L'objectif de ce TP est de créer et manipuler un Generative Adversarial Networks (GAN) afin de générer des images de chiffres manuscrits à partir d'une base de données d'exemples.
Vous aurez besoin des outils présents dans le fichier gan_tools_cuda.py que vous devrez copier dans votre répertoire Jupyter afin de pouvoir l'importer. Regardez bien les fonctions et classes définies dans ce fichier : il y a les codes permettant de télécharger la base de donnée d'images, de faire les conversions entre images et vecteurs, de générer les vecteurs de bruit à donner en entrée du générateur et de gérer l’affichage des résultats sous forme d'exemples d’images générées par le générateur et des indices de qualité du générateur et du discriminateur.
import torch
from torch import nn, optim
from torch.autograd.variable import Variable
import gan_tools_cuda as gt
Le loader présentera au réseau les données par paquet de 100, dans un ordre aléatoire.
# Load data
data = gt.mnist_data()
# Create loader with data, so that we can iterate over it
data_loader = torch.utils.data.DataLoader(data, batch_size=100, shuffle=True)
# Num batches
num_batches = len(data_loader)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-images-idx3-ubyte.gz
Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-labels-idx1-ubyte.gz
Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/train-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-images-idx3-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting ./torch_data/VGAN/MNIST/dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./torch_data/VGAN/MNIST/dataset/MNIST/raw Processing... Done!
/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:479: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.) return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
Créez une classe Python DiscriminatorNet décrivant la structure du réseau discriminateur.
Le réseau est composé de trois couches cachées et d'une couche de sortie. Nous utiliserons le module "séquentiel" de Pytorch qui décrit l’ordre des différentes transformations à appliquer aux données dans chaque couche.
L'entrée du réseau est un vecteur avec 784 valeurs (une image 28x28), la sortie est une valeur unique, allant de 0 (fausse image) à 1 (vraie image) grâce à l'utilisation d'une fonction sigmoïde.
Les trois couches cachées utilisent une fonction LeakyReLU pour transformer leur sortie en valeurs quasi positives, avec alpha = 0,2. Elles utilisent également une fonction Dropout pour définir aléatoirement à zéro 30% des valeurs de sortie des neurones des couches cachées (il a été prouvé que cela augmentait les performances du réseau en empêchant le sur-apprentissage). Le nombre de neurones dans chaque couche cachée est respectivement de 1024, 512 et 256.
La classe possède une méthode "forward" permettant de calculer la sortie du réseau à partir d'un vecteur d'entrée (représenaant une image).
class DiscriminatorNet(torch.nn.Module):
def __init__(self):
super(DiscriminatorNet, self).__init__()
n_features = 784
n_out = 1
self.hidden0 = nn.Sequential(
nn.Linear(n_features, 1024),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden1 = nn.Sequential(
nn.Linear(1024, 512),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden2 = nn.Sequential(
nn.Linear(512, 256),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.out = nn.Sequential(
torch.nn.Linear(256, n_out),
torch.nn.Sigmoid()
)
def forward(self, x):
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.out(x)
return x
Créez une classe Python GeneratorNet décrivant la structure du réseau générateurs. Le réseau est composé de trois couches cachées et d'une couche de sortie. L'entrée du réseau est un vecteur avec 100 valeurs, la sortie est une image avec 784 valeurs (utilisez nn.Tanh() au lieu de nn.Sigmoid() pour convertir les valeurs de sortie). Les trois couches cachées utilisent une fonction LeakyReLU pour transformer leur sortie en valeurs quasi positives, avec alpha = 0,2, mais pas de Dropout ici. Le nombre de neurones dans chaque couche cachée est respectivement de 256, 512 et 1024
class GeneratorNet(torch.nn.Module):
"""
3 couches cachées generative neural network
"""
def __init__(self):
super(GeneratorNet, self).__init__()
n_features = 100
n_out = 784
self.hidden0 = nn.Sequential(
nn.Linear(n_features, 256),
nn.LeakyReLU(0.2)
)
self.hidden1 = nn.Sequential(
nn.Linear(256, 512),
nn.LeakyReLU(0.2)
)
self.hidden2 = nn.Sequential(
nn.Linear(512, 1024),
nn.LeakyReLU(0.2)
)
self.out = nn.Sequential(
nn.Linear(1024, n_out),
nn.Tanh()
)
def forward(self, x):
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.out(x)
return x
Initialisez les instances d'un discriminateur et d'un générateurs. Créez deux optimiseurs de type Adam pour les deux réseaux. Définissez la fonction de perte: ici, nous choisissons une fonction d'entropie croisée binaire pour vérifier si le discriminateur trouve la bonne réponse.
discriminator = DiscriminatorNet()
generator = GeneratorNet()
if torch.cuda.is_available():
discriminator.cuda()
generator.cuda()
# Optimizers
d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)
g_optimizer = optim.Adam(generator.parameters(), lr=0.0002)
# Loss function
loss = nn.BCELoss()
Créez une fonction train_generator et une fonction train_discriminator. Expliquez les différentes étapes du processus d'apprentissage.
D'après le cours, si nous remplaçons vᵢ = D(xᵢ) et yᵢ=1 (pour tout i) dans la définition de la perte BCE, nous obtenons la perte liée aux images réelles. A l'inverse, si on fixe vᵢ = D(G(zᵢ)) et yᵢ=0 pour tout i, on obtient la perte liée aux images fake.
Dans le modèle mathématique d'un GAN , le gradient de celle-ci devait être ascendant, mais PyTorch et la plupart des autres frameworks de Machine Learning minimisent généralement les fonctions à la place. Puisque la maximisation d'une fonction est équivalente à la minimisation de son négatif, et que le terme BCE-Loss a un signe moins, nous n'avons pas besoin de nous soucier du signe.
De plus, nous pouvons observer que les cibles des images réelles sont toujours des 1, tandis que les cibles des images fausses sont des 0. On utilise les fonctions reel_data_target et fake_data_target définies dans le fichier gan_tools.
En additionnant ces deux pertes du discriminateur, nous obtenons la perte totale du mini-batch pour le discriminateur.
def train_discriminator(optimizer, real_data, fake_data):
# Reset gradients
optimizer.zero_grad()
# 1.1 Train on Real Data
prediction_real = discriminator(real_data)
# Calculate error and backpropagate
error_real = loss(prediction_real, gt.real_data_target(real_data.size(0)))
error_real.backward()
# 1.2 Train on Fake Data
prediction_fake = discriminator(fake_data)
# Calculate error and backpropagate
error_fake = loss(prediction_fake, gt.fake_data_target(real_data.size(0)))
error_fake.backward()
# 1.3 Update weights with gradients
optimizer.step()
# Return error
return error_real + error_fake, prediction_real, prediction_fake
Nous calculerons les gradients séparément, puis nous les mettrons à jour ensemble.
Plutôt que de minimiser log(1- D(G(z)), l'entraînement du générateur pour maximiser log D(G(z)) fournira des gradients beaucoup plus forts au début de l'entraînement. Les deux pertes peuvent être échangées de manière interchangeable puisqu'elles entraînent la même dynamique pour le Générateur et le Discriminateur. La maximisation de log D(G(z)) est équivalente à la minimisation de sa valeur négative et, comme la définition de la perte BCE-Loss comporte un signe moins, il n'est pas nécessaire de tenir compte du signe. De même que pour le Discriminateur, si nous fixons vᵢ = D(G(zᵢ)) et yᵢ=1 ∀ i, nous obtenons la perte souhaitée à minimiser.
def train_generator(optimizer, fake_data):
# 2. Train Generator
# Reset gradients
optimizer.zero_grad()
# Sample noise and generate fake data
prediction = discriminator(fake_data)
# Calculate error and backpropagate
error = loss(prediction, gt.real_data_target(prediction.size(0)))
error.backward()
# Update weights with gradients
optimizer.step()
# Return error
return error
Pour chaque itération et pour chaque lot ("batch") de données, les étapes d'apprentissage sont les suivantes:
Transformez le lot de données en variables Torch à l'aide des fonctions Variable et gt.images_to_vectors. Ce sont les vraies données pour l'étape suivante.
Créez des vecteurs de bruit de taille 100 avec gt.noise (autant que de vraies images dans le lot), puis générez de fausses données avec le générateur.
Former le discriminateur sur les fausses données et les données réelles, en utilisant l'optimiseur défini ci-dessus (d_optimizer).
Créez d'autres vecteurs de bruit, taille 100, avec gt.noise.
Entrainez le générateur sur les vecteurs de bruit.
Visualisez un échantillon d'images générées avec gt.plot_gan. Cette fonction nécessite le numéro d'itération et de lot, ainsi que le nombre total de lots et le nom du générateur.
Il se peut que le système se "bloque" dans une configuration où la sortie du générateur est la même quelle que soit l'entrée. Si toutes les images des exemples de sortie sont identiques après une dizaine d'itérations et que D(x) = 1 et D(G(z)) = 0, il faut relancer l'apprentissage (redémarrer le noyau).
num_epochs=20
for epoch in range(num_epochs):
for n_batch, (real_batch,_) in enumerate(data_loader):
n = real_batch.size(0)
#Transformez le lot de données en variables Torch à l'aide des fonctions Variable et gt.images_to_vectors.
#Ce sont les vraies données pour l'étape suivante.
# 1 Train Discriminator
# 1.1 Prepare real data
real_data = Variable(gt.images_to_vectors(real_batch))
# 1.2 Generate fake data with the Generator
#""" A COMPLETER : Créer des vecteurs bruit """
#(les gradients ne sont donc pas calculés pour le générateur)
fake_data = generator(gt.noise(n)).detach()
#""" A COMPLETER : Générer des images avec le générateur """
# 1.3 Train Discriminator
# """ A COMPLETER : Entrainer le discriminateur """
d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, real_data, fake_data)
# 2 Train Generator
# 2.1 Generate noise
fake_data = generator(gt.noise(n))
# 2.2 Train Generator
#""" A COMPLETER : Entrainer le generateur """
g_error = train_generator(g_optimizer, fake_data)
# Generate images from a fixed noise input and visualize the output
gt.plot_gan(epoch, n_batch, num_batches, generator)
# Display status Logs
gt.logger.display_status(
epoch, num_epochs, n_batch, num_batches,
d_error, g_error, d_pred_real, d_pred_fake
)
Epoch: 0, Batch Num: [0/600]
Epoch: [0/20], Batch Num: [0/600] Discriminator Loss: 1.3757, Generator Loss: 0.6997 D(x): 0.5020, D(G(z)): 0.4965 Epoch: [0/20], Batch Num: [1/600] Discriminator Loss: 1.2533, Generator Loss: 0.6947 D(x): 0.5697, D(G(z)): 0.4985 Epoch: [0/20], Batch Num: [2/600] Discriminator Loss: 1.1399, Generator Loss: 0.6885 D(x): 0.6421, D(G(z)): 0.5015 Epoch: [0/20], Batch Num: [3/600] Discriminator Loss: 1.0170, Generator Loss: 0.6786 D(x): 0.7323, D(G(z)): 0.5057 Epoch: [0/20], Batch Num: [4/600] Discriminator Loss: 0.9390, Generator Loss: 0.6612 D(x): 0.8037, D(G(z)): 0.5131 Epoch: [0/20], Batch Num: [5/600] Discriminator Loss: 0.8756, Generator Loss: 0.6338 D(x): 0.8770, D(G(z)): 0.5247 Epoch: [0/20], Batch Num: [6/600] Discriminator Loss: 0.8576, Generator Loss: 0.5975 D(x): 0.9311, D(G(z)): 0.5442 Epoch: [0/20], Batch Num: [7/600] Discriminator Loss: 0.8783, Generator Loss: 0.5560 D(x): 0.9677, D(G(z)): 0.5704 Epoch: [0/20], Batch Num: [8/600] Discriminator Loss: 0.9397, Generator Loss: 0.5054 D(x): 0.9818, D(G(z)): 0.6015 Epoch: [0/20], Batch Num: [9/600] Discriminator Loss: 1.0182, Generator Loss: 0.4571 D(x): 0.9894, D(G(z)): 0.6342 Epoch: [0/20], Batch Num: [10/600] Discriminator Loss: 1.1329, Generator Loss: 0.4214 D(x): 0.9917, D(G(z)): 0.6743 Epoch: [0/20], Batch Num: [11/600] Discriminator Loss: 1.2116, Generator Loss: 0.4033 D(x): 0.9912, D(G(z)): 0.6983 Epoch: [0/20], Batch Num: [12/600] Discriminator Loss: 1.2775, Generator Loss: 0.3852 D(x): 0.9894, D(G(z)): 0.7168 Epoch: [0/20], Batch Num: [13/600] Discriminator Loss: 1.3234, Generator Loss: 0.3940 D(x): 0.9852, D(G(z)): 0.7279 Epoch: [0/20], Batch Num: [14/600] Discriminator Loss: 1.2922, Generator Loss: 0.4148 D(x): 0.9753, D(G(z)): 0.7168 Epoch: [0/20], Batch Num: [15/600] Discriminator Loss: 1.2624, Generator Loss: 0.4539 D(x): 0.9585, D(G(z)): 0.7034 Epoch: [0/20], Batch Num: [16/600] Discriminator Loss: 1.2129, Generator Loss: 0.5100 D(x): 0.9253, D(G(z)): 0.6771 Epoch: [0/20], Batch Num: [17/600] Discriminator Loss: 1.1521, Generator Loss: 0.5946 D(x): 0.8661, D(G(z)): 0.6338 Epoch: [0/20], Batch Num: [18/600] Discriminator Loss: 1.0823, Generator Loss: 0.7206 D(x): 0.7975, D(G(z)): 0.5742 Epoch: [0/20], Batch Num: [19/600] Discriminator Loss: 1.0828, Generator Loss: 0.8469 D(x): 0.7008, D(G(z)): 0.5150 Epoch: [0/20], Batch Num: [20/600] Discriminator Loss: 1.0993, Generator Loss: 0.9372 D(x): 0.6058, D(G(z)): 0.4478 Epoch: [0/20], Batch Num: [21/600] Discriminator Loss: 1.1323, Generator Loss: 1.0164 D(x): 0.5413, D(G(z)): 0.4012 Epoch: [0/20], Batch Num: [22/600] Discriminator Loss: 1.1501, Generator Loss: 1.0378 D(x): 0.5153, D(G(z)): 0.3812 Epoch: [0/20], Batch Num: [23/600] Discriminator Loss: 1.1575, Generator Loss: 0.9534 D(x): 0.5174, D(G(z)): 0.3881 Epoch: [0/20], Batch Num: [24/600] Discriminator Loss: 1.1552, Generator Loss: 0.8324 D(x): 0.5576, D(G(z)): 0.4304 Epoch: [0/20], Batch Num: [25/600] Discriminator Loss: 1.1874, Generator Loss: 0.6943 D(x): 0.5998, D(G(z)): 0.4872 Epoch: [0/20], Batch Num: [26/600] Discriminator Loss: 1.2665, Generator Loss: 0.6062 D(x): 0.6455, D(G(z)): 0.5598 Epoch: [0/20], Batch Num: [27/600] Discriminator Loss: 1.4218, Generator Loss: 0.5423 D(x): 0.6558, D(G(z)): 0.6277 Epoch: [0/20], Batch Num: [28/600] Discriminator Loss: 1.5001, Generator Loss: 0.5569 D(x): 0.6419, D(G(z)): 0.6477 Epoch: [0/20], Batch Num: [29/600] Discriminator Loss: 1.5554, Generator Loss: 0.6346 D(x): 0.6102, D(G(z)): 0.6479 Epoch: [0/20], Batch Num: [30/600] Discriminator Loss: 1.6531, Generator Loss: 0.7014 D(x): 0.5315, D(G(z)): 0.6269 Epoch: [0/20], Batch Num: [31/600] Discriminator Loss: 1.6781, Generator Loss: 0.7924 D(x): 0.4631, D(G(z)): 0.5802 Epoch: [0/20], Batch Num: [32/600] Discriminator Loss: 1.7457, Generator Loss: 0.8311 D(x): 0.3935, D(G(z)): 0.5352 Epoch: [0/20], Batch Num: [33/600] Discriminator Loss: 1.7417, Generator Loss: 0.8616 D(x): 0.3742, D(G(z)): 0.5153 Epoch: [0/20], Batch Num: [34/600] Discriminator Loss: 1.6515, Generator Loss: 0.8278 D(x): 0.4019, D(G(z)): 0.5099 Epoch: [0/20], Batch Num: [35/600] Discriminator Loss: 1.5516, Generator Loss: 0.8314 D(x): 0.4403, D(G(z)): 0.5055 Epoch: [0/20], Batch Num: [36/600] Discriminator Loss: 1.4324, Generator Loss: 0.8331 D(x): 0.4787, D(G(z)): 0.4918 Epoch: [0/20], Batch Num: [37/600] Discriminator Loss: 1.3024, Generator Loss: 0.8581 D(x): 0.5380, D(G(z)): 0.4891 Epoch: [0/20], Batch Num: [38/600] Discriminator Loss: 1.1468, Generator Loss: 0.9232 D(x): 0.6018, D(G(z)): 0.4669 Epoch: [0/20], Batch Num: [39/600] Discriminator Loss: 1.0202, Generator Loss: 1.0655 D(x): 0.6513, D(G(z)): 0.4417 Epoch: [0/20], Batch Num: [40/600] Discriminator Loss: 0.8959, Generator Loss: 1.1934 D(x): 0.6860, D(G(z)): 0.4015 Epoch: [0/20], Batch Num: [41/600] Discriminator Loss: 0.7477, Generator Loss: 1.3638 D(x): 0.7228, D(G(z)): 0.3417 Epoch: [0/20], Batch Num: [42/600] Discriminator Loss: 0.6334, Generator Loss: 1.5860 D(x): 0.7651, D(G(z)): 0.3026 Epoch: [0/20], Batch Num: [43/600] Discriminator Loss: 0.5010, Generator Loss: 1.8039 D(x): 0.8094, D(G(z)): 0.2490 Epoch: [0/20], Batch Num: [44/600] Discriminator Loss: 0.4071, Generator Loss: 2.0720 D(x): 0.8376, D(G(z)): 0.2032 Epoch: [0/20], Batch Num: [45/600] Discriminator Loss: 0.3182, Generator Loss: 2.2691 D(x): 0.8673, D(G(z)): 0.1589 Epoch: [0/20], Batch Num: [46/600] Discriminator Loss: 0.2585, Generator Loss: 2.5395 D(x): 0.9028, D(G(z)): 0.1429 Epoch: [0/20], Batch Num: [47/600] Discriminator Loss: 0.2147, Generator Loss: 2.5651 D(x): 0.9261, D(G(z)): 0.1271 Epoch: [0/20], Batch Num: [48/600] Discriminator Loss: 0.1938, Generator Loss: 2.7570 D(x): 0.9452, D(G(z)): 0.1269 Epoch: [0/20], Batch Num: [49/600] Discriminator Loss: 0.1694, Generator Loss: 2.7636 D(x): 0.9674, D(G(z)): 0.1262 Epoch: [0/20], Batch Num: [50/600] Discriminator Loss: 0.1716, Generator Loss: 2.7915 D(x): 0.9700, D(G(z)): 0.1299 Epoch: [0/20], Batch Num: [51/600] Discriminator Loss: 0.1924, Generator Loss: 2.6971 D(x): 0.9700, D(G(z)): 0.1481 Epoch: [0/20], Batch Num: [52/600] Discriminator Loss: 0.2190, Generator Loss: 2.5869 D(x): 0.9731, D(G(z)): 0.1723 Epoch: [0/20], Batch Num: [53/600] Discriminator Loss: 0.2729, Generator Loss: 2.3712 D(x): 0.9607, D(G(z)): 0.2033 Epoch: [0/20], Batch Num: [54/600] Discriminator Loss: 0.3433, Generator Loss: 2.0457 D(x): 0.9607, D(G(z)): 0.2577 Epoch: [0/20], Batch Num: [55/600] Discriminator Loss: 0.5324, Generator Loss: 1.6961 D(x): 0.9153, D(G(z)): 0.3379 Epoch: [0/20], Batch Num: [56/600] Discriminator Loss: 0.8184, Generator Loss: 1.2114 D(x): 0.8659, D(G(z)): 0.4533 Epoch: [0/20], Batch Num: [57/600] Discriminator Loss: 1.0401, Generator Loss: 0.8257 D(x): 0.8643, D(G(z)): 0.5675 Epoch: [0/20], Batch Num: [58/600] Discriminator Loss: 1.2592, Generator Loss: 0.6095 D(x): 0.8683, D(G(z)): 0.6518 Epoch: [0/20], Batch Num: [59/600] Discriminator Loss: 1.3357, Generator Loss: 0.4950 D(x): 0.8982, D(G(z)): 0.6840 Epoch: [0/20], Batch Num: [60/600] Discriminator Loss: 1.3016, Generator Loss: 0.4461 D(x): 0.9417, D(G(z)): 0.7032 Epoch: [0/20], Batch Num: [61/600] Discriminator Loss: 1.2379, Generator Loss: 0.4373 D(x): 0.9880, D(G(z)): 0.7041 Epoch: [0/20], Batch Num: [62/600] Discriminator Loss: 1.2578, Generator Loss: 0.4644 D(x): 0.9912, D(G(z)): 0.7103 Epoch: [0/20], Batch Num: [63/600] Discriminator Loss: 1.1733, Generator Loss: 0.5047 D(x): 0.9958, D(G(z)): 0.6868 Epoch: [0/20], Batch Num: [64/600] Discriminator Loss: 1.0763, Generator Loss: 0.5611 D(x): 0.9980, D(G(z)): 0.6567 Epoch: [0/20], Batch Num: [65/600] Discriminator Loss: 0.9887, Generator Loss: 0.6345 D(x): 0.9973, D(G(z)): 0.6256 Epoch: [0/20], Batch Num: [66/600] Discriminator Loss: 0.9078, Generator Loss: 0.6993 D(x): 0.9985, D(G(z)): 0.5945 Epoch: [0/20], Batch Num: [67/600] Discriminator Loss: 0.8236, Generator Loss: 0.7486 D(x): 0.9978, D(G(z)): 0.5588 Epoch: [0/20], Batch Num: [68/600] Discriminator Loss: 0.7643, Generator Loss: 0.8112 D(x): 0.9986, D(G(z)): 0.5324 Epoch: [0/20], Batch Num: [69/600] Discriminator Loss: 0.7060, Generator Loss: 0.8751 D(x): 0.9973, D(G(z)): 0.5040 Epoch: [0/20], Batch Num: [70/600] Discriminator Loss: 0.6686, Generator Loss: 0.9255 D(x): 0.9986, D(G(z)): 0.4858 Epoch: [0/20], Batch Num: [71/600] Discriminator Loss: 0.6529, Generator Loss: 0.9738 D(x): 0.9986, D(G(z)): 0.4777 Epoch: [0/20], Batch Num: [72/600] Discriminator Loss: 0.6296, Generator Loss: 0.9920 D(x): 0.9984, D(G(z)): 0.4653 Epoch: [0/20], Batch Num: [73/600] Discriminator Loss: 0.6636, Generator Loss: 0.9755 D(x): 0.9985, D(G(z)): 0.4823 Epoch: [0/20], Batch Num: [74/600] Discriminator Loss: 0.6659, Generator Loss: 0.9740 D(x): 0.9965, D(G(z)): 0.4819 Epoch: [0/20], Batch Num: [75/600] Discriminator Loss: 0.6878, Generator Loss: 0.9486 D(x): 0.9988, D(G(z)): 0.4945 Epoch: [0/20], Batch Num: [76/600] Discriminator Loss: 0.7214, Generator Loss: 0.9181 D(x): 0.9974, D(G(z)): 0.5094 Epoch: [0/20], Batch Num: [77/600] Discriminator Loss: 0.7737, Generator Loss: 0.9087 D(x): 0.9985, D(G(z)): 0.5334 Epoch: [0/20], Batch Num: [78/600] Discriminator Loss: 0.8364, Generator Loss: 0.8875 D(x): 0.9985, D(G(z)): 0.5606 Epoch: [0/20], Batch Num: [79/600] Discriminator Loss: 0.9387, Generator Loss: 0.9201 D(x): 0.9981, D(G(z)): 0.5983 Epoch: [0/20], Batch Num: [80/600] Discriminator Loss: 0.9371, Generator Loss: 0.9294 D(x): 0.9961, D(G(z)): 0.5995 Epoch: [0/20], Batch Num: [81/600] Discriminator Loss: 0.8749, Generator Loss: 1.0195 D(x): 0.9957, D(G(z)): 0.5743 Epoch: [0/20], Batch Num: [82/600] Discriminator Loss: 0.8026, Generator Loss: 1.2085 D(x): 0.9891, D(G(z)): 0.5405 Epoch: [0/20], Batch Num: [83/600] Discriminator Loss: 0.6476, Generator Loss: 1.4802 D(x): 0.9806, D(G(z)): 0.4604 Epoch: [0/20], Batch Num: [84/600] Discriminator Loss: 0.4984, Generator Loss: 1.8845 D(x): 0.9684, D(G(z)): 0.3670 Epoch: [0/20], Batch Num: [85/600] Discriminator Loss: 0.3566, Generator Loss: 2.4574 D(x): 0.9203, D(G(z)): 0.2314 Epoch: [0/20], Batch Num: [86/600] Discriminator Loss: 0.2886, Generator Loss: 2.9607 D(x): 0.9030, D(G(z)): 0.1646 Epoch: [0/20], Batch Num: [87/600] Discriminator Loss: 0.2324, Generator Loss: 3.3977 D(x): 0.9119, D(G(z)): 0.1261 Epoch: [0/20], Batch Num: [88/600] Discriminator Loss: 0.1666, Generator Loss: 3.4194 D(x): 0.9203, D(G(z)): 0.0760 Epoch: [0/20], Batch Num: [89/600] Discriminator Loss: 0.1434, Generator Loss: 3.5815 D(x): 0.9496, D(G(z)): 0.0845 Epoch: [0/20], Batch Num: [90/600] Discriminator Loss: 0.1398, Generator Loss: 3.3752 D(x): 0.9745, D(G(z)): 0.1055 Epoch: [0/20], Batch Num: [91/600] Discriminator Loss: 0.1742, Generator Loss: 3.0946 D(x): 0.9831, D(G(z)): 0.1417 Epoch: [0/20], Batch Num: [92/600] Discriminator Loss: 0.2990, Generator Loss: 2.7808 D(x): 0.9862, D(G(z)): 0.2341 Epoch: [0/20], Batch Num: [93/600] Discriminator Loss: 0.4710, Generator Loss: 2.6480 D(x): 0.9834, D(G(z)): 0.3487 Epoch: [0/20], Batch Num: [94/600] Discriminator Loss: 0.4440, Generator Loss: 3.0964 D(x): 0.9635, D(G(z)): 0.3193 Epoch: [0/20], Batch Num: [95/600] Discriminator Loss: 0.5219, Generator Loss: 3.3658 D(x): 0.8752, D(G(z)): 0.3004 Epoch: [0/20], Batch Num: [96/600] Discriminator Loss: 1.2233, Generator Loss: 2.5355 D(x): 0.5219, D(G(z)): 0.2663 Epoch: [0/20], Batch Num: [97/600] Discriminator Loss: 1.5884, Generator Loss: 1.2441 D(x): 0.4920, D(G(z)): 0.4067 Epoch: [0/20], Batch Num: [98/600] Discriminator Loss: 1.8034, Generator Loss: 0.6282 D(x): 0.6402, D(G(z)): 0.6709 Epoch: [0/20], Batch Num: [99/600] Discriminator Loss: 2.0546, Generator Loss: 0.6380 D(x): 0.6808, D(G(z)): 0.7555 Epoch: 0, Batch Num: [100/600]
Epoch: [0/20], Batch Num: [100/600] Discriminator Loss: 2.0057, Generator Loss: 0.7656 D(x): 0.7039, D(G(z)): 0.7589 Epoch: [0/20], Batch Num: [101/600] Discriminator Loss: 2.0250, Generator Loss: 1.1344 D(x): 0.6194, D(G(z)): 0.6761 Epoch: [0/20], Batch Num: [102/600] Discriminator Loss: 1.6289, Generator Loss: 1.3661 D(x): 0.5502, D(G(z)): 0.4884 Epoch: [0/20], Batch Num: [103/600] Discriminator Loss: 1.2518, Generator Loss: 1.5980 D(x): 0.6560, D(G(z)): 0.4149 Epoch: [0/20], Batch Num: [104/600] Discriminator Loss: 0.7981, Generator Loss: 1.8476 D(x): 0.7662, D(G(z)): 0.3217 Epoch: [0/20], Batch Num: [105/600] Discriminator Loss: 0.5618, Generator Loss: 2.3718 D(x): 0.8224, D(G(z)): 0.2550 Epoch: [0/20], Batch Num: [106/600] Discriminator Loss: 0.4530, Generator Loss: 2.7787 D(x): 0.8677, D(G(z)): 0.2268 Epoch: [0/20], Batch Num: [107/600] Discriminator Loss: 0.3115, Generator Loss: 2.9076 D(x): 0.9033, D(G(z)): 0.1719 Epoch: [0/20], Batch Num: [108/600] Discriminator Loss: 0.3001, Generator Loss: 3.2308 D(x): 0.8968, D(G(z)): 0.1429 Epoch: [0/20], Batch Num: [109/600] Discriminator Loss: 0.2864, Generator Loss: 3.1464 D(x): 0.9199, D(G(z)): 0.1650 Epoch: [0/20], Batch Num: [110/600] Discriminator Loss: 0.2895, Generator Loss: 3.3187 D(x): 0.9270, D(G(z)): 0.1796 Epoch: [0/20], Batch Num: [111/600] Discriminator Loss: 0.3755, Generator Loss: 2.8984 D(x): 0.9124, D(G(z)): 0.2203 Epoch: [0/20], Batch Num: [112/600] Discriminator Loss: 0.4398, Generator Loss: 2.6751 D(x): 0.8507, D(G(z)): 0.1858 Epoch: [0/20], Batch Num: [113/600] Discriminator Loss: 0.3714, Generator Loss: 2.6324 D(x): 0.8860, D(G(z)): 0.1808 Epoch: [0/20], Batch Num: [114/600] Discriminator Loss: 0.3039, Generator Loss: 2.7980 D(x): 0.9056, D(G(z)): 0.1571 Epoch: [0/20], Batch Num: [115/600] Discriminator Loss: 0.2057, Generator Loss: 2.9515 D(x): 0.9415, D(G(z)): 0.1239 Epoch: [0/20], Batch Num: [116/600] Discriminator Loss: 0.1903, Generator Loss: 3.0568 D(x): 0.9648, D(G(z)): 0.1353 Epoch: [0/20], Batch Num: [117/600] Discriminator Loss: 0.1890, Generator Loss: 3.0937 D(x): 0.9754, D(G(z)): 0.1457 Epoch: [0/20], Batch Num: [118/600] Discriminator Loss: 0.2634, Generator Loss: 3.2799 D(x): 0.9653, D(G(z)): 0.1898 Epoch: [0/20], Batch Num: [119/600] Discriminator Loss: 0.2386, Generator Loss: 3.2300 D(x): 0.9631, D(G(z)): 0.1619 Epoch: [0/20], Batch Num: [120/600] Discriminator Loss: 0.2684, Generator Loss: 3.4721 D(x): 0.9554, D(G(z)): 0.1813 Epoch: [0/20], Batch Num: [121/600] Discriminator Loss: 0.3528, Generator Loss: 3.5226 D(x): 0.8916, D(G(z)): 0.1728 Epoch: [0/20], Batch Num: [122/600] Discriminator Loss: 0.3932, Generator Loss: 3.4124 D(x): 0.8850, D(G(z)): 0.1959 Epoch: [0/20], Batch Num: [123/600] Discriminator Loss: 0.4349, Generator Loss: 3.3915 D(x): 0.8791, D(G(z)): 0.2198 Epoch: [0/20], Batch Num: [124/600] Discriminator Loss: 0.5224, Generator Loss: 3.2788 D(x): 0.8699, D(G(z)): 0.2663 Epoch: [0/20], Batch Num: [125/600] Discriminator Loss: 0.7063, Generator Loss: 3.0236 D(x): 0.8080, D(G(z)): 0.3151 Epoch: [0/20], Batch Num: [126/600] Discriminator Loss: 0.9791, Generator Loss: 2.3889 D(x): 0.7672, D(G(z)): 0.4154 Epoch: [0/20], Batch Num: [127/600] Discriminator Loss: 1.6270, Generator Loss: 1.6560 D(x): 0.6527, D(G(z)): 0.5714 Epoch: [0/20], Batch Num: [128/600] Discriminator Loss: 2.3135, Generator Loss: 1.0631 D(x): 0.6043, D(G(z)): 0.7322 Epoch: [0/20], Batch Num: [129/600] Discriminator Loss: 3.3252, Generator Loss: 0.5853 D(x): 0.5236, D(G(z)): 0.8656 Epoch: [0/20], Batch Num: [130/600] Discriminator Loss: 4.0215, Generator Loss: 0.2905 D(x): 0.5304, D(G(z)): 0.9252 Epoch: [0/20], Batch Num: [131/600] Discriminator Loss: 4.5425, Generator Loss: 0.1464 D(x): 0.5152, D(G(z)): 0.9545 Epoch: [0/20], Batch Num: [132/600] Discriminator Loss: 4.6483, Generator Loss: 0.1203 D(x): 0.5145, D(G(z)): 0.9604 Epoch: [0/20], Batch Num: [133/600] Discriminator Loss: 4.5506, Generator Loss: 0.1208 D(x): 0.4705, D(G(z)): 0.9590 Epoch: [0/20], Batch Num: [134/600] Discriminator Loss: 4.1835, Generator Loss: 0.1369 D(x): 0.4786, D(G(z)): 0.9429 Epoch: [0/20], Batch Num: [135/600] Discriminator Loss: 3.4126, Generator Loss: 0.2342 D(x): 0.5837, D(G(z)): 0.9210 Epoch: [0/20], Batch Num: [136/600] Discriminator Loss: 2.6324, Generator Loss: 0.4036 D(x): 0.6446, D(G(z)): 0.8661 Epoch: [0/20], Batch Num: [137/600] Discriminator Loss: 1.9153, Generator Loss: 0.7278 D(x): 0.6619, D(G(z)): 0.7449 Epoch: [0/20], Batch Num: [138/600] Discriminator Loss: 1.4548, Generator Loss: 1.1038 D(x): 0.5977, D(G(z)): 0.5576 Epoch: [0/20], Batch Num: [139/600] Discriminator Loss: 1.0761, Generator Loss: 1.4134 D(x): 0.6160, D(G(z)): 0.4031 Epoch: [0/20], Batch Num: [140/600] Discriminator Loss: 0.8119, Generator Loss: 1.7640 D(x): 0.6759, D(G(z)): 0.3083 Epoch: [0/20], Batch Num: [141/600] Discriminator Loss: 0.7429, Generator Loss: 1.9919 D(x): 0.6613, D(G(z)): 0.2273 Epoch: [0/20], Batch Num: [142/600] Discriminator Loss: 0.5646, Generator Loss: 2.1301 D(x): 0.7626, D(G(z)): 0.2289 Epoch: [0/20], Batch Num: [143/600] Discriminator Loss: 0.5102, Generator Loss: 2.1464 D(x): 0.7785, D(G(z)): 0.2090 Epoch: [0/20], Batch Num: [144/600] Discriminator Loss: 0.4331, Generator Loss: 2.0524 D(x): 0.8613, D(G(z)): 0.2373 Epoch: [0/20], Batch Num: [145/600] Discriminator Loss: 0.4878, Generator Loss: 1.8296 D(x): 0.8755, D(G(z)): 0.2875 Epoch: [0/20], Batch Num: [146/600] Discriminator Loss: 0.5691, Generator Loss: 1.6582 D(x): 0.9043, D(G(z)): 0.3652 Epoch: [0/20], Batch Num: [147/600] Discriminator Loss: 0.6970, Generator Loss: 1.6181 D(x): 0.9036, D(G(z)): 0.4314 Epoch: [0/20], Batch Num: [148/600] Discriminator Loss: 0.8336, Generator Loss: 1.4877 D(x): 0.8885, D(G(z)): 0.4894 Epoch: [0/20], Batch Num: [149/600] Discriminator Loss: 0.9185, Generator Loss: 1.6651 D(x): 0.8533, D(G(z)): 0.5129 Epoch: [0/20], Batch Num: [150/600] Discriminator Loss: 0.9745, Generator Loss: 1.7946 D(x): 0.7520, D(G(z)): 0.4655 Epoch: [0/20], Batch Num: [151/600] Discriminator Loss: 1.3522, Generator Loss: 1.8568 D(x): 0.5674, D(G(z)): 0.4696 Epoch: [0/20], Batch Num: [152/600] Discriminator Loss: 1.4831, Generator Loss: 1.6831 D(x): 0.4961, D(G(z)): 0.4592 Epoch: [0/20], Batch Num: [153/600] Discriminator Loss: 1.8555, Generator Loss: 1.4483 D(x): 0.3750, D(G(z)): 0.4657 Epoch: [0/20], Batch Num: [154/600] Discriminator Loss: 2.3840, Generator Loss: 1.0873 D(x): 0.2793, D(G(z)): 0.5554 Epoch: [0/20], Batch Num: [155/600] Discriminator Loss: 2.5592, Generator Loss: 0.8425 D(x): 0.3098, D(G(z)): 0.6657 Epoch: [0/20], Batch Num: [156/600] Discriminator Loss: 2.7537, Generator Loss: 0.7005 D(x): 0.3332, D(G(z)): 0.7338 Epoch: [0/20], Batch Num: [157/600] Discriminator Loss: 2.8382, Generator Loss: 0.7531 D(x): 0.3216, D(G(z)): 0.7407 Epoch: [0/20], Batch Num: [158/600] Discriminator Loss: 2.7322, Generator Loss: 0.8191 D(x): 0.3435, D(G(z)): 0.7207 Epoch: [0/20], Batch Num: [159/600] Discriminator Loss: 2.1532, Generator Loss: 1.1463 D(x): 0.4042, D(G(z)): 0.6271 Epoch: [0/20], Batch Num: [160/600] Discriminator Loss: 1.7042, Generator Loss: 1.6353 D(x): 0.4595, D(G(z)): 0.5319 Epoch: [0/20], Batch Num: [161/600] Discriminator Loss: 1.4963, Generator Loss: 1.9447 D(x): 0.4473, D(G(z)): 0.4194 Epoch: [0/20], Batch Num: [162/600] Discriminator Loss: 1.2780, Generator Loss: 2.3246 D(x): 0.4593, D(G(z)): 0.2986 Epoch: [0/20], Batch Num: [163/600] Discriminator Loss: 1.1015, Generator Loss: 2.1820 D(x): 0.5048, D(G(z)): 0.2592 Epoch: [0/20], Batch Num: [164/600] Discriminator Loss: 0.9572, Generator Loss: 2.2909 D(x): 0.5741, D(G(z)): 0.2818 Epoch: [0/20], Batch Num: [165/600] Discriminator Loss: 0.8590, Generator Loss: 2.0758 D(x): 0.6387, D(G(z)): 0.2985 Epoch: [0/20], Batch Num: [166/600] Discriminator Loss: 0.8500, Generator Loss: 2.0418 D(x): 0.7050, D(G(z)): 0.3491 Epoch: [0/20], Batch Num: [167/600] Discriminator Loss: 0.8134, Generator Loss: 2.2371 D(x): 0.7248, D(G(z)): 0.3416 Epoch: [0/20], Batch Num: [168/600] Discriminator Loss: 0.7569, Generator Loss: 2.5149 D(x): 0.7520, D(G(z)): 0.3397 Epoch: [0/20], Batch Num: [169/600] Discriminator Loss: 0.7376, Generator Loss: 2.8681 D(x): 0.7346, D(G(z)): 0.3097 Epoch: [0/20], Batch Num: [170/600] Discriminator Loss: 0.8856, Generator Loss: 3.0549 D(x): 0.6582, D(G(z)): 0.3129 Epoch: [0/20], Batch Num: [171/600] Discriminator Loss: 0.9488, Generator Loss: 2.9372 D(x): 0.6192, D(G(z)): 0.2714 Epoch: [0/20], Batch Num: [172/600] Discriminator Loss: 1.1630, Generator Loss: 2.4872 D(x): 0.5530, D(G(z)): 0.2882 Epoch: [0/20], Batch Num: [173/600] Discriminator Loss: 1.2253, Generator Loss: 2.0293 D(x): 0.5919, D(G(z)): 0.3943 Epoch: [0/20], Batch Num: [174/600] Discriminator Loss: 1.1550, Generator Loss: 1.9717 D(x): 0.6662, D(G(z)): 0.4420 Epoch: [0/20], Batch Num: [175/600] Discriminator Loss: 1.4188, Generator Loss: 1.8792 D(x): 0.6209, D(G(z)): 0.5219 Epoch: [0/20], Batch Num: [176/600] Discriminator Loss: 1.5129, Generator Loss: 2.0554 D(x): 0.5929, D(G(z)): 0.5384 Epoch: [0/20], Batch Num: [177/600] Discriminator Loss: 1.6055, Generator Loss: 1.9217 D(x): 0.4794, D(G(z)): 0.4466 Epoch: [0/20], Batch Num: [178/600] Discriminator Loss: 1.5354, Generator Loss: 1.9267 D(x): 0.5363, D(G(z)): 0.4640 Epoch: [0/20], Batch Num: [179/600] Discriminator Loss: 1.5365, Generator Loss: 1.8693 D(x): 0.5542, D(G(z)): 0.4971 Epoch: [0/20], Batch Num: [180/600] Discriminator Loss: 1.5685, Generator Loss: 1.6900 D(x): 0.5531, D(G(z)): 0.5089 Epoch: [0/20], Batch Num: [181/600] Discriminator Loss: 1.3998, Generator Loss: 1.8437 D(x): 0.6168, D(G(z)): 0.5190 Epoch: [0/20], Batch Num: [182/600] Discriminator Loss: 1.4275, Generator Loss: 2.1761 D(x): 0.5807, D(G(z)): 0.4679 Epoch: [0/20], Batch Num: [183/600] Discriminator Loss: 1.2889, Generator Loss: 2.0070 D(x): 0.5728, D(G(z)): 0.4082 Epoch: [0/20], Batch Num: [184/600] Discriminator Loss: 1.2336, Generator Loss: 1.8823 D(x): 0.5808, D(G(z)): 0.3866 Epoch: [0/20], Batch Num: [185/600] Discriminator Loss: 1.0873, Generator Loss: 1.9294 D(x): 0.6637, D(G(z)): 0.4191 Epoch: [0/20], Batch Num: [186/600] Discriminator Loss: 1.0522, Generator Loss: 1.9696 D(x): 0.6867, D(G(z)): 0.4189 Epoch: [0/20], Batch Num: [187/600] Discriminator Loss: 0.8979, Generator Loss: 2.1970 D(x): 0.7452, D(G(z)): 0.3877 Epoch: [0/20], Batch Num: [188/600] Discriminator Loss: 0.6575, Generator Loss: 2.4053 D(x): 0.7830, D(G(z)): 0.3052 Epoch: [0/20], Batch Num: [189/600] Discriminator Loss: 0.5843, Generator Loss: 2.5890 D(x): 0.8193, D(G(z)): 0.2889 Epoch: [0/20], Batch Num: [190/600] Discriminator Loss: 0.5317, Generator Loss: 2.7182 D(x): 0.8279, D(G(z)): 0.2608 Epoch: [0/20], Batch Num: [191/600] Discriminator Loss: 0.5641, Generator Loss: 2.9022 D(x): 0.7709, D(G(z)): 0.2174 Epoch: [0/20], Batch Num: [192/600] Discriminator Loss: 0.4754, Generator Loss: 2.7208 D(x): 0.8240, D(G(z)): 0.2120 Epoch: [0/20], Batch Num: [193/600] Discriminator Loss: 0.5251, Generator Loss: 2.7104 D(x): 0.8163, D(G(z)): 0.2372 Epoch: [0/20], Batch Num: [194/600] Discriminator Loss: 0.5117, Generator Loss: 2.5804 D(x): 0.8407, D(G(z)): 0.2527 Epoch: [0/20], Batch Num: [195/600] Discriminator Loss: 0.5327, Generator Loss: 2.4419 D(x): 0.8800, D(G(z)): 0.2912 Epoch: [0/20], Batch Num: [196/600] Discriminator Loss: 0.6154, Generator Loss: 2.3948 D(x): 0.8686, D(G(z)): 0.3437 Epoch: [0/20], Batch Num: [197/600] Discriminator Loss: 0.6782, Generator Loss: 2.4333 D(x): 0.8502, D(G(z)): 0.3625 Epoch: [0/20], Batch Num: [198/600] Discriminator Loss: 0.7137, Generator Loss: 2.1507 D(x): 0.8151, D(G(z)): 0.3544 Epoch: [0/20], Batch Num: [199/600] Discriminator Loss: 0.9986, Generator Loss: 2.1189 D(x): 0.7806, D(G(z)): 0.4560 Epoch: 0, Batch Num: [200/600]
Epoch: [0/20], Batch Num: [200/600] Discriminator Loss: 0.9086, Generator Loss: 2.0211 D(x): 0.8197, D(G(z)): 0.4333 Epoch: [0/20], Batch Num: [201/600] Discriminator Loss: 0.9055, Generator Loss: 2.0067 D(x): 0.7908, D(G(z)): 0.4377 Epoch: [0/20], Batch Num: [202/600] Discriminator Loss: 0.8385, Generator Loss: 2.0879 D(x): 0.8175, D(G(z)): 0.4100 Epoch: [0/20], Batch Num: [203/600] Discriminator Loss: 0.6694, Generator Loss: 2.6079 D(x): 0.8426, D(G(z)): 0.3517 Epoch: [0/20], Batch Num: [204/600] Discriminator Loss: 0.5598, Generator Loss: 2.8838 D(x): 0.8498, D(G(z)): 0.2806 Epoch: [0/20], Batch Num: [205/600] Discriminator Loss: 0.4382, Generator Loss: 3.1786 D(x): 0.8889, D(G(z)): 0.2179 Epoch: [0/20], Batch Num: [206/600] Discriminator Loss: 0.3378, Generator Loss: 3.6241 D(x): 0.9109, D(G(z)): 0.1870 Epoch: [0/20], Batch Num: [207/600] Discriminator Loss: 0.2100, Generator Loss: 3.8903 D(x): 0.9392, D(G(z)): 0.1267 Epoch: [0/20], Batch Num: [208/600] Discriminator Loss: 0.1872, Generator Loss: 3.8112 D(x): 0.9524, D(G(z)): 0.1212 Epoch: [0/20], Batch Num: [209/600] Discriminator Loss: 0.2041, Generator Loss: 3.7566 D(x): 0.9628, D(G(z)): 0.1400 Epoch: [0/20], Batch Num: [210/600] Discriminator Loss: 0.2619, Generator Loss: 3.6942 D(x): 0.9677, D(G(z)): 0.1890 Epoch: [0/20], Batch Num: [211/600] Discriminator Loss: 0.2675, Generator Loss: 3.3797 D(x): 0.9692, D(G(z)): 0.1978 Epoch: [0/20], Batch Num: [212/600] Discriminator Loss: 0.3590, Generator Loss: 2.9914 D(x): 0.9764, D(G(z)): 0.2688 Epoch: [0/20], Batch Num: [213/600] Discriminator Loss: 0.4237, Generator Loss: 2.9625 D(x): 0.9682, D(G(z)): 0.2909 Epoch: [0/20], Batch Num: [214/600] Discriminator Loss: 0.4924, Generator Loss: 2.9242 D(x): 0.9705, D(G(z)): 0.3416 Epoch: [0/20], Batch Num: [215/600] Discriminator Loss: 0.6234, Generator Loss: 2.8422 D(x): 0.9479, D(G(z)): 0.3907 Epoch: [0/20], Batch Num: [216/600] Discriminator Loss: 0.6726, Generator Loss: 2.9595 D(x): 0.9298, D(G(z)): 0.3948 Epoch: [0/20], Batch Num: [217/600] Discriminator Loss: 0.8335, Generator Loss: 2.9506 D(x): 0.8965, D(G(z)): 0.4508 Epoch: [0/20], Batch Num: [218/600] Discriminator Loss: 1.0198, Generator Loss: 2.7615 D(x): 0.7836, D(G(z)): 0.4316 Epoch: [0/20], Batch Num: [219/600] Discriminator Loss: 1.3710, Generator Loss: 2.3265 D(x): 0.7505, D(G(z)): 0.5249 Epoch: [0/20], Batch Num: [220/600] Discriminator Loss: 1.6645, Generator Loss: 1.8983 D(x): 0.7498, D(G(z)): 0.6652 Epoch: [0/20], Batch Num: [221/600] Discriminator Loss: 2.1903, Generator Loss: 1.3833 D(x): 0.7528, D(G(z)): 0.7828 Epoch: [0/20], Batch Num: [222/600] Discriminator Loss: 2.7846, Generator Loss: 1.1118 D(x): 0.7619, D(G(z)): 0.8626 Epoch: [0/20], Batch Num: [223/600] Discriminator Loss: 3.1516, Generator Loss: 0.7451 D(x): 0.7429, D(G(z)): 0.9099 Epoch: [0/20], Batch Num: [224/600] Discriminator Loss: 3.4502, Generator Loss: 0.4626 D(x): 0.6580, D(G(z)): 0.9191 Epoch: [0/20], Batch Num: [225/600] Discriminator Loss: 3.8689, Generator Loss: 0.4014 D(x): 0.5788, D(G(z)): 0.9288 Epoch: [0/20], Batch Num: [226/600] Discriminator Loss: 4.1244, Generator Loss: 0.4059 D(x): 0.4931, D(G(z)): 0.9321 Epoch: [0/20], Batch Num: [227/600] Discriminator Loss: 3.6857, Generator Loss: 0.4365 D(x): 0.5729, D(G(z)): 0.9180 Epoch: [0/20], Batch Num: [228/600] Discriminator Loss: 3.1983, Generator Loss: 0.7137 D(x): 0.5785, D(G(z)): 0.8782 Epoch: [0/20], Batch Num: [229/600] Discriminator Loss: 2.5742, Generator Loss: 1.2122 D(x): 0.6557, D(G(z)): 0.8231 Epoch: [0/20], Batch Num: [230/600] Discriminator Loss: 2.0029, Generator Loss: 2.0332 D(x): 0.6684, D(G(z)): 0.6982 Epoch: [0/20], Batch Num: [231/600] Discriminator Loss: 1.3821, Generator Loss: 3.6414 D(x): 0.6537, D(G(z)): 0.4803 Epoch: [0/20], Batch Num: [232/600] Discriminator Loss: 1.1986, Generator Loss: 4.8706 D(x): 0.6045, D(G(z)): 0.3288 Epoch: [0/20], Batch Num: [233/600] Discriminator Loss: 1.2239, Generator Loss: 5.2146 D(x): 0.4872, D(G(z)): 0.1799 Epoch: [0/20], Batch Num: [234/600] Discriminator Loss: 1.2970, Generator Loss: 4.8424 D(x): 0.4805, D(G(z)): 0.1455 Epoch: [0/20], Batch Num: [235/600] Discriminator Loss: 0.8832, Generator Loss: 4.1107 D(x): 0.6294, D(G(z)): 0.1865 Epoch: [0/20], Batch Num: [236/600] Discriminator Loss: 0.9230, Generator Loss: 3.7021 D(x): 0.6983, D(G(z)): 0.2893 Epoch: [0/20], Batch Num: [237/600] Discriminator Loss: 1.1147, Generator Loss: 3.6811 D(x): 0.7233, D(G(z)): 0.4258 Epoch: [0/20], Batch Num: [238/600] Discriminator Loss: 0.9427, Generator Loss: 3.8535 D(x): 0.7152, D(G(z)): 0.3127 Epoch: [0/20], Batch Num: [239/600] Discriminator Loss: 1.3040, Generator Loss: 4.1377 D(x): 0.5689, D(G(z)): 0.2845 Epoch: [0/20], Batch Num: [240/600] Discriminator Loss: 1.2124, Generator Loss: 3.8424 D(x): 0.5810, D(G(z)): 0.2978 Epoch: [0/20], Batch Num: [241/600] Discriminator Loss: 1.4023, Generator Loss: 3.1724 D(x): 0.5452, D(G(z)): 0.3327 Epoch: [0/20], Batch Num: [242/600] Discriminator Loss: 1.4698, Generator Loss: 2.9145 D(x): 0.5864, D(G(z)): 0.3599 Epoch: [0/20], Batch Num: [243/600] Discriminator Loss: 1.4432, Generator Loss: 2.5519 D(x): 0.6519, D(G(z)): 0.4249 Epoch: [0/20], Batch Num: [244/600] Discriminator Loss: 1.6462, Generator Loss: 2.6780 D(x): 0.6372, D(G(z)): 0.4593 Epoch: [0/20], Batch Num: [245/600] Discriminator Loss: 1.4753, Generator Loss: 3.1936 D(x): 0.5806, D(G(z)): 0.4117 Epoch: [0/20], Batch Num: [246/600] Discriminator Loss: 1.6904, Generator Loss: 2.7215 D(x): 0.5042, D(G(z)): 0.3362 Epoch: [0/20], Batch Num: [247/600] Discriminator Loss: 1.4428, Generator Loss: 2.4317 D(x): 0.6080, D(G(z)): 0.4065 Epoch: [0/20], Batch Num: [248/600] Discriminator Loss: 1.7317, Generator Loss: 2.5980 D(x): 0.5633, D(G(z)): 0.4570 Epoch: [0/20], Batch Num: [249/600] Discriminator Loss: 2.0449, Generator Loss: 2.1086 D(x): 0.4631, D(G(z)): 0.4101 Epoch: [0/20], Batch Num: [250/600] Discriminator Loss: 2.0100, Generator Loss: 1.7823 D(x): 0.5111, D(G(z)): 0.5429 Epoch: [0/20], Batch Num: [251/600] Discriminator Loss: 2.4089, Generator Loss: 1.6016 D(x): 0.4946, D(G(z)): 0.5957 Epoch: [0/20], Batch Num: [252/600] Discriminator Loss: 2.2947, Generator Loss: 1.6971 D(x): 0.4943, D(G(z)): 0.5506 Epoch: [0/20], Batch Num: [253/600] Discriminator Loss: 2.5003, Generator Loss: 1.5763 D(x): 0.3989, D(G(z)): 0.5616 Epoch: [0/20], Batch Num: [254/600] Discriminator Loss: 2.6157, Generator Loss: 1.6664 D(x): 0.4193, D(G(z)): 0.5546 Epoch: [0/20], Batch Num: [255/600] Discriminator Loss: 2.1071, Generator Loss: 1.5496 D(x): 0.4873, D(G(z)): 0.5196 Epoch: [0/20], Batch Num: [256/600] Discriminator Loss: 1.8149, Generator Loss: 1.7503 D(x): 0.4820, D(G(z)): 0.4274 Epoch: [0/20], Batch Num: [257/600] Discriminator Loss: 1.6699, Generator Loss: 1.8347 D(x): 0.5744, D(G(z)): 0.5022 Epoch: [0/20], Batch Num: [258/600] Discriminator Loss: 1.0655, Generator Loss: 2.3592 D(x): 0.7107, D(G(z)): 0.3988 Epoch: [0/20], Batch Num: [259/600] Discriminator Loss: 1.0344, Generator Loss: 2.4587 D(x): 0.6506, D(G(z)): 0.3040 Epoch: [0/20], Batch Num: [260/600] Discriminator Loss: 0.8716, Generator Loss: 2.7074 D(x): 0.6824, D(G(z)): 0.2422 Epoch: [0/20], Batch Num: [261/600] Discriminator Loss: 0.4163, Generator Loss: 3.2893 D(x): 0.8099, D(G(z)): 0.1551 Epoch: [0/20], Batch Num: [262/600] Discriminator Loss: 0.4096, Generator Loss: 3.4558 D(x): 0.8224, D(G(z)): 0.1444 Epoch: [0/20], Batch Num: [263/600] Discriminator Loss: 0.3184, Generator Loss: 3.5489 D(x): 0.8336, D(G(z)): 0.0902 Epoch: [0/20], Batch Num: [264/600] Discriminator Loss: 0.2801, Generator Loss: 3.7337 D(x): 0.8491, D(G(z)): 0.0827 Epoch: [0/20], Batch Num: [265/600] Discriminator Loss: 0.2361, Generator Loss: 4.1468 D(x): 0.8858, D(G(z)): 0.0662 Epoch: [0/20], Batch Num: [266/600] Discriminator Loss: 0.1748, Generator Loss: 4.0025 D(x): 0.9143, D(G(z)): 0.0665 Epoch: [0/20], Batch Num: [267/600] Discriminator Loss: 0.1312, Generator Loss: 4.1672 D(x): 0.9359, D(G(z)): 0.0553 Epoch: [0/20], Batch Num: [268/600] Discriminator Loss: 0.1615, Generator Loss: 4.4024 D(x): 0.9334, D(G(z)): 0.0651 Epoch: [0/20], Batch Num: [269/600] Discriminator Loss: 0.1176, Generator Loss: 4.5093 D(x): 0.9410, D(G(z)): 0.0482 Epoch: [0/20], Batch Num: [270/600] Discriminator Loss: 0.0938, Generator Loss: 4.7856 D(x): 0.9635, D(G(z)): 0.0517 Epoch: [0/20], Batch Num: [271/600] Discriminator Loss: 0.0810, Generator Loss: 4.4808 D(x): 0.9593, D(G(z)): 0.0357 Epoch: [0/20], Batch Num: [272/600] Discriminator Loss: 0.0900, Generator Loss: 4.3926 D(x): 0.9639, D(G(z)): 0.0464 Epoch: [0/20], Batch Num: [273/600] Discriminator Loss: 0.0808, Generator Loss: 4.5429 D(x): 0.9717, D(G(z)): 0.0487 Epoch: [0/20], Batch Num: [274/600] Discriminator Loss: 0.0916, Generator Loss: 4.3356 D(x): 0.9674, D(G(z)): 0.0539 Epoch: [0/20], Batch Num: [275/600] Discriminator Loss: 0.1133, Generator Loss: 3.9325 D(x): 0.9564, D(G(z)): 0.0611 Epoch: [0/20], Batch Num: [276/600] Discriminator Loss: 0.1539, Generator Loss: 4.3792 D(x): 0.9538, D(G(z)): 0.0891 Epoch: [0/20], Batch Num: [277/600] Discriminator Loss: 0.1395, Generator Loss: 4.6449 D(x): 0.9459, D(G(z)): 0.0685 Epoch: [0/20], Batch Num: [278/600] Discriminator Loss: 0.1330, Generator Loss: 4.8289 D(x): 0.9521, D(G(z)): 0.0723 Epoch: [0/20], Batch Num: [279/600] Discriminator Loss: 0.1440, Generator Loss: 4.7882 D(x): 0.9436, D(G(z)): 0.0757 Epoch: [0/20], Batch Num: [280/600] Discriminator Loss: 0.1555, Generator Loss: 5.1488 D(x): 0.9328, D(G(z)): 0.0721 Epoch: [0/20], Batch Num: [281/600] Discriminator Loss: 0.2216, Generator Loss: 5.2763 D(x): 0.8892, D(G(z)): 0.0673 Epoch: [0/20], Batch Num: [282/600] Discriminator Loss: 0.2164, Generator Loss: 4.9849 D(x): 0.8759, D(G(z)): 0.0573 Epoch: [0/20], Batch Num: [283/600] Discriminator Loss: 0.2838, Generator Loss: 5.0490 D(x): 0.8629, D(G(z)): 0.0686 Epoch: [0/20], Batch Num: [284/600] Discriminator Loss: 0.1968, Generator Loss: 4.7905 D(x): 0.9079, D(G(z)): 0.0836 Epoch: [0/20], Batch Num: [285/600] Discriminator Loss: 0.2356, Generator Loss: 4.3804 D(x): 0.8919, D(G(z)): 0.0842 Epoch: [0/20], Batch Num: [286/600] Discriminator Loss: 0.2215, Generator Loss: 4.1736 D(x): 0.9076, D(G(z)): 0.0947 Epoch: [0/20], Batch Num: [287/600] Discriminator Loss: 0.2095, Generator Loss: 4.0381 D(x): 0.9183, D(G(z)): 0.1050 Epoch: [0/20], Batch Num: [288/600] Discriminator Loss: 0.2304, Generator Loss: 4.4215 D(x): 0.9248, D(G(z)): 0.1172 Epoch: [0/20], Batch Num: [289/600] Discriminator Loss: 0.2084, Generator Loss: 4.7047 D(x): 0.9254, D(G(z)): 0.1064 Epoch: [0/20], Batch Num: [290/600] Discriminator Loss: 0.2328, Generator Loss: 4.6516 D(x): 0.8906, D(G(z)): 0.0903 Epoch: [0/20], Batch Num: [291/600] Discriminator Loss: 0.1920, Generator Loss: 4.8816 D(x): 0.9265, D(G(z)): 0.0914 Epoch: [0/20], Batch Num: [292/600] Discriminator Loss: 0.2264, Generator Loss: 5.2001 D(x): 0.8966, D(G(z)): 0.0811 Epoch: [0/20], Batch Num: [293/600] Discriminator Loss: 0.2442, Generator Loss: 4.7204 D(x): 0.8922, D(G(z)): 0.0918 Epoch: [0/20], Batch Num: [294/600] Discriminator Loss: 0.2652, Generator Loss: 4.4690 D(x): 0.9024, D(G(z)): 0.1201 Epoch: [0/20], Batch Num: [295/600] Discriminator Loss: 0.2115, Generator Loss: 4.1499 D(x): 0.9308, D(G(z)): 0.1137 Epoch: [0/20], Batch Num: [296/600] Discriminator Loss: 0.2321, Generator Loss: 4.6099 D(x): 0.9269, D(G(z)): 0.1277 Epoch: [0/20], Batch Num: [297/600] Discriminator Loss: 0.2710, Generator Loss: 4.4073 D(x): 0.9163, D(G(z)): 0.1284 Epoch: [0/20], Batch Num: [298/600] Discriminator Loss: 0.3584, Generator Loss: 4.5914 D(x): 0.8850, D(G(z)): 0.1474 Epoch: [0/20], Batch Num: [299/600] Discriminator Loss: 0.3294, Generator Loss: 4.4059 D(x): 0.8963, D(G(z)): 0.1613 Epoch: 0, Batch Num: [300/600]
Epoch: [0/20], Batch Num: [300/600] Discriminator Loss: 0.3378, Generator Loss: 4.4186 D(x): 0.8675, D(G(z)): 0.1273 Epoch: [0/20], Batch Num: [301/600] Discriminator Loss: 0.4077, Generator Loss: 4.3169 D(x): 0.8571, D(G(z)): 0.1558 Epoch: [0/20], Batch Num: [302/600] Discriminator Loss: 0.4715, Generator Loss: 4.0077 D(x): 0.8285, D(G(z)): 0.1581 Epoch: [0/20], Batch Num: [303/600] Discriminator Loss: 0.5292, Generator Loss: 3.6661 D(x): 0.8507, D(G(z)): 0.2064 Epoch: [0/20], Batch Num: [304/600] Discriminator Loss: 0.5801, Generator Loss: 3.5109 D(x): 0.8343, D(G(z)): 0.2295 Epoch: [0/20], Batch Num: [305/600] Discriminator Loss: 0.5487, Generator Loss: 4.1193 D(x): 0.8902, D(G(z)): 0.3074 Epoch: [0/20], Batch Num: [306/600] Discriminator Loss: 0.4946, Generator Loss: 4.4040 D(x): 0.8420, D(G(z)): 0.2181 Epoch: [0/20], Batch Num: [307/600] Discriminator Loss: 0.5920, Generator Loss: 4.4235 D(x): 0.8268, D(G(z)): 0.2416 Epoch: [0/20], Batch Num: [308/600] Discriminator Loss: 0.6398, Generator Loss: 4.2284 D(x): 0.8224, D(G(z)): 0.2246 Epoch: [0/20], Batch Num: [309/600] Discriminator Loss: 0.7078, Generator Loss: 3.9516 D(x): 0.8109, D(G(z)): 0.2872 Epoch: [0/20], Batch Num: [310/600] Discriminator Loss: 0.5823, Generator Loss: 4.0908 D(x): 0.8709, D(G(z)): 0.2844 Epoch: [0/20], Batch Num: [311/600] Discriminator Loss: 0.6484, Generator Loss: 4.1293 D(x): 0.8631, D(G(z)): 0.3056 Epoch: [0/20], Batch Num: [312/600] Discriminator Loss: 0.6123, Generator Loss: 3.7420 D(x): 0.8523, D(G(z)): 0.2774 Epoch: [0/20], Batch Num: [313/600] Discriminator Loss: 0.6142, Generator Loss: 3.7879 D(x): 0.8713, D(G(z)): 0.3241 Epoch: [0/20], Batch Num: [314/600] Discriminator Loss: 0.6176, Generator Loss: 3.4247 D(x): 0.8799, D(G(z)): 0.3153 Epoch: [0/20], Batch Num: [315/600] Discriminator Loss: 0.5878, Generator Loss: 3.7192 D(x): 0.8503, D(G(z)): 0.2774 Epoch: [0/20], Batch Num: [316/600] Discriminator Loss: 0.5088, Generator Loss: 3.8903 D(x): 0.8703, D(G(z)): 0.2698 Epoch: [0/20], Batch Num: [317/600] Discriminator Loss: 0.5097, Generator Loss: 4.1630 D(x): 0.8598, D(G(z)): 0.2175 Epoch: [0/20], Batch Num: [318/600] Discriminator Loss: 0.5365, Generator Loss: 4.0946 D(x): 0.8161, D(G(z)): 0.1743 Epoch: [0/20], Batch Num: [319/600] Discriminator Loss: 0.2974, Generator Loss: 4.8686 D(x): 0.9298, D(G(z)): 0.1600 Epoch: [0/20], Batch Num: [320/600] Discriminator Loss: 0.2551, Generator Loss: 4.8189 D(x): 0.9063, D(G(z)): 0.0935 Epoch: [0/20], Batch Num: [321/600] Discriminator Loss: 0.2400, Generator Loss: 4.7758 D(x): 0.9305, D(G(z)): 0.1047 Epoch: [0/20], Batch Num: [322/600] Discriminator Loss: 0.1495, Generator Loss: 5.1347 D(x): 0.9612, D(G(z)): 0.0884 Epoch: [0/20], Batch Num: [323/600] Discriminator Loss: 0.1547, Generator Loss: 6.0189 D(x): 0.9762, D(G(z)): 0.1095 Epoch: [0/20], Batch Num: [324/600] Discriminator Loss: 0.1670, Generator Loss: 5.4164 D(x): 0.9753, D(G(z)): 0.1122 Epoch: [0/20], Batch Num: [325/600] Discriminator Loss: 0.1739, Generator Loss: 5.2561 D(x): 0.9803, D(G(z)): 0.1218 Epoch: [0/20], Batch Num: [326/600] Discriminator Loss: 0.1957, Generator Loss: 4.1296 D(x): 0.9809, D(G(z)): 0.1426 Epoch: [0/20], Batch Num: [327/600] Discriminator Loss: 0.1950, Generator Loss: 4.4456 D(x): 0.9791, D(G(z)): 0.1417 Epoch: [0/20], Batch Num: [328/600] Discriminator Loss: 0.2748, Generator Loss: 4.5575 D(x): 0.9744, D(G(z)): 0.1940 Epoch: [0/20], Batch Num: [329/600] Discriminator Loss: 0.3059, Generator Loss: 4.4916 D(x): 0.9854, D(G(z)): 0.2148 Epoch: [0/20], Batch Num: [330/600] Discriminator Loss: 0.2334, Generator Loss: 4.8871 D(x): 0.9771, D(G(z)): 0.1700 Epoch: [0/20], Batch Num: [331/600] Discriminator Loss: 0.2066, Generator Loss: 5.4307 D(x): 0.9760, D(G(z)): 0.1512 Epoch: [0/20], Batch Num: [332/600] Discriminator Loss: 0.1633, Generator Loss: 5.2715 D(x): 0.9731, D(G(z)): 0.1149 Epoch: [0/20], Batch Num: [333/600] Discriminator Loss: 0.2096, Generator Loss: 5.9224 D(x): 0.9366, D(G(z)): 0.1162 Epoch: [0/20], Batch Num: [334/600] Discriminator Loss: 0.2443, Generator Loss: 5.2586 D(x): 0.9232, D(G(z)): 0.1258 Epoch: [0/20], Batch Num: [335/600] Discriminator Loss: 0.2647, Generator Loss: 4.8717 D(x): 0.8936, D(G(z)): 0.1077 Epoch: [0/20], Batch Num: [336/600] Discriminator Loss: 0.3286, Generator Loss: 4.4946 D(x): 0.8676, D(G(z)): 0.1311 Epoch: [0/20], Batch Num: [337/600] Discriminator Loss: 0.3014, Generator Loss: 3.8580 D(x): 0.8753, D(G(z)): 0.1359 Epoch: [0/20], Batch Num: [338/600] Discriminator Loss: 0.4657, Generator Loss: 3.4570 D(x): 0.8498, D(G(z)): 0.2205 Epoch: [0/20], Batch Num: [339/600] Discriminator Loss: 0.5175, Generator Loss: 2.9128 D(x): 0.8587, D(G(z)): 0.2714 Epoch: [0/20], Batch Num: [340/600] Discriminator Loss: 0.7476, Generator Loss: 2.9062 D(x): 0.7871, D(G(z)): 0.2940 Epoch: [0/20], Batch Num: [341/600] Discriminator Loss: 0.9251, Generator Loss: 2.3504 D(x): 0.7261, D(G(z)): 0.3415 Epoch: [0/20], Batch Num: [342/600] Discriminator Loss: 1.1914, Generator Loss: 2.0162 D(x): 0.6800, D(G(z)): 0.4262 Epoch: [0/20], Batch Num: [343/600] Discriminator Loss: 1.5330, Generator Loss: 1.8931 D(x): 0.6103, D(G(z)): 0.4815 Epoch: [0/20], Batch Num: [344/600] Discriminator Loss: 1.5510, Generator Loss: 1.7129 D(x): 0.6655, D(G(z)): 0.5245 Epoch: [0/20], Batch Num: [345/600] Discriminator Loss: 1.5849, Generator Loss: 1.5490 D(x): 0.6838, D(G(z)): 0.5592 Epoch: [0/20], Batch Num: [346/600] Discriminator Loss: 1.5829, Generator Loss: 1.1877 D(x): 0.6436, D(G(z)): 0.5055 Epoch: [0/20], Batch Num: [347/600] Discriminator Loss: 1.4055, Generator Loss: 1.5322 D(x): 0.7102, D(G(z)): 0.5312 Epoch: [0/20], Batch Num: [348/600] Discriminator Loss: 1.2521, Generator Loss: 1.6479 D(x): 0.7364, D(G(z)): 0.5101 Epoch: [0/20], Batch Num: [349/600] Discriminator Loss: 1.1557, Generator Loss: 1.7793 D(x): 0.7388, D(G(z)): 0.4847 Epoch: [0/20], Batch Num: [350/600] Discriminator Loss: 1.3250, Generator Loss: 1.8958 D(x): 0.7003, D(G(z)): 0.4750 Epoch: [0/20], Batch Num: [351/600] Discriminator Loss: 1.2288, Generator Loss: 1.9919 D(x): 0.7205, D(G(z)): 0.4936 Epoch: [0/20], Batch Num: [352/600] Discriminator Loss: 0.9772, Generator Loss: 2.1081 D(x): 0.7385, D(G(z)): 0.3933 Epoch: [0/20], Batch Num: [353/600] Discriminator Loss: 0.9898, Generator Loss: 2.2842 D(x): 0.7301, D(G(z)): 0.4145 Epoch: [0/20], Batch Num: [354/600] Discriminator Loss: 0.9732, Generator Loss: 2.3749 D(x): 0.6853, D(G(z)): 0.3409 Epoch: [0/20], Batch Num: [355/600] Discriminator Loss: 0.8764, Generator Loss: 2.3410 D(x): 0.7081, D(G(z)): 0.3178 Epoch: [0/20], Batch Num: [356/600] Discriminator Loss: 0.9863, Generator Loss: 2.3610 D(x): 0.6530, D(G(z)): 0.3327 Epoch: [0/20], Batch Num: [357/600] Discriminator Loss: 1.0412, Generator Loss: 2.1764 D(x): 0.6662, D(G(z)): 0.3406 Epoch: [0/20], Batch Num: [358/600] Discriminator Loss: 0.8415, Generator Loss: 2.2480 D(x): 0.7666, D(G(z)): 0.3726 Epoch: [0/20], Batch Num: [359/600] Discriminator Loss: 0.8847, Generator Loss: 2.5500 D(x): 0.7512, D(G(z)): 0.3469 Epoch: [0/20], Batch Num: [360/600] Discriminator Loss: 0.8525, Generator Loss: 2.7454 D(x): 0.7385, D(G(z)): 0.3187 Epoch: [0/20], Batch Num: [361/600] Discriminator Loss: 0.8883, Generator Loss: 3.0345 D(x): 0.6984, D(G(z)): 0.2491 Epoch: [0/20], Batch Num: [362/600] Discriminator Loss: 0.6770, Generator Loss: 3.2126 D(x): 0.7642, D(G(z)): 0.2394 Epoch: [0/20], Batch Num: [363/600] Discriminator Loss: 0.6818, Generator Loss: 3.6033 D(x): 0.7424, D(G(z)): 0.2228 Epoch: [0/20], Batch Num: [364/600] Discriminator Loss: 0.8657, Generator Loss: 3.5201 D(x): 0.6610, D(G(z)): 0.1718 Epoch: [0/20], Batch Num: [365/600] Discriminator Loss: 0.7704, Generator Loss: 3.1906 D(x): 0.6986, D(G(z)): 0.2068 Epoch: [0/20], Batch Num: [366/600] Discriminator Loss: 0.6863, Generator Loss: 2.9705 D(x): 0.7384, D(G(z)): 0.2110 Epoch: [0/20], Batch Num: [367/600] Discriminator Loss: 0.7620, Generator Loss: 3.0217 D(x): 0.7373, D(G(z)): 0.2138 Epoch: [0/20], Batch Num: [368/600] Discriminator Loss: 0.7306, Generator Loss: 3.0261 D(x): 0.7514, D(G(z)): 0.2508 Epoch: [0/20], Batch Num: [369/600] Discriminator Loss: 0.5604, Generator Loss: 3.0055 D(x): 0.7998, D(G(z)): 0.2193 Epoch: [0/20], Batch Num: [370/600] Discriminator Loss: 0.8189, Generator Loss: 3.2061 D(x): 0.7349, D(G(z)): 0.2355 Epoch: [0/20], Batch Num: [371/600] Discriminator Loss: 0.8259, Generator Loss: 2.8850 D(x): 0.7013, D(G(z)): 0.2174 Epoch: [0/20], Batch Num: [372/600] Discriminator Loss: 1.0870, Generator Loss: 2.5400 D(x): 0.6417, D(G(z)): 0.2385 Epoch: [0/20], Batch Num: [373/600] Discriminator Loss: 0.9984, Generator Loss: 2.0578 D(x): 0.6919, D(G(z)): 0.3125 Epoch: [0/20], Batch Num: [374/600] Discriminator Loss: 1.0087, Generator Loss: 2.1666 D(x): 0.7480, D(G(z)): 0.3971 Epoch: [0/20], Batch Num: [375/600] Discriminator Loss: 1.1479, Generator Loss: 2.3249 D(x): 0.6966, D(G(z)): 0.3655 Epoch: [0/20], Batch Num: [376/600] Discriminator Loss: 0.9389, Generator Loss: 2.7871 D(x): 0.7302, D(G(z)): 0.3498 Epoch: [0/20], Batch Num: [377/600] Discriminator Loss: 0.7718, Generator Loss: 3.2262 D(x): 0.7398, D(G(z)): 0.2620 Epoch: [0/20], Batch Num: [378/600] Discriminator Loss: 0.6473, Generator Loss: 3.3029 D(x): 0.7499, D(G(z)): 0.2045 Epoch: [0/20], Batch Num: [379/600] Discriminator Loss: 0.6817, Generator Loss: 3.1151 D(x): 0.7233, D(G(z)): 0.1618 Epoch: [0/20], Batch Num: [380/600] Discriminator Loss: 0.5807, Generator Loss: 2.9312 D(x): 0.7709, D(G(z)): 0.1775 Epoch: [0/20], Batch Num: [381/600] Discriminator Loss: 0.4236, Generator Loss: 2.9158 D(x): 0.8563, D(G(z)): 0.1749 Epoch: [0/20], Batch Num: [382/600] Discriminator Loss: 0.3306, Generator Loss: 3.2439 D(x): 0.9140, D(G(z)): 0.1737 Epoch: [0/20], Batch Num: [383/600] Discriminator Loss: 0.2632, Generator Loss: 3.6253 D(x): 0.9311, D(G(z)): 0.1474 Epoch: [0/20], Batch Num: [384/600] Discriminator Loss: 0.1779, Generator Loss: 4.6465 D(x): 0.9368, D(G(z)): 0.0905 Epoch: [0/20], Batch Num: [385/600] Discriminator Loss: 0.2007, Generator Loss: 5.2087 D(x): 0.9201, D(G(z)): 0.0925 Epoch: [0/20], Batch Num: [386/600] Discriminator Loss: 0.1361, Generator Loss: 5.6504 D(x): 0.9253, D(G(z)): 0.0448 Epoch: [0/20], Batch Num: [387/600] Discriminator Loss: 0.1723, Generator Loss: 5.4397 D(x): 0.9152, D(G(z)): 0.0332 Epoch: [0/20], Batch Num: [388/600] Discriminator Loss: 0.1206, Generator Loss: 6.1569 D(x): 0.9430, D(G(z)): 0.0390 Epoch: [0/20], Batch Num: [389/600] Discriminator Loss: 0.1155, Generator Loss: 5.5913 D(x): 0.9386, D(G(z)): 0.0378 Epoch: [0/20], Batch Num: [390/600] Discriminator Loss: 0.1269, Generator Loss: 4.9678 D(x): 0.9491, D(G(z)): 0.0597 Epoch: [0/20], Batch Num: [391/600] Discriminator Loss: 0.1634, Generator Loss: 4.7428 D(x): 0.9493, D(G(z)): 0.0751 Epoch: [0/20], Batch Num: [392/600] Discriminator Loss: 0.1940, Generator Loss: 4.8659 D(x): 0.9641, D(G(z)): 0.1206 Epoch: [0/20], Batch Num: [393/600] Discriminator Loss: 0.2070, Generator Loss: 4.7886 D(x): 0.9711, D(G(z)): 0.1414 Epoch: [0/20], Batch Num: [394/600] Discriminator Loss: 0.2567, Generator Loss: 5.8451 D(x): 0.9452, D(G(z)): 0.1476 Epoch: [0/20], Batch Num: [395/600] Discriminator Loss: 0.2658, Generator Loss: 5.6535 D(x): 0.9275, D(G(z)): 0.1347 Epoch: [0/20], Batch Num: [396/600] Discriminator Loss: 0.3585, Generator Loss: 5.3229 D(x): 0.8706, D(G(z)): 0.1294 Epoch: [0/20], Batch Num: [397/600] Discriminator Loss: 0.4910, Generator Loss: 5.3154 D(x): 0.8239, D(G(z)): 0.1482 Epoch: [0/20], Batch Num: [398/600] Discriminator Loss: 0.6025, Generator Loss: 4.1603 D(x): 0.8322, D(G(z)): 0.2335 Epoch: [0/20], Batch Num: [399/600] Discriminator Loss: 0.6588, Generator Loss: 4.4807 D(x): 0.8618, D(G(z)): 0.3018 Epoch: 0, Batch Num: [400/600]
Epoch: [0/20], Batch Num: [400/600] Discriminator Loss: 0.8637, Generator Loss: 3.8195 D(x): 0.7915, D(G(z)): 0.3371 Epoch: [0/20], Batch Num: [401/600] Discriminator Loss: 1.1138, Generator Loss: 3.1757 D(x): 0.7231, D(G(z)): 0.3864 Epoch: [0/20], Batch Num: [402/600] Discriminator Loss: 1.5666, Generator Loss: 2.7434 D(x): 0.7470, D(G(z)): 0.5415 Epoch: [0/20], Batch Num: [403/600] Discriminator Loss: 1.8187, Generator Loss: 2.6013 D(x): 0.6786, D(G(z)): 0.6137 Epoch: [0/20], Batch Num: [404/600] Discriminator Loss: 2.5149, Generator Loss: 1.5563 D(x): 0.5964, D(G(z)): 0.7367 Epoch: [0/20], Batch Num: [405/600] Discriminator Loss: 2.8111, Generator Loss: 0.7896 D(x): 0.5935, D(G(z)): 0.8126 Epoch: [0/20], Batch Num: [406/600] Discriminator Loss: 2.9330, Generator Loss: 0.5005 D(x): 0.6860, D(G(z)): 0.8784 Epoch: [0/20], Batch Num: [407/600] Discriminator Loss: 3.0145, Generator Loss: 0.3111 D(x): 0.7703, D(G(z)): 0.9052 Epoch: [0/20], Batch Num: [408/600] Discriminator Loss: 2.9508, Generator Loss: 0.2850 D(x): 0.7619, D(G(z)): 0.9126 Epoch: [0/20], Batch Num: [409/600] Discriminator Loss: 2.9008, Generator Loss: 0.2856 D(x): 0.7282, D(G(z)): 0.8778 Epoch: [0/20], Batch Num: [410/600] Discriminator Loss: 2.2671, Generator Loss: 0.6555 D(x): 0.8052, D(G(z)): 0.8255 Epoch: [0/20], Batch Num: [411/600] Discriminator Loss: 1.6237, Generator Loss: 1.1743 D(x): 0.8351, D(G(z)): 0.7028 Epoch: [0/20], Batch Num: [412/600] Discriminator Loss: 1.3443, Generator Loss: 1.8150 D(x): 0.7760, D(G(z)): 0.5617 Epoch: [0/20], Batch Num: [413/600] Discriminator Loss: 1.1458, Generator Loss: 1.9022 D(x): 0.8387, D(G(z)): 0.5271 Epoch: [0/20], Batch Num: [414/600] Discriminator Loss: 1.3398, Generator Loss: 1.7302 D(x): 0.8493, D(G(z)): 0.5665 Epoch: [0/20], Batch Num: [415/600] Discriminator Loss: 1.2380, Generator Loss: 1.1740 D(x): 0.8716, D(G(z)): 0.6396 Epoch: [0/20], Batch Num: [416/600] Discriminator Loss: 1.5691, Generator Loss: 0.8763 D(x): 0.8362, D(G(z)): 0.7196 Epoch: [0/20], Batch Num: [417/600] Discriminator Loss: 1.6766, Generator Loss: 0.4624 D(x): 0.8669, D(G(z)): 0.7723 Epoch: [0/20], Batch Num: [418/600] Discriminator Loss: 1.8481, Generator Loss: 0.3247 D(x): 0.8534, D(G(z)): 0.8032 Epoch: [0/20], Batch Num: [419/600] Discriminator Loss: 1.8624, Generator Loss: 0.3008 D(x): 0.8527, D(G(z)): 0.8074 Epoch: [0/20], Batch Num: [420/600] Discriminator Loss: 1.8794, Generator Loss: 0.2948 D(x): 0.8618, D(G(z)): 0.8091 Epoch: [0/20], Batch Num: [421/600] Discriminator Loss: 1.7642, Generator Loss: 0.3647 D(x): 0.8253, D(G(z)): 0.7593 Epoch: [0/20], Batch Num: [422/600] Discriminator Loss: 1.4364, Generator Loss: 0.4694 D(x): 0.8551, D(G(z)): 0.7003 Epoch: [0/20], Batch Num: [423/600] Discriminator Loss: 1.2661, Generator Loss: 0.6384 D(x): 0.8717, D(G(z)): 0.6370 Epoch: [0/20], Batch Num: [424/600] Discriminator Loss: 1.1418, Generator Loss: 0.9491 D(x): 0.8711, D(G(z)): 0.5926 Epoch: [0/20], Batch Num: [425/600] Discriminator Loss: 0.9564, Generator Loss: 1.1156 D(x): 0.9174, D(G(z)): 0.5572 Epoch: [0/20], Batch Num: [426/600] Discriminator Loss: 0.9834, Generator Loss: 1.2297 D(x): 0.8968, D(G(z)): 0.5508 Epoch: [0/20], Batch Num: [427/600] Discriminator Loss: 0.9950, Generator Loss: 1.0573 D(x): 0.8894, D(G(z)): 0.5552 Epoch: [0/20], Batch Num: [428/600] Discriminator Loss: 1.0495, Generator Loss: 1.0541 D(x): 0.9108, D(G(z)): 0.5976 Epoch: [0/20], Batch Num: [429/600] Discriminator Loss: 1.0904, Generator Loss: 0.9624 D(x): 0.8999, D(G(z)): 0.6134 Epoch: [0/20], Batch Num: [430/600] Discriminator Loss: 1.1362, Generator Loss: 0.9815 D(x): 0.8894, D(G(z)): 0.6262 Epoch: [0/20], Batch Num: [431/600] Discriminator Loss: 1.0766, Generator Loss: 1.1177 D(x): 0.8730, D(G(z)): 0.5925 Epoch: [0/20], Batch Num: [432/600] Discriminator Loss: 0.9921, Generator Loss: 1.3311 D(x): 0.8675, D(G(z)): 0.5596 Epoch: [0/20], Batch Num: [433/600] Discriminator Loss: 0.8906, Generator Loss: 1.5313 D(x): 0.8492, D(G(z)): 0.5012 Epoch: [0/20], Batch Num: [434/600] Discriminator Loss: 0.7964, Generator Loss: 1.9437 D(x): 0.8010, D(G(z)): 0.4149 Epoch: [0/20], Batch Num: [435/600] Discriminator Loss: 0.7392, Generator Loss: 2.3470 D(x): 0.7797, D(G(z)): 0.3585 Epoch: [0/20], Batch Num: [436/600] Discriminator Loss: 0.7456, Generator Loss: 2.7541 D(x): 0.6985, D(G(z)): 0.2838 Epoch: [0/20], Batch Num: [437/600] Discriminator Loss: 0.7623, Generator Loss: 3.2118 D(x): 0.6397, D(G(z)): 0.2150 Epoch: [0/20], Batch Num: [438/600] Discriminator Loss: 0.8586, Generator Loss: 2.9694 D(x): 0.5496, D(G(z)): 0.1778 Epoch: [0/20], Batch Num: [439/600] Discriminator Loss: 1.0126, Generator Loss: 2.6910 D(x): 0.4825, D(G(z)): 0.1834 Epoch: [0/20], Batch Num: [440/600] Discriminator Loss: 1.0929, Generator Loss: 2.0530 D(x): 0.4684, D(G(z)): 0.2210 Epoch: [0/20], Batch Num: [441/600] Discriminator Loss: 1.1882, Generator Loss: 1.4595 D(x): 0.5184, D(G(z)): 0.3663 Epoch: [0/20], Batch Num: [442/600] Discriminator Loss: 1.3562, Generator Loss: 1.0952 D(x): 0.5165, D(G(z)): 0.4566 Epoch: [0/20], Batch Num: [443/600] Discriminator Loss: 1.3760, Generator Loss: 0.8087 D(x): 0.6128, D(G(z)): 0.5585 Epoch: [0/20], Batch Num: [444/600] Discriminator Loss: 1.5048, Generator Loss: 0.8477 D(x): 0.6391, D(G(z)): 0.6219 Epoch: [0/20], Batch Num: [445/600] Discriminator Loss: 1.4412, Generator Loss: 0.9277 D(x): 0.6475, D(G(z)): 0.6138 Epoch: [0/20], Batch Num: [446/600] Discriminator Loss: 1.4301, Generator Loss: 1.0640 D(x): 0.6293, D(G(z)): 0.5865 Epoch: [0/20], Batch Num: [447/600] Discriminator Loss: 1.2869, Generator Loss: 1.3038 D(x): 0.6142, D(G(z)): 0.5161 Epoch: [0/20], Batch Num: [448/600] Discriminator Loss: 1.2356, Generator Loss: 1.6444 D(x): 0.5797, D(G(z)): 0.4517 Epoch: [0/20], Batch Num: [449/600] Discriminator Loss: 1.1447, Generator Loss: 1.9549 D(x): 0.5765, D(G(z)): 0.4004 Epoch: [0/20], Batch Num: [450/600] Discriminator Loss: 0.9784, Generator Loss: 2.0486 D(x): 0.5874, D(G(z)): 0.3154 Epoch: [0/20], Batch Num: [451/600] Discriminator Loss: 1.0952, Generator Loss: 1.8060 D(x): 0.5500, D(G(z)): 0.3265 Epoch: [0/20], Batch Num: [452/600] Discriminator Loss: 0.9854, Generator Loss: 1.6963 D(x): 0.6403, D(G(z)): 0.3897 Epoch: [0/20], Batch Num: [453/600] Discriminator Loss: 0.9131, Generator Loss: 1.6264 D(x): 0.6889, D(G(z)): 0.3915 Epoch: [0/20], Batch Num: [454/600] Discriminator Loss: 0.9527, Generator Loss: 1.5740 D(x): 0.7157, D(G(z)): 0.4345 Epoch: [0/20], Batch Num: [455/600] Discriminator Loss: 0.9495, Generator Loss: 1.6693 D(x): 0.7098, D(G(z)): 0.4339 Epoch: [0/20], Batch Num: [456/600] Discriminator Loss: 0.9575, Generator Loss: 1.6531 D(x): 0.7057, D(G(z)): 0.4247 Epoch: [0/20], Batch Num: [457/600] Discriminator Loss: 0.8948, Generator Loss: 2.0974 D(x): 0.7011, D(G(z)): 0.3927 Epoch: [0/20], Batch Num: [458/600] Discriminator Loss: 0.9058, Generator Loss: 2.0725 D(x): 0.6624, D(G(z)): 0.3580 Epoch: [0/20], Batch Num: [459/600] Discriminator Loss: 0.9951, Generator Loss: 2.2703 D(x): 0.6189, D(G(z)): 0.3489 Epoch: [0/20], Batch Num: [460/600] Discriminator Loss: 1.0670, Generator Loss: 2.2192 D(x): 0.5913, D(G(z)): 0.3660 Epoch: [0/20], Batch Num: [461/600] Discriminator Loss: 1.0103, Generator Loss: 2.0042 D(x): 0.6365, D(G(z)): 0.3811 Epoch: [0/20], Batch Num: [462/600] Discriminator Loss: 1.0494, Generator Loss: 2.1390 D(x): 0.6728, D(G(z)): 0.4380 Epoch: [0/20], Batch Num: [463/600] Discriminator Loss: 1.0843, Generator Loss: 1.7961 D(x): 0.6679, D(G(z)): 0.4423 Epoch: [0/20], Batch Num: [464/600] Discriminator Loss: 1.0367, Generator Loss: 1.9582 D(x): 0.7137, D(G(z)): 0.4652 Epoch: [0/20], Batch Num: [465/600] Discriminator Loss: 1.0717, Generator Loss: 1.8306 D(x): 0.6884, D(G(z)): 0.4710 Epoch: [0/20], Batch Num: [466/600] Discriminator Loss: 1.1154, Generator Loss: 1.9660 D(x): 0.6624, D(G(z)): 0.4589 Epoch: [0/20], Batch Num: [467/600] Discriminator Loss: 1.2075, Generator Loss: 1.9492 D(x): 0.6153, D(G(z)): 0.4649 Epoch: [0/20], Batch Num: [468/600] Discriminator Loss: 1.3347, Generator Loss: 1.7411 D(x): 0.5606, D(G(z)): 0.4733 Epoch: [0/20], Batch Num: [469/600] Discriminator Loss: 1.3871, Generator Loss: 1.6085 D(x): 0.5650, D(G(z)): 0.4809 Epoch: [0/20], Batch Num: [470/600] Discriminator Loss: 1.5842, Generator Loss: 1.3166 D(x): 0.5260, D(G(z)): 0.5508 Epoch: [0/20], Batch Num: [471/600] Discriminator Loss: 1.7264, Generator Loss: 0.9696 D(x): 0.5363, D(G(z)): 0.5869 Epoch: [0/20], Batch Num: [472/600] Discriminator Loss: 1.9889, Generator Loss: 0.6204 D(x): 0.5201, D(G(z)): 0.6905 Epoch: [0/20], Batch Num: [473/600] Discriminator Loss: 2.1805, Generator Loss: 0.4402 D(x): 0.5215, D(G(z)): 0.7436 Epoch: [0/20], Batch Num: [474/600] Discriminator Loss: 2.0492, Generator Loss: 0.4010 D(x): 0.5884, D(G(z)): 0.7544 Epoch: [0/20], Batch Num: [475/600] Discriminator Loss: 2.2361, Generator Loss: 0.3614 D(x): 0.5732, D(G(z)): 0.7880 Epoch: [0/20], Batch Num: [476/600] Discriminator Loss: 2.1687, Generator Loss: 0.3923 D(x): 0.6164, D(G(z)): 0.7891 Epoch: [0/20], Batch Num: [477/600] Discriminator Loss: 2.1202, Generator Loss: 0.4029 D(x): 0.5900, D(G(z)): 0.7740 Epoch: [0/20], Batch Num: [478/600] Discriminator Loss: 2.0298, Generator Loss: 0.4168 D(x): 0.5784, D(G(z)): 0.7473 Epoch: [0/20], Batch Num: [479/600] Discriminator Loss: 1.9404, Generator Loss: 0.4578 D(x): 0.5974, D(G(z)): 0.7362 Epoch: [0/20], Batch Num: [480/600] Discriminator Loss: 1.7845, Generator Loss: 0.5338 D(x): 0.5892, D(G(z)): 0.6810 Epoch: [0/20], Batch Num: [481/600] Discriminator Loss: 1.7554, Generator Loss: 0.6433 D(x): 0.5511, D(G(z)): 0.6490 Epoch: [0/20], Batch Num: [482/600] Discriminator Loss: 1.6627, Generator Loss: 0.7037 D(x): 0.5317, D(G(z)): 0.5978 Epoch: [0/20], Batch Num: [483/600] Discriminator Loss: 1.5099, Generator Loss: 0.8377 D(x): 0.5440, D(G(z)): 0.5526 Epoch: [0/20], Batch Num: [484/600] Discriminator Loss: 1.5169, Generator Loss: 0.9287 D(x): 0.5028, D(G(z)): 0.5075 Epoch: [0/20], Batch Num: [485/600] Discriminator Loss: 1.5134, Generator Loss: 0.9106 D(x): 0.5016, D(G(z)): 0.5007 Epoch: [0/20], Batch Num: [486/600] Discriminator Loss: 1.4228, Generator Loss: 0.9163 D(x): 0.5131, D(G(z)): 0.4865 Epoch: [0/20], Batch Num: [487/600] Discriminator Loss: 1.4457, Generator Loss: 0.8474 D(x): 0.5150, D(G(z)): 0.4914 Epoch: [0/20], Batch Num: [488/600] Discriminator Loss: 1.3729, Generator Loss: 0.8347 D(x): 0.5501, D(G(z)): 0.4900 Epoch: [0/20], Batch Num: [489/600] Discriminator Loss: 1.3466, Generator Loss: 0.8285 D(x): 0.5548, D(G(z)): 0.5007 Epoch: [0/20], Batch Num: [490/600] Discriminator Loss: 1.3227, Generator Loss: 0.7707 D(x): 0.5751, D(G(z)): 0.5108 Epoch: [0/20], Batch Num: [491/600] Discriminator Loss: 1.3361, Generator Loss: 0.9200 D(x): 0.5637, D(G(z)): 0.4975 Epoch: [0/20], Batch Num: [492/600] Discriminator Loss: 1.3108, Generator Loss: 0.9030 D(x): 0.5753, D(G(z)): 0.5008 Epoch: [0/20], Batch Num: [493/600] Discriminator Loss: 1.2987, Generator Loss: 1.0030 D(x): 0.5352, D(G(z)): 0.4585 Epoch: [0/20], Batch Num: [494/600] Discriminator Loss: 1.3362, Generator Loss: 1.1080 D(x): 0.5130, D(G(z)): 0.4473 Epoch: [0/20], Batch Num: [495/600] Discriminator Loss: 1.3319, Generator Loss: 1.1165 D(x): 0.4955, D(G(z)): 0.4183 Epoch: [0/20], Batch Num: [496/600] Discriminator Loss: 1.2648, Generator Loss: 1.0867 D(x): 0.5191, D(G(z)): 0.4232 Epoch: [0/20], Batch Num: [497/600] Discriminator Loss: 1.1916, Generator Loss: 1.0685 D(x): 0.5296, D(G(z)): 0.3943 Epoch: [0/20], Batch Num: [498/600] Discriminator Loss: 1.2433, Generator Loss: 1.1376 D(x): 0.5181, D(G(z)): 0.4052 Epoch: [0/20], Batch Num: [499/600] Discriminator Loss: 1.2194, Generator Loss: 1.1542 D(x): 0.5253, D(G(z)): 0.4003 Epoch: 0, Batch Num: [500/600]
Epoch: [0/20], Batch Num: [500/600] Discriminator Loss: 1.1486, Generator Loss: 1.1845 D(x): 0.5528, D(G(z)): 0.3996 Epoch: [0/20], Batch Num: [501/600] Discriminator Loss: 1.0560, Generator Loss: 1.2711 D(x): 0.5840, D(G(z)): 0.3790 Epoch: [0/20], Batch Num: [502/600] Discriminator Loss: 1.0526, Generator Loss: 1.3692 D(x): 0.5795, D(G(z)): 0.3676 Epoch: [0/20], Batch Num: [503/600] Discriminator Loss: 1.0229, Generator Loss: 1.4933 D(x): 0.5674, D(G(z)): 0.3344 Epoch: [0/20], Batch Num: [504/600] Discriminator Loss: 0.9839, Generator Loss: 1.5374 D(x): 0.5705, D(G(z)): 0.3162 Epoch: [0/20], Batch Num: [505/600] Discriminator Loss: 1.0121, Generator Loss: 1.6814 D(x): 0.5647, D(G(z)): 0.3146 Epoch: [0/20], Batch Num: [506/600] Discriminator Loss: 0.9718, Generator Loss: 1.6185 D(x): 0.5732, D(G(z)): 0.2973 Epoch: [0/20], Batch Num: [507/600] Discriminator Loss: 0.8523, Generator Loss: 1.7571 D(x): 0.6166, D(G(z)): 0.2725 Epoch: [0/20], Batch Num: [508/600] Discriminator Loss: 0.8095, Generator Loss: 1.7674 D(x): 0.6354, D(G(z)): 0.2754 Epoch: [0/20], Batch Num: [509/600] Discriminator Loss: 0.8257, Generator Loss: 1.8444 D(x): 0.6200, D(G(z)): 0.2657 Epoch: [0/20], Batch Num: [510/600] Discriminator Loss: 0.8307, Generator Loss: 1.8691 D(x): 0.6408, D(G(z)): 0.2766 Epoch: [0/20], Batch Num: [511/600] Discriminator Loss: 0.7211, Generator Loss: 1.9878 D(x): 0.6750, D(G(z)): 0.2495 Epoch: [0/20], Batch Num: [512/600] Discriminator Loss: 0.6933, Generator Loss: 2.0553 D(x): 0.6877, D(G(z)): 0.2499 Epoch: [0/20], Batch Num: [513/600] Discriminator Loss: 0.6338, Generator Loss: 2.1294 D(x): 0.7101, D(G(z)): 0.2281 Epoch: [0/20], Batch Num: [514/600] Discriminator Loss: 0.6418, Generator Loss: 2.3913 D(x): 0.6942, D(G(z)): 0.2151 Epoch: [0/20], Batch Num: [515/600] Discriminator Loss: 0.5960, Generator Loss: 2.4338 D(x): 0.7075, D(G(z)): 0.1997 Epoch: [0/20], Batch Num: [516/600] Discriminator Loss: 0.6030, Generator Loss: 2.2881 D(x): 0.7241, D(G(z)): 0.2146 Epoch: [0/20], Batch Num: [517/600] Discriminator Loss: 0.5676, Generator Loss: 2.5491 D(x): 0.7283, D(G(z)): 0.1988 Epoch: [0/20], Batch Num: [518/600] Discriminator Loss: 0.6106, Generator Loss: 2.2876 D(x): 0.7029, D(G(z)): 0.1967 Epoch: [0/20], Batch Num: [519/600] Discriminator Loss: 0.6054, Generator Loss: 2.4001 D(x): 0.7319, D(G(z)): 0.2265 Epoch: [0/20], Batch Num: [520/600] Discriminator Loss: 0.6297, Generator Loss: 2.2620 D(x): 0.7320, D(G(z)): 0.2324 Epoch: [0/20], Batch Num: [521/600] Discriminator Loss: 0.7399, Generator Loss: 2.2095 D(x): 0.7024, D(G(z)): 0.2765 Epoch: [0/20], Batch Num: [522/600] Discriminator Loss: 0.7701, Generator Loss: 2.2951 D(x): 0.7139, D(G(z)): 0.3085 Epoch: [0/20], Batch Num: [523/600] Discriminator Loss: 0.8351, Generator Loss: 2.2933 D(x): 0.6676, D(G(z)): 0.2877 Epoch: [0/20], Batch Num: [524/600] Discriminator Loss: 0.8405, Generator Loss: 2.0897 D(x): 0.6711, D(G(z)): 0.3035 Epoch: [0/20], Batch Num: [525/600] Discriminator Loss: 0.9873, Generator Loss: 1.8748 D(x): 0.6187, D(G(z)): 0.3139 Epoch: [0/20], Batch Num: [526/600] Discriminator Loss: 0.8235, Generator Loss: 2.0938 D(x): 0.6953, D(G(z)): 0.3059 Epoch: [0/20], Batch Num: [527/600] Discriminator Loss: 0.9736, Generator Loss: 1.8095 D(x): 0.6204, D(G(z)): 0.2931 Epoch: [0/20], Batch Num: [528/600] Discriminator Loss: 0.9134, Generator Loss: 1.8510 D(x): 0.6943, D(G(z)): 0.3521 Epoch: [0/20], Batch Num: [529/600] Discriminator Loss: 0.8840, Generator Loss: 2.0397 D(x): 0.7014, D(G(z)): 0.3389 Epoch: [0/20], Batch Num: [530/600] Discriminator Loss: 0.8072, Generator Loss: 2.3508 D(x): 0.7012, D(G(z)): 0.3063 Epoch: [0/20], Batch Num: [531/600] Discriminator Loss: 0.7169, Generator Loss: 2.4544 D(x): 0.7072, D(G(z)): 0.2520 Epoch: [0/20], Batch Num: [532/600] Discriminator Loss: 0.7669, Generator Loss: 2.5117 D(x): 0.6643, D(G(z)): 0.2106 Epoch: [0/20], Batch Num: [533/600] Discriminator Loss: 0.6541, Generator Loss: 2.5520 D(x): 0.7486, D(G(z)): 0.2371 Epoch: [0/20], Batch Num: [534/600] Discriminator Loss: 0.5366, Generator Loss: 2.5924 D(x): 0.7786, D(G(z)): 0.1968 Epoch: [0/20], Batch Num: [535/600] Discriminator Loss: 0.4829, Generator Loss: 2.7945 D(x): 0.8093, D(G(z)): 0.1958 Epoch: [0/20], Batch Num: [536/600] Discriminator Loss: 0.4426, Generator Loss: 3.0354 D(x): 0.8076, D(G(z)): 0.1659 Epoch: [0/20], Batch Num: [537/600] Discriminator Loss: 0.5152, Generator Loss: 2.9206 D(x): 0.7823, D(G(z)): 0.1602 Epoch: [0/20], Batch Num: [538/600] Discriminator Loss: 0.4681, Generator Loss: 3.4649 D(x): 0.8471, D(G(z)): 0.2271 Epoch: [0/20], Batch Num: [539/600] Discriminator Loss: 0.4049, Generator Loss: 3.4827 D(x): 0.8454, D(G(z)): 0.1802 Epoch: [0/20], Batch Num: [540/600] Discriminator Loss: 0.5233, Generator Loss: 3.3731 D(x): 0.8105, D(G(z)): 0.1842 Epoch: [0/20], Batch Num: [541/600] Discriminator Loss: 0.4343, Generator Loss: 3.6656 D(x): 0.8215, D(G(z)): 0.1549 Epoch: [0/20], Batch Num: [542/600] Discriminator Loss: 0.5151, Generator Loss: 3.0899 D(x): 0.7780, D(G(z)): 0.1752 Epoch: [0/20], Batch Num: [543/600] Discriminator Loss: 0.6280, Generator Loss: 2.8664 D(x): 0.7558, D(G(z)): 0.2150 Epoch: [0/20], Batch Num: [544/600] Discriminator Loss: 0.8383, Generator Loss: 2.4980 D(x): 0.7286, D(G(z)): 0.3034 Epoch: [0/20], Batch Num: [545/600] Discriminator Loss: 1.1020, Generator Loss: 1.8477 D(x): 0.6766, D(G(z)): 0.4093 Epoch: [0/20], Batch Num: [546/600] Discriminator Loss: 1.6987, Generator Loss: 1.6460 D(x): 0.5912, D(G(z)): 0.5453 Epoch: [0/20], Batch Num: [547/600] Discriminator Loss: 1.8480, Generator Loss: 1.5389 D(x): 0.5727, D(G(z)): 0.6116 Epoch: [0/20], Batch Num: [548/600] Discriminator Loss: 2.3487, Generator Loss: 1.0578 D(x): 0.4355, D(G(z)): 0.6251 Epoch: [0/20], Batch Num: [549/600] Discriminator Loss: 2.2174, Generator Loss: 1.0377 D(x): 0.5092, D(G(z)): 0.6789 Epoch: [0/20], Batch Num: [550/600] Discriminator Loss: 2.7294, Generator Loss: 1.0003 D(x): 0.4230, D(G(z)): 0.7273 Epoch: [0/20], Batch Num: [551/600] Discriminator Loss: 2.5798, Generator Loss: 0.9633 D(x): 0.4298, D(G(z)): 0.6772 Epoch: [0/20], Batch Num: [552/600] Discriminator Loss: 2.3156, Generator Loss: 1.0999 D(x): 0.4805, D(G(z)): 0.6486 Epoch: [0/20], Batch Num: [553/600] Discriminator Loss: 1.8557, Generator Loss: 1.4084 D(x): 0.4941, D(G(z)): 0.5454 Epoch: [0/20], Batch Num: [554/600] Discriminator Loss: 1.2408, Generator Loss: 2.5082 D(x): 0.5922, D(G(z)): 0.4050 Epoch: [0/20], Batch Num: [555/600] Discriminator Loss: 0.9540, Generator Loss: 3.1253 D(x): 0.6246, D(G(z)): 0.2896 Epoch: [0/20], Batch Num: [556/600] Discriminator Loss: 0.7917, Generator Loss: 3.7396 D(x): 0.6234, D(G(z)): 0.1698 Epoch: [0/20], Batch Num: [557/600] Discriminator Loss: 0.5222, Generator Loss: 4.2356 D(x): 0.7388, D(G(z)): 0.1366 Epoch: [0/20], Batch Num: [558/600] Discriminator Loss: 0.5701, Generator Loss: 3.9677 D(x): 0.6888, D(G(z)): 0.1081 Epoch: [0/20], Batch Num: [559/600] Discriminator Loss: 0.5119, Generator Loss: 4.1337 D(x): 0.7525, D(G(z)): 0.1348 Epoch: [0/20], Batch Num: [560/600] Discriminator Loss: 0.4672, Generator Loss: 3.6722 D(x): 0.7574, D(G(z)): 0.0931 Epoch: [0/20], Batch Num: [561/600] Discriminator Loss: 0.3737, Generator Loss: 3.4717 D(x): 0.8335, D(G(z)): 0.1392 Epoch: [0/20], Batch Num: [562/600] Discriminator Loss: 0.4166, Generator Loss: 3.8651 D(x): 0.8125, D(G(z)): 0.1482 Epoch: [0/20], Batch Num: [563/600] Discriminator Loss: 0.3404, Generator Loss: 3.7055 D(x): 0.8484, D(G(z)): 0.1289 Epoch: [0/20], Batch Num: [564/600] Discriminator Loss: 0.4481, Generator Loss: 3.7489 D(x): 0.7913, D(G(z)): 0.1376 Epoch: [0/20], Batch Num: [565/600] Discriminator Loss: 0.4358, Generator Loss: 3.6423 D(x): 0.8149, D(G(z)): 0.1517 Epoch: [0/20], Batch Num: [566/600] Discriminator Loss: 0.5364, Generator Loss: 3.6154 D(x): 0.7789, D(G(z)): 0.1786 Epoch: [0/20], Batch Num: [567/600] Discriminator Loss: 0.4864, Generator Loss: 3.7216 D(x): 0.7874, D(G(z)): 0.1529 Epoch: [0/20], Batch Num: [568/600] Discriminator Loss: 0.6548, Generator Loss: 3.5130 D(x): 0.7395, D(G(z)): 0.1588 Epoch: [0/20], Batch Num: [569/600] Discriminator Loss: 0.5863, Generator Loss: 3.2726 D(x): 0.7865, D(G(z)): 0.2041 Epoch: [0/20], Batch Num: [570/600] Discriminator Loss: 0.6460, Generator Loss: 3.0779 D(x): 0.7444, D(G(z)): 0.1894 Epoch: [0/20], Batch Num: [571/600] Discriminator Loss: 0.9295, Generator Loss: 2.9488 D(x): 0.7108, D(G(z)): 0.2886 Epoch: [0/20], Batch Num: [572/600] Discriminator Loss: 0.8421, Generator Loss: 2.5525 D(x): 0.7113, D(G(z)): 0.2444 Epoch: [0/20], Batch Num: [573/600] Discriminator Loss: 0.9099, Generator Loss: 3.0412 D(x): 0.7398, D(G(z)): 0.3109 Epoch: [0/20], Batch Num: [574/600] Discriminator Loss: 0.7702, Generator Loss: 3.0890 D(x): 0.8201, D(G(z)): 0.3372 Epoch: [0/20], Batch Num: [575/600] Discriminator Loss: 0.7253, Generator Loss: 3.5575 D(x): 0.7548, D(G(z)): 0.2338 Epoch: [0/20], Batch Num: [576/600] Discriminator Loss: 0.5552, Generator Loss: 4.1565 D(x): 0.7841, D(G(z)): 0.1815 Epoch: [0/20], Batch Num: [577/600] Discriminator Loss: 0.6895, Generator Loss: 3.9858 D(x): 0.7307, D(G(z)): 0.1820 Epoch: [0/20], Batch Num: [578/600] Discriminator Loss: 0.7562, Generator Loss: 3.8925 D(x): 0.6887, D(G(z)): 0.1423 Epoch: [0/20], Batch Num: [579/600] Discriminator Loss: 0.6630, Generator Loss: 2.8363 D(x): 0.7587, D(G(z)): 0.1939 Epoch: [0/20], Batch Num: [580/600] Discriminator Loss: 0.6217, Generator Loss: 2.7417 D(x): 0.8328, D(G(z)): 0.2677 Epoch: [0/20], Batch Num: [581/600] Discriminator Loss: 0.6751, Generator Loss: 3.1393 D(x): 0.8489, D(G(z)): 0.2945 Epoch: [0/20], Batch Num: [582/600] Discriminator Loss: 0.5685, Generator Loss: 4.0275 D(x): 0.8782, D(G(z)): 0.2839 Epoch: [0/20], Batch Num: [583/600] Discriminator Loss: 0.4684, Generator Loss: 4.6400 D(x): 0.8307, D(G(z)): 0.1593 Epoch: [0/20], Batch Num: [584/600] Discriminator Loss: 0.3629, Generator Loss: 4.9793 D(x): 0.8156, D(G(z)): 0.0756 Epoch: [0/20], Batch Num: [585/600] Discriminator Loss: 0.3336, Generator Loss: 5.4266 D(x): 0.8382, D(G(z)): 0.0872 Epoch: [0/20], Batch Num: [586/600] Discriminator Loss: 0.3088, Generator Loss: 5.3266 D(x): 0.8428, D(G(z)): 0.0745 Epoch: [0/20], Batch Num: [587/600] Discriminator Loss: 0.1994, Generator Loss: 4.6596 D(x): 0.8995, D(G(z)): 0.0676 Epoch: [0/20], Batch Num: [588/600] Discriminator Loss: 0.1892, Generator Loss: 5.3610 D(x): 0.9064, D(G(z)): 0.0691 Epoch: [0/20], Batch Num: [589/600] Discriminator Loss: 0.1751, Generator Loss: 4.9585 D(x): 0.9365, D(G(z)): 0.0849 Epoch: [0/20], Batch Num: [590/600] Discriminator Loss: 0.1222, Generator Loss: 5.9010 D(x): 0.9523, D(G(z)): 0.0614 Epoch: [0/20], Batch Num: [591/600] Discriminator Loss: 0.0827, Generator Loss: 6.5904 D(x): 0.9674, D(G(z)): 0.0413 Epoch: [0/20], Batch Num: [592/600] Discriminator Loss: 0.0821, Generator Loss: 7.0481 D(x): 0.9559, D(G(z)): 0.0286 Epoch: [0/20], Batch Num: [593/600] Discriminator Loss: 0.0637, Generator Loss: 6.4821 D(x): 0.9630, D(G(z)): 0.0217 Epoch: [0/20], Batch Num: [594/600] Discriminator Loss: 0.0811, Generator Loss: 7.0804 D(x): 0.9604, D(G(z)): 0.0175 Epoch: [0/20], Batch Num: [595/600] Discriminator Loss: 0.0481, Generator Loss: 7.1159 D(x): 0.9759, D(G(z)): 0.0220 Epoch: [0/20], Batch Num: [596/600] Discriminator Loss: 0.0535, Generator Loss: 6.1330 D(x): 0.9749, D(G(z)): 0.0216 Epoch: [0/20], Batch Num: [597/600] Discriminator Loss: 0.1048, Generator Loss: 5.7750 D(x): 0.9836, D(G(z)): 0.0729 Epoch: [0/20], Batch Num: [598/600] Discriminator Loss: 0.1471, Generator Loss: 4.8065 D(x): 0.9812, D(G(z)): 0.1006 Epoch: [0/20], Batch Num: [599/600] Discriminator Loss: 0.2751, Generator Loss: 5.0674 D(x): 0.9801, D(G(z)): 0.1850 Epoch: 1, Batch Num: [0/600]
Epoch: [1/20], Batch Num: [0/600] Discriminator Loss: 0.2440, Generator Loss: 5.3547 D(x): 0.9780, D(G(z)): 0.1700 Epoch: [1/20], Batch Num: [1/600] Discriminator Loss: 0.2805, Generator Loss: 6.0067 D(x): 0.9577, D(G(z)): 0.1718 Epoch: [1/20], Batch Num: [2/600] Discriminator Loss: 0.2998, Generator Loss: 5.9869 D(x): 0.9411, D(G(z)): 0.1764 Epoch: [1/20], Batch Num: [3/600] Discriminator Loss: 0.2472, Generator Loss: 6.2311 D(x): 0.9187, D(G(z)): 0.1165 Epoch: [1/20], Batch Num: [4/600] Discriminator Loss: 0.3954, Generator Loss: 5.6887 D(x): 0.8293, D(G(z)): 0.0892 Epoch: [1/20], Batch Num: [5/600] Discriminator Loss: 0.4845, Generator Loss: 5.6694 D(x): 0.8141, D(G(z)): 0.1390 Epoch: [1/20], Batch Num: [6/600] Discriminator Loss: 0.4590, Generator Loss: 4.6255 D(x): 0.8266, D(G(z)): 0.1601 Epoch: [1/20], Batch Num: [7/600] Discriminator Loss: 0.6753, Generator Loss: 3.8724 D(x): 0.7977, D(G(z)): 0.2463 Epoch: [1/20], Batch Num: [8/600] Discriminator Loss: 0.7337, Generator Loss: 3.9309 D(x): 0.8271, D(G(z)): 0.3128 Epoch: [1/20], Batch Num: [9/600] Discriminator Loss: 0.6241, Generator Loss: 4.2000 D(x): 0.8396, D(G(z)): 0.2828 Epoch: [1/20], Batch Num: [10/600] Discriminator Loss: 0.6892, Generator Loss: 5.1277 D(x): 0.7994, D(G(z)): 0.2845 Epoch: [1/20], Batch Num: [11/600] Discriminator Loss: 0.4678, Generator Loss: 5.6946 D(x): 0.8005, D(G(z)): 0.1439 Epoch: [1/20], Batch Num: [12/600] Discriminator Loss: 0.6080, Generator Loss: 5.1809 D(x): 0.7313, D(G(z)): 0.1222 Epoch: [1/20], Batch Num: [13/600] Discriminator Loss: 0.5089, Generator Loss: 4.9032 D(x): 0.7553, D(G(z)): 0.1090 Epoch: [1/20], Batch Num: [14/600] Discriminator Loss: 0.4940, Generator Loss: 4.5344 D(x): 0.7464, D(G(z)): 0.1111 Epoch: [1/20], Batch Num: [15/600] Discriminator Loss: 0.5630, Generator Loss: 3.2114 D(x): 0.7555, D(G(z)): 0.1323 Epoch: [1/20], Batch Num: [16/600] Discriminator Loss: 0.6074, Generator Loss: 3.3786 D(x): 0.8324, D(G(z)): 0.2530 Epoch: [1/20], Batch Num: [17/600] Discriminator Loss: 0.5794, Generator Loss: 3.4535 D(x): 0.8210, D(G(z)): 0.2353 Epoch: [1/20], Batch Num: [18/600] Discriminator Loss: 0.6746, Generator Loss: 3.0433 D(x): 0.7590, D(G(z)): 0.2492 Epoch: [1/20], Batch Num: [19/600] Discriminator Loss: 0.8054, Generator Loss: 2.8170 D(x): 0.6959, D(G(z)): 0.2038 Epoch: [1/20], Batch Num: [20/600] Discriminator Loss: 0.8450, Generator Loss: 2.4325 D(x): 0.7118, D(G(z)): 0.2633 Epoch: [1/20], Batch Num: [21/600] Discriminator Loss: 0.8644, Generator Loss: 2.0715 D(x): 0.7380, D(G(z)): 0.2931 Epoch: [1/20], Batch Num: [22/600] Discriminator Loss: 1.0922, Generator Loss: 1.9181 D(x): 0.7041, D(G(z)): 0.3460 Epoch: [1/20], Batch Num: [23/600] Discriminator Loss: 1.0371, Generator Loss: 1.8601 D(x): 0.7735, D(G(z)): 0.4590 Epoch: [1/20], Batch Num: [24/600] Discriminator Loss: 1.0787, Generator Loss: 2.2901 D(x): 0.7382, D(G(z)): 0.3990 Epoch: [1/20], Batch Num: [25/600] Discriminator Loss: 1.3555, Generator Loss: 2.2120 D(x): 0.6415, D(G(z)): 0.3759 Epoch: [1/20], Batch Num: [26/600] Discriminator Loss: 1.4471, Generator Loss: 1.8218 D(x): 0.5923, D(G(z)): 0.3740 Epoch: [1/20], Batch Num: [27/600] Discriminator Loss: 1.2946, Generator Loss: 1.5933 D(x): 0.6525, D(G(z)): 0.4348 Epoch: [1/20], Batch Num: [28/600] Discriminator Loss: 1.3042, Generator Loss: 1.6352 D(x): 0.6925, D(G(z)): 0.4556 Epoch: [1/20], Batch Num: [29/600] Discriminator Loss: 1.2611, Generator Loss: 1.5015 D(x): 0.7245, D(G(z)): 0.4842 Epoch: [1/20], Batch Num: [30/600] Discriminator Loss: 1.4540, Generator Loss: 1.9068 D(x): 0.6609, D(G(z)): 0.4666 Epoch: [1/20], Batch Num: [31/600] Discriminator Loss: 1.3638, Generator Loss: 1.7375 D(x): 0.6636, D(G(z)): 0.4577 Epoch: [1/20], Batch Num: [32/600] Discriminator Loss: 1.2123, Generator Loss: 2.1615 D(x): 0.6786, D(G(z)): 0.3877 Epoch: [1/20], Batch Num: [33/600] Discriminator Loss: 1.1330, Generator Loss: 1.9546 D(x): 0.6648, D(G(z)): 0.3730 Epoch: [1/20], Batch Num: [34/600] Discriminator Loss: 1.3297, Generator Loss: 1.7346 D(x): 0.6422, D(G(z)): 0.3961 Epoch: [1/20], Batch Num: [35/600] Discriminator Loss: 1.2936, Generator Loss: 1.6407 D(x): 0.6969, D(G(z)): 0.4683 Epoch: [1/20], Batch Num: [36/600] Discriminator Loss: 0.9108, Generator Loss: 2.0477 D(x): 0.8168, D(G(z)): 0.4307 Epoch: [1/20], Batch Num: [37/600] Discriminator Loss: 0.9377, Generator Loss: 2.5519 D(x): 0.8323, D(G(z)): 0.4484 Epoch: [1/20], Batch Num: [38/600] Discriminator Loss: 0.7558, Generator Loss: 2.8456 D(x): 0.7475, D(G(z)): 0.2712 Epoch: [1/20], Batch Num: [39/600] Discriminator Loss: 0.8263, Generator Loss: 3.1043 D(x): 0.7358, D(G(z)): 0.2338 Epoch: [1/20], Batch Num: [40/600] Discriminator Loss: 0.7602, Generator Loss: 3.0713 D(x): 0.7115, D(G(z)): 0.1801 Epoch: [1/20], Batch Num: [41/600] Discriminator Loss: 0.6777, Generator Loss: 2.8073 D(x): 0.7912, D(G(z)): 0.2480 Epoch: [1/20], Batch Num: [42/600] Discriminator Loss: 0.7111, Generator Loss: 2.9474 D(x): 0.8124, D(G(z)): 0.2701 Epoch: [1/20], Batch Num: [43/600] Discriminator Loss: 0.6299, Generator Loss: 2.6301 D(x): 0.8529, D(G(z)): 0.3022 Epoch: [1/20], Batch Num: [44/600] Discriminator Loss: 0.6219, Generator Loss: 3.2359 D(x): 0.8883, D(G(z)): 0.3250 Epoch: [1/20], Batch Num: [45/600] Discriminator Loss: 0.5019, Generator Loss: 3.7616 D(x): 0.8927, D(G(z)): 0.2659 Epoch: [1/20], Batch Num: [46/600] Discriminator Loss: 0.4230, Generator Loss: 4.7722 D(x): 0.8774, D(G(z)): 0.1960 Epoch: [1/20], Batch Num: [47/600] Discriminator Loss: 0.4787, Generator Loss: 5.1251 D(x): 0.8385, D(G(z)): 0.1595 Epoch: [1/20], Batch Num: [48/600] Discriminator Loss: 0.5350, Generator Loss: 4.9347 D(x): 0.8184, D(G(z)): 0.1478 Epoch: [1/20], Batch Num: [49/600] Discriminator Loss: 0.6426, Generator Loss: 4.0593 D(x): 0.7461, D(G(z)): 0.1746 Epoch: [1/20], Batch Num: [50/600] Discriminator Loss: 0.5462, Generator Loss: 3.3777 D(x): 0.8233, D(G(z)): 0.1969 Epoch: [1/20], Batch Num: [51/600] Discriminator Loss: 0.6741, Generator Loss: 2.8187 D(x): 0.8489, D(G(z)): 0.3075 Epoch: [1/20], Batch Num: [52/600] Discriminator Loss: 0.7461, Generator Loss: 3.0868 D(x): 0.8596, D(G(z)): 0.3614 Epoch: [1/20], Batch Num: [53/600] Discriminator Loss: 0.8438, Generator Loss: 3.0248 D(x): 0.8354, D(G(z)): 0.3710 Epoch: [1/20], Batch Num: [54/600] Discriminator Loss: 0.9015, Generator Loss: 3.1065 D(x): 0.8014, D(G(z)): 0.3786 Epoch: [1/20], Batch Num: [55/600] Discriminator Loss: 0.8907, Generator Loss: 2.9403 D(x): 0.7306, D(G(z)): 0.3085 Epoch: [1/20], Batch Num: [56/600] Discriminator Loss: 1.1243, Generator Loss: 2.4102 D(x): 0.7037, D(G(z)): 0.3500 Epoch: [1/20], Batch Num: [57/600] Discriminator Loss: 1.2254, Generator Loss: 2.2462 D(x): 0.7453, D(G(z)): 0.4394 Epoch: [1/20], Batch Num: [58/600] Discriminator Loss: 1.2264, Generator Loss: 2.2007 D(x): 0.7579, D(G(z)): 0.4954 Epoch: [1/20], Batch Num: [59/600] Discriminator Loss: 1.2572, Generator Loss: 1.9949 D(x): 0.7845, D(G(z)): 0.5043 Epoch: [1/20], Batch Num: [60/600] Discriminator Loss: 1.1215, Generator Loss: 2.2597 D(x): 0.7573, D(G(z)): 0.4676 Epoch: [1/20], Batch Num: [61/600] Discriminator Loss: 1.3260, Generator Loss: 2.3041 D(x): 0.7149, D(G(z)): 0.4900 Epoch: [1/20], Batch Num: [62/600] Discriminator Loss: 1.5219, Generator Loss: 1.7998 D(x): 0.6726, D(G(z)): 0.4926 Epoch: [1/20], Batch Num: [63/600] Discriminator Loss: 1.5194, Generator Loss: 1.6543 D(x): 0.6506, D(G(z)): 0.4793 Epoch: [1/20], Batch Num: [64/600] Discriminator Loss: 1.5112, Generator Loss: 1.5239 D(x): 0.7388, D(G(z)): 0.5631 Epoch: [1/20], Batch Num: [65/600] Discriminator Loss: 1.4737, Generator Loss: 1.3360 D(x): 0.7910, D(G(z)): 0.6103 Epoch: [1/20], Batch Num: [66/600] Discriminator Loss: 1.4563, Generator Loss: 1.3232 D(x): 0.8095, D(G(z)): 0.6203 Epoch: [1/20], Batch Num: [67/600] Discriminator Loss: 1.4418, Generator Loss: 1.7050 D(x): 0.7244, D(G(z)): 0.5739 Epoch: [1/20], Batch Num: [68/600] Discriminator Loss: 1.5152, Generator Loss: 1.5211 D(x): 0.6710, D(G(z)): 0.5345 Epoch: [1/20], Batch Num: [69/600] Discriminator Loss: 1.6335, Generator Loss: 1.4354 D(x): 0.6705, D(G(z)): 0.5534 Epoch: [1/20], Batch Num: [70/600] Discriminator Loss: 1.8528, Generator Loss: 1.1562 D(x): 0.6130, D(G(z)): 0.5988 Epoch: [1/20], Batch Num: [71/600] Discriminator Loss: 2.0192, Generator Loss: 0.8365 D(x): 0.6236, D(G(z)): 0.6652 Epoch: [1/20], Batch Num: [72/600] Discriminator Loss: 1.9184, Generator Loss: 0.5612 D(x): 0.6997, D(G(z)): 0.7078 Epoch: [1/20], Batch Num: [73/600] Discriminator Loss: 2.0063, Generator Loss: 0.5304 D(x): 0.7473, D(G(z)): 0.7634 Epoch: [1/20], Batch Num: [74/600] Discriminator Loss: 1.8117, Generator Loss: 0.7827 D(x): 0.8139, D(G(z)): 0.7566 Epoch: [1/20], Batch Num: [75/600] Discriminator Loss: 1.6704, Generator Loss: 0.8657 D(x): 0.7813, D(G(z)): 0.6804 Epoch: [1/20], Batch Num: [76/600] Discriminator Loss: 1.5192, Generator Loss: 1.2827 D(x): 0.7695, D(G(z)): 0.6212 Epoch: [1/20], Batch Num: [77/600] Discriminator Loss: 1.1717, Generator Loss: 1.6369 D(x): 0.7841, D(G(z)): 0.5297 Epoch: [1/20], Batch Num: [78/600] Discriminator Loss: 1.0687, Generator Loss: 1.9764 D(x): 0.7721, D(G(z)): 0.4372 Epoch: [1/20], Batch Num: [79/600] Discriminator Loss: 0.8959, Generator Loss: 2.3163 D(x): 0.7687, D(G(z)): 0.3632 Epoch: [1/20], Batch Num: [80/600] Discriminator Loss: 0.9095, Generator Loss: 2.3784 D(x): 0.7218, D(G(z)): 0.3512 Epoch: [1/20], Batch Num: [81/600] Discriminator Loss: 0.8895, Generator Loss: 2.2051 D(x): 0.7042, D(G(z)): 0.3030 Epoch: [1/20], Batch Num: [82/600] Discriminator Loss: 0.9384, Generator Loss: 1.8834 D(x): 0.7540, D(G(z)): 0.3551 Epoch: [1/20], Batch Num: [83/600] Discriminator Loss: 0.9284, Generator Loss: 1.7420 D(x): 0.7568, D(G(z)): 0.3986 Epoch: [1/20], Batch Num: [84/600] Discriminator Loss: 0.9498, Generator Loss: 1.6822 D(x): 0.7764, D(G(z)): 0.4310 Epoch: [1/20], Batch Num: [85/600] Discriminator Loss: 0.9544, Generator Loss: 1.5711 D(x): 0.8209, D(G(z)): 0.4738 Epoch: [1/20], Batch Num: [86/600] Discriminator Loss: 0.9747, Generator Loss: 1.6747 D(x): 0.7834, D(G(z)): 0.4535 Epoch: [1/20], Batch Num: [87/600] Discriminator Loss: 1.0437, Generator Loss: 1.8452 D(x): 0.7460, D(G(z)): 0.4594 Epoch: [1/20], Batch Num: [88/600] Discriminator Loss: 0.9637, Generator Loss: 1.8636 D(x): 0.7780, D(G(z)): 0.4324 Epoch: [1/20], Batch Num: [89/600] Discriminator Loss: 1.3828, Generator Loss: 1.7207 D(x): 0.6216, D(G(z)): 0.4380 Epoch: [1/20], Batch Num: [90/600] Discriminator Loss: 1.2900, Generator Loss: 1.5660 D(x): 0.6462, D(G(z)): 0.4407 Epoch: [1/20], Batch Num: [91/600] Discriminator Loss: 1.2834, Generator Loss: 1.2699 D(x): 0.6949, D(G(z)): 0.5023 Epoch: [1/20], Batch Num: [92/600] Discriminator Loss: 1.3948, Generator Loss: 1.1245 D(x): 0.6785, D(G(z)): 0.5468 Epoch: [1/20], Batch Num: [93/600] Discriminator Loss: 1.4562, Generator Loss: 1.1115 D(x): 0.7148, D(G(z)): 0.5887 Epoch: [1/20], Batch Num: [94/600] Discriminator Loss: 1.4675, Generator Loss: 1.1008 D(x): 0.7458, D(G(z)): 0.6283 Epoch: [1/20], Batch Num: [95/600] Discriminator Loss: 1.4814, Generator Loss: 0.9876 D(x): 0.7381, D(G(z)): 0.6322 Epoch: [1/20], Batch Num: [96/600] Discriminator Loss: 1.6665, Generator Loss: 1.0028 D(x): 0.6871, D(G(z)): 0.6475 Epoch: [1/20], Batch Num: [97/600] Discriminator Loss: 1.7653, Generator Loss: 0.9827 D(x): 0.6587, D(G(z)): 0.6919 Epoch: [1/20], Batch Num: [98/600] Discriminator Loss: 1.8093, Generator Loss: 0.9017 D(x): 0.6584, D(G(z)): 0.6848 Epoch: [1/20], Batch Num: [99/600] Discriminator Loss: 1.8452, Generator Loss: 0.7056 D(x): 0.6255, D(G(z)): 0.6911 Epoch: 1, Batch Num: [100/600]
Epoch: [1/20], Batch Num: [100/600] Discriminator Loss: 2.0245, Generator Loss: 0.5976 D(x): 0.6104, D(G(z)): 0.7205 Epoch: [1/20], Batch Num: [101/600] Discriminator Loss: 2.0362, Generator Loss: 0.5873 D(x): 0.6118, D(G(z)): 0.7375 Epoch: [1/20], Batch Num: [102/600] Discriminator Loss: 2.0667, Generator Loss: 0.5805 D(x): 0.6083, D(G(z)): 0.7209 Epoch: [1/20], Batch Num: [103/600] Discriminator Loss: 1.7915, Generator Loss: 0.5704 D(x): 0.7168, D(G(z)): 0.7262 Epoch: [1/20], Batch Num: [104/600] Discriminator Loss: 1.8837, Generator Loss: 0.6831 D(x): 0.6597, D(G(z)): 0.7104 Epoch: [1/20], Batch Num: [105/600] Discriminator Loss: 1.7493, Generator Loss: 0.6629 D(x): 0.6986, D(G(z)): 0.6991 Epoch: [1/20], Batch Num: [106/600] Discriminator Loss: 1.7695, Generator Loss: 0.7572 D(x): 0.6626, D(G(z)): 0.6658 Epoch: [1/20], Batch Num: [107/600] Discriminator Loss: 1.6814, Generator Loss: 0.9166 D(x): 0.6398, D(G(z)): 0.6263 Epoch: [1/20], Batch Num: [108/600] Discriminator Loss: 1.4949, Generator Loss: 1.0250 D(x): 0.6613, D(G(z)): 0.5931 Epoch: [1/20], Batch Num: [109/600] Discriminator Loss: 1.6324, Generator Loss: 1.1906 D(x): 0.6101, D(G(z)): 0.5872 Epoch: [1/20], Batch Num: [110/600] Discriminator Loss: 1.5546, Generator Loss: 0.9736 D(x): 0.6026, D(G(z)): 0.5669 Epoch: [1/20], Batch Num: [111/600] Discriminator Loss: 1.6785, Generator Loss: 0.9903 D(x): 0.5754, D(G(z)): 0.5960 Epoch: [1/20], Batch Num: [112/600] Discriminator Loss: 1.6222, Generator Loss: 0.8207 D(x): 0.6009, D(G(z)): 0.6025 Epoch: [1/20], Batch Num: [113/600] Discriminator Loss: 1.7293, Generator Loss: 0.7476 D(x): 0.5792, D(G(z)): 0.6160 Epoch: [1/20], Batch Num: [114/600] Discriminator Loss: 1.9161, Generator Loss: 0.6711 D(x): 0.5708, D(G(z)): 0.6288 Epoch: [1/20], Batch Num: [115/600] Discriminator Loss: 1.6583, Generator Loss: 0.6529 D(x): 0.6201, D(G(z)): 0.6299 Epoch: [1/20], Batch Num: [116/600] Discriminator Loss: 1.6623, Generator Loss: 0.6249 D(x): 0.6222, D(G(z)): 0.6458 Epoch: [1/20], Batch Num: [117/600] Discriminator Loss: 1.7189, Generator Loss: 0.7067 D(x): 0.6307, D(G(z)): 0.6483 Epoch: [1/20], Batch Num: [118/600] Discriminator Loss: 1.6539, Generator Loss: 0.7962 D(x): 0.6068, D(G(z)): 0.6202 Epoch: [1/20], Batch Num: [119/600] Discriminator Loss: 1.8402, Generator Loss: 0.8498 D(x): 0.5259, D(G(z)): 0.6016 Epoch: [1/20], Batch Num: [120/600] Discriminator Loss: 1.7243, Generator Loss: 0.9497 D(x): 0.5261, D(G(z)): 0.5507 Epoch: [1/20], Batch Num: [121/600] Discriminator Loss: 1.6600, Generator Loss: 1.0020 D(x): 0.5063, D(G(z)): 0.5287 Epoch: [1/20], Batch Num: [122/600] Discriminator Loss: 1.5038, Generator Loss: 0.9344 D(x): 0.5527, D(G(z)): 0.5311 Epoch: [1/20], Batch Num: [123/600] Discriminator Loss: 1.5437, Generator Loss: 0.9785 D(x): 0.5601, D(G(z)): 0.5452 Epoch: [1/20], Batch Num: [124/600] Discriminator Loss: 1.4784, Generator Loss: 1.0525 D(x): 0.5631, D(G(z)): 0.5317 Epoch: [1/20], Batch Num: [125/600] Discriminator Loss: 1.4288, Generator Loss: 1.1007 D(x): 0.5631, D(G(z)): 0.4860 Epoch: [1/20], Batch Num: [126/600] Discriminator Loss: 1.4199, Generator Loss: 1.1417 D(x): 0.5216, D(G(z)): 0.4670 Epoch: [1/20], Batch Num: [127/600] Discriminator Loss: 1.4773, Generator Loss: 1.1955 D(x): 0.4978, D(G(z)): 0.4397 Epoch: [1/20], Batch Num: [128/600] Discriminator Loss: 1.2760, Generator Loss: 1.2308 D(x): 0.5472, D(G(z)): 0.4390 Epoch: [1/20], Batch Num: [129/600] Discriminator Loss: 1.2079, Generator Loss: 1.4230 D(x): 0.5567, D(G(z)): 0.4002 Epoch: [1/20], Batch Num: [130/600] Discriminator Loss: 1.2110, Generator Loss: 1.3123 D(x): 0.5557, D(G(z)): 0.3948 Epoch: [1/20], Batch Num: [131/600] Discriminator Loss: 1.1346, Generator Loss: 1.4915 D(x): 0.5916, D(G(z)): 0.4045 Epoch: [1/20], Batch Num: [132/600] Discriminator Loss: 1.0618, Generator Loss: 1.3682 D(x): 0.6005, D(G(z)): 0.3782 Epoch: [1/20], Batch Num: [133/600] Discriminator Loss: 1.0348, Generator Loss: 1.6657 D(x): 0.5988, D(G(z)): 0.3507 Epoch: [1/20], Batch Num: [134/600] Discriminator Loss: 0.9344, Generator Loss: 1.7401 D(x): 0.6270, D(G(z)): 0.3203 Epoch: [1/20], Batch Num: [135/600] Discriminator Loss: 0.9539, Generator Loss: 1.8596 D(x): 0.6028, D(G(z)): 0.2703 Epoch: [1/20], Batch Num: [136/600] Discriminator Loss: 0.8831, Generator Loss: 1.6557 D(x): 0.6083, D(G(z)): 0.2689 Epoch: [1/20], Batch Num: [137/600] Discriminator Loss: 0.8215, Generator Loss: 1.9150 D(x): 0.6713, D(G(z)): 0.2893 Epoch: [1/20], Batch Num: [138/600] Discriminator Loss: 0.7657, Generator Loss: 1.9076 D(x): 0.6916, D(G(z)): 0.2770 Epoch: [1/20], Batch Num: [139/600] Discriminator Loss: 0.6801, Generator Loss: 1.9343 D(x): 0.6999, D(G(z)): 0.2412 Epoch: [1/20], Batch Num: [140/600] Discriminator Loss: 0.7680, Generator Loss: 2.0374 D(x): 0.6882, D(G(z)): 0.2767 Epoch: [1/20], Batch Num: [141/600] Discriminator Loss: 0.7040, Generator Loss: 1.9666 D(x): 0.6985, D(G(z)): 0.2472 Epoch: [1/20], Batch Num: [142/600] Discriminator Loss: 0.6880, Generator Loss: 2.1051 D(x): 0.7251, D(G(z)): 0.2580 Epoch: [1/20], Batch Num: [143/600] Discriminator Loss: 0.6634, Generator Loss: 1.9951 D(x): 0.7422, D(G(z)): 0.2535 Epoch: [1/20], Batch Num: [144/600] Discriminator Loss: 0.7027, Generator Loss: 1.9948 D(x): 0.7365, D(G(z)): 0.2727 Epoch: [1/20], Batch Num: [145/600] Discriminator Loss: 0.6725, Generator Loss: 2.1110 D(x): 0.7287, D(G(z)): 0.2421 Epoch: [1/20], Batch Num: [146/600] Discriminator Loss: 0.7168, Generator Loss: 2.2380 D(x): 0.7262, D(G(z)): 0.2682 Epoch: [1/20], Batch Num: [147/600] Discriminator Loss: 0.7324, Generator Loss: 2.1367 D(x): 0.7024, D(G(z)): 0.2488 Epoch: [1/20], Batch Num: [148/600] Discriminator Loss: 0.7200, Generator Loss: 1.8305 D(x): 0.7369, D(G(z)): 0.2953 Epoch: [1/20], Batch Num: [149/600] Discriminator Loss: 0.7264, Generator Loss: 1.7049 D(x): 0.7232, D(G(z)): 0.2713 Epoch: [1/20], Batch Num: [150/600] Discriminator Loss: 0.7690, Generator Loss: 1.9191 D(x): 0.7431, D(G(z)): 0.3104 Epoch: [1/20], Batch Num: [151/600] Discriminator Loss: 0.8586, Generator Loss: 1.8314 D(x): 0.7000, D(G(z)): 0.3245 Epoch: [1/20], Batch Num: [152/600] Discriminator Loss: 0.8807, Generator Loss: 1.7711 D(x): 0.6793, D(G(z)): 0.2906 Epoch: [1/20], Batch Num: [153/600] Discriminator Loss: 0.9751, Generator Loss: 1.9320 D(x): 0.6900, D(G(z)): 0.3596 Epoch: [1/20], Batch Num: [154/600] Discriminator Loss: 0.8514, Generator Loss: 1.7860 D(x): 0.7010, D(G(z)): 0.3147 Epoch: [1/20], Batch Num: [155/600] Discriminator Loss: 1.0897, Generator Loss: 1.7451 D(x): 0.6160, D(G(z)): 0.3455 Epoch: [1/20], Batch Num: [156/600] Discriminator Loss: 1.0089, Generator Loss: 1.5329 D(x): 0.6804, D(G(z)): 0.3642 Epoch: [1/20], Batch Num: [157/600] Discriminator Loss: 1.0857, Generator Loss: 1.4200 D(x): 0.6796, D(G(z)): 0.4080 Epoch: [1/20], Batch Num: [158/600] Discriminator Loss: 1.0444, Generator Loss: 1.6959 D(x): 0.6561, D(G(z)): 0.3886 Epoch: [1/20], Batch Num: [159/600] Discriminator Loss: 1.0547, Generator Loss: 1.5199 D(x): 0.6492, D(G(z)): 0.3657 Epoch: [1/20], Batch Num: [160/600] Discriminator Loss: 1.3742, Generator Loss: 1.2914 D(x): 0.5769, D(G(z)): 0.4061 Epoch: [1/20], Batch Num: [161/600] Discriminator Loss: 1.1642, Generator Loss: 1.2459 D(x): 0.6962, D(G(z)): 0.4742 Epoch: [1/20], Batch Num: [162/600] Discriminator Loss: 1.1186, Generator Loss: 1.5679 D(x): 0.6639, D(G(z)): 0.4078 Epoch: [1/20], Batch Num: [163/600] Discriminator Loss: 1.1589, Generator Loss: 1.5593 D(x): 0.6808, D(G(z)): 0.4466 Epoch: [1/20], Batch Num: [164/600] Discriminator Loss: 1.1507, Generator Loss: 1.5149 D(x): 0.6176, D(G(z)): 0.3613 Epoch: [1/20], Batch Num: [165/600] Discriminator Loss: 1.1540, Generator Loss: 1.6016 D(x): 0.6058, D(G(z)): 0.3518 Epoch: [1/20], Batch Num: [166/600] Discriminator Loss: 1.4157, Generator Loss: 1.3640 D(x): 0.5556, D(G(z)): 0.4232 Epoch: [1/20], Batch Num: [167/600] Discriminator Loss: 1.0116, Generator Loss: 1.4225 D(x): 0.7635, D(G(z)): 0.4545 Epoch: [1/20], Batch Num: [168/600] Discriminator Loss: 1.0827, Generator Loss: 1.3821 D(x): 0.6924, D(G(z)): 0.4321 Epoch: [1/20], Batch Num: [169/600] Discriminator Loss: 1.1501, Generator Loss: 1.6141 D(x): 0.6942, D(G(z)): 0.4300 Epoch: [1/20], Batch Num: [170/600] Discriminator Loss: 0.9788, Generator Loss: 1.8569 D(x): 0.7053, D(G(z)): 0.3812 Epoch: [1/20], Batch Num: [171/600] Discriminator Loss: 1.0219, Generator Loss: 2.0618 D(x): 0.6398, D(G(z)): 0.2848 Epoch: [1/20], Batch Num: [172/600] Discriminator Loss: 0.9441, Generator Loss: 1.8397 D(x): 0.6377, D(G(z)): 0.2808 Epoch: [1/20], Batch Num: [173/600] Discriminator Loss: 0.8263, Generator Loss: 1.6090 D(x): 0.6849, D(G(z)): 0.2674 Epoch: [1/20], Batch Num: [174/600] Discriminator Loss: 0.8413, Generator Loss: 1.6117 D(x): 0.7685, D(G(z)): 0.3617 Epoch: [1/20], Batch Num: [175/600] Discriminator Loss: 0.8024, Generator Loss: 1.5888 D(x): 0.7688, D(G(z)): 0.3567 Epoch: [1/20], Batch Num: [176/600] Discriminator Loss: 0.8616, Generator Loss: 1.8042 D(x): 0.7786, D(G(z)): 0.3884 Epoch: [1/20], Batch Num: [177/600] Discriminator Loss: 0.8142, Generator Loss: 2.1080 D(x): 0.7641, D(G(z)): 0.3364 Epoch: [1/20], Batch Num: [178/600] Discriminator Loss: 0.7450, Generator Loss: 2.1220 D(x): 0.7143, D(G(z)): 0.2521 Epoch: [1/20], Batch Num: [179/600] Discriminator Loss: 0.8113, Generator Loss: 2.1478 D(x): 0.6814, D(G(z)): 0.2563 Epoch: [1/20], Batch Num: [180/600] Discriminator Loss: 0.7620, Generator Loss: 1.8858 D(x): 0.7023, D(G(z)): 0.2446 Epoch: [1/20], Batch Num: [181/600] Discriminator Loss: 0.6939, Generator Loss: 1.8813 D(x): 0.7985, D(G(z)): 0.3154 Epoch: [1/20], Batch Num: [182/600] Discriminator Loss: 0.7821, Generator Loss: 1.9502 D(x): 0.7893, D(G(z)): 0.3487 Epoch: [1/20], Batch Num: [183/600] Discriminator Loss: 0.7485, Generator Loss: 2.2225 D(x): 0.7701, D(G(z)): 0.3107 Epoch: [1/20], Batch Num: [184/600] Discriminator Loss: 0.6232, Generator Loss: 2.2277 D(x): 0.7677, D(G(z)): 0.2464 Epoch: [1/20], Batch Num: [185/600] Discriminator Loss: 0.9001, Generator Loss: 2.1033 D(x): 0.6742, D(G(z)): 0.2722 Epoch: [1/20], Batch Num: [186/600] Discriminator Loss: 0.7880, Generator Loss: 1.7829 D(x): 0.7262, D(G(z)): 0.2938 Epoch: [1/20], Batch Num: [187/600] Discriminator Loss: 0.7748, Generator Loss: 1.7835 D(x): 0.7747, D(G(z)): 0.3090 Epoch: [1/20], Batch Num: [188/600] Discriminator Loss: 0.7217, Generator Loss: 2.1105 D(x): 0.7745, D(G(z)): 0.3110 Epoch: [1/20], Batch Num: [189/600] Discriminator Loss: 0.7717, Generator Loss: 2.1561 D(x): 0.7526, D(G(z)): 0.3127 Epoch: [1/20], Batch Num: [190/600] Discriminator Loss: 0.8601, Generator Loss: 2.2600 D(x): 0.7288, D(G(z)): 0.3268 Epoch: [1/20], Batch Num: [191/600] Discriminator Loss: 0.8588, Generator Loss: 2.0742 D(x): 0.6990, D(G(z)): 0.2829 Epoch: [1/20], Batch Num: [192/600] Discriminator Loss: 0.8707, Generator Loss: 2.1126 D(x): 0.7270, D(G(z)): 0.3159 Epoch: [1/20], Batch Num: [193/600] Discriminator Loss: 1.0008, Generator Loss: 1.8880 D(x): 0.7106, D(G(z)): 0.3424 Epoch: [1/20], Batch Num: [194/600] Discriminator Loss: 0.7387, Generator Loss: 1.9103 D(x): 0.7827, D(G(z)): 0.3140 Epoch: [1/20], Batch Num: [195/600] Discriminator Loss: 0.8711, Generator Loss: 2.0345 D(x): 0.7347, D(G(z)): 0.3465 Epoch: [1/20], Batch Num: [196/600] Discriminator Loss: 0.8018, Generator Loss: 2.0687 D(x): 0.7401, D(G(z)): 0.2866 Epoch: [1/20], Batch Num: [197/600] Discriminator Loss: 0.8803, Generator Loss: 2.0682 D(x): 0.6924, D(G(z)): 0.2781 Epoch: [1/20], Batch Num: [198/600] Discriminator Loss: 0.8696, Generator Loss: 2.0922 D(x): 0.7228, D(G(z)): 0.3133 Epoch: [1/20], Batch Num: [199/600] Discriminator Loss: 0.7694, Generator Loss: 2.1709 D(x): 0.7486, D(G(z)): 0.2786 Epoch: 1, Batch Num: [200/600]
Epoch: [1/20], Batch Num: [200/600] Discriminator Loss: 0.9038, Generator Loss: 2.1094 D(x): 0.7586, D(G(z)): 0.3472 Epoch: [1/20], Batch Num: [201/600] Discriminator Loss: 0.7788, Generator Loss: 2.1873 D(x): 0.7591, D(G(z)): 0.3081 Epoch: [1/20], Batch Num: [202/600] Discriminator Loss: 0.7284, Generator Loss: 2.3894 D(x): 0.7721, D(G(z)): 0.2817 Epoch: [1/20], Batch Num: [203/600] Discriminator Loss: 0.7022, Generator Loss: 2.7067 D(x): 0.7747, D(G(z)): 0.2526 Epoch: [1/20], Batch Num: [204/600] Discriminator Loss: 0.6716, Generator Loss: 2.5858 D(x): 0.7594, D(G(z)): 0.2501 Epoch: [1/20], Batch Num: [205/600] Discriminator Loss: 0.6784, Generator Loss: 2.5104 D(x): 0.7460, D(G(z)): 0.2193 Epoch: [1/20], Batch Num: [206/600] Discriminator Loss: 0.6629, Generator Loss: 2.4898 D(x): 0.7864, D(G(z)): 0.2551 Epoch: [1/20], Batch Num: [207/600] Discriminator Loss: 0.4907, Generator Loss: 2.5240 D(x): 0.8522, D(G(z)): 0.2358 Epoch: [1/20], Batch Num: [208/600] Discriminator Loss: 0.5672, Generator Loss: 2.6999 D(x): 0.7914, D(G(z)): 0.1994 Epoch: [1/20], Batch Num: [209/600] Discriminator Loss: 0.5156, Generator Loss: 2.6685 D(x): 0.8313, D(G(z)): 0.2184 Epoch: [1/20], Batch Num: [210/600] Discriminator Loss: 0.5367, Generator Loss: 3.2010 D(x): 0.8435, D(G(z)): 0.2415 Epoch: [1/20], Batch Num: [211/600] Discriminator Loss: 0.5352, Generator Loss: 2.9677 D(x): 0.7979, D(G(z)): 0.1732 Epoch: [1/20], Batch Num: [212/600] Discriminator Loss: 0.3726, Generator Loss: 2.7089 D(x): 0.8435, D(G(z)): 0.1386 Epoch: [1/20], Batch Num: [213/600] Discriminator Loss: 0.4727, Generator Loss: 3.1640 D(x): 0.8326, D(G(z)): 0.1825 Epoch: [1/20], Batch Num: [214/600] Discriminator Loss: 0.5267, Generator Loss: 2.7425 D(x): 0.8193, D(G(z)): 0.1752 Epoch: [1/20], Batch Num: [215/600] Discriminator Loss: 0.3355, Generator Loss: 2.9898 D(x): 0.8915, D(G(z)): 0.1714 Epoch: [1/20], Batch Num: [216/600] Discriminator Loss: 0.5171, Generator Loss: 3.0616 D(x): 0.8362, D(G(z)): 0.1919 Epoch: [1/20], Batch Num: [217/600] Discriminator Loss: 0.5493, Generator Loss: 3.0614 D(x): 0.8293, D(G(z)): 0.2140 Epoch: [1/20], Batch Num: [218/600] Discriminator Loss: 0.5376, Generator Loss: 3.0975 D(x): 0.8389, D(G(z)): 0.2184 Epoch: [1/20], Batch Num: [219/600] Discriminator Loss: 0.6040, Generator Loss: 2.8980 D(x): 0.7839, D(G(z)): 0.1856 Epoch: [1/20], Batch Num: [220/600] Discriminator Loss: 0.5571, Generator Loss: 2.8367 D(x): 0.8203, D(G(z)): 0.2147 Epoch: [1/20], Batch Num: [221/600] Discriminator Loss: 0.5779, Generator Loss: 2.8794 D(x): 0.8079, D(G(z)): 0.2163 Epoch: [1/20], Batch Num: [222/600] Discriminator Loss: 0.5458, Generator Loss: 2.7755 D(x): 0.8687, D(G(z)): 0.2487 Epoch: [1/20], Batch Num: [223/600] Discriminator Loss: 0.6102, Generator Loss: 2.9927 D(x): 0.7825, D(G(z)): 0.2060 Epoch: [1/20], Batch Num: [224/600] Discriminator Loss: 0.5444, Generator Loss: 2.9940 D(x): 0.8166, D(G(z)): 0.2046 Epoch: [1/20], Batch Num: [225/600] Discriminator Loss: 0.6883, Generator Loss: 2.9943 D(x): 0.7816, D(G(z)): 0.2386 Epoch: [1/20], Batch Num: [226/600] Discriminator Loss: 0.7443, Generator Loss: 2.8804 D(x): 0.7661, D(G(z)): 0.2561 Epoch: [1/20], Batch Num: [227/600] Discriminator Loss: 0.7861, Generator Loss: 2.6626 D(x): 0.7720, D(G(z)): 0.2880 Epoch: [1/20], Batch Num: [228/600] Discriminator Loss: 0.9284, Generator Loss: 2.3715 D(x): 0.7365, D(G(z)): 0.3031 Epoch: [1/20], Batch Num: [229/600] Discriminator Loss: 1.0718, Generator Loss: 2.2454 D(x): 0.6974, D(G(z)): 0.3448 Epoch: [1/20], Batch Num: [230/600] Discriminator Loss: 1.2177, Generator Loss: 1.8470 D(x): 0.6515, D(G(z)): 0.3621 Epoch: [1/20], Batch Num: [231/600] Discriminator Loss: 1.3543, Generator Loss: 1.8831 D(x): 0.7035, D(G(z)): 0.4731 Epoch: [1/20], Batch Num: [232/600] Discriminator Loss: 1.4018, Generator Loss: 2.0650 D(x): 0.6872, D(G(z)): 0.4703 Epoch: [1/20], Batch Num: [233/600] Discriminator Loss: 1.3646, Generator Loss: 1.9105 D(x): 0.6455, D(G(z)): 0.4041 Epoch: [1/20], Batch Num: [234/600] Discriminator Loss: 1.6171, Generator Loss: 1.7461 D(x): 0.5819, D(G(z)): 0.4155 Epoch: [1/20], Batch Num: [235/600] Discriminator Loss: 1.4976, Generator Loss: 1.6019 D(x): 0.6528, D(G(z)): 0.4279 Epoch: [1/20], Batch Num: [236/600] Discriminator Loss: 1.2554, Generator Loss: 1.6993 D(x): 0.6994, D(G(z)): 0.4518 Epoch: [1/20], Batch Num: [237/600] Discriminator Loss: 1.3390, Generator Loss: 1.7638 D(x): 0.6832, D(G(z)): 0.4494 Epoch: [1/20], Batch Num: [238/600] Discriminator Loss: 1.0554, Generator Loss: 1.9368 D(x): 0.7104, D(G(z)): 0.3895 Epoch: [1/20], Batch Num: [239/600] Discriminator Loss: 1.2125, Generator Loss: 1.8775 D(x): 0.6102, D(G(z)): 0.3417 Epoch: [1/20], Batch Num: [240/600] Discriminator Loss: 1.1382, Generator Loss: 2.1234 D(x): 0.7038, D(G(z)): 0.3551 Epoch: [1/20], Batch Num: [241/600] Discriminator Loss: 1.0686, Generator Loss: 1.9584 D(x): 0.6880, D(G(z)): 0.3406 Epoch: [1/20], Batch Num: [242/600] Discriminator Loss: 1.0293, Generator Loss: 1.7722 D(x): 0.6723, D(G(z)): 0.3311 Epoch: [1/20], Batch Num: [243/600] Discriminator Loss: 0.8833, Generator Loss: 1.6316 D(x): 0.7646, D(G(z)): 0.3673 Epoch: [1/20], Batch Num: [244/600] Discriminator Loss: 1.0127, Generator Loss: 1.8280 D(x): 0.7450, D(G(z)): 0.4003 Epoch: [1/20], Batch Num: [245/600] Discriminator Loss: 0.9296, Generator Loss: 2.2694 D(x): 0.7829, D(G(z)): 0.4018 Epoch: [1/20], Batch Num: [246/600] Discriminator Loss: 0.9952, Generator Loss: 2.3325 D(x): 0.6769, D(G(z)): 0.3080 Epoch: [1/20], Batch Num: [247/600] Discriminator Loss: 0.9803, Generator Loss: 2.2664 D(x): 0.6827, D(G(z)): 0.2922 Epoch: [1/20], Batch Num: [248/600] Discriminator Loss: 1.2185, Generator Loss: 1.9713 D(x): 0.5702, D(G(z)): 0.3045 Epoch: [1/20], Batch Num: [249/600] Discriminator Loss: 0.8702, Generator Loss: 1.7014 D(x): 0.7260, D(G(z)): 0.3253 Epoch: [1/20], Batch Num: [250/600] Discriminator Loss: 1.0325, Generator Loss: 1.6680 D(x): 0.7377, D(G(z)): 0.4231 Epoch: [1/20], Batch Num: [251/600] Discriminator Loss: 0.9821, Generator Loss: 1.6656 D(x): 0.7452, D(G(z)): 0.4037 Epoch: [1/20], Batch Num: [252/600] Discriminator Loss: 0.9210, Generator Loss: 1.8789 D(x): 0.7503, D(G(z)): 0.3860 Epoch: [1/20], Batch Num: [253/600] Discriminator Loss: 0.8481, Generator Loss: 2.4204 D(x): 0.7672, D(G(z)): 0.3519 Epoch: [1/20], Batch Num: [254/600] Discriminator Loss: 1.0265, Generator Loss: 2.2742 D(x): 0.6149, D(G(z)): 0.2464 Epoch: [1/20], Batch Num: [255/600] Discriminator Loss: 0.9905, Generator Loss: 2.0476 D(x): 0.6369, D(G(z)): 0.2775 Epoch: [1/20], Batch Num: [256/600] Discriminator Loss: 1.0234, Generator Loss: 1.7832 D(x): 0.6754, D(G(z)): 0.3284 Epoch: [1/20], Batch Num: [257/600] Discriminator Loss: 0.9815, Generator Loss: 1.5655 D(x): 0.7049, D(G(z)): 0.3654 Epoch: [1/20], Batch Num: [258/600] Discriminator Loss: 0.8935, Generator Loss: 1.7980 D(x): 0.7352, D(G(z)): 0.3475 Epoch: [1/20], Batch Num: [259/600] Discriminator Loss: 0.9874, Generator Loss: 1.9922 D(x): 0.7684, D(G(z)): 0.4327 Epoch: [1/20], Batch Num: [260/600] Discriminator Loss: 0.9892, Generator Loss: 2.1566 D(x): 0.6785, D(G(z)): 0.3388 Epoch: [1/20], Batch Num: [261/600] Discriminator Loss: 0.9689, Generator Loss: 2.1780 D(x): 0.6983, D(G(z)): 0.3415 Epoch: [1/20], Batch Num: [262/600] Discriminator Loss: 0.9561, Generator Loss: 2.3758 D(x): 0.6801, D(G(z)): 0.2881 Epoch: [1/20], Batch Num: [263/600] Discriminator Loss: 0.9540, Generator Loss: 2.0937 D(x): 0.6988, D(G(z)): 0.3221 Epoch: [1/20], Batch Num: [264/600] Discriminator Loss: 0.9259, Generator Loss: 2.1812 D(x): 0.7147, D(G(z)): 0.3229 Epoch: [1/20], Batch Num: [265/600] Discriminator Loss: 0.9273, Generator Loss: 2.2571 D(x): 0.7132, D(G(z)): 0.2921 Epoch: [1/20], Batch Num: [266/600] Discriminator Loss: 0.7961, Generator Loss: 2.2735 D(x): 0.7639, D(G(z)): 0.3201 Epoch: [1/20], Batch Num: [267/600] Discriminator Loss: 0.6315, Generator Loss: 2.2869 D(x): 0.8211, D(G(z)): 0.2953 Epoch: [1/20], Batch Num: [268/600] Discriminator Loss: 0.6316, Generator Loss: 2.9530 D(x): 0.8386, D(G(z)): 0.2745 Epoch: [1/20], Batch Num: [269/600] Discriminator Loss: 0.6677, Generator Loss: 2.5744 D(x): 0.7803, D(G(z)): 0.2625 Epoch: [1/20], Batch Num: [270/600] Discriminator Loss: 0.6093, Generator Loss: 3.4905 D(x): 0.7777, D(G(z)): 0.2129 Epoch: [1/20], Batch Num: [271/600] Discriminator Loss: 0.5362, Generator Loss: 3.8831 D(x): 0.8249, D(G(z)): 0.2327 Epoch: [1/20], Batch Num: [272/600] Discriminator Loss: 0.6385, Generator Loss: 4.1762 D(x): 0.7727, D(G(z)): 0.2269 Epoch: [1/20], Batch Num: [273/600] Discriminator Loss: 0.6147, Generator Loss: 3.8705 D(x): 0.7954, D(G(z)): 0.2237 Epoch: [1/20], Batch Num: [274/600] Discriminator Loss: 0.6158, Generator Loss: 3.4176 D(x): 0.8040, D(G(z)): 0.2562 Epoch: [1/20], Batch Num: [275/600] Discriminator Loss: 0.6720, Generator Loss: 3.8937 D(x): 0.8134, D(G(z)): 0.2838 Epoch: [1/20], Batch Num: [276/600] Discriminator Loss: 0.7709, Generator Loss: 3.8525 D(x): 0.8074, D(G(z)): 0.3177 Epoch: [1/20], Batch Num: [277/600] Discriminator Loss: 0.8458, Generator Loss: 3.7535 D(x): 0.7728, D(G(z)): 0.3288 Epoch: [1/20], Batch Num: [278/600] Discriminator Loss: 1.0031, Generator Loss: 4.1187 D(x): 0.7317, D(G(z)): 0.3408 Epoch: [1/20], Batch Num: [279/600] Discriminator Loss: 0.9799, Generator Loss: 4.1339 D(x): 0.7026, D(G(z)): 0.2910 Epoch: [1/20], Batch Num: [280/600] Discriminator Loss: 0.8693, Generator Loss: 3.8413 D(x): 0.7969, D(G(z)): 0.3719 Epoch: [1/20], Batch Num: [281/600] Discriminator Loss: 0.9295, Generator Loss: 4.9272 D(x): 0.7789, D(G(z)): 0.3696 Epoch: [1/20], Batch Num: [282/600] Discriminator Loss: 0.8473, Generator Loss: 5.2160 D(x): 0.7373, D(G(z)): 0.2486 Epoch: [1/20], Batch Num: [283/600] Discriminator Loss: 0.7372, Generator Loss: 5.7717 D(x): 0.7798, D(G(z)): 0.2448 Epoch: [1/20], Batch Num: [284/600] Discriminator Loss: 0.6286, Generator Loss: 6.0482 D(x): 0.8528, D(G(z)): 0.2610 Epoch: [1/20], Batch Num: [285/600] Discriminator Loss: 0.6191, Generator Loss: 6.8693 D(x): 0.9004, D(G(z)): 0.2849 Epoch: [1/20], Batch Num: [286/600] Discriminator Loss: 0.7551, Generator Loss: 6.7564 D(x): 0.8971, D(G(z)): 0.3230 Epoch: [1/20], Batch Num: [287/600] Discriminator Loss: 1.1677, Generator Loss: 5.8796 D(x): 0.8249, D(G(z)): 0.3978 Epoch: [1/20], Batch Num: [288/600] Discriminator Loss: 1.5860, Generator Loss: 6.7582 D(x): 0.6888, D(G(z)): 0.4279 Epoch: [1/20], Batch Num: [289/600] Discriminator Loss: 1.9986, Generator Loss: 5.1930 D(x): 0.6559, D(G(z)): 0.5134 Epoch: [1/20], Batch Num: [290/600] Discriminator Loss: 2.2458, Generator Loss: 4.8630 D(x): 0.6467, D(G(z)): 0.6318 Epoch: [1/20], Batch Num: [291/600] Discriminator Loss: 1.8884, Generator Loss: 4.7339 D(x): 0.6653, D(G(z)): 0.5514 Epoch: [1/20], Batch Num: [292/600] Discriminator Loss: 2.4206, Generator Loss: 5.5290 D(x): 0.5706, D(G(z)): 0.5823 Epoch: [1/20], Batch Num: [293/600] Discriminator Loss: 1.7899, Generator Loss: 5.2521 D(x): 0.5962, D(G(z)): 0.4101 Epoch: [1/20], Batch Num: [294/600] Discriminator Loss: 1.8470, Generator Loss: 5.0267 D(x): 0.5980, D(G(z)): 0.4294 Epoch: [1/20], Batch Num: [295/600] Discriminator Loss: 1.6758, Generator Loss: 4.8769 D(x): 0.6051, D(G(z)): 0.3984 Epoch: [1/20], Batch Num: [296/600] Discriminator Loss: 1.7690, Generator Loss: 4.0716 D(x): 0.6759, D(G(z)): 0.4945 Epoch: [1/20], Batch Num: [297/600] Discriminator Loss: 1.4975, Generator Loss: 4.8097 D(x): 0.7332, D(G(z)): 0.4864 Epoch: [1/20], Batch Num: [298/600] Discriminator Loss: 1.7299, Generator Loss: 4.5633 D(x): 0.6356, D(G(z)): 0.4269 Epoch: [1/20], Batch Num: [299/600] Discriminator Loss: 1.3674, Generator Loss: 5.7512 D(x): 0.7053, D(G(z)): 0.4017 Epoch: 1, Batch Num: [300/600]
Epoch: [1/20], Batch Num: [300/600] Discriminator Loss: 1.6676, Generator Loss: 4.9633 D(x): 0.5705, D(G(z)): 0.3676 Epoch: [1/20], Batch Num: [301/600] Discriminator Loss: 1.7584, Generator Loss: 3.4557 D(x): 0.5805, D(G(z)): 0.4028 Epoch: [1/20], Batch Num: [302/600] Discriminator Loss: 1.4705, Generator Loss: 3.8597 D(x): 0.7296, D(G(z)): 0.4577 Epoch: [1/20], Batch Num: [303/600] Discriminator Loss: 1.8006, Generator Loss: 4.2917 D(x): 0.7088, D(G(z)): 0.5720 Epoch: [1/20], Batch Num: [304/600] Discriminator Loss: 1.3667, Generator Loss: 4.6967 D(x): 0.7488, D(G(z)): 0.4546 Epoch: [1/20], Batch Num: [305/600] Discriminator Loss: 1.5296, Generator Loss: 5.2579 D(x): 0.6347, D(G(z)): 0.3259 Epoch: [1/20], Batch Num: [306/600] Discriminator Loss: 1.7292, Generator Loss: 3.8928 D(x): 0.5008, D(G(z)): 0.2267 Epoch: [1/20], Batch Num: [307/600] Discriminator Loss: 1.3779, Generator Loss: 3.2693 D(x): 0.6928, D(G(z)): 0.4184 Epoch: [1/20], Batch Num: [308/600] Discriminator Loss: 1.8731, Generator Loss: 2.7374 D(x): 0.6520, D(G(z)): 0.5584 Epoch: [1/20], Batch Num: [309/600] Discriminator Loss: 2.1731, Generator Loss: 2.4443 D(x): 0.6630, D(G(z)): 0.6015 Epoch: [1/20], Batch Num: [310/600] Discriminator Loss: 2.1248, Generator Loss: 2.3872 D(x): 0.5738, D(G(z)): 0.4986 Epoch: [1/20], Batch Num: [311/600] Discriminator Loss: 2.1092, Generator Loss: 2.4286 D(x): 0.5986, D(G(z)): 0.5638 Epoch: [1/20], Batch Num: [312/600] Discriminator Loss: 2.3625, Generator Loss: 1.6674 D(x): 0.4802, D(G(z)): 0.4363 Epoch: [1/20], Batch Num: [313/600] Discriminator Loss: 2.0480, Generator Loss: 1.4653 D(x): 0.6395, D(G(z)): 0.5887 Epoch: [1/20], Batch Num: [314/600] Discriminator Loss: 2.0334, Generator Loss: 1.3353 D(x): 0.6175, D(G(z)): 0.5549 Epoch: [1/20], Batch Num: [315/600] Discriminator Loss: 1.6639, Generator Loss: 1.7351 D(x): 0.7292, D(G(z)): 0.6011 Epoch: [1/20], Batch Num: [316/600] Discriminator Loss: 1.5892, Generator Loss: 2.1493 D(x): 0.6433, D(G(z)): 0.4619 Epoch: [1/20], Batch Num: [317/600] Discriminator Loss: 1.4092, Generator Loss: 2.1781 D(x): 0.6070, D(G(z)): 0.3772 Epoch: [1/20], Batch Num: [318/600] Discriminator Loss: 1.3430, Generator Loss: 2.0116 D(x): 0.6421, D(G(z)): 0.3579 Epoch: [1/20], Batch Num: [319/600] Discriminator Loss: 1.7594, Generator Loss: 1.3643 D(x): 0.5790, D(G(z)): 0.3994 Epoch: [1/20], Batch Num: [320/600] Discriminator Loss: 1.3329, Generator Loss: 1.3386 D(x): 0.7014, D(G(z)): 0.4809 Epoch: [1/20], Batch Num: [321/600] Discriminator Loss: 1.1442, Generator Loss: 1.6497 D(x): 0.7565, D(G(z)): 0.4634 Epoch: [1/20], Batch Num: [322/600] Discriminator Loss: 1.1851, Generator Loss: 1.9470 D(x): 0.7652, D(G(z)): 0.4783 Epoch: [1/20], Batch Num: [323/600] Discriminator Loss: 0.9987, Generator Loss: 2.0318 D(x): 0.7053, D(G(z)): 0.3354 Epoch: [1/20], Batch Num: [324/600] Discriminator Loss: 1.1575, Generator Loss: 2.1115 D(x): 0.6018, D(G(z)): 0.2463 Epoch: [1/20], Batch Num: [325/600] Discriminator Loss: 0.9048, Generator Loss: 1.9772 D(x): 0.6723, D(G(z)): 0.2470 Epoch: [1/20], Batch Num: [326/600] Discriminator Loss: 0.8574, Generator Loss: 2.0428 D(x): 0.7064, D(G(z)): 0.2523 Epoch: [1/20], Batch Num: [327/600] Discriminator Loss: 0.8630, Generator Loss: 1.5463 D(x): 0.7392, D(G(z)): 0.2985 Epoch: [1/20], Batch Num: [328/600] Discriminator Loss: 0.9120, Generator Loss: 1.5308 D(x): 0.7844, D(G(z)): 0.3872 Epoch: [1/20], Batch Num: [329/600] Discriminator Loss: 0.6842, Generator Loss: 1.5571 D(x): 0.8533, D(G(z)): 0.3367 Epoch: [1/20], Batch Num: [330/600] Discriminator Loss: 0.7099, Generator Loss: 2.1164 D(x): 0.8256, D(G(z)): 0.3216 Epoch: [1/20], Batch Num: [331/600] Discriminator Loss: 0.7287, Generator Loss: 2.0506 D(x): 0.7933, D(G(z)): 0.2865 Epoch: [1/20], Batch Num: [332/600] Discriminator Loss: 0.8352, Generator Loss: 2.3132 D(x): 0.7188, D(G(z)): 0.2573 Epoch: [1/20], Batch Num: [333/600] Discriminator Loss: 0.6900, Generator Loss: 2.1394 D(x): 0.7437, D(G(z)): 0.2139 Epoch: [1/20], Batch Num: [334/600] Discriminator Loss: 0.7320, Generator Loss: 1.9427 D(x): 0.7516, D(G(z)): 0.2235 Epoch: [1/20], Batch Num: [335/600] Discriminator Loss: 0.6657, Generator Loss: 1.9720 D(x): 0.7744, D(G(z)): 0.2226 Epoch: [1/20], Batch Num: [336/600] Discriminator Loss: 0.7277, Generator Loss: 1.6719 D(x): 0.8092, D(G(z)): 0.3002 Epoch: [1/20], Batch Num: [337/600] Discriminator Loss: 0.7720, Generator Loss: 1.5913 D(x): 0.7925, D(G(z)): 0.3212 Epoch: [1/20], Batch Num: [338/600] Discriminator Loss: 0.7159, Generator Loss: 1.7539 D(x): 0.8433, D(G(z)): 0.3344 Epoch: [1/20], Batch Num: [339/600] Discriminator Loss: 0.6708, Generator Loss: 2.1097 D(x): 0.8306, D(G(z)): 0.3050 Epoch: [1/20], Batch Num: [340/600] Discriminator Loss: 0.7043, Generator Loss: 2.0116 D(x): 0.7783, D(G(z)): 0.2789 Epoch: [1/20], Batch Num: [341/600] Discriminator Loss: 0.7334, Generator Loss: 2.1344 D(x): 0.7616, D(G(z)): 0.2550 Epoch: [1/20], Batch Num: [342/600] Discriminator Loss: 0.7177, Generator Loss: 1.9192 D(x): 0.7858, D(G(z)): 0.3018 Epoch: [1/20], Batch Num: [343/600] Discriminator Loss: 0.8283, Generator Loss: 2.0467 D(x): 0.7665, D(G(z)): 0.2874 Epoch: [1/20], Batch Num: [344/600] Discriminator Loss: 0.7063, Generator Loss: 1.6935 D(x): 0.7801, D(G(z)): 0.2732 Epoch: [1/20], Batch Num: [345/600] Discriminator Loss: 0.7409, Generator Loss: 1.7666 D(x): 0.7799, D(G(z)): 0.3004 Epoch: [1/20], Batch Num: [346/600] Discriminator Loss: 0.6485, Generator Loss: 1.6801 D(x): 0.8198, D(G(z)): 0.2889 Epoch: [1/20], Batch Num: [347/600] Discriminator Loss: 0.7787, Generator Loss: 1.7609 D(x): 0.8066, D(G(z)): 0.3586 Epoch: [1/20], Batch Num: [348/600] Discriminator Loss: 0.7479, Generator Loss: 1.7680 D(x): 0.7946, D(G(z)): 0.3234 Epoch: [1/20], Batch Num: [349/600] Discriminator Loss: 0.7787, Generator Loss: 1.8165 D(x): 0.8222, D(G(z)): 0.3687 Epoch: [1/20], Batch Num: [350/600] Discriminator Loss: 0.9153, Generator Loss: 2.0216 D(x): 0.7368, D(G(z)): 0.3185 Epoch: [1/20], Batch Num: [351/600] Discriminator Loss: 0.7612, Generator Loss: 1.8232 D(x): 0.7616, D(G(z)): 0.2618 Epoch: [1/20], Batch Num: [352/600] Discriminator Loss: 0.7644, Generator Loss: 1.8080 D(x): 0.7498, D(G(z)): 0.2621 Epoch: [1/20], Batch Num: [353/600] Discriminator Loss: 0.7319, Generator Loss: 1.6882 D(x): 0.7918, D(G(z)): 0.2811 Epoch: [1/20], Batch Num: [354/600] Discriminator Loss: 0.8495, Generator Loss: 1.5749 D(x): 0.7882, D(G(z)): 0.3636 Epoch: [1/20], Batch Num: [355/600] Discriminator Loss: 0.6547, Generator Loss: 1.4382 D(x): 0.8471, D(G(z)): 0.3260 Epoch: [1/20], Batch Num: [356/600] Discriminator Loss: 0.7399, Generator Loss: 1.6330 D(x): 0.7982, D(G(z)): 0.3236 Epoch: [1/20], Batch Num: [357/600] Discriminator Loss: 0.7427, Generator Loss: 1.7017 D(x): 0.8022, D(G(z)): 0.3269 Epoch: [1/20], Batch Num: [358/600] Discriminator Loss: 0.8140, Generator Loss: 1.6682 D(x): 0.7861, D(G(z)): 0.3418 Epoch: [1/20], Batch Num: [359/600] Discriminator Loss: 0.8737, Generator Loss: 1.7116 D(x): 0.7530, D(G(z)): 0.3314 Epoch: [1/20], Batch Num: [360/600] Discriminator Loss: 0.7284, Generator Loss: 1.6502 D(x): 0.7662, D(G(z)): 0.2786 Epoch: [1/20], Batch Num: [361/600] Discriminator Loss: 0.7305, Generator Loss: 1.6169 D(x): 0.7959, D(G(z)): 0.2957 Epoch: [1/20], Batch Num: [362/600] Discriminator Loss: 0.7316, Generator Loss: 1.8570 D(x): 0.8159, D(G(z)): 0.3228 Epoch: [1/20], Batch Num: [363/600] Discriminator Loss: 0.6715, Generator Loss: 1.6803 D(x): 0.8290, D(G(z)): 0.2883 Epoch: [1/20], Batch Num: [364/600] Discriminator Loss: 0.7009, Generator Loss: 1.7591 D(x): 0.8359, D(G(z)): 0.3417 Epoch: [1/20], Batch Num: [365/600] Discriminator Loss: 0.7863, Generator Loss: 1.8742 D(x): 0.7739, D(G(z)): 0.3022 Epoch: [1/20], Batch Num: [366/600] Discriminator Loss: 0.7232, Generator Loss: 1.5337 D(x): 0.7823, D(G(z)): 0.2936 Epoch: [1/20], Batch Num: [367/600] Discriminator Loss: 0.6419, Generator Loss: 1.7182 D(x): 0.8411, D(G(z)): 0.3215 Epoch: [1/20], Batch Num: [368/600] Discriminator Loss: 0.7108, Generator Loss: 1.6879 D(x): 0.8303, D(G(z)): 0.3443 Epoch: [1/20], Batch Num: [369/600] Discriminator Loss: 0.7670, Generator Loss: 1.6667 D(x): 0.8214, D(G(z)): 0.3564 Epoch: [1/20], Batch Num: [370/600] Discriminator Loss: 0.8319, Generator Loss: 1.8269 D(x): 0.7803, D(G(z)): 0.3390 Epoch: [1/20], Batch Num: [371/600] Discriminator Loss: 0.6741, Generator Loss: 1.8886 D(x): 0.8213, D(G(z)): 0.3245 Epoch: [1/20], Batch Num: [372/600] Discriminator Loss: 0.7910, Generator Loss: 1.7078 D(x): 0.8058, D(G(z)): 0.3501 Epoch: [1/20], Batch Num: [373/600] Discriminator Loss: 0.7221, Generator Loss: 1.7613 D(x): 0.8130, D(G(z)): 0.3400 Epoch: [1/20], Batch Num: [374/600] Discriminator Loss: 0.8187, Generator Loss: 1.7656 D(x): 0.7937, D(G(z)): 0.3561 Epoch: [1/20], Batch Num: [375/600] Discriminator Loss: 0.7272, Generator Loss: 1.5846 D(x): 0.8305, D(G(z)): 0.3484 Epoch: [1/20], Batch Num: [376/600] Discriminator Loss: 0.7954, Generator Loss: 1.6335 D(x): 0.7989, D(G(z)): 0.3516 Epoch: [1/20], Batch Num: [377/600] Discriminator Loss: 0.7863, Generator Loss: 1.7241 D(x): 0.8313, D(G(z)): 0.3757 Epoch: [1/20], Batch Num: [378/600] Discriminator Loss: 0.7073, Generator Loss: 1.5703 D(x): 0.8460, D(G(z)): 0.3413 Epoch: [1/20], Batch Num: [379/600] Discriminator Loss: 0.7470, Generator Loss: 1.5937 D(x): 0.8641, D(G(z)): 0.3898 Epoch: [1/20], Batch Num: [380/600] Discriminator Loss: 0.8856, Generator Loss: 1.5415 D(x): 0.7920, D(G(z)): 0.3951 Epoch: [1/20], Batch Num: [381/600] Discriminator Loss: 1.0919, Generator Loss: 1.4449 D(x): 0.7425, D(G(z)): 0.4285 Epoch: [1/20], Batch Num: [382/600] Discriminator Loss: 0.8830, Generator Loss: 1.4380 D(x): 0.8109, D(G(z)): 0.4215 Epoch: [1/20], Batch Num: [383/600] Discriminator Loss: 0.8926, Generator Loss: 1.4877 D(x): 0.8375, D(G(z)): 0.4539 Epoch: [1/20], Batch Num: [384/600] Discriminator Loss: 0.9613, Generator Loss: 1.4732 D(x): 0.8247, D(G(z)): 0.4779 Epoch: [1/20], Batch Num: [385/600] Discriminator Loss: 0.9555, Generator Loss: 1.3703 D(x): 0.8196, D(G(z)): 0.4569 Epoch: [1/20], Batch Num: [386/600] Discriminator Loss: 0.9755, Generator Loss: 1.5196 D(x): 0.8139, D(G(z)): 0.4790 Epoch: [1/20], Batch Num: [387/600] Discriminator Loss: 0.8442, Generator Loss: 1.5870 D(x): 0.8395, D(G(z)): 0.4272 Epoch: [1/20], Batch Num: [388/600] Discriminator Loss: 0.9484, Generator Loss: 1.5640 D(x): 0.8101, D(G(z)): 0.4560 Epoch: [1/20], Batch Num: [389/600] Discriminator Loss: 0.7833, Generator Loss: 1.4547 D(x): 0.8114, D(G(z)): 0.3842 Epoch: [1/20], Batch Num: [390/600] Discriminator Loss: 0.8768, Generator Loss: 1.8017 D(x): 0.8423, D(G(z)): 0.4572 Epoch: [1/20], Batch Num: [391/600] Discriminator Loss: 0.9379, Generator Loss: 1.3495 D(x): 0.8130, D(G(z)): 0.4525 Epoch: [1/20], Batch Num: [392/600] Discriminator Loss: 0.9442, Generator Loss: 1.4669 D(x): 0.8733, D(G(z)): 0.5091 Epoch: [1/20], Batch Num: [393/600] Discriminator Loss: 1.0477, Generator Loss: 1.3215 D(x): 0.8609, D(G(z)): 0.5401 Epoch: [1/20], Batch Num: [394/600] Discriminator Loss: 1.0716, Generator Loss: 1.3067 D(x): 0.8688, D(G(z)): 0.5666 Epoch: [1/20], Batch Num: [395/600] Discriminator Loss: 1.0941, Generator Loss: 1.4600 D(x): 0.8066, D(G(z)): 0.5148 Epoch: [1/20], Batch Num: [396/600] Discriminator Loss: 1.0950, Generator Loss: 1.3903 D(x): 0.8063, D(G(z)): 0.5124 Epoch: [1/20], Batch Num: [397/600] Discriminator Loss: 1.1339, Generator Loss: 1.5538 D(x): 0.7855, D(G(z)): 0.5059 Epoch: [1/20], Batch Num: [398/600] Discriminator Loss: 1.1776, Generator Loss: 1.7259 D(x): 0.7085, D(G(z)): 0.4785 Epoch: [1/20], Batch Num: [399/600] Discriminator Loss: 1.2767, Generator Loss: 1.6338 D(x): 0.7131, D(G(z)): 0.4931 Epoch: 1, Batch Num: [400/600]
Epoch: [1/20], Batch Num: [400/600] Discriminator Loss: 1.0677, Generator Loss: 1.7115 D(x): 0.7602, D(G(z)): 0.4906 Epoch: [1/20], Batch Num: [401/600] Discriminator Loss: 1.2620, Generator Loss: 1.7033 D(x): 0.7224, D(G(z)): 0.4991 Epoch: [1/20], Batch Num: [402/600] Discriminator Loss: 1.0641, Generator Loss: 1.6412 D(x): 0.7874, D(G(z)): 0.5078 Epoch: [1/20], Batch Num: [403/600] Discriminator Loss: 1.0644, Generator Loss: 1.8727 D(x): 0.7624, D(G(z)): 0.4878 Epoch: [1/20], Batch Num: [404/600] Discriminator Loss: 1.0221, Generator Loss: 1.9554 D(x): 0.7732, D(G(z)): 0.4763 Epoch: [1/20], Batch Num: [405/600] Discriminator Loss: 0.9621, Generator Loss: 2.0803 D(x): 0.7564, D(G(z)): 0.4309 Epoch: [1/20], Batch Num: [406/600] Discriminator Loss: 1.0160, Generator Loss: 2.4440 D(x): 0.7701, D(G(z)): 0.4705 Epoch: [1/20], Batch Num: [407/600] Discriminator Loss: 0.9189, Generator Loss: 2.6161 D(x): 0.7080, D(G(z)): 0.3241 Epoch: [1/20], Batch Num: [408/600] Discriminator Loss: 0.8820, Generator Loss: 2.7662 D(x): 0.6791, D(G(z)): 0.2953 Epoch: [1/20], Batch Num: [409/600] Discriminator Loss: 0.9088, Generator Loss: 2.6145 D(x): 0.6504, D(G(z)): 0.2966 Epoch: [1/20], Batch Num: [410/600] Discriminator Loss: 0.9956, Generator Loss: 2.6331 D(x): 0.6222, D(G(z)): 0.3036 Epoch: [1/20], Batch Num: [411/600] Discriminator Loss: 1.1916, Generator Loss: 2.2518 D(x): 0.5916, D(G(z)): 0.3751 Epoch: [1/20], Batch Num: [412/600] Discriminator Loss: 1.3114, Generator Loss: 1.8948 D(x): 0.5932, D(G(z)): 0.4524 Epoch: [1/20], Batch Num: [413/600] Discriminator Loss: 1.6807, Generator Loss: 1.7759 D(x): 0.5512, D(G(z)): 0.5556 Epoch: [1/20], Batch Num: [414/600] Discriminator Loss: 2.0762, Generator Loss: 1.6067 D(x): 0.4726, D(G(z)): 0.5826 Epoch: [1/20], Batch Num: [415/600] Discriminator Loss: 2.2468, Generator Loss: 1.1165 D(x): 0.4192, D(G(z)): 0.5728 Epoch: [1/20], Batch Num: [416/600] Discriminator Loss: 2.8064, Generator Loss: 0.7112 D(x): 0.3859, D(G(z)): 0.7154 Epoch: [1/20], Batch Num: [417/600] Discriminator Loss: 3.0877, Generator Loss: 0.5900 D(x): 0.4078, D(G(z)): 0.7900 Epoch: [1/20], Batch Num: [418/600] Discriminator Loss: 3.6201, Generator Loss: 0.3080 D(x): 0.4102, D(G(z)): 0.8296 Epoch: [1/20], Batch Num: [419/600] Discriminator Loss: 3.5458, Generator Loss: 0.1845 D(x): 0.4509, D(G(z)): 0.8820 Epoch: [1/20], Batch Num: [420/600] Discriminator Loss: 3.5324, Generator Loss: 0.2086 D(x): 0.5607, D(G(z)): 0.9031 Epoch: [1/20], Batch Num: [421/600] Discriminator Loss: 3.0920, Generator Loss: 0.2339 D(x): 0.5934, D(G(z)): 0.8806 Epoch: [1/20], Batch Num: [422/600] Discriminator Loss: 2.8135, Generator Loss: 0.3981 D(x): 0.6240, D(G(z)): 0.8495 Epoch: [1/20], Batch Num: [423/600] Discriminator Loss: 2.8667, Generator Loss: 0.5785 D(x): 0.4964, D(G(z)): 0.7881 Epoch: [1/20], Batch Num: [424/600] Discriminator Loss: 2.5456, Generator Loss: 0.6973 D(x): 0.4235, D(G(z)): 0.6804 Epoch: [1/20], Batch Num: [425/600] Discriminator Loss: 2.1277, Generator Loss: 0.7587 D(x): 0.4375, D(G(z)): 0.5662 Epoch: [1/20], Batch Num: [426/600] Discriminator Loss: 2.2133, Generator Loss: 0.8710 D(x): 0.3919, D(G(z)): 0.5667 Epoch: [1/20], Batch Num: [427/600] Discriminator Loss: 1.8264, Generator Loss: 0.7942 D(x): 0.4600, D(G(z)): 0.5426 Epoch: [1/20], Batch Num: [428/600] Discriminator Loss: 1.8705, Generator Loss: 0.7732 D(x): 0.4911, D(G(z)): 0.5820 Epoch: [1/20], Batch Num: [429/600] Discriminator Loss: 1.8852, Generator Loss: 0.7020 D(x): 0.4833, D(G(z)): 0.5957 Epoch: [1/20], Batch Num: [430/600] Discriminator Loss: 1.7135, Generator Loss: 0.7387 D(x): 0.5482, D(G(z)): 0.5960 Epoch: [1/20], Batch Num: [431/600] Discriminator Loss: 1.6524, Generator Loss: 0.8429 D(x): 0.5421, D(G(z)): 0.5684 Epoch: [1/20], Batch Num: [432/600] Discriminator Loss: 1.3772, Generator Loss: 0.7545 D(x): 0.5901, D(G(z)): 0.5135 Epoch: [1/20], Batch Num: [433/600] Discriminator Loss: 1.5242, Generator Loss: 0.8580 D(x): 0.5500, D(G(z)): 0.5421 Epoch: [1/20], Batch Num: [434/600] Discriminator Loss: 1.3809, Generator Loss: 0.9228 D(x): 0.5336, D(G(z)): 0.4607 Epoch: [1/20], Batch Num: [435/600] Discriminator Loss: 1.2801, Generator Loss: 0.9593 D(x): 0.5463, D(G(z)): 0.4326 Epoch: [1/20], Batch Num: [436/600] Discriminator Loss: 1.4434, Generator Loss: 1.0106 D(x): 0.5100, D(G(z)): 0.4718 Epoch: [1/20], Batch Num: [437/600] Discriminator Loss: 1.3434, Generator Loss: 1.0656 D(x): 0.5062, D(G(z)): 0.4061 Epoch: [1/20], Batch Num: [438/600] Discriminator Loss: 1.3510, Generator Loss: 0.8886 D(x): 0.5407, D(G(z)): 0.4456 Epoch: [1/20], Batch Num: [439/600] Discriminator Loss: 1.3030, Generator Loss: 0.8766 D(x): 0.5539, D(G(z)): 0.4466 Epoch: [1/20], Batch Num: [440/600] Discriminator Loss: 1.3479, Generator Loss: 0.9100 D(x): 0.5946, D(G(z)): 0.5116 Epoch: [1/20], Batch Num: [441/600] Discriminator Loss: 1.2781, Generator Loss: 0.8971 D(x): 0.6328, D(G(z)): 0.5190 Epoch: [1/20], Batch Num: [442/600] Discriminator Loss: 1.1070, Generator Loss: 0.8653 D(x): 0.6562, D(G(z)): 0.4525 Epoch: [1/20], Batch Num: [443/600] Discriminator Loss: 1.0673, Generator Loss: 0.9760 D(x): 0.6724, D(G(z)): 0.4510 Epoch: [1/20], Batch Num: [444/600] Discriminator Loss: 1.1253, Generator Loss: 1.0408 D(x): 0.6414, D(G(z)): 0.4527 Epoch: [1/20], Batch Num: [445/600] Discriminator Loss: 1.0519, Generator Loss: 1.1280 D(x): 0.6134, D(G(z)): 0.3874 Epoch: [1/20], Batch Num: [446/600] Discriminator Loss: 1.0746, Generator Loss: 1.1480 D(x): 0.6018, D(G(z)): 0.3881 Epoch: [1/20], Batch Num: [447/600] Discriminator Loss: 1.0307, Generator Loss: 1.1871 D(x): 0.6457, D(G(z)): 0.3867 Epoch: [1/20], Batch Num: [448/600] Discriminator Loss: 1.0469, Generator Loss: 1.2516 D(x): 0.6121, D(G(z)): 0.3789 Epoch: [1/20], Batch Num: [449/600] Discriminator Loss: 0.9501, Generator Loss: 1.1879 D(x): 0.6559, D(G(z)): 0.3692 Epoch: [1/20], Batch Num: [450/600] Discriminator Loss: 0.9211, Generator Loss: 1.1518 D(x): 0.6560, D(G(z)): 0.3592 Epoch: [1/20], Batch Num: [451/600] Discriminator Loss: 0.9050, Generator Loss: 1.2440 D(x): 0.6731, D(G(z)): 0.3552 Epoch: [1/20], Batch Num: [452/600] Discriminator Loss: 0.9871, Generator Loss: 1.2694 D(x): 0.6574, D(G(z)): 0.3947 Epoch: [1/20], Batch Num: [453/600] Discriminator Loss: 0.8810, Generator Loss: 1.2922 D(x): 0.6850, D(G(z)): 0.3570 Epoch: [1/20], Batch Num: [454/600] Discriminator Loss: 0.9033, Generator Loss: 1.3042 D(x): 0.6808, D(G(z)): 0.3609 Epoch: [1/20], Batch Num: [455/600] Discriminator Loss: 0.8915, Generator Loss: 1.3437 D(x): 0.6863, D(G(z)): 0.3602 Epoch: [1/20], Batch Num: [456/600] Discriminator Loss: 0.7407, Generator Loss: 1.3230 D(x): 0.7628, D(G(z)): 0.3452 Epoch: [1/20], Batch Num: [457/600] Discriminator Loss: 0.7700, Generator Loss: 1.4263 D(x): 0.7432, D(G(z)): 0.3436 Epoch: [1/20], Batch Num: [458/600] Discriminator Loss: 0.7512, Generator Loss: 1.4986 D(x): 0.7113, D(G(z)): 0.2996 Epoch: [1/20], Batch Num: [459/600] Discriminator Loss: 0.7414, Generator Loss: 1.5566 D(x): 0.7182, D(G(z)): 0.2963 Epoch: [1/20], Batch Num: [460/600] Discriminator Loss: 0.7880, Generator Loss: 1.6073 D(x): 0.6770, D(G(z)): 0.2738 Epoch: [1/20], Batch Num: [461/600] Discriminator Loss: 0.9225, Generator Loss: 1.5149 D(x): 0.6434, D(G(z)): 0.3086 Epoch: [1/20], Batch Num: [462/600] Discriminator Loss: 0.6934, Generator Loss: 1.3673 D(x): 0.7580, D(G(z)): 0.3096 Epoch: [1/20], Batch Num: [463/600] Discriminator Loss: 0.7885, Generator Loss: 1.3722 D(x): 0.7234, D(G(z)): 0.3223 Epoch: [1/20], Batch Num: [464/600] Discriminator Loss: 0.8407, Generator Loss: 1.2616 D(x): 0.7507, D(G(z)): 0.3777 Epoch: [1/20], Batch Num: [465/600] Discriminator Loss: 0.7799, Generator Loss: 1.3686 D(x): 0.7724, D(G(z)): 0.3693 Epoch: [1/20], Batch Num: [466/600] Discriminator Loss: 0.7838, Generator Loss: 1.4073 D(x): 0.7414, D(G(z)): 0.3401 Epoch: [1/20], Batch Num: [467/600] Discriminator Loss: 0.7458, Generator Loss: 1.5388 D(x): 0.7178, D(G(z)): 0.2786 Epoch: [1/20], Batch Num: [468/600] Discriminator Loss: 0.8377, Generator Loss: 1.6441 D(x): 0.6843, D(G(z)): 0.3068 Epoch: [1/20], Batch Num: [469/600] Discriminator Loss: 0.8736, Generator Loss: 1.5156 D(x): 0.7010, D(G(z)): 0.3269 Epoch: [1/20], Batch Num: [470/600] Discriminator Loss: 0.7891, Generator Loss: 1.3745 D(x): 0.7306, D(G(z)): 0.3361 Epoch: [1/20], Batch Num: [471/600] Discriminator Loss: 0.8633, Generator Loss: 1.2802 D(x): 0.6918, D(G(z)): 0.3189 Epoch: [1/20], Batch Num: [472/600] Discriminator Loss: 0.9118, Generator Loss: 1.2901 D(x): 0.7424, D(G(z)): 0.3931 Epoch: [1/20], Batch Num: [473/600] Discriminator Loss: 0.9210, Generator Loss: 1.2470 D(x): 0.7267, D(G(z)): 0.3773 Epoch: [1/20], Batch Num: [474/600] Discriminator Loss: 0.9114, Generator Loss: 1.3767 D(x): 0.7619, D(G(z)): 0.4099 Epoch: [1/20], Batch Num: [475/600] Discriminator Loss: 0.9425, Generator Loss: 1.5005 D(x): 0.7116, D(G(z)): 0.3808 Epoch: [1/20], Batch Num: [476/600] Discriminator Loss: 0.9796, Generator Loss: 1.4130 D(x): 0.6721, D(G(z)): 0.3297 Epoch: [1/20], Batch Num: [477/600] Discriminator Loss: 1.0085, Generator Loss: 1.3435 D(x): 0.6984, D(G(z)): 0.3915 Epoch: [1/20], Batch Num: [478/600] Discriminator Loss: 1.1210, Generator Loss: 1.3059 D(x): 0.6146, D(G(z)): 0.3621 Epoch: [1/20], Batch Num: [479/600] Discriminator Loss: 0.9934, Generator Loss: 1.0244 D(x): 0.7346, D(G(z)): 0.4175 Epoch: [1/20], Batch Num: [480/600] Discriminator Loss: 1.0309, Generator Loss: 1.1787 D(x): 0.7381, D(G(z)): 0.4450 Epoch: [1/20], Batch Num: [481/600] Discriminator Loss: 1.0793, Generator Loss: 1.1685 D(x): 0.7253, D(G(z)): 0.4684 Epoch: [1/20], Batch Num: [482/600] Discriminator Loss: 1.0152, Generator Loss: 1.3707 D(x): 0.7021, D(G(z)): 0.4220 Epoch: [1/20], Batch Num: [483/600] Discriminator Loss: 1.1721, Generator Loss: 1.3686 D(x): 0.6115, D(G(z)): 0.3864 Epoch: [1/20], Batch Num: [484/600] Discriminator Loss: 1.1648, Generator Loss: 1.2398 D(x): 0.6262, D(G(z)): 0.3686 Epoch: [1/20], Batch Num: [485/600] Discriminator Loss: 0.9916, Generator Loss: 1.1631 D(x): 0.7213, D(G(z)): 0.4315 Epoch: [1/20], Batch Num: [486/600] Discriminator Loss: 1.1852, Generator Loss: 1.2646 D(x): 0.6792, D(G(z)): 0.4659 Epoch: [1/20], Batch Num: [487/600] Discriminator Loss: 1.2465, Generator Loss: 1.1352 D(x): 0.6527, D(G(z)): 0.4566 Epoch: [1/20], Batch Num: [488/600] Discriminator Loss: 1.2930, Generator Loss: 1.0781 D(x): 0.6253, D(G(z)): 0.4771 Epoch: [1/20], Batch Num: [489/600] Discriminator Loss: 1.0561, Generator Loss: 1.1686 D(x): 0.7075, D(G(z)): 0.4353 Epoch: [1/20], Batch Num: [490/600] Discriminator Loss: 1.1480, Generator Loss: 1.1969 D(x): 0.6784, D(G(z)): 0.4453 Epoch: [1/20], Batch Num: [491/600] Discriminator Loss: 1.0439, Generator Loss: 1.1579 D(x): 0.6794, D(G(z)): 0.4087 Epoch: [1/20], Batch Num: [492/600] Discriminator Loss: 1.0690, Generator Loss: 1.1378 D(x): 0.6948, D(G(z)): 0.4191 Epoch: [1/20], Batch Num: [493/600] Discriminator Loss: 1.1876, Generator Loss: 1.1837 D(x): 0.6480, D(G(z)): 0.4444 Epoch: [1/20], Batch Num: [494/600] Discriminator Loss: 1.1236, Generator Loss: 1.2547 D(x): 0.6665, D(G(z)): 0.4103 Epoch: [1/20], Batch Num: [495/600] Discriminator Loss: 1.1418, Generator Loss: 1.1029 D(x): 0.6766, D(G(z)): 0.4485 Epoch: [1/20], Batch Num: [496/600] Discriminator Loss: 1.0475, Generator Loss: 1.1080 D(x): 0.7298, D(G(z)): 0.4623 Epoch: [1/20], Batch Num: [497/600] Discriminator Loss: 0.9767, Generator Loss: 1.2860 D(x): 0.7704, D(G(z)): 0.4591 Epoch: [1/20], Batch Num: [498/600] Discriminator Loss: 0.8418, Generator Loss: 1.5934 D(x): 0.7513, D(G(z)): 0.3745 Epoch: [1/20], Batch Num: [499/600] Discriminator Loss: 0.9080, Generator Loss: 1.6105 D(x): 0.6714, D(G(z)): 0.3214 Epoch: 1, Batch Num: [500/600]
Epoch: [1/20], Batch Num: [500/600] Discriminator Loss: 1.0737, Generator Loss: 1.3525 D(x): 0.5968, D(G(z)): 0.3256 Epoch: [1/20], Batch Num: [501/600] Discriminator Loss: 0.8757, Generator Loss: 1.2705 D(x): 0.7538, D(G(z)): 0.3917 Epoch: [1/20], Batch Num: [502/600] Discriminator Loss: 0.9055, Generator Loss: 1.2476 D(x): 0.7407, D(G(z)): 0.3872 Epoch: [1/20], Batch Num: [503/600] Discriminator Loss: 0.8020, Generator Loss: 1.2401 D(x): 0.7764, D(G(z)): 0.3817 Epoch: [1/20], Batch Num: [504/600] Discriminator Loss: 0.8513, Generator Loss: 1.4685 D(x): 0.7427, D(G(z)): 0.3576 Epoch: [1/20], Batch Num: [505/600] Discriminator Loss: 0.6886, Generator Loss: 1.6290 D(x): 0.7850, D(G(z)): 0.3169 Epoch: [1/20], Batch Num: [506/600] Discriminator Loss: 0.7186, Generator Loss: 1.6723 D(x): 0.7327, D(G(z)): 0.2724 Epoch: [1/20], Batch Num: [507/600] Discriminator Loss: 0.7083, Generator Loss: 1.9497 D(x): 0.7429, D(G(z)): 0.2983 Epoch: [1/20], Batch Num: [508/600] Discriminator Loss: 0.7000, Generator Loss: 1.9070 D(x): 0.7368, D(G(z)): 0.2706 Epoch: [1/20], Batch Num: [509/600] Discriminator Loss: 0.6702, Generator Loss: 1.8727 D(x): 0.7468, D(G(z)): 0.2655 Epoch: [1/20], Batch Num: [510/600] Discriminator Loss: 0.7218, Generator Loss: 1.8643 D(x): 0.7400, D(G(z)): 0.2640 Epoch: [1/20], Batch Num: [511/600] Discriminator Loss: 0.6078, Generator Loss: 1.9370 D(x): 0.7969, D(G(z)): 0.2722 Epoch: [1/20], Batch Num: [512/600] Discriminator Loss: 0.5172, Generator Loss: 2.1070 D(x): 0.8231, D(G(z)): 0.2466 Epoch: [1/20], Batch Num: [513/600] Discriminator Loss: 0.5320, Generator Loss: 2.1740 D(x): 0.8250, D(G(z)): 0.2582 Epoch: [1/20], Batch Num: [514/600] Discriminator Loss: 0.5297, Generator Loss: 2.4829 D(x): 0.7897, D(G(z)): 0.2081 Epoch: [1/20], Batch Num: [515/600] Discriminator Loss: 0.5134, Generator Loss: 2.6342 D(x): 0.7597, D(G(z)): 0.1712 Epoch: [1/20], Batch Num: [516/600] Discriminator Loss: 0.4386, Generator Loss: 2.7179 D(x): 0.7918, D(G(z)): 0.1401 Epoch: [1/20], Batch Num: [517/600] Discriminator Loss: 0.4224, Generator Loss: 2.8451 D(x): 0.7858, D(G(z)): 0.1275 Epoch: [1/20], Batch Num: [518/600] Discriminator Loss: 0.3907, Generator Loss: 2.6147 D(x): 0.8225, D(G(z)): 0.1446 Epoch: [1/20], Batch Num: [519/600] Discriminator Loss: 0.3565, Generator Loss: 2.7024 D(x): 0.8377, D(G(z)): 0.1344 Epoch: [1/20], Batch Num: [520/600] Discriminator Loss: 0.4102, Generator Loss: 2.7513 D(x): 0.8315, D(G(z)): 0.1675 Epoch: [1/20], Batch Num: [521/600] Discriminator Loss: 0.3779, Generator Loss: 2.7842 D(x): 0.8284, D(G(z)): 0.1442 Epoch: [1/20], Batch Num: [522/600] Discriminator Loss: 0.4593, Generator Loss: 2.7615 D(x): 0.8013, D(G(z)): 0.1539 Epoch: [1/20], Batch Num: [523/600] Discriminator Loss: 0.4475, Generator Loss: 2.6470 D(x): 0.8230, D(G(z)): 0.1813 Epoch: [1/20], Batch Num: [524/600] Discriminator Loss: 0.5360, Generator Loss: 2.6050 D(x): 0.7847, D(G(z)): 0.1869 Epoch: [1/20], Batch Num: [525/600] Discriminator Loss: 0.4264, Generator Loss: 2.2686 D(x): 0.8246, D(G(z)): 0.1659 Epoch: [1/20], Batch Num: [526/600] Discriminator Loss: 0.5024, Generator Loss: 2.5455 D(x): 0.8188, D(G(z)): 0.2059 Epoch: [1/20], Batch Num: [527/600] Discriminator Loss: 0.5227, Generator Loss: 2.2170 D(x): 0.8107, D(G(z)): 0.2055 Epoch: [1/20], Batch Num: [528/600] Discriminator Loss: 0.5158, Generator Loss: 2.2783 D(x): 0.7956, D(G(z)): 0.1957 Epoch: [1/20], Batch Num: [529/600] Discriminator Loss: 0.5161, Generator Loss: 2.3100 D(x): 0.8166, D(G(z)): 0.2196 Epoch: [1/20], Batch Num: [530/600] Discriminator Loss: 0.6167, Generator Loss: 2.2440 D(x): 0.7520, D(G(z)): 0.2189 Epoch: [1/20], Batch Num: [531/600] Discriminator Loss: 0.6712, Generator Loss: 2.1805 D(x): 0.7643, D(G(z)): 0.2527 Epoch: [1/20], Batch Num: [532/600] Discriminator Loss: 0.6747, Generator Loss: 2.0073 D(x): 0.7782, D(G(z)): 0.2676 Epoch: [1/20], Batch Num: [533/600] Discriminator Loss: 0.7513, Generator Loss: 1.9177 D(x): 0.7230, D(G(z)): 0.2608 Epoch: 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[1/20], Batch Num: [568/600] Discriminator Loss: 0.8053, Generator Loss: 1.4049 D(x): 0.7341, D(G(z)): 0.3260 Epoch: [1/20], Batch Num: [569/600] Discriminator Loss: 0.8792, Generator Loss: 1.5280 D(x): 0.7573, D(G(z)): 0.3736 Epoch: [1/20], Batch Num: [570/600] Discriminator Loss: 0.9386, Generator Loss: 1.4607 D(x): 0.6990, D(G(z)): 0.3409 Epoch: [1/20], Batch Num: [571/600] Discriminator Loss: 0.9476, Generator Loss: 1.6390 D(x): 0.6989, D(G(z)): 0.3504 Epoch: [1/20], Batch Num: [572/600] Discriminator Loss: 0.8255, Generator Loss: 1.4826 D(x): 0.7311, D(G(z)): 0.3076 Epoch: [1/20], Batch Num: [573/600] Discriminator Loss: 0.8984, Generator Loss: 1.5296 D(x): 0.7514, D(G(z)): 0.3807 Epoch: [1/20], Batch Num: [574/600] Discriminator Loss: 0.8031, Generator Loss: 1.6083 D(x): 0.7473, D(G(z)): 0.3273 Epoch: [1/20], Batch Num: [575/600] Discriminator Loss: 0.7777, Generator Loss: 1.5768 D(x): 0.7479, D(G(z)): 0.3183 Epoch: [1/20], Batch Num: [576/600] Discriminator Loss: 0.8455, Generator Loss: 1.5442 D(x): 0.7304, D(G(z)): 0.3344 Epoch: [1/20], Batch Num: [577/600] Discriminator Loss: 0.6554, Generator Loss: 1.6081 D(x): 0.7965, D(G(z)): 0.3043 Epoch: [1/20], Batch Num: [578/600] Discriminator Loss: 0.8109, Generator Loss: 1.6248 D(x): 0.7763, D(G(z)): 0.3289 Epoch: [1/20], Batch Num: [579/600] Discriminator Loss: 0.8257, Generator Loss: 1.7049 D(x): 0.7541, D(G(z)): 0.3226 Epoch: [1/20], Batch Num: [580/600] Discriminator Loss: 0.9365, Generator Loss: 1.6645 D(x): 0.6960, D(G(z)): 0.3339 Epoch: [1/20], Batch Num: [581/600] Discriminator Loss: 0.8432, Generator Loss: 1.4379 D(x): 0.7343, D(G(z)): 0.3263 Epoch: [1/20], Batch Num: [582/600] Discriminator Loss: 0.7845, Generator Loss: 1.5320 D(x): 0.7717, D(G(z)): 0.3314 Epoch: [1/20], Batch Num: [583/600] Discriminator Loss: 0.7766, Generator Loss: 1.4887 D(x): 0.7904, D(G(z)): 0.3470 Epoch: [1/20], Batch Num: [584/600] Discriminator Loss: 0.7985, Generator Loss: 1.5807 D(x): 0.7736, D(G(z)): 0.3474 Epoch: [1/20], Batch Num: [585/600] Discriminator Loss: 0.8849, Generator Loss: 1.6394 D(x): 0.7368, D(G(z)): 0.3513 Epoch: [1/20], Batch Num: [586/600] Discriminator Loss: 0.8510, Generator Loss: 1.7921 D(x): 0.7825, D(G(z)): 0.3707 Epoch: [1/20], Batch Num: [587/600] Discriminator Loss: 0.8445, Generator Loss: 1.5548 D(x): 0.7781, D(G(z)): 0.3690 Epoch: [1/20], Batch Num: [588/600] Discriminator Loss: 0.9062, Generator Loss: 1.7754 D(x): 0.7119, D(G(z)): 0.3306 Epoch: [1/20], Batch Num: [589/600] Discriminator Loss: 0.9493, Generator Loss: 1.8484 D(x): 0.6950, D(G(z)): 0.2975 Epoch: [1/20], Batch Num: [590/600] Discriminator Loss: 0.6729, Generator Loss: 1.7359 D(x): 0.7926, D(G(z)): 0.2902 Epoch: [1/20], Batch Num: [591/600] Discriminator Loss: 0.7571, Generator Loss: 1.7232 D(x): 0.7791, D(G(z)): 0.3163 Epoch: [1/20], Batch Num: [592/600] Discriminator Loss: 0.8374, Generator Loss: 1.6968 D(x): 0.7520, D(G(z)): 0.3307 Epoch: [1/20], Batch Num: [593/600] Discriminator Loss: 0.8650, Generator Loss: 1.7531 D(x): 0.7847, D(G(z)): 0.3708 Epoch: [1/20], Batch Num: [594/600] Discriminator Loss: 0.6942, Generator Loss: 1.9544 D(x): 0.8452, D(G(z)): 0.3374 Epoch: [1/20], Batch Num: [595/600] Discriminator Loss: 0.8431, Generator Loss: 2.2027 D(x): 0.7368, D(G(z)): 0.2758 Epoch: [1/20], Batch Num: [596/600] Discriminator Loss: 0.6473, Generator Loss: 2.3307 D(x): 0.7960, D(G(z)): 0.2490 Epoch: [1/20], Batch Num: [597/600] Discriminator Loss: 0.6803, Generator Loss: 2.6721 D(x): 0.7581, D(G(z)): 0.2242 Epoch: [1/20], Batch Num: [598/600] Discriminator Loss: 0.6201, Generator Loss: 2.7715 D(x): 0.7854, D(G(z)): 0.2059 Epoch: [1/20], Batch Num: [599/600] Discriminator Loss: 0.4793, Generator Loss: 2.9589 D(x): 0.8845, D(G(z)): 0.2398 Epoch: 2, Batch Num: [0/600]
Epoch: [2/20], Batch Num: [0/600] Discriminator Loss: 0.5326, Generator Loss: 3.5064 D(x): 0.8527, D(G(z)): 0.2352 Epoch: [2/20], Batch Num: [1/600] Discriminator Loss: 0.4195, Generator Loss: 4.2679 D(x): 0.8619, D(G(z)): 0.1631 Epoch: [2/20], Batch Num: [2/600] Discriminator Loss: 0.3451, Generator Loss: 4.3464 D(x): 0.8623, D(G(z)): 0.1251 Epoch: [2/20], Batch Num: [3/600] Discriminator Loss: 0.3478, Generator Loss: 4.1882 D(x): 0.8470, D(G(z)): 0.1093 Epoch: [2/20], Batch Num: [4/600] Discriminator Loss: 0.4371, Generator Loss: 4.3283 D(x): 0.8091, D(G(z)): 0.1093 Epoch: [2/20], Batch Num: [5/600] Discriminator Loss: 0.4540, Generator Loss: 3.7972 D(x): 0.8421, D(G(z)): 0.1433 Epoch: [2/20], Batch Num: [6/600] Discriminator Loss: 0.5518, Generator Loss: 3.3194 D(x): 0.8627, D(G(z)): 0.2256 Epoch: [2/20], Batch Num: [7/600] Discriminator Loss: 0.4841, Generator Loss: 3.9664 D(x): 0.8695, D(G(z)): 0.1868 Epoch: [2/20], Batch Num: [8/600] Discriminator Loss: 0.4873, Generator Loss: 3.9310 D(x): 0.8426, D(G(z)): 0.1678 Epoch: [2/20], Batch Num: [9/600] Discriminator Loss: 0.6264, Generator Loss: 3.4663 D(x): 0.7332, D(G(z)): 0.1243 Epoch: [2/20], Batch Num: [10/600] Discriminator Loss: 0.7494, Generator Loss: 2.8890 D(x): 0.8333, D(G(z)): 0.2766 Epoch: [2/20], Batch Num: [11/600] Discriminator Loss: 0.9091, Generator Loss: 2.9574 D(x): 0.7742, D(G(z)): 0.2987 Epoch: [2/20], Batch Num: [12/600] Discriminator Loss: 0.7866, Generator Loss: 2.9150 D(x): 0.8161, D(G(z)): 0.2889 Epoch: [2/20], Batch Num: [13/600] Discriminator Loss: 1.5388, Generator Loss: 2.2699 D(x): 0.5891, D(G(z)): 0.2921 Epoch: [2/20], Batch Num: [14/600] Discriminator Loss: 1.3289, Generator Loss: 2.2625 D(x): 0.7915, D(G(z)): 0.4682 Epoch: [2/20], Batch Num: [15/600] Discriminator Loss: 1.3794, Generator Loss: 2.4113 D(x): 0.6656, D(G(z)): 0.4248 Epoch: [2/20], Batch Num: [16/600] Discriminator Loss: 1.7118, Generator Loss: 1.7444 D(x): 0.5970, D(G(z)): 0.3747 Epoch: [2/20], Batch Num: [17/600] Discriminator Loss: 1.8264, Generator Loss: 1.6033 D(x): 0.6480, D(G(z)): 0.5244 Epoch: [2/20], Batch Num: [18/600] Discriminator Loss: 2.2045, Generator Loss: 1.3117 D(x): 0.5985, D(G(z)): 0.5229 Epoch: [2/20], Batch Num: [19/600] Discriminator Loss: 2.1623, Generator Loss: 1.5729 D(x): 0.6181, D(G(z)): 0.5950 Epoch: [2/20], Batch Num: [20/600] Discriminator Loss: 2.4625, Generator Loss: 0.9298 D(x): 0.4772, D(G(z)): 0.4948 Epoch: [2/20], Batch Num: [21/600] Discriminator Loss: 2.3170, Generator Loss: 0.9521 D(x): 0.6465, D(G(z)): 0.6779 Epoch: [2/20], Batch Num: [22/600] Discriminator Loss: 2.2959, Generator Loss: 1.0593 D(x): 0.5990, D(G(z)): 0.6134 Epoch: [2/20], Batch Num: [23/600] Discriminator Loss: 2.2351, Generator Loss: 1.2667 D(x): 0.5851, D(G(z)): 0.6243 Epoch: [2/20], Batch Num: [24/600] Discriminator Loss: 2.2503, Generator Loss: 1.1655 D(x): 0.4940, D(G(z)): 0.5046 Epoch: [2/20], Batch Num: [25/600] Discriminator Loss: 2.3790, Generator Loss: 0.6502 D(x): 0.5054, D(G(z)): 0.5414 Epoch: [2/20], Batch Num: [26/600] Discriminator Loss: 2.1488, Generator Loss: 0.5906 D(x): 0.6580, D(G(z)): 0.6762 Epoch: [2/20], Batch Num: [27/600] Discriminator Loss: 2.1077, Generator Loss: 1.0245 D(x): 0.7063, D(G(z)): 0.7195 Epoch: [2/20], Batch Num: [28/600] Discriminator Loss: 2.1797, Generator Loss: 1.0598 D(x): 0.5220, D(G(z)): 0.5559 Epoch: [2/20], Batch Num: [29/600] Discriminator Loss: 2.0926, Generator Loss: 1.1624 D(x): 0.4890, D(G(z)): 0.5009 Epoch: [2/20], Batch Num: [30/600] Discriminator Loss: 1.9854, Generator Loss: 0.7021 D(x): 0.4959, D(G(z)): 0.5323 Epoch: [2/20], Batch Num: [31/600] Discriminator Loss: 2.1161, Generator Loss: 0.5165 D(x): 0.5638, D(G(z)): 0.6408 Epoch: [2/20], Batch Num: [32/600] Discriminator Loss: 2.0502, Generator Loss: 0.5998 D(x): 0.6658, D(G(z)): 0.7125 Epoch: [2/20], Batch Num: [33/600] Discriminator Loss: 1.8771, Generator Loss: 0.7940 D(x): 0.6344, D(G(z)): 0.6501 Epoch: [2/20], Batch Num: [34/600] Discriminator Loss: 2.0866, Generator Loss: 0.9782 D(x): 0.5242, D(G(z)): 0.5903 Epoch: [2/20], Batch Num: [35/600] Discriminator Loss: 1.7921, Generator Loss: 0.9615 D(x): 0.5372, D(G(z)): 0.5319 Epoch: [2/20], Batch Num: [36/600] Discriminator Loss: 1.9022, Generator Loss: 0.7502 D(x): 0.5276, D(G(z)): 0.5578 Epoch: [2/20], Batch Num: [37/600] Discriminator Loss: 1.8531, Generator Loss: 0.7357 D(x): 0.5742, D(G(z)): 0.6140 Epoch: [2/20], Batch Num: [38/600] Discriminator Loss: 1.8592, Generator Loss: 0.6477 D(x): 0.5724, D(G(z)): 0.6193 Epoch: [2/20], Batch Num: [39/600] Discriminator Loss: 1.6378, Generator Loss: 0.6440 D(x): 0.6739, D(G(z)): 0.6423 Epoch: [2/20], Batch Num: [40/600] Discriminator Loss: 1.6886, Generator Loss: 0.6758 D(x): 0.6310, D(G(z)): 0.6247 Epoch: [2/20], Batch Num: [41/600] Discriminator Loss: 1.5680, Generator Loss: 0.8043 D(x): 0.6019, D(G(z)): 0.5816 Epoch: [2/20], Batch Num: [42/600] Discriminator Loss: 1.6167, Generator Loss: 0.8472 D(x): 0.5840, D(G(z)): 0.5522 Epoch: [2/20], Batch Num: [43/600] Discriminator Loss: 1.5010, Generator Loss: 0.8871 D(x): 0.6091, D(G(z)): 0.5345 Epoch: [2/20], Batch Num: [44/600] Discriminator Loss: 1.4255, Generator Loss: 0.9391 D(x): 0.5954, D(G(z)): 0.5158 Epoch: [2/20], Batch Num: [45/600] Discriminator Loss: 1.4404, Generator Loss: 0.8065 D(x): 0.5563, D(G(z)): 0.4865 Epoch: [2/20], Batch Num: [46/600] Discriminator Loss: 1.4216, Generator Loss: 0.8289 D(x): 0.6137, D(G(z)): 0.5399 Epoch: [2/20], Batch Num: [47/600] Discriminator Loss: 1.4742, Generator Loss: 0.7746 D(x): 0.6433, D(G(z)): 0.5805 Epoch: [2/20], Batch Num: [48/600] Discriminator Loss: 1.2597, Generator Loss: 0.7544 D(x): 0.6789, D(G(z)): 0.5191 Epoch: [2/20], Batch Num: [49/600] Discriminator Loss: 1.1480, Generator Loss: 0.9563 D(x): 0.7209, D(G(z)): 0.5032 Epoch: [2/20], Batch Num: [50/600] Discriminator Loss: 1.1351, Generator Loss: 0.9869 D(x): 0.7086, D(G(z)): 0.4929 Epoch: [2/20], Batch Num: [51/600] Discriminator Loss: 1.1555, Generator Loss: 1.0298 D(x): 0.6566, D(G(z)): 0.4571 Epoch: [2/20], Batch Num: [52/600] Discriminator Loss: 1.0188, Generator Loss: 1.2253 D(x): 0.6628, D(G(z)): 0.4093 Epoch: [2/20], Batch Num: [53/600] Discriminator Loss: 1.0286, Generator Loss: 1.2673 D(x): 0.6429, D(G(z)): 0.3847 Epoch: [2/20], Batch Num: [54/600] Discriminator Loss: 1.0324, Generator Loss: 1.2969 D(x): 0.6274, D(G(z)): 0.3835 Epoch: [2/20], Batch Num: [55/600] Discriminator Loss: 1.0601, Generator Loss: 1.2184 D(x): 0.6196, D(G(z)): 0.3807 Epoch: [2/20], Batch Num: [56/600] Discriminator Loss: 1.0099, Generator Loss: 1.1287 D(x): 0.6566, D(G(z)): 0.3918 Epoch: [2/20], Batch Num: [57/600] Discriminator Loss: 1.0451, Generator Loss: 1.0364 D(x): 0.6783, D(G(z)): 0.4235 Epoch: [2/20], Batch Num: [58/600] Discriminator Loss: 0.9386, Generator Loss: 1.0445 D(x): 0.6994, D(G(z)): 0.4033 Epoch: [2/20], Batch Num: [59/600] Discriminator Loss: 0.9210, Generator Loss: 1.1644 D(x): 0.7190, D(G(z)): 0.4110 Epoch: [2/20], Batch Num: [60/600] Discriminator Loss: 0.9394, Generator Loss: 1.1187 D(x): 0.7301, D(G(z)): 0.4258 Epoch: [2/20], Batch Num: [61/600] Discriminator Loss: 0.9094, Generator Loss: 1.1834 D(x): 0.7297, D(G(z)): 0.4094 Epoch: [2/20], Batch Num: [62/600] Discriminator Loss: 0.8457, Generator Loss: 1.4128 D(x): 0.7280, D(G(z)): 0.3725 Epoch: [2/20], Batch Num: [63/600] Discriminator Loss: 0.8934, Generator Loss: 1.4670 D(x): 0.7016, D(G(z)): 0.3674 Epoch: [2/20], Batch Num: [64/600] Discriminator Loss: 0.8812, Generator Loss: 1.4788 D(x): 0.6759, D(G(z)): 0.3448 Epoch: [2/20], Batch Num: [65/600] Discriminator Loss: 0.8059, Generator Loss: 1.4597 D(x): 0.7019, D(G(z)): 0.3172 Epoch: [2/20], Batch Num: [66/600] Discriminator Loss: 0.8473, Generator Loss: 1.5327 D(x): 0.6980, D(G(z)): 0.3373 Epoch: [2/20], Batch Num: [67/600] Discriminator Loss: 0.7726, Generator Loss: 1.5668 D(x): 0.7060, D(G(z)): 0.3154 Epoch: [2/20], Batch Num: [68/600] Discriminator Loss: 0.7704, Generator Loss: 1.7068 D(x): 0.7156, D(G(z)): 0.3162 Epoch: [2/20], Batch Num: [69/600] Discriminator Loss: 0.8234, Generator Loss: 1.5448 D(x): 0.7032, D(G(z)): 0.3340 Epoch: [2/20], Batch Num: [70/600] Discriminator Loss: 0.8392, Generator Loss: 1.7735 D(x): 0.6896, D(G(z)): 0.3349 Epoch: [2/20], Batch Num: [71/600] Discriminator Loss: 0.8074, Generator Loss: 1.7651 D(x): 0.6964, D(G(z)): 0.3217 Epoch: [2/20], Batch Num: [72/600] Discriminator Loss: 0.7541, Generator Loss: 1.6608 D(x): 0.7117, D(G(z)): 0.3093 Epoch: [2/20], Batch Num: [73/600] Discriminator Loss: 0.8469, Generator Loss: 1.7540 D(x): 0.6810, D(G(z)): 0.3147 Epoch: [2/20], Batch Num: [74/600] Discriminator Loss: 0.8751, Generator Loss: 1.9914 D(x): 0.6774, D(G(z)): 0.3351 Epoch: [2/20], Batch Num: [75/600] Discriminator Loss: 0.9011, Generator Loss: 1.9427 D(x): 0.6698, D(G(z)): 0.3441 Epoch: [2/20], Batch Num: [76/600] Discriminator Loss: 0.8586, Generator Loss: 1.8935 D(x): 0.6717, D(G(z)): 0.3169 Epoch: [2/20], Batch Num: [77/600] Discriminator Loss: 0.8927, Generator Loss: 1.9809 D(x): 0.6493, D(G(z)): 0.3198 Epoch: [2/20], Batch Num: [78/600] Discriminator Loss: 1.0494, Generator Loss: 1.8814 D(x): 0.6144, D(G(z)): 0.3487 Epoch: [2/20], Batch Num: [79/600] Discriminator Loss: 0.9756, Generator Loss: 1.9533 D(x): 0.6305, D(G(z)): 0.3467 Epoch: [2/20], Batch Num: [80/600] Discriminator Loss: 1.1091, Generator Loss: 1.8548 D(x): 0.6368, D(G(z)): 0.3975 Epoch: [2/20], Batch Num: [81/600] Discriminator Loss: 1.1429, Generator Loss: 1.4397 D(x): 0.6038, D(G(z)): 0.3885 Epoch: [2/20], Batch Num: [82/600] Discriminator Loss: 1.2640, Generator Loss: 1.4754 D(x): 0.5732, D(G(z)): 0.4182 Epoch: [2/20], Batch Num: [83/600] Discriminator Loss: 1.3534, Generator Loss: 1.5234 D(x): 0.5695, D(G(z)): 0.4555 Epoch: [2/20], Batch Num: [84/600] Discriminator Loss: 1.4308, Generator Loss: 1.5920 D(x): 0.5753, D(G(z)): 0.4631 Epoch: [2/20], Batch Num: [85/600] Discriminator Loss: 1.4678, Generator Loss: 1.3137 D(x): 0.5352, D(G(z)): 0.4553 Epoch: [2/20], Batch Num: [86/600] Discriminator Loss: 1.5004, Generator Loss: 1.3786 D(x): 0.5419, D(G(z)): 0.4565 Epoch: [2/20], Batch Num: [87/600] Discriminator Loss: 1.5793, Generator Loss: 1.2503 D(x): 0.5418, D(G(z)): 0.4944 Epoch: [2/20], Batch Num: [88/600] Discriminator Loss: 1.3885, Generator Loss: 1.4485 D(x): 0.6343, D(G(z)): 0.5198 Epoch: [2/20], Batch Num: [89/600] Discriminator Loss: 1.4015, Generator Loss: 1.7728 D(x): 0.6035, D(G(z)): 0.4699 Epoch: [2/20], Batch Num: [90/600] Discriminator Loss: 1.4890, Generator Loss: 1.6567 D(x): 0.4975, D(G(z)): 0.3872 Epoch: [2/20], Batch Num: [91/600] Discriminator Loss: 1.4446, Generator Loss: 1.5962 D(x): 0.5028, D(G(z)): 0.3870 Epoch: [2/20], Batch Num: [92/600] Discriminator Loss: 1.3911, Generator Loss: 1.5209 D(x): 0.5377, D(G(z)): 0.4079 Epoch: [2/20], Batch Num: [93/600] Discriminator Loss: 1.3891, Generator Loss: 1.4846 D(x): 0.5701, D(G(z)): 0.4261 Epoch: [2/20], Batch Num: [94/600] Discriminator Loss: 1.2111, Generator Loss: 1.4932 D(x): 0.6709, D(G(z)): 0.4682 Epoch: [2/20], Batch Num: [95/600] Discriminator Loss: 1.2302, Generator Loss: 1.4972 D(x): 0.6458, D(G(z)): 0.4216 Epoch: [2/20], Batch Num: [96/600] Discriminator Loss: 1.1821, Generator Loss: 1.6850 D(x): 0.6089, D(G(z)): 0.3935 Epoch: [2/20], Batch Num: [97/600] Discriminator Loss: 1.2368, Generator Loss: 1.8061 D(x): 0.5912, D(G(z)): 0.3629 Epoch: [2/20], Batch Num: [98/600] Discriminator Loss: 1.1611, Generator Loss: 1.5892 D(x): 0.5889, D(G(z)): 0.3493 Epoch: [2/20], Batch Num: [99/600] Discriminator Loss: 1.2364, Generator Loss: 1.8703 D(x): 0.5973, D(G(z)): 0.3945 Epoch: 2, Batch Num: [100/600]
Epoch: [2/20], Batch Num: [100/600] Discriminator Loss: 1.1596, Generator Loss: 1.6338 D(x): 0.5914, D(G(z)): 0.3578 Epoch: [2/20], Batch Num: [101/600] Discriminator Loss: 1.1299, Generator Loss: 1.5753 D(x): 0.6411, D(G(z)): 0.3990 Epoch: [2/20], Batch Num: [102/600] Discriminator Loss: 1.2569, Generator Loss: 1.6689 D(x): 0.6680, D(G(z)): 0.4576 Epoch: [2/20], Batch Num: [103/600] Discriminator Loss: 1.2973, Generator Loss: 1.6585 D(x): 0.6086, D(G(z)): 0.4495 Epoch: [2/20], Batch Num: [104/600] Discriminator Loss: 1.2633, Generator Loss: 1.5719 D(x): 0.5810, D(G(z)): 0.3941 Epoch: [2/20], Batch Num: [105/600] Discriminator Loss: 1.4159, Generator Loss: 1.4184 D(x): 0.5291, D(G(z)): 0.4014 Epoch: [2/20], Batch Num: [106/600] Discriminator Loss: 1.4213, Generator Loss: 1.4331 D(x): 0.5488, D(G(z)): 0.4313 Epoch: [2/20], Batch Num: [107/600] Discriminator Loss: 1.4790, Generator Loss: 1.3004 D(x): 0.6097, D(G(z)): 0.4812 Epoch: [2/20], Batch Num: [108/600] Discriminator Loss: 1.3824, Generator Loss: 1.4026 D(x): 0.6513, D(G(z)): 0.5147 Epoch: [2/20], Batch Num: [109/600] Discriminator Loss: 1.5662, Generator Loss: 1.3202 D(x): 0.6175, D(G(z)): 0.5306 Epoch: [2/20], Batch Num: [110/600] Discriminator Loss: 1.7521, Generator Loss: 1.4062 D(x): 0.5274, D(G(z)): 0.4596 Epoch: [2/20], Batch Num: [111/600] Discriminator Loss: 1.5249, Generator Loss: 1.3372 D(x): 0.5681, D(G(z)): 0.4730 Epoch: [2/20], Batch Num: [112/600] Discriminator Loss: 1.3032, Generator Loss: 1.2986 D(x): 0.6233, D(G(z)): 0.4442 Epoch: [2/20], Batch Num: [113/600] Discriminator Loss: 1.4096, Generator Loss: 1.3708 D(x): 0.5861, D(G(z)): 0.4401 Epoch: [2/20], Batch Num: [114/600] Discriminator Loss: 1.3504, Generator Loss: 1.3540 D(x): 0.5807, D(G(z)): 0.4213 Epoch: [2/20], Batch Num: [115/600] Discriminator Loss: 1.0233, Generator Loss: 1.3550 D(x): 0.6767, D(G(z)): 0.3858 Epoch: [2/20], Batch Num: [116/600] Discriminator Loss: 1.1938, Generator Loss: 1.6392 D(x): 0.6298, D(G(z)): 0.3959 Epoch: [2/20], Batch Num: [117/600] Discriminator Loss: 1.0927, Generator Loss: 1.5693 D(x): 0.6408, D(G(z)): 0.3620 Epoch: [2/20], Batch Num: [118/600] Discriminator Loss: 1.1060, Generator Loss: 1.8312 D(x): 0.6274, D(G(z)): 0.3579 Epoch: [2/20], Batch Num: [119/600] Discriminator Loss: 0.8017, Generator Loss: 1.7183 D(x): 0.6890, D(G(z)): 0.2705 Epoch: [2/20], Batch Num: [120/600] Discriminator Loss: 1.0658, Generator Loss: 1.6590 D(x): 0.6305, D(G(z)): 0.3343 Epoch: [2/20], Batch Num: [121/600] Discriminator Loss: 0.7824, Generator Loss: 1.8488 D(x): 0.7170, D(G(z)): 0.2936 Epoch: [2/20], Batch Num: [122/600] Discriminator Loss: 0.7937, Generator Loss: 1.8934 D(x): 0.7371, D(G(z)): 0.3269 Epoch: [2/20], Batch Num: [123/600] Discriminator Loss: 0.7826, Generator Loss: 2.1326 D(x): 0.7173, D(G(z)): 0.2701 Epoch: [2/20], Batch Num: [124/600] Discriminator Loss: 0.7705, Generator Loss: 2.5403 D(x): 0.7302, D(G(z)): 0.2482 Epoch: [2/20], Batch Num: [125/600] Discriminator Loss: 0.8829, Generator Loss: 2.1459 D(x): 0.6768, D(G(z)): 0.2546 Epoch: [2/20], Batch Num: [126/600] Discriminator Loss: 0.7983, Generator Loss: 2.1202 D(x): 0.7064, D(G(z)): 0.2394 Epoch: [2/20], Batch Num: [127/600] Discriminator Loss: 0.8108, Generator Loss: 1.9246 D(x): 0.7522, D(G(z)): 0.3031 Epoch: [2/20], Batch Num: [128/600] Discriminator Loss: 0.7294, Generator Loss: 2.1843 D(x): 0.7846, D(G(z)): 0.3094 Epoch: [2/20], Batch Num: [129/600] Discriminator Loss: 0.8306, Generator Loss: 2.1449 D(x): 0.7288, D(G(z)): 0.2837 Epoch: [2/20], Batch Num: [130/600] Discriminator Loss: 0.8522, Generator Loss: 2.3994 D(x): 0.7270, D(G(z)): 0.3010 Epoch: [2/20], Batch Num: [131/600] Discriminator Loss: 0.9287, Generator Loss: 2.1278 D(x): 0.6873, D(G(z)): 0.2912 Epoch: [2/20], Batch Num: [132/600] Discriminator Loss: 0.8194, Generator Loss: 1.7450 D(x): 0.7296, D(G(z)): 0.2612 Epoch: [2/20], Batch Num: [133/600] Discriminator Loss: 0.9164, Generator Loss: 1.7812 D(x): 0.7177, D(G(z)): 0.3362 Epoch: [2/20], Batch Num: [134/600] Discriminator Loss: 0.9350, Generator Loss: 1.3594 D(x): 0.7078, D(G(z)): 0.3113 Epoch: [2/20], Batch Num: [135/600] Discriminator Loss: 1.0940, Generator Loss: 1.3523 D(x): 0.7236, D(G(z)): 0.4099 Epoch: [2/20], Batch Num: [136/600] Discriminator Loss: 1.0625, Generator Loss: 1.6920 D(x): 0.7455, D(G(z)): 0.4109 Epoch: [2/20], Batch Num: [137/600] Discriminator Loss: 1.0479, Generator Loss: 1.7081 D(x): 0.6941, D(G(z)): 0.3590 Epoch: [2/20], Batch Num: [138/600] Discriminator Loss: 1.0030, Generator Loss: 1.6778 D(x): 0.6784, D(G(z)): 0.3296 Epoch: [2/20], Batch Num: [139/600] Discriminator Loss: 0.8105, Generator Loss: 1.6238 D(x): 0.7310, D(G(z)): 0.3099 Epoch: [2/20], Batch Num: [140/600] Discriminator Loss: 0.9371, Generator Loss: 1.6616 D(x): 0.6974, D(G(z)): 0.3285 Epoch: [2/20], Batch Num: [141/600] Discriminator Loss: 0.9200, Generator Loss: 1.4729 D(x): 0.7357, D(G(z)): 0.3720 Epoch: [2/20], Batch Num: [142/600] Discriminator Loss: 0.8410, Generator Loss: 1.7147 D(x): 0.7793, D(G(z)): 0.3742 Epoch: [2/20], Batch Num: [143/600] Discriminator Loss: 0.8587, Generator Loss: 1.9233 D(x): 0.7399, D(G(z)): 0.3350 Epoch: [2/20], Batch Num: [144/600] Discriminator Loss: 0.8517, Generator Loss: 1.7507 D(x): 0.6599, D(G(z)): 0.2318 Epoch: [2/20], Batch Num: [145/600] Discriminator Loss: 0.8073, Generator Loss: 1.5989 D(x): 0.7246, D(G(z)): 0.2812 Epoch: [2/20], Batch Num: [146/600] Discriminator Loss: 0.8702, Generator Loss: 1.5290 D(x): 0.7377, D(G(z)): 0.3070 Epoch: [2/20], Batch Num: [147/600] Discriminator Loss: 0.7994, Generator Loss: 1.4857 D(x): 0.7900, D(G(z)): 0.3561 Epoch: [2/20], Batch Num: [148/600] Discriminator Loss: 0.6976, Generator Loss: 1.5359 D(x): 0.8172, D(G(z)): 0.3383 Epoch: [2/20], Batch Num: [149/600] Discriminator Loss: 0.7322, Generator Loss: 1.6676 D(x): 0.7795, D(G(z)): 0.3136 Epoch: [2/20], Batch Num: [150/600] Discriminator Loss: 0.7256, Generator Loss: 2.0858 D(x): 0.7371, D(G(z)): 0.2729 Epoch: [2/20], Batch Num: [151/600] Discriminator Loss: 0.7405, Generator Loss: 2.0263 D(x): 0.7187, D(G(z)): 0.2639 Epoch: [2/20], Batch Num: [152/600] Discriminator Loss: 0.8105, Generator Loss: 2.0129 D(x): 0.6992, D(G(z)): 0.2627 Epoch: [2/20], Batch Num: [153/600] Discriminator Loss: 0.7154, Generator Loss: 1.6309 D(x): 0.7451, D(G(z)): 0.2644 Epoch: [2/20], Batch Num: [154/600] Discriminator Loss: 0.7806, Generator Loss: 1.4792 D(x): 0.7814, D(G(z)): 0.3229 Epoch: [2/20], Batch Num: [155/600] Discriminator Loss: 0.8531, Generator Loss: 1.4594 D(x): 0.7386, D(G(z)): 0.3309 Epoch: [2/20], Batch Num: [156/600] Discriminator Loss: 0.7969, Generator Loss: 1.6487 D(x): 0.7824, D(G(z)): 0.3351 Epoch: [2/20], Batch Num: [157/600] Discriminator Loss: 0.9412, Generator Loss: 1.6547 D(x): 0.7503, D(G(z)): 0.3764 Epoch: [2/20], Batch Num: [158/600] Discriminator Loss: 0.8226, Generator Loss: 1.8665 D(x): 0.7569, D(G(z)): 0.3243 Epoch: [2/20], Batch Num: [159/600] Discriminator Loss: 0.7820, Generator Loss: 1.8500 D(x): 0.7422, D(G(z)): 0.2854 Epoch: [2/20], Batch Num: [160/600] Discriminator Loss: 0.8171, Generator Loss: 2.0058 D(x): 0.7048, D(G(z)): 0.2681 Epoch: [2/20], Batch Num: [161/600] Discriminator Loss: 0.7674, Generator Loss: 1.7151 D(x): 0.7143, D(G(z)): 0.2647 Epoch: [2/20], Batch Num: [162/600] Discriminator Loss: 0.7618, Generator Loss: 1.6789 D(x): 0.7484, D(G(z)): 0.2968 Epoch: [2/20], Batch Num: [163/600] Discriminator Loss: 0.8007, Generator Loss: 2.0145 D(x): 0.7775, D(G(z)): 0.3387 Epoch: [2/20], Batch Num: [164/600] Discriminator Loss: 0.8123, Generator Loss: 1.9268 D(x): 0.7567, D(G(z)): 0.3419 Epoch: [2/20], Batch Num: [165/600] Discriminator Loss: 0.7858, Generator Loss: 1.9711 D(x): 0.7438, D(G(z)): 0.2946 Epoch: [2/20], Batch Num: [166/600] Discriminator Loss: 0.8210, Generator Loss: 1.9154 D(x): 0.7269, D(G(z)): 0.2676 Epoch: [2/20], Batch Num: [167/600] Discriminator Loss: 0.8691, Generator Loss: 1.7828 D(x): 0.6942, D(G(z)): 0.2835 Epoch: [2/20], Batch Num: [168/600] Discriminator Loss: 0.8974, Generator Loss: 1.6877 D(x): 0.7077, D(G(z)): 0.3122 Epoch: [2/20], Batch Num: [169/600] Discriminator Loss: 0.9393, Generator Loss: 1.5578 D(x): 0.7074, D(G(z)): 0.3472 Epoch: [2/20], Batch Num: [170/600] Discriminator Loss: 1.0212, Generator Loss: 1.4170 D(x): 0.6968, D(G(z)): 0.3675 Epoch: [2/20], Batch Num: [171/600] Discriminator Loss: 1.0158, Generator Loss: 1.4972 D(x): 0.7420, D(G(z)): 0.4074 Epoch: [2/20], Batch Num: [172/600] Discriminator Loss: 1.0815, Generator Loss: 1.5257 D(x): 0.7209, D(G(z)): 0.4247 Epoch: [2/20], Batch Num: [173/600] Discriminator Loss: 1.1945, Generator Loss: 1.5440 D(x): 0.6277, D(G(z)): 0.3786 Epoch: [2/20], Batch Num: [174/600] Discriminator Loss: 1.3077, Generator Loss: 1.5420 D(x): 0.6167, D(G(z)): 0.3821 Epoch: [2/20], Batch Num: [175/600] Discriminator Loss: 1.1811, Generator Loss: 1.4048 D(x): 0.6397, D(G(z)): 0.4034 Epoch: [2/20], Batch Num: [176/600] Discriminator Loss: 1.2686, Generator Loss: 1.3251 D(x): 0.6340, D(G(z)): 0.4402 Epoch: [2/20], Batch Num: [177/600] Discriminator Loss: 1.3130, Generator Loss: 1.1276 D(x): 0.6578, D(G(z)): 0.4714 Epoch: [2/20], Batch Num: [178/600] Discriminator Loss: 1.2780, Generator Loss: 1.2504 D(x): 0.6666, D(G(z)): 0.4770 Epoch: [2/20], Batch Num: [179/600] Discriminator Loss: 1.3397, Generator Loss: 1.3410 D(x): 0.6836, D(G(z)): 0.5133 Epoch: [2/20], Batch Num: [180/600] Discriminator Loss: 1.4525, Generator Loss: 1.6120 D(x): 0.6188, D(G(z)): 0.4765 Epoch: [2/20], Batch Num: [181/600] Discriminator Loss: 1.3853, Generator Loss: 1.5314 D(x): 0.5533, D(G(z)): 0.3735 Epoch: [2/20], Batch Num: [182/600] Discriminator Loss: 1.5378, Generator Loss: 1.3203 D(x): 0.5631, D(G(z)): 0.4729 Epoch: [2/20], Batch Num: [183/600] Discriminator Loss: 1.6121, Generator Loss: 0.9641 D(x): 0.5833, D(G(z)): 0.4857 Epoch: [2/20], Batch Num: [184/600] Discriminator Loss: 1.5474, Generator Loss: 0.9109 D(x): 0.6401, D(G(z)): 0.5360 Epoch: [2/20], Batch Num: [185/600] Discriminator Loss: 1.6315, Generator Loss: 0.9052 D(x): 0.6581, D(G(z)): 0.6036 Epoch: [2/20], Batch Num: [186/600] Discriminator Loss: 1.6086, Generator Loss: 1.1498 D(x): 0.6274, D(G(z)): 0.5657 Epoch: [2/20], Batch Num: [187/600] Discriminator Loss: 1.6802, Generator Loss: 1.1530 D(x): 0.5728, D(G(z)): 0.5414 Epoch: [2/20], Batch Num: [188/600] Discriminator Loss: 2.0813, Generator Loss: 0.8799 D(x): 0.5046, D(G(z)): 0.5819 Epoch: [2/20], Batch Num: [189/600] Discriminator Loss: 1.8708, Generator Loss: 0.8495 D(x): 0.5006, D(G(z)): 0.5276 Epoch: [2/20], Batch Num: [190/600] Discriminator Loss: 1.7551, Generator Loss: 0.7131 D(x): 0.5633, D(G(z)): 0.5531 Epoch: [2/20], Batch Num: [191/600] Discriminator Loss: 1.7628, Generator Loss: 0.6111 D(x): 0.6196, D(G(z)): 0.6184 Epoch: [2/20], Batch Num: [192/600] Discriminator Loss: 1.7101, Generator Loss: 0.7144 D(x): 0.6658, D(G(z)): 0.6248 Epoch: [2/20], Batch Num: [193/600] Discriminator Loss: 1.7699, Generator Loss: 0.8368 D(x): 0.6499, D(G(z)): 0.6411 Epoch: [2/20], Batch Num: [194/600] Discriminator Loss: 1.6705, Generator Loss: 0.8582 D(x): 0.6421, D(G(z)): 0.6246 Epoch: [2/20], Batch Num: [195/600] Discriminator Loss: 1.6599, Generator Loss: 0.9661 D(x): 0.6169, D(G(z)): 0.5878 Epoch: [2/20], Batch Num: [196/600] Discriminator Loss: 1.7230, Generator Loss: 0.9762 D(x): 0.5332, D(G(z)): 0.5550 Epoch: [2/20], Batch Num: [197/600] Discriminator Loss: 1.4609, Generator Loss: 1.1374 D(x): 0.5762, D(G(z)): 0.4992 Epoch: [2/20], Batch Num: [198/600] Discriminator Loss: 1.4002, Generator Loss: 1.1451 D(x): 0.5688, D(G(z)): 0.4671 Epoch: [2/20], Batch Num: [199/600] Discriminator Loss: 1.3443, Generator Loss: 0.9925 D(x): 0.5864, D(G(z)): 0.4817 Epoch: 2, Batch Num: [200/600]
Epoch: [2/20], Batch Num: [200/600] Discriminator Loss: 1.4261, Generator Loss: 0.9632 D(x): 0.5930, D(G(z)): 0.4967 Epoch: [2/20], Batch Num: [201/600] Discriminator Loss: 1.2394, Generator Loss: 1.0489 D(x): 0.6301, D(G(z)): 0.4753 Epoch: [2/20], Batch Num: [202/600] Discriminator Loss: 1.1750, Generator Loss: 1.1082 D(x): 0.6525, D(G(z)): 0.4550 Epoch: [2/20], Batch Num: [203/600] Discriminator Loss: 1.1290, Generator Loss: 1.1444 D(x): 0.6792, D(G(z)): 0.4717 Epoch: [2/20], Batch Num: [204/600] Discriminator Loss: 0.9256, Generator Loss: 1.4243 D(x): 0.7180, D(G(z)): 0.3962 Epoch: [2/20], Batch Num: [205/600] Discriminator Loss: 0.9139, Generator Loss: 1.4302 D(x): 0.6912, D(G(z)): 0.3746 Epoch: [2/20], Batch Num: [206/600] Discriminator Loss: 0.7973, Generator Loss: 1.7956 D(x): 0.7075, D(G(z)): 0.3109 Epoch: [2/20], Batch Num: [207/600] Discriminator Loss: 0.7089, Generator Loss: 2.0060 D(x): 0.6889, D(G(z)): 0.2437 Epoch: [2/20], Batch Num: [208/600] Discriminator Loss: 0.5989, Generator Loss: 2.3132 D(x): 0.7237, D(G(z)): 0.2058 Epoch: [2/20], Batch Num: [209/600] Discriminator Loss: 0.5588, Generator Loss: 2.6978 D(x): 0.7544, D(G(z)): 0.2102 Epoch: [2/20], Batch Num: [210/600] Discriminator Loss: 0.5487, Generator Loss: 2.6275 D(x): 0.7292, D(G(z)): 0.1756 Epoch: [2/20], Batch Num: [211/600] Discriminator Loss: 0.5462, Generator Loss: 2.7728 D(x): 0.7491, D(G(z)): 0.1909 Epoch: [2/20], Batch Num: [212/600] Discriminator Loss: 0.4837, Generator Loss: 2.6659 D(x): 0.7729, D(G(z)): 0.1726 Epoch: [2/20], Batch Num: [213/600] Discriminator Loss: 0.4763, Generator Loss: 2.6402 D(x): 0.7824, D(G(z)): 0.1715 Epoch: [2/20], Batch Num: [214/600] Discriminator Loss: 0.4380, Generator Loss: 2.7057 D(x): 0.8042, D(G(z)): 0.1705 Epoch: [2/20], Batch Num: [215/600] Discriminator Loss: 0.3757, Generator Loss: 2.9660 D(x): 0.8326, D(G(z)): 0.1544 Epoch: [2/20], Batch Num: [216/600] Discriminator Loss: 0.4119, Generator Loss: 2.8270 D(x): 0.8248, D(G(z)): 0.1689 Epoch: [2/20], Batch Num: [217/600] Discriminator Loss: 0.3568, Generator Loss: 3.1514 D(x): 0.8493, D(G(z)): 0.1550 Epoch: [2/20], Batch Num: [218/600] Discriminator Loss: 0.3775, Generator Loss: 2.9785 D(x): 0.8266, D(G(z)): 0.1442 Epoch: [2/20], Batch Num: [219/600] Discriminator Loss: 0.3892, Generator Loss: 3.1918 D(x): 0.8270, D(G(z)): 0.1536 Epoch: [2/20], Batch Num: [220/600] Discriminator Loss: 0.3725, Generator Loss: 2.9769 D(x): 0.8126, D(G(z)): 0.1284 Epoch: [2/20], Batch Num: [221/600] Discriminator Loss: 0.3941, Generator Loss: 3.0446 D(x): 0.8260, D(G(z)): 0.1571 Epoch: [2/20], Batch Num: [222/600] Discriminator Loss: 0.4547, Generator Loss: 3.1784 D(x): 0.7888, D(G(z)): 0.1583 Epoch: [2/20], Batch Num: [223/600] Discriminator Loss: 0.4599, Generator Loss: 2.9096 D(x): 0.7931, D(G(z)): 0.1550 Epoch: [2/20], Batch Num: [224/600] Discriminator Loss: 0.5707, Generator Loss: 2.6627 D(x): 0.7570, D(G(z)): 0.1945 Epoch: [2/20], Batch Num: [225/600] Discriminator Loss: 0.6349, Generator Loss: 2.7054 D(x): 0.7407, D(G(z)): 0.2204 Epoch: [2/20], Batch Num: [226/600] Discriminator Loss: 0.5760, Generator Loss: 2.4994 D(x): 0.7892, D(G(z)): 0.2306 Epoch: [2/20], Batch Num: [227/600] Discriminator Loss: 0.8135, Generator Loss: 2.1325 D(x): 0.7099, D(G(z)): 0.2727 Epoch: [2/20], Batch Num: [228/600] Discriminator Loss: 0.8168, Generator Loss: 2.1529 D(x): 0.7240, D(G(z)): 0.2901 Epoch: [2/20], Batch Num: [229/600] Discriminator Loss: 0.9440, Generator Loss: 1.9893 D(x): 0.7154, D(G(z)): 0.3457 Epoch: [2/20], Batch Num: [230/600] Discriminator Loss: 0.9196, Generator Loss: 2.1040 D(x): 0.7014, D(G(z)): 0.3492 Epoch: [2/20], Batch Num: [231/600] Discriminator Loss: 1.1244, Generator Loss: 2.0967 D(x): 0.6409, D(G(z)): 0.3583 Epoch: [2/20], Batch Num: [232/600] Discriminator Loss: 1.1547, Generator Loss: 1.8127 D(x): 0.6086, D(G(z)): 0.3237 Epoch: [2/20], Batch Num: [233/600] Discriminator Loss: 1.1322, Generator Loss: 1.7432 D(x): 0.7167, D(G(z)): 0.4195 Epoch: [2/20], Batch Num: [234/600] Discriminator Loss: 1.1359, Generator Loss: 1.7022 D(x): 0.6685, D(G(z)): 0.3981 Epoch: [2/20], Batch Num: [235/600] Discriminator Loss: 1.1640, Generator Loss: 1.7127 D(x): 0.6964, D(G(z)): 0.4460 Epoch: [2/20], Batch Num: [236/600] Discriminator Loss: 1.1827, Generator Loss: 1.7944 D(x): 0.6102, D(G(z)): 0.3701 Epoch: [2/20], Batch Num: [237/600] Discriminator Loss: 1.3219, Generator Loss: 1.8784 D(x): 0.5765, D(G(z)): 0.3767 Epoch: [2/20], Batch Num: [238/600] Discriminator Loss: 1.3621, Generator Loss: 1.3377 D(x): 0.6228, D(G(z)): 0.4390 Epoch: [2/20], Batch Num: [239/600] Discriminator Loss: 1.3330, Generator Loss: 1.3532 D(x): 0.6264, D(G(z)): 0.4424 Epoch: [2/20], Batch Num: [240/600] Discriminator Loss: 1.3337, Generator Loss: 1.5376 D(x): 0.6946, D(G(z)): 0.4963 Epoch: [2/20], Batch Num: [241/600] Discriminator Loss: 1.3087, Generator Loss: 1.6700 D(x): 0.5756, D(G(z)): 0.4163 Epoch: [2/20], Batch Num: [242/600] Discriminator Loss: 1.4873, Generator Loss: 1.5634 D(x): 0.5591, D(G(z)): 0.4291 Epoch: [2/20], Batch Num: [243/600] Discriminator Loss: 1.4555, Generator Loss: 1.3088 D(x): 0.6050, D(G(z)): 0.4591 Epoch: [2/20], Batch Num: [244/600] Discriminator Loss: 1.5853, Generator Loss: 1.1379 D(x): 0.5454, D(G(z)): 0.4510 Epoch: [2/20], Batch Num: [245/600] Discriminator Loss: 1.7168, Generator Loss: 1.0702 D(x): 0.5747, D(G(z)): 0.5306 Epoch: [2/20], Batch Num: [246/600] Discriminator Loss: 1.9206, Generator Loss: 1.0287 D(x): 0.6063, D(G(z)): 0.6106 Epoch: [2/20], Batch Num: [247/600] Discriminator Loss: 1.7553, Generator Loss: 1.0866 D(x): 0.5733, D(G(z)): 0.5344 Epoch: [2/20], Batch Num: [248/600] Discriminator Loss: 1.6805, Generator Loss: 1.1127 D(x): 0.5881, D(G(z)): 0.5270 Epoch: [2/20], Batch Num: [249/600] Discriminator Loss: 1.8872, Generator Loss: 1.2448 D(x): 0.5159, D(G(z)): 0.5180 Epoch: [2/20], Batch Num: [250/600] Discriminator Loss: 2.1262, Generator Loss: 0.9949 D(x): 0.4549, D(G(z)): 0.5583 Epoch: [2/20], Batch Num: [251/600] Discriminator Loss: 2.1483, Generator Loss: 0.7428 D(x): 0.4818, D(G(z)): 0.5469 Epoch: [2/20], Batch Num: [252/600] Discriminator Loss: 2.0045, Generator Loss: 0.6580 D(x): 0.5619, D(G(z)): 0.6044 Epoch: [2/20], Batch Num: [253/600] Discriminator Loss: 2.0077, Generator Loss: 0.7338 D(x): 0.6487, D(G(z)): 0.6703 Epoch: [2/20], Batch Num: [254/600] Discriminator Loss: 1.8604, Generator Loss: 0.9103 D(x): 0.5471, D(G(z)): 0.5727 Epoch: [2/20], Batch Num: [255/600] Discriminator Loss: 2.0755, Generator Loss: 1.0252 D(x): 0.4908, D(G(z)): 0.5875 Epoch: [2/20], Batch Num: [256/600] Discriminator Loss: 2.1586, Generator Loss: 0.8466 D(x): 0.4655, D(G(z)): 0.5592 Epoch: [2/20], Batch Num: [257/600] Discriminator Loss: 2.1421, Generator Loss: 0.8338 D(x): 0.4629, D(G(z)): 0.5860 Epoch: [2/20], Batch Num: [258/600] Discriminator Loss: 1.8072, Generator Loss: 0.8122 D(x): 0.5807, D(G(z)): 0.6109 Epoch: [2/20], Batch Num: [259/600] Discriminator Loss: 1.8529, Generator Loss: 0.6964 D(x): 0.5378, D(G(z)): 0.5898 Epoch: [2/20], Batch Num: [260/600] Discriminator Loss: 1.9330, Generator Loss: 0.6182 D(x): 0.5550, D(G(z)): 0.6454 Epoch: [2/20], Batch Num: [261/600] Discriminator Loss: 1.7176, Generator Loss: 0.8147 D(x): 0.5804, D(G(z)): 0.5773 Epoch: [2/20], Batch Num: [262/600] Discriminator Loss: 1.7588, Generator Loss: 0.8283 D(x): 0.5883, D(G(z)): 0.5943 Epoch: [2/20], Batch Num: [263/600] Discriminator Loss: 1.4326, Generator Loss: 0.9973 D(x): 0.6176, D(G(z)): 0.5392 Epoch: [2/20], Batch Num: [264/600] Discriminator Loss: 1.5051, Generator Loss: 0.9051 D(x): 0.5732, D(G(z)): 0.5156 Epoch: [2/20], Batch Num: [265/600] Discriminator Loss: 1.5012, Generator Loss: 1.1323 D(x): 0.5745, D(G(z)): 0.4972 Epoch: [2/20], Batch Num: [266/600] Discriminator Loss: 1.3118, Generator Loss: 1.0754 D(x): 0.5703, D(G(z)): 0.4126 Epoch: [2/20], Batch Num: [267/600] Discriminator Loss: 1.5299, Generator Loss: 0.9888 D(x): 0.5221, D(G(z)): 0.4481 Epoch: [2/20], Batch Num: [268/600] Discriminator Loss: 1.2689, Generator Loss: 0.8987 D(x): 0.6159, D(G(z)): 0.4558 Epoch: [2/20], Batch Num: [269/600] Discriminator Loss: 1.2184, Generator Loss: 0.8025 D(x): 0.6705, D(G(z)): 0.4909 Epoch: [2/20], Batch Num: [270/600] Discriminator Loss: 1.1773, Generator Loss: 1.0154 D(x): 0.6519, D(G(z)): 0.4600 Epoch: [2/20], Batch Num: [271/600] Discriminator Loss: 1.0355, Generator Loss: 1.0584 D(x): 0.7044, D(G(z)): 0.4355 Epoch: [2/20], Batch Num: [272/600] Discriminator Loss: 1.1049, Generator Loss: 1.2162 D(x): 0.6401, D(G(z)): 0.3886 Epoch: [2/20], Batch Num: [273/600] Discriminator Loss: 1.0107, Generator Loss: 1.2534 D(x): 0.6433, D(G(z)): 0.3735 Epoch: [2/20], Batch Num: [274/600] Discriminator Loss: 1.0265, Generator Loss: 1.2856 D(x): 0.6518, D(G(z)): 0.3887 Epoch: [2/20], Batch Num: [275/600] Discriminator Loss: 0.9262, Generator Loss: 1.3964 D(x): 0.6538, D(G(z)): 0.3198 Epoch: [2/20], Batch Num: [276/600] Discriminator Loss: 0.8339, Generator Loss: 1.5697 D(x): 0.6712, D(G(z)): 0.2964 Epoch: [2/20], Batch Num: [277/600] Discriminator Loss: 0.8954, Generator Loss: 1.3509 D(x): 0.6576, D(G(z)): 0.3122 Epoch: [2/20], Batch Num: [278/600] Discriminator Loss: 0.7843, Generator Loss: 1.3570 D(x): 0.7097, D(G(z)): 0.2966 Epoch: [2/20], Batch Num: [279/600] Discriminator Loss: 0.7445, Generator Loss: 1.4095 D(x): 0.7530, D(G(z)): 0.3224 Epoch: [2/20], Batch Num: [280/600] Discriminator Loss: 0.7133, Generator Loss: 1.5117 D(x): 0.7589, D(G(z)): 0.3147 Epoch: [2/20], Batch Num: [281/600] Discriminator Loss: 0.7047, Generator Loss: 1.6562 D(x): 0.7335, D(G(z)): 0.2823 Epoch: [2/20], Batch Num: [282/600] Discriminator Loss: 0.6808, Generator Loss: 1.8087 D(x): 0.7470, D(G(z)): 0.2775 Epoch: [2/20], Batch Num: [283/600] Discriminator Loss: 0.6416, Generator Loss: 1.9126 D(x): 0.7726, D(G(z)): 0.2795 Epoch: [2/20], Batch Num: [284/600] Discriminator Loss: 0.6327, Generator Loss: 2.0916 D(x): 0.7303, D(G(z)): 0.2301 Epoch: [2/20], Batch Num: [285/600] Discriminator Loss: 0.6013, Generator Loss: 1.9606 D(x): 0.7427, D(G(z)): 0.2120 Epoch: [2/20], Batch Num: [286/600] Discriminator Loss: 0.5641, Generator Loss: 1.8240 D(x): 0.7493, D(G(z)): 0.2016 Epoch: [2/20], Batch Num: [287/600] Discriminator Loss: 0.5781, Generator Loss: 2.0232 D(x): 0.7485, D(G(z)): 0.2100 Epoch: [2/20], Batch Num: [288/600] Discriminator Loss: 0.5460, Generator Loss: 1.8787 D(x): 0.7731, D(G(z)): 0.2200 Epoch: [2/20], Batch Num: [289/600] Discriminator Loss: 0.5175, Generator Loss: 1.8814 D(x): 0.7946, D(G(z)): 0.2015 Epoch: [2/20], Batch Num: [290/600] Discriminator Loss: 0.5699, Generator Loss: 1.9270 D(x): 0.7692, D(G(z)): 0.2151 Epoch: [2/20], Batch Num: [291/600] Discriminator Loss: 0.4979, Generator Loss: 2.0373 D(x): 0.7973, D(G(z)): 0.2098 Epoch: [2/20], Batch Num: [292/600] Discriminator Loss: 0.5170, Generator Loss: 2.1221 D(x): 0.7755, D(G(z)): 0.1749 Epoch: [2/20], Batch Num: [293/600] Discriminator Loss: 0.5119, Generator Loss: 2.1822 D(x): 0.7890, D(G(z)): 0.2051 Epoch: [2/20], Batch Num: [294/600] Discriminator Loss: 0.5237, Generator Loss: 2.1963 D(x): 0.7781, D(G(z)): 0.2018 Epoch: [2/20], Batch Num: [295/600] Discriminator Loss: 0.5187, Generator Loss: 2.2603 D(x): 0.7857, D(G(z)): 0.2111 Epoch: [2/20], Batch Num: [296/600] Discriminator Loss: 0.5857, Generator Loss: 2.0685 D(x): 0.7708, D(G(z)): 0.2316 Epoch: [2/20], Batch Num: [297/600] Discriminator Loss: 0.5768, Generator Loss: 2.0682 D(x): 0.7630, D(G(z)): 0.2123 Epoch: [2/20], Batch Num: [298/600] Discriminator Loss: 0.6187, Generator Loss: 1.9450 D(x): 0.7189, D(G(z)): 0.1974 Epoch: [2/20], Batch Num: [299/600] Discriminator Loss: 0.6011, Generator Loss: 1.9603 D(x): 0.7550, D(G(z)): 0.2261 Epoch: 2, Batch Num: [300/600]
Epoch: [2/20], Batch Num: [300/600] Discriminator Loss: 0.6291, Generator Loss: 1.7361 D(x): 0.7398, D(G(z)): 0.2309 Epoch: [2/20], Batch Num: [301/600] Discriminator Loss: 0.6346, Generator Loss: 1.7763 D(x): 0.7597, D(G(z)): 0.2516 Epoch: [2/20], Batch Num: [302/600] Discriminator Loss: 0.6542, Generator Loss: 1.7841 D(x): 0.7587, D(G(z)): 0.2619 Epoch: [2/20], Batch Num: [303/600] Discriminator Loss: 0.7111, Generator Loss: 1.6555 D(x): 0.7541, D(G(z)): 0.2781 Epoch: [2/20], Batch Num: [304/600] Discriminator Loss: 0.8046, Generator Loss: 1.8072 D(x): 0.7106, D(G(z)): 0.3008 Epoch: [2/20], Batch Num: [305/600] Discriminator Loss: 0.8128, Generator Loss: 1.8384 D(x): 0.7073, D(G(z)): 0.3184 Epoch: [2/20], Batch Num: [306/600] Discriminator Loss: 0.8071, Generator Loss: 1.8892 D(x): 0.6885, D(G(z)): 0.2835 Epoch: [2/20], Batch Num: [307/600] Discriminator Loss: 1.0246, Generator Loss: 1.6783 D(x): 0.6188, D(G(z)): 0.2862 Epoch: [2/20], Batch Num: [308/600] Discriminator Loss: 0.8756, Generator Loss: 1.4865 D(x): 0.6794, D(G(z)): 0.3069 Epoch: [2/20], Batch Num: [309/600] Discriminator Loss: 0.9681, Generator Loss: 1.2381 D(x): 0.6769, D(G(z)): 0.3695 Epoch: [2/20], Batch Num: [310/600] Discriminator Loss: 0.8632, Generator Loss: 1.3262 D(x): 0.7488, D(G(z)): 0.3717 Epoch: [2/20], Batch Num: [311/600] Discriminator Loss: 1.1089, Generator Loss: 1.1464 D(x): 0.6629, D(G(z)): 0.4233 Epoch: [2/20], Batch Num: [312/600] Discriminator Loss: 1.1443, Generator Loss: 1.3345 D(x): 0.6476, D(G(z)): 0.4229 Epoch: [2/20], Batch Num: [313/600] Discriminator Loss: 1.1075, Generator Loss: 1.2591 D(x): 0.6302, D(G(z)): 0.3946 Epoch: [2/20], Batch Num: [314/600] Discriminator Loss: 1.1686, Generator Loss: 1.2358 D(x): 0.6328, D(G(z)): 0.4048 Epoch: [2/20], Batch Num: [315/600] Discriminator Loss: 1.3809, Generator Loss: 1.0008 D(x): 0.5473, D(G(z)): 0.4218 Epoch: [2/20], Batch Num: [316/600] Discriminator Loss: 1.3529, Generator Loss: 0.8889 D(x): 0.5885, D(G(z)): 0.4633 Epoch: [2/20], Batch Num: [317/600] Discriminator Loss: 1.4361, Generator Loss: 0.7320 D(x): 0.6199, D(G(z)): 0.5148 Epoch: [2/20], Batch Num: [318/600] Discriminator Loss: 1.4616, Generator Loss: 0.7235 D(x): 0.6299, D(G(z)): 0.5539 Epoch: [2/20], Batch Num: [319/600] Discriminator Loss: 1.5375, Generator Loss: 0.8445 D(x): 0.6131, D(G(z)): 0.5666 Epoch: [2/20], Batch Num: [320/600] Discriminator Loss: 1.5967, Generator Loss: 0.8230 D(x): 0.5395, D(G(z)): 0.5228 Epoch: [2/20], Batch Num: [321/600] Discriminator Loss: 1.5599, Generator Loss: 0.7921 D(x): 0.5895, D(G(z)): 0.5415 Epoch: [2/20], Batch Num: [322/600] Discriminator Loss: 1.7170, Generator Loss: 0.8444 D(x): 0.5096, D(G(z)): 0.5168 Epoch: [2/20], Batch Num: [323/600] Discriminator Loss: 1.4930, Generator Loss: 0.7905 D(x): 0.6103, D(G(z)): 0.5507 Epoch: [2/20], Batch Num: [324/600] Discriminator Loss: 1.7259, Generator Loss: 0.7557 D(x): 0.5665, D(G(z)): 0.5874 Epoch: [2/20], Batch Num: [325/600] Discriminator Loss: 1.6458, Generator Loss: 0.6810 D(x): 0.5956, D(G(z)): 0.5952 Epoch: [2/20], Batch Num: [326/600] Discriminator Loss: 1.5834, Generator Loss: 0.7151 D(x): 0.5951, D(G(z)): 0.5643 Epoch: [2/20], Batch Num: [327/600] Discriminator Loss: 1.4623, Generator Loss: 0.7780 D(x): 0.6127, D(G(z)): 0.5561 Epoch: [2/20], Batch Num: [328/600] Discriminator Loss: 1.5115, Generator Loss: 0.6979 D(x): 0.5851, D(G(z)): 0.5382 Epoch: [2/20], Batch Num: [329/600] Discriminator Loss: 1.4455, Generator Loss: 0.8530 D(x): 0.5990, D(G(z)): 0.5336 Epoch: [2/20], Batch Num: [330/600] Discriminator Loss: 1.3846, Generator Loss: 0.7839 D(x): 0.5922, D(G(z)): 0.5073 Epoch: [2/20], Batch Num: [331/600] Discriminator Loss: 1.2801, Generator Loss: 0.9062 D(x): 0.6320, D(G(z)): 0.4866 Epoch: [2/20], Batch Num: [332/600] Discriminator Loss: 1.4011, Generator Loss: 0.7994 D(x): 0.5865, D(G(z)): 0.5091 Epoch: [2/20], Batch Num: [333/600] Discriminator Loss: 1.2932, Generator Loss: 1.0054 D(x): 0.6123, D(G(z)): 0.4809 Epoch: [2/20], Batch Num: [334/600] Discriminator Loss: 1.4113, Generator Loss: 1.0430 D(x): 0.5509, D(G(z)): 0.4494 Epoch: [2/20], Batch Num: [335/600] Discriminator Loss: 1.1673, Generator Loss: 1.0352 D(x): 0.6326, D(G(z)): 0.4494 Epoch: [2/20], Batch Num: [336/600] Discriminator Loss: 1.1692, Generator Loss: 1.0440 D(x): 0.6373, D(G(z)): 0.4563 Epoch: [2/20], Batch Num: [337/600] Discriminator Loss: 1.1204, Generator Loss: 0.9980 D(x): 0.6319, D(G(z)): 0.4241 Epoch: [2/20], Batch Num: [338/600] Discriminator Loss: 1.2521, Generator Loss: 0.9934 D(x): 0.5851, D(G(z)): 0.4291 Epoch: [2/20], Batch Num: [339/600] Discriminator Loss: 1.1591, Generator Loss: 0.9418 D(x): 0.6485, D(G(z)): 0.4543 Epoch: [2/20], Batch Num: [340/600] Discriminator Loss: 1.0699, Generator Loss: 1.0783 D(x): 0.6657, D(G(z)): 0.4369 Epoch: [2/20], Batch Num: [341/600] Discriminator Loss: 1.0225, Generator Loss: 1.0862 D(x): 0.6703, D(G(z)): 0.4024 Epoch: [2/20], Batch Num: [342/600] Discriminator Loss: 1.1183, Generator Loss: 1.0717 D(x): 0.6466, D(G(z)): 0.4131 Epoch: [2/20], Batch Num: [343/600] Discriminator Loss: 0.9546, Generator Loss: 1.0663 D(x): 0.7092, D(G(z)): 0.4061 Epoch: [2/20], Batch Num: [344/600] Discriminator Loss: 0.9778, Generator Loss: 1.1244 D(x): 0.6717, D(G(z)): 0.3720 Epoch: [2/20], Batch Num: [345/600] Discriminator Loss: 0.9698, Generator Loss: 1.2096 D(x): 0.6665, D(G(z)): 0.3596 Epoch: [2/20], Batch Num: [346/600] Discriminator Loss: 0.9636, Generator Loss: 1.1243 D(x): 0.6804, D(G(z)): 0.3735 Epoch: [2/20], Batch Num: [347/600] Discriminator Loss: 0.9642, Generator Loss: 1.2048 D(x): 0.6770, D(G(z)): 0.3750 Epoch: [2/20], Batch Num: [348/600] Discriminator Loss: 0.9087, Generator Loss: 1.2084 D(x): 0.7176, D(G(z)): 0.3902 Epoch: [2/20], Batch Num: [349/600] Discriminator Loss: 0.9788, Generator Loss: 1.2602 D(x): 0.6832, D(G(z)): 0.3796 Epoch: [2/20], Batch Num: [350/600] Discriminator Loss: 0.9312, Generator Loss: 1.1832 D(x): 0.7077, D(G(z)): 0.3855 Epoch: [2/20], Batch Num: [351/600] Discriminator Loss: 0.9366, Generator Loss: 1.3058 D(x): 0.6855, D(G(z)): 0.3607 Epoch: [2/20], Batch Num: [352/600] Discriminator Loss: 0.9447, Generator Loss: 1.2722 D(x): 0.6806, D(G(z)): 0.3678 Epoch: [2/20], Batch Num: [353/600] Discriminator Loss: 1.0095, Generator Loss: 1.2247 D(x): 0.6628, D(G(z)): 0.3809 Epoch: [2/20], Batch Num: [354/600] Discriminator Loss: 0.8604, Generator Loss: 1.4224 D(x): 0.7071, D(G(z)): 0.3425 Epoch: [2/20], Batch Num: [355/600] Discriminator Loss: 0.8182, Generator Loss: 1.2408 D(x): 0.7636, D(G(z)): 0.3664 Epoch: [2/20], Batch Num: [356/600] Discriminator Loss: 0.8487, Generator Loss: 1.4133 D(x): 0.7144, D(G(z)): 0.3350 Epoch: [2/20], Batch Num: [357/600] Discriminator Loss: 0.8371, Generator Loss: 1.4276 D(x): 0.6815, D(G(z)): 0.3026 Epoch: [2/20], Batch Num: [358/600] Discriminator Loss: 0.6925, Generator Loss: 1.3303 D(x): 0.7634, D(G(z)): 0.3074 Epoch: [2/20], Batch Num: [359/600] Discriminator Loss: 0.8944, Generator Loss: 1.6390 D(x): 0.7081, D(G(z)): 0.3568 Epoch: [2/20], Batch Num: [360/600] Discriminator Loss: 0.8816, Generator Loss: 1.3648 D(x): 0.6598, D(G(z)): 0.2936 Epoch: [2/20], Batch Num: [361/600] Discriminator Loss: 0.7575, Generator Loss: 1.3520 D(x): 0.7321, D(G(z)): 0.3032 Epoch: [2/20], Batch Num: [362/600] Discriminator Loss: 0.7845, Generator Loss: 1.3834 D(x): 0.7500, D(G(z)): 0.3344 Epoch: [2/20], Batch Num: [363/600] Discriminator Loss: 0.7552, Generator Loss: 1.4128 D(x): 0.7621, D(G(z)): 0.3321 Epoch: [2/20], Batch Num: [364/600] Discriminator Loss: 0.7800, Generator Loss: 1.4548 D(x): 0.7593, D(G(z)): 0.3331 Epoch: [2/20], Batch Num: [365/600] Discriminator Loss: 0.8266, Generator Loss: 1.5008 D(x): 0.7342, D(G(z)): 0.3322 Epoch: [2/20], Batch Num: [366/600] Discriminator Loss: 0.8882, Generator Loss: 1.5368 D(x): 0.7439, D(G(z)): 0.3833 Epoch: [2/20], Batch Num: [367/600] Discriminator Loss: 0.8342, Generator Loss: 1.5107 D(x): 0.7332, D(G(z)): 0.3467 Epoch: [2/20], Batch Num: [368/600] Discriminator Loss: 0.7611, Generator Loss: 1.4657 D(x): 0.7538, D(G(z)): 0.3224 Epoch: [2/20], Batch Num: [369/600] Discriminator Loss: 0.7071, Generator Loss: 1.4951 D(x): 0.7676, D(G(z)): 0.3113 Epoch: [2/20], Batch Num: [370/600] Discriminator Loss: 0.8656, Generator Loss: 1.5833 D(x): 0.6902, D(G(z)): 0.3022 Epoch: [2/20], Batch Num: [371/600] Discriminator Loss: 1.0097, Generator Loss: 1.2396 D(x): 0.6450, D(G(z)): 0.3185 Epoch: [2/20], Batch Num: [372/600] Discriminator Loss: 0.8337, Generator Loss: 1.1628 D(x): 0.7557, D(G(z)): 0.3475 Epoch: [2/20], Batch Num: [373/600] Discriminator Loss: 0.8875, Generator Loss: 1.2544 D(x): 0.7712, D(G(z)): 0.4021 Epoch: [2/20], Batch Num: [374/600] Discriminator Loss: 0.8489, Generator Loss: 1.1858 D(x): 0.7846, D(G(z)): 0.4038 Epoch: [2/20], Batch Num: [375/600] Discriminator Loss: 0.8875, Generator Loss: 1.4198 D(x): 0.7858, D(G(z)): 0.4200 Epoch: [2/20], Batch Num: [376/600] Discriminator Loss: 0.8043, Generator Loss: 1.4791 D(x): 0.7643, D(G(z)): 0.3528 Epoch: [2/20], Batch Num: [377/600] Discriminator Loss: 0.9316, Generator Loss: 1.6865 D(x): 0.6721, D(G(z)): 0.3234 Epoch: [2/20], Batch Num: [378/600] Discriminator Loss: 0.9630, Generator Loss: 1.5169 D(x): 0.6716, D(G(z)): 0.3036 Epoch: [2/20], Batch Num: [379/600] Discriminator Loss: 0.8930, Generator Loss: 1.3235 D(x): 0.6777, D(G(z)): 0.3014 Epoch: [2/20], Batch Num: [380/600] Discriminator Loss: 0.9016, Generator Loss: 1.0703 D(x): 0.7598, D(G(z)): 0.3975 Epoch: [2/20], Batch Num: [381/600] Discriminator Loss: 0.9102, Generator Loss: 1.1593 D(x): 0.7563, D(G(z)): 0.3970 Epoch: [2/20], Batch Num: [382/600] Discriminator Loss: 0.9498, Generator Loss: 1.2211 D(x): 0.7526, D(G(z)): 0.4190 Epoch: [2/20], Batch Num: [383/600] Discriminator Loss: 0.9739, Generator Loss: 1.2495 D(x): 0.7704, D(G(z)): 0.4258 Epoch: [2/20], Batch Num: [384/600] Discriminator Loss: 0.9893, Generator Loss: 1.4269 D(x): 0.7426, D(G(z)): 0.4271 Epoch: [2/20], Batch Num: [385/600] Discriminator Loss: 1.0256, Generator Loss: 1.5944 D(x): 0.6806, D(G(z)): 0.3753 Epoch: [2/20], Batch Num: [386/600] Discriminator Loss: 1.1032, Generator Loss: 1.3649 D(x): 0.6389, D(G(z)): 0.3645 Epoch: [2/20], Batch Num: [387/600] Discriminator Loss: 1.0521, Generator Loss: 1.3265 D(x): 0.6417, D(G(z)): 0.3478 Epoch: [2/20], Batch Num: [388/600] Discriminator Loss: 1.0501, Generator Loss: 1.0461 D(x): 0.7251, D(G(z)): 0.4344 Epoch: [2/20], Batch Num: [389/600] Discriminator Loss: 1.1246, Generator Loss: 1.0725 D(x): 0.7239, D(G(z)): 0.4774 Epoch: [2/20], Batch Num: [390/600] Discriminator Loss: 1.0343, Generator Loss: 1.0959 D(x): 0.7631, D(G(z)): 0.4726 Epoch: [2/20], Batch Num: [391/600] Discriminator Loss: 1.1670, Generator Loss: 1.1215 D(x): 0.7113, D(G(z)): 0.4645 Epoch: [2/20], Batch Num: [392/600] Discriminator Loss: 1.0551, Generator Loss: 1.2334 D(x): 0.7175, D(G(z)): 0.4162 Epoch: [2/20], Batch Num: [393/600] Discriminator Loss: 1.0953, Generator Loss: 1.1827 D(x): 0.6931, D(G(z)): 0.4138 Epoch: [2/20], Batch Num: [394/600] Discriminator Loss: 1.0505, Generator Loss: 1.2023 D(x): 0.6690, D(G(z)): 0.3707 Epoch: [2/20], Batch Num: [395/600] Discriminator Loss: 1.2225, Generator Loss: 0.9646 D(x): 0.6302, D(G(z)): 0.4247 Epoch: [2/20], Batch Num: [396/600] Discriminator Loss: 1.3443, Generator Loss: 0.9494 D(x): 0.6786, D(G(z)): 0.5124 Epoch: [2/20], Batch Num: [397/600] Discriminator Loss: 1.2014, Generator Loss: 0.8476 D(x): 0.7258, D(G(z)): 0.5076 Epoch: [2/20], Batch Num: [398/600] Discriminator Loss: 1.0869, Generator Loss: 0.9510 D(x): 0.7917, D(G(z)): 0.5165 Epoch: [2/20], Batch Num: [399/600] Discriminator Loss: 1.0697, Generator Loss: 1.0097 D(x): 0.7359, D(G(z)): 0.4740 Epoch: 2, Batch Num: [400/600]
Epoch: [2/20], Batch Num: [400/600] Discriminator Loss: 1.1870, Generator Loss: 1.0130 D(x): 0.7014, D(G(z)): 0.4730 Epoch: [2/20], Batch Num: [401/600] Discriminator Loss: 1.0862, Generator Loss: 1.0395 D(x): 0.7126, D(G(z)): 0.4604 Epoch: [2/20], Batch Num: [402/600] Discriminator Loss: 1.0733, Generator Loss: 1.0099 D(x): 0.6953, D(G(z)): 0.4273 Epoch: [2/20], Batch Num: [403/600] Discriminator Loss: 1.1475, Generator Loss: 0.9640 D(x): 0.6432, D(G(z)): 0.4067 Epoch: [2/20], Batch Num: [404/600] Discriminator Loss: 1.1758, Generator Loss: 0.9123 D(x): 0.6790, D(G(z)): 0.4564 Epoch: [2/20], Batch Num: [405/600] Discriminator Loss: 1.0537, Generator Loss: 0.9130 D(x): 0.7410, D(G(z)): 0.4612 Epoch: [2/20], Batch Num: [406/600] Discriminator Loss: 1.0562, Generator Loss: 0.8211 D(x): 0.7782, D(G(z)): 0.4989 Epoch: [2/20], Batch Num: [407/600] Discriminator Loss: 1.0561, Generator Loss: 0.8825 D(x): 0.7747, D(G(z)): 0.5035 Epoch: [2/20], Batch Num: [408/600] Discriminator Loss: 0.9464, Generator Loss: 1.0675 D(x): 0.7582, D(G(z)): 0.4332 Epoch: [2/20], Batch Num: [409/600] Discriminator Loss: 0.9676, Generator Loss: 1.1693 D(x): 0.7408, D(G(z)): 0.4168 Epoch: [2/20], Batch Num: [410/600] Discriminator Loss: 0.9095, Generator Loss: 1.1945 D(x): 0.7015, D(G(z)): 0.3743 Epoch: [2/20], Batch Num: [411/600] Discriminator Loss: 0.8520, Generator Loss: 1.1568 D(x): 0.7443, D(G(z)): 0.3774 Epoch: [2/20], Batch Num: [412/600] Discriminator Loss: 0.8929, Generator Loss: 1.1204 D(x): 0.7418, D(G(z)): 0.3910 Epoch: [2/20], Batch Num: [413/600] Discriminator Loss: 0.7839, Generator Loss: 1.1739 D(x): 0.7988, D(G(z)): 0.3955 Epoch: [2/20], Batch Num: [414/600] Discriminator Loss: 0.8398, Generator Loss: 1.1236 D(x): 0.7730, D(G(z)): 0.3878 Epoch: [2/20], Batch Num: [415/600] Discriminator Loss: 0.8134, Generator Loss: 1.2126 D(x): 0.7590, D(G(z)): 0.3545 Epoch: [2/20], Batch Num: [416/600] Discriminator Loss: 0.7417, Generator Loss: 1.1797 D(x): 0.7994, D(G(z)): 0.3672 Epoch: [2/20], Batch Num: [417/600] Discriminator Loss: 0.7718, Generator Loss: 1.1481 D(x): 0.7903, D(G(z)): 0.3593 Epoch: [2/20], Batch Num: [418/600] Discriminator Loss: 0.7904, Generator Loss: 1.3022 D(x): 0.7861, D(G(z)): 0.3706 Epoch: [2/20], Batch Num: [419/600] Discriminator Loss: 0.6842, Generator Loss: 1.3966 D(x): 0.8403, D(G(z)): 0.3565 Epoch: [2/20], Batch Num: [420/600] Discriminator Loss: 0.5685, Generator Loss: 1.3694 D(x): 0.8311, D(G(z)): 0.2865 Epoch: [2/20], Batch Num: [421/600] Discriminator Loss: 0.5527, Generator Loss: 1.5730 D(x): 0.8301, D(G(z)): 0.2752 Epoch: [2/20], Batch Num: [422/600] Discriminator Loss: 0.7201, Generator Loss: 1.5960 D(x): 0.7574, D(G(z)): 0.2860 Epoch: [2/20], Batch Num: [423/600] Discriminator Loss: 0.6525, Generator Loss: 1.4804 D(x): 0.7910, D(G(z)): 0.2786 Epoch: [2/20], Batch Num: [424/600] Discriminator Loss: 0.5707, Generator Loss: 1.6337 D(x): 0.8373, D(G(z)): 0.2791 Epoch: [2/20], Batch Num: [425/600] Discriminator Loss: 0.6312, Generator Loss: 1.4971 D(x): 0.8501, D(G(z)): 0.3065 Epoch: [2/20], Batch Num: [426/600] Discriminator Loss: 0.5209, Generator Loss: 1.7054 D(x): 0.8916, D(G(z)): 0.2995 Epoch: [2/20], Batch Num: [427/600] Discriminator Loss: 0.5812, Generator Loss: 1.8716 D(x): 0.8312, D(G(z)): 0.2565 Epoch: [2/20], Batch Num: [428/600] Discriminator Loss: 0.5586, Generator Loss: 1.8535 D(x): 0.8014, D(G(z)): 0.2325 Epoch: [2/20], Batch Num: [429/600] Discriminator Loss: 0.5179, Generator Loss: 1.9004 D(x): 0.8184, D(G(z)): 0.2081 Epoch: [2/20], Batch Num: [430/600] Discriminator Loss: 0.5927, Generator Loss: 1.9747 D(x): 0.8171, D(G(z)): 0.2260 Epoch: [2/20], Batch Num: [431/600] Discriminator Loss: 0.4885, Generator Loss: 1.7781 D(x): 0.8691, D(G(z)): 0.2438 Epoch: [2/20], Batch Num: [432/600] Discriminator Loss: 0.4435, Generator Loss: 1.9938 D(x): 0.8850, D(G(z)): 0.2318 Epoch: [2/20], Batch Num: [433/600] Discriminator Loss: 0.3854, Generator Loss: 2.2409 D(x): 0.8610, D(G(z)): 0.1858 Epoch: [2/20], Batch Num: [434/600] Discriminator Loss: 0.5204, Generator Loss: 2.3758 D(x): 0.8276, D(G(z)): 0.1910 Epoch: [2/20], Batch Num: [435/600] Discriminator Loss: 0.4920, Generator Loss: 1.9747 D(x): 0.8137, D(G(z)): 0.1708 Epoch: [2/20], Batch Num: [436/600] Discriminator Loss: 0.4148, Generator Loss: 2.0851 D(x): 0.8839, D(G(z)): 0.2079 Epoch: [2/20], Batch Num: [437/600] Discriminator Loss: 0.4984, Generator Loss: 2.1033 D(x): 0.8760, D(G(z)): 0.2522 Epoch: [2/20], Batch Num: [438/600] Discriminator Loss: 0.4068, Generator Loss: 2.3395 D(x): 0.8663, D(G(z)): 0.1860 Epoch: [2/20], Batch Num: [439/600] Discriminator Loss: 0.4614, Generator Loss: 2.4611 D(x): 0.8327, D(G(z)): 0.1737 Epoch: [2/20], Batch Num: [440/600] Discriminator Loss: 0.5672, Generator Loss: 2.2878 D(x): 0.8062, D(G(z)): 0.1946 Epoch: [2/20], Batch Num: [441/600] Discriminator Loss: 0.5789, Generator Loss: 2.3076 D(x): 0.8432, D(G(z)): 0.2537 Epoch: [2/20], Batch Num: [442/600] Discriminator Loss: 0.7085, Generator Loss: 2.5104 D(x): 0.7969, D(G(z)): 0.2542 Epoch: [2/20], Batch Num: [443/600] Discriminator Loss: 0.7041, Generator Loss: 2.4165 D(x): 0.8003, D(G(z)): 0.2447 Epoch: [2/20], Batch Num: [444/600] Discriminator Loss: 0.6892, Generator Loss: 2.3024 D(x): 0.7612, D(G(z)): 0.1918 Epoch: [2/20], Batch Num: [445/600] Discriminator Loss: 0.8916, Generator Loss: 1.9941 D(x): 0.7465, D(G(z)): 0.2647 Epoch: [2/20], Batch Num: [446/600] Discriminator Loss: 1.1299, Generator Loss: 1.8155 D(x): 0.7261, D(G(z)): 0.3668 Epoch: [2/20], Batch Num: [447/600] Discriminator Loss: 1.1162, Generator Loss: 2.2858 D(x): 0.7539, D(G(z)): 0.3591 Epoch: [2/20], Batch Num: [448/600] Discriminator Loss: 1.4029, Generator Loss: 1.9879 D(x): 0.6347, D(G(z)): 0.3361 Epoch: [2/20], Batch Num: [449/600] Discriminator Loss: 1.4950, Generator Loss: 1.7651 D(x): 0.5810, D(G(z)): 0.3695 Epoch: [2/20], Batch Num: [450/600] Discriminator Loss: 1.3670, Generator Loss: 1.5121 D(x): 0.6503, D(G(z)): 0.3869 Epoch: [2/20], Batch Num: [451/600] Discriminator Loss: 1.5856, Generator Loss: 1.7284 D(x): 0.6428, D(G(z)): 0.4370 Epoch: [2/20], Batch Num: [452/600] Discriminator Loss: 1.5138, Generator Loss: 1.7111 D(x): 0.5887, D(G(z)): 0.4180 Epoch: [2/20], Batch Num: [453/600] Discriminator Loss: 1.7574, Generator Loss: 1.5359 D(x): 0.5231, D(G(z)): 0.3887 Epoch: [2/20], Batch Num: [454/600] Discriminator Loss: 1.7171, Generator Loss: 1.5660 D(x): 0.5558, D(G(z)): 0.4504 Epoch: [2/20], Batch Num: [455/600] Discriminator Loss: 2.2362, Generator Loss: 1.3267 D(x): 0.4625, D(G(z)): 0.4626 Epoch: [2/20], Batch Num: [456/600] Discriminator Loss: 1.7129, Generator Loss: 1.4239 D(x): 0.5238, D(G(z)): 0.4458 Epoch: [2/20], Batch Num: [457/600] Discriminator Loss: 1.9662, Generator Loss: 1.3488 D(x): 0.5154, D(G(z)): 0.4557 Epoch: [2/20], Batch Num: [458/600] Discriminator Loss: 1.7513, Generator Loss: 1.6238 D(x): 0.5819, D(G(z)): 0.4913 Epoch: [2/20], Batch Num: [459/600] Discriminator Loss: 1.6220, Generator Loss: 2.0302 D(x): 0.5452, D(G(z)): 0.4211 Epoch: [2/20], Batch Num: [460/600] Discriminator Loss: 1.6476, Generator Loss: 1.6065 D(x): 0.4629, D(G(z)): 0.2926 Epoch: [2/20], Batch Num: [461/600] Discriminator Loss: 1.2727, Generator Loss: 1.4864 D(x): 0.5910, D(G(z)): 0.3140 Epoch: [2/20], Batch Num: [462/600] Discriminator Loss: 1.1292, Generator Loss: 1.4118 D(x): 0.6840, D(G(z)): 0.3772 Epoch: [2/20], Batch Num: [463/600] Discriminator Loss: 1.1493, Generator Loss: 1.9496 D(x): 0.7159, D(G(z)): 0.4239 Epoch: [2/20], Batch Num: [464/600] Discriminator Loss: 0.9893, Generator Loss: 2.2813 D(x): 0.6952, D(G(z)): 0.3100 Epoch: [2/20], Batch Num: [465/600] Discriminator Loss: 0.9276, Generator Loss: 2.5962 D(x): 0.6344, D(G(z)): 0.1832 Epoch: [2/20], Batch Num: [466/600] Discriminator Loss: 0.7483, Generator Loss: 2.6471 D(x): 0.6703, D(G(z)): 0.1664 Epoch: [2/20], Batch Num: [467/600] Discriminator Loss: 0.6156, Generator Loss: 2.3805 D(x): 0.7360, D(G(z)): 0.1466 Epoch: [2/20], Batch Num: [468/600] Discriminator Loss: 0.3931, Generator Loss: 2.2527 D(x): 0.8907, D(G(z)): 0.2000 Epoch: [2/20], Batch Num: [469/600] Discriminator Loss: 0.3712, Generator Loss: 2.7253 D(x): 0.8940, D(G(z)): 0.1918 Epoch: [2/20], Batch Num: [470/600] Discriminator Loss: 0.3337, Generator Loss: 3.4772 D(x): 0.8728, D(G(z)): 0.1341 Epoch: [2/20], Batch Num: [471/600] Discriminator Loss: 0.2461, Generator Loss: 3.9060 D(x): 0.8889, D(G(z)): 0.0950 Epoch: [2/20], Batch Num: [472/600] Discriminator Loss: 0.2926, Generator Loss: 4.3667 D(x): 0.8427, D(G(z)): 0.0603 Epoch: [2/20], Batch Num: [473/600] Discriminator Loss: 0.1862, Generator Loss: 4.6651 D(x): 0.9085, D(G(z)): 0.0665 Epoch: [2/20], Batch Num: [474/600] Discriminator Loss: 0.2467, Generator Loss: 4.5824 D(x): 0.8541, D(G(z)): 0.0371 Epoch: [2/20], Batch Num: [475/600] Discriminator Loss: 0.1942, Generator Loss: 4.5876 D(x): 0.9041, D(G(z)): 0.0596 Epoch: [2/20], Batch Num: [476/600] Discriminator Loss: 0.2352, Generator Loss: 4.2811 D(x): 0.9089, D(G(z)): 0.0770 Epoch: [2/20], Batch Num: [477/600] Discriminator Loss: 0.2224, Generator Loss: 4.1087 D(x): 0.9305, D(G(z)): 0.0947 Epoch: [2/20], Batch Num: [478/600] Discriminator Loss: 0.1813, Generator Loss: 4.1088 D(x): 0.9426, D(G(z)): 0.0891 Epoch: [2/20], Batch Num: [479/600] Discriminator Loss: 0.2625, Generator Loss: 4.2616 D(x): 0.9419, D(G(z)): 0.1425 Epoch: [2/20], Batch Num: [480/600] Discriminator Loss: 0.2183, Generator Loss: 4.7844 D(x): 0.9368, D(G(z)): 0.1064 Epoch: [2/20], Batch Num: [481/600] Discriminator Loss: 0.3251, Generator Loss: 4.5340 D(x): 0.8478, D(G(z)): 0.0625 Epoch: [2/20], Batch Num: [482/600] Discriminator Loss: 0.4811, Generator Loss: 3.4476 D(x): 0.7935, D(G(z)): 0.0906 Epoch: [2/20], Batch Num: [483/600] Discriminator Loss: 0.3885, Generator Loss: 2.8447 D(x): 0.8540, D(G(z)): 0.1447 Epoch: [2/20], Batch Num: [484/600] Discriminator Loss: 0.6408, Generator Loss: 2.9422 D(x): 0.8640, D(G(z)): 0.2802 Epoch: [2/20], Batch Num: [485/600] Discriminator Loss: 0.6961, Generator Loss: 2.9475 D(x): 0.8475, D(G(z)): 0.2908 Epoch: [2/20], Batch Num: [486/600] Discriminator Loss: 0.8693, Generator Loss: 3.0533 D(x): 0.7582, D(G(z)): 0.2369 Epoch: [2/20], Batch Num: [487/600] Discriminator Loss: 0.7742, Generator Loss: 2.5942 D(x): 0.7010, D(G(z)): 0.1769 Epoch: [2/20], Batch Num: [488/600] Discriminator Loss: 0.8951, Generator Loss: 1.7452 D(x): 0.7238, D(G(z)): 0.2378 Epoch: [2/20], Batch Num: [489/600] Discriminator Loss: 1.2141, Generator Loss: 1.7526 D(x): 0.8274, D(G(z)): 0.5011 Epoch: [2/20], Batch Num: [490/600] Discriminator Loss: 1.1615, Generator Loss: 2.1738 D(x): 0.7189, D(G(z)): 0.3887 Epoch: [2/20], Batch Num: [491/600] Discriminator Loss: 1.5318, Generator Loss: 2.1772 D(x): 0.6127, D(G(z)): 0.3428 Epoch: [2/20], Batch Num: [492/600] Discriminator Loss: 1.6492, Generator Loss: 1.5787 D(x): 0.5412, D(G(z)): 0.3562 Epoch: [2/20], Batch Num: [493/600] Discriminator Loss: 1.5099, Generator Loss: 1.1380 D(x): 0.6396, D(G(z)): 0.4524 Epoch: [2/20], Batch Num: [494/600] Discriminator Loss: 1.7517, Generator Loss: 1.0239 D(x): 0.7249, D(G(z)): 0.5484 Epoch: [2/20], Batch Num: [495/600] Discriminator Loss: 1.5005, Generator Loss: 1.7590 D(x): 0.7485, D(G(z)): 0.5121 Epoch: [2/20], Batch Num: [496/600] Discriminator Loss: 1.5942, Generator Loss: 2.0729 D(x): 0.5862, D(G(z)): 0.4353 Epoch: [2/20], Batch Num: [497/600] Discriminator Loss: 1.6100, Generator Loss: 1.8254 D(x): 0.5774, D(G(z)): 0.3483 Epoch: [2/20], Batch Num: [498/600] Discriminator Loss: 1.5286, Generator Loss: 1.3242 D(x): 0.5734, D(G(z)): 0.3923 Epoch: [2/20], Batch Num: [499/600] Discriminator Loss: 1.5298, Generator Loss: 1.1042 D(x): 0.7326, D(G(z)): 0.5223 Epoch: 2, Batch Num: [500/600]
Epoch: [2/20], Batch Num: [500/600] Discriminator Loss: 1.5758, Generator Loss: 1.1615 D(x): 0.7716, D(G(z)): 0.5983 Epoch: [2/20], Batch Num: [501/600] Discriminator Loss: 1.2731, Generator Loss: 1.5648 D(x): 0.7611, D(G(z)): 0.5029 Epoch: [2/20], Batch Num: [502/600] Discriminator Loss: 1.3638, Generator Loss: 1.4750 D(x): 0.5938, D(G(z)): 0.3862 Epoch: [2/20], Batch Num: [503/600] Discriminator Loss: 1.2590, Generator Loss: 1.4242 D(x): 0.5987, D(G(z)): 0.3291 Epoch: [2/20], Batch Num: [504/600] Discriminator Loss: 1.3726, Generator Loss: 0.9464 D(x): 0.6124, D(G(z)): 0.4109 Epoch: [2/20], Batch Num: [505/600] Discriminator Loss: 1.3263, Generator Loss: 0.8463 D(x): 0.7730, D(G(z)): 0.4999 Epoch: [2/20], Batch Num: [506/600] Discriminator Loss: 1.2707, Generator Loss: 0.9819 D(x): 0.8287, D(G(z)): 0.5270 Epoch: [2/20], Batch Num: [507/600] Discriminator Loss: 1.1285, Generator Loss: 1.3307 D(x): 0.7647, D(G(z)): 0.4519 Epoch: [2/20], Batch Num: [508/600] Discriminator Loss: 1.2323, Generator Loss: 1.6300 D(x): 0.6909, D(G(z)): 0.4315 Epoch: [2/20], Batch Num: [509/600] Discriminator Loss: 1.1411, Generator Loss: 1.6418 D(x): 0.6490, D(G(z)): 0.3702 Epoch: [2/20], Batch Num: [510/600] Discriminator Loss: 1.4284, Generator Loss: 1.3500 D(x): 0.5831, D(G(z)): 0.3839 Epoch: [2/20], Batch Num: [511/600] Discriminator Loss: 1.2982, Generator Loss: 1.1551 D(x): 0.5910, D(G(z)): 0.3885 Epoch: [2/20], Batch Num: [512/600] Discriminator Loss: 1.1586, Generator Loss: 0.8841 D(x): 0.6935, D(G(z)): 0.4085 Epoch: [2/20], Batch Num: [513/600] Discriminator Loss: 1.3448, Generator Loss: 0.7277 D(x): 0.7540, D(G(z)): 0.5516 Epoch: [2/20], Batch Num: [514/600] Discriminator Loss: 1.1562, Generator Loss: 0.8884 D(x): 0.7760, D(G(z)): 0.5037 Epoch: [2/20], Batch Num: [515/600] Discriminator Loss: 1.3345, Generator Loss: 1.2100 D(x): 0.7116, D(G(z)): 0.5251 Epoch: [2/20], Batch Num: [516/600] Discriminator Loss: 1.2276, Generator Loss: 1.1549 D(x): 0.6503, D(G(z)): 0.4314 Epoch: [2/20], Batch Num: [517/600] Discriminator Loss: 1.3051, Generator Loss: 1.2932 D(x): 0.5732, D(G(z)): 0.3802 Epoch: [2/20], Batch Num: [518/600] Discriminator Loss: 1.1435, Generator Loss: 1.1412 D(x): 0.6687, D(G(z)): 0.4187 Epoch: [2/20], Batch Num: [519/600] Discriminator Loss: 1.2668, Generator Loss: 1.0269 D(x): 0.6040, D(G(z)): 0.4299 Epoch: [2/20], Batch Num: [520/600] Discriminator Loss: 1.4175, Generator Loss: 0.8408 D(x): 0.6501, D(G(z)): 0.4994 Epoch: [2/20], Batch Num: [521/600] Discriminator Loss: 1.3148, Generator Loss: 0.8340 D(x): 0.6828, D(G(z)): 0.5155 Epoch: [2/20], Batch Num: [522/600] Discriminator Loss: 1.2081, Generator Loss: 0.7988 D(x): 0.6972, D(G(z)): 0.5006 Epoch: [2/20], Batch Num: [523/600] Discriminator Loss: 1.2871, Generator Loss: 0.7856 D(x): 0.6774, D(G(z)): 0.5254 Epoch: [2/20], Batch Num: [524/600] Discriminator Loss: 1.2701, Generator Loss: 0.8995 D(x): 0.6878, D(G(z)): 0.5226 Epoch: [2/20], Batch Num: [525/600] Discriminator Loss: 1.1694, Generator Loss: 0.9912 D(x): 0.6542, D(G(z)): 0.4500 Epoch: [2/20], Batch Num: [526/600] Discriminator Loss: 1.1287, Generator Loss: 1.1060 D(x): 0.6316, D(G(z)): 0.4155 Epoch: [2/20], Batch Num: [527/600] Discriminator Loss: 1.1817, Generator Loss: 0.9921 D(x): 0.5923, D(G(z)): 0.4113 Epoch: [2/20], Batch Num: [528/600] Discriminator Loss: 1.1478, Generator Loss: 0.9514 D(x): 0.5967, D(G(z)): 0.3778 Epoch: [2/20], Batch Num: [529/600] Discriminator Loss: 1.0410, Generator Loss: 0.9128 D(x): 0.6696, D(G(z)): 0.4209 Epoch: [2/20], Batch Num: [530/600] Discriminator Loss: 1.0378, Generator Loss: 1.0226 D(x): 0.6798, D(G(z)): 0.4333 Epoch: [2/20], Batch Num: [531/600] Discriminator Loss: 0.9603, Generator Loss: 0.9646 D(x): 0.7328, D(G(z)): 0.4431 Epoch: [2/20], Batch Num: [532/600] Discriminator Loss: 0.9393, Generator Loss: 1.0906 D(x): 0.7129, D(G(z)): 0.4032 Epoch: [2/20], Batch Num: [533/600] Discriminator Loss: 0.8675, Generator Loss: 1.2049 D(x): 0.7332, D(G(z)): 0.3900 Epoch: [2/20], Batch Num: [534/600] Discriminator Loss: 0.9574, Generator Loss: 1.2526 D(x): 0.6587, D(G(z)): 0.3659 Epoch: [2/20], Batch Num: [535/600] Discriminator Loss: 0.7624, Generator Loss: 1.3450 D(x): 0.7210, D(G(z)): 0.3201 Epoch: [2/20], Batch Num: [536/600] Discriminator Loss: 0.6752, Generator Loss: 1.4280 D(x): 0.7433, D(G(z)): 0.2831 Epoch: [2/20], Batch Num: [537/600] Discriminator Loss: 0.7110, Generator Loss: 1.3652 D(x): 0.7407, D(G(z)): 0.3042 Epoch: [2/20], Batch Num: [538/600] Discriminator Loss: 0.6394, Generator Loss: 1.4992 D(x): 0.7691, D(G(z)): 0.2799 Epoch: [2/20], Batch Num: [539/600] Discriminator Loss: 0.5823, Generator Loss: 1.4923 D(x): 0.7851, D(G(z)): 0.2689 Epoch: [2/20], Batch Num: [540/600] Discriminator Loss: 0.5353, Generator Loss: 1.6014 D(x): 0.8239, D(G(z)): 0.2711 Epoch: [2/20], Batch Num: [541/600] Discriminator Loss: 0.4842, Generator Loss: 1.7752 D(x): 0.8269, D(G(z)): 0.2383 Epoch: [2/20], Batch Num: [542/600] Discriminator Loss: 0.4667, Generator Loss: 1.8015 D(x): 0.8224, D(G(z)): 0.2201 Epoch: [2/20], Batch Num: [543/600] Discriminator Loss: 0.4335, Generator Loss: 1.9926 D(x): 0.8204, D(G(z)): 0.1949 Epoch: [2/20], Batch Num: [544/600] Discriminator Loss: 0.4282, Generator Loss: 2.1122 D(x): 0.8245, D(G(z)): 0.1862 Epoch: [2/20], Batch Num: [545/600] Discriminator Loss: 0.4058, Generator Loss: 2.0505 D(x): 0.8186, D(G(z)): 0.1635 Epoch: [2/20], Batch Num: [546/600] Discriminator Loss: 0.3141, Generator Loss: 2.1264 D(x): 0.8787, D(G(z)): 0.1564 Epoch: [2/20], Batch Num: [547/600] Discriminator Loss: 0.3774, Generator Loss: 2.0828 D(x): 0.8466, D(G(z)): 0.1704 Epoch: [2/20], Batch Num: [548/600] Discriminator Loss: 0.3512, Generator Loss: 2.0957 D(x): 0.8606, D(G(z)): 0.1618 Epoch: [2/20], Batch Num: [549/600] Discriminator Loss: 0.3385, Generator Loss: 2.1022 D(x): 0.8822, D(G(z)): 0.1756 Epoch: [2/20], Batch Num: [550/600] Discriminator Loss: 0.3434, Generator Loss: 1.9226 D(x): 0.8765, D(G(z)): 0.1703 Epoch: [2/20], Batch Num: [551/600] Discriminator Loss: 0.3040, Generator Loss: 1.9776 D(x): 0.9088, D(G(z)): 0.1756 Epoch: [2/20], Batch Num: [552/600] Discriminator Loss: 0.3710, Generator Loss: 1.8852 D(x): 0.8878, D(G(z)): 0.2008 Epoch: [2/20], Batch Num: [553/600] Discriminator Loss: 0.3733, Generator Loss: 1.9512 D(x): 0.8623, D(G(z)): 0.1778 Epoch: [2/20], Batch Num: [554/600] Discriminator Loss: 0.3979, Generator Loss: 1.8673 D(x): 0.8744, D(G(z)): 0.1997 Epoch: [2/20], Batch Num: [555/600] Discriminator Loss: 0.4579, Generator Loss: 1.8822 D(x): 0.8753, D(G(z)): 0.2477 Epoch: [2/20], Batch Num: [556/600] Discriminator Loss: 0.5175, Generator Loss: 1.8943 D(x): 0.8337, D(G(z)): 0.2429 Epoch: [2/20], Batch Num: [557/600] Discriminator Loss: 0.5744, Generator Loss: 1.7107 D(x): 0.8236, D(G(z)): 0.2531 Epoch: [2/20], Batch Num: [558/600] Discriminator Loss: 0.5649, Generator Loss: 1.6894 D(x): 0.8507, D(G(z)): 0.2871 Epoch: [2/20], Batch Num: [559/600] Discriminator Loss: 0.6684, Generator Loss: 1.6256 D(x): 0.7802, D(G(z)): 0.2686 Epoch: [2/20], Batch Num: [560/600] Discriminator Loss: 0.8516, Generator Loss: 1.5338 D(x): 0.7434, D(G(z)): 0.3195 Epoch: [2/20], Batch Num: [561/600] Discriminator Loss: 0.9630, Generator Loss: 1.2576 D(x): 0.7553, D(G(z)): 0.3900 Epoch: [2/20], Batch Num: [562/600] Discriminator Loss: 1.2145, Generator Loss: 1.2807 D(x): 0.6971, D(G(z)): 0.4388 Epoch: [2/20], Batch Num: [563/600] Discriminator Loss: 1.4317, Generator Loss: 1.0001 D(x): 0.6548, D(G(z)): 0.4651 Epoch: [2/20], Batch Num: [564/600] Discriminator Loss: 1.6550, Generator Loss: 0.9721 D(x): 0.5644, D(G(z)): 0.4877 Epoch: [2/20], Batch Num: [565/600] Discriminator Loss: 1.9686, Generator Loss: 0.8175 D(x): 0.5933, D(G(z)): 0.6124 Epoch: [2/20], Batch Num: [566/600] Discriminator Loss: 2.1011, Generator Loss: 0.7103 D(x): 0.4694, D(G(z)): 0.5572 Epoch: [2/20], Batch Num: [567/600] Discriminator Loss: 2.5155, Generator Loss: 0.4803 D(x): 0.5147, D(G(z)): 0.6753 Epoch: [2/20], Batch Num: [568/600] Discriminator Loss: 2.7921, Generator Loss: 0.4487 D(x): 0.4891, D(G(z)): 0.7361 Epoch: [2/20], Batch Num: [569/600] Discriminator Loss: 3.0945, Generator Loss: 0.3742 D(x): 0.4281, D(G(z)): 0.7599 Epoch: [2/20], Batch Num: [570/600] Discriminator Loss: 3.5556, Generator Loss: 0.3049 D(x): 0.3321, D(G(z)): 0.7772 Epoch: [2/20], Batch Num: [571/600] Discriminator Loss: 3.5802, Generator Loss: 0.2839 D(x): 0.3454, D(G(z)): 0.8060 Epoch: [2/20], Batch Num: [572/600] Discriminator Loss: 3.4813, Generator Loss: 0.2574 D(x): 0.3812, D(G(z)): 0.8306 Epoch: [2/20], Batch Num: [573/600] Discriminator Loss: 3.5452, Generator Loss: 0.2225 D(x): 0.3975, D(G(z)): 0.8587 Epoch: [2/20], Batch Num: [574/600] Discriminator Loss: 3.4656, Generator Loss: 0.2086 D(x): 0.4238, D(G(z)): 0.8533 Epoch: [2/20], Batch Num: [575/600] Discriminator Loss: 3.0563, Generator Loss: 0.2396 D(x): 0.4917, D(G(z)): 0.8542 Epoch: [2/20], Batch Num: [576/600] Discriminator Loss: 3.1137, Generator Loss: 0.2615 D(x): 0.4521, D(G(z)): 0.8479 Epoch: [2/20], Batch Num: [577/600] Discriminator Loss: 2.8796, Generator Loss: 0.2981 D(x): 0.4354, D(G(z)): 0.8213 Epoch: [2/20], Batch Num: [578/600] Discriminator Loss: 2.8211, Generator Loss: 0.3173 D(x): 0.4020, D(G(z)): 0.7966 Epoch: [2/20], Batch Num: [579/600] Discriminator Loss: 2.5940, Generator Loss: 0.3585 D(x): 0.3856, D(G(z)): 0.7399 Epoch: [2/20], Batch Num: [580/600] Discriminator Loss: 2.6864, Generator Loss: 0.3934 D(x): 0.3464, D(G(z)): 0.7445 Epoch: [2/20], Batch Num: [581/600] Discriminator Loss: 2.4583, Generator Loss: 0.4341 D(x): 0.3892, D(G(z)): 0.7298 Epoch: [2/20], Batch Num: [582/600] Discriminator Loss: 2.2958, Generator Loss: 0.4437 D(x): 0.3899, D(G(z)): 0.6889 Epoch: [2/20], Batch Num: [583/600] Discriminator Loss: 2.1089, Generator Loss: 0.4767 D(x): 0.3994, D(G(z)): 0.6564 Epoch: [2/20], Batch Num: [584/600] Discriminator Loss: 2.0286, Generator Loss: 0.5322 D(x): 0.3901, D(G(z)): 0.6260 Epoch: [2/20], Batch Num: [585/600] Discriminator Loss: 1.9939, Generator Loss: 0.5164 D(x): 0.4064, D(G(z)): 0.6308 Epoch: [2/20], Batch Num: [586/600] Discriminator Loss: 1.9026, Generator Loss: 0.5162 D(x): 0.4256, D(G(z)): 0.6223 Epoch: [2/20], Batch Num: [587/600] Discriminator Loss: 1.7600, Generator Loss: 0.5475 D(x): 0.4582, D(G(z)): 0.6011 Epoch: [2/20], Batch Num: [588/600] Discriminator Loss: 1.6767, Generator Loss: 0.5430 D(x): 0.4836, D(G(z)): 0.5925 Epoch: [2/20], Batch Num: [589/600] Discriminator Loss: 1.6703, Generator Loss: 0.5486 D(x): 0.4987, D(G(z)): 0.6037 Epoch: [2/20], Batch Num: [590/600] Discriminator Loss: 1.5334, Generator Loss: 0.5456 D(x): 0.5354, D(G(z)): 0.5832 Epoch: [2/20], Batch Num: [591/600] Discriminator Loss: 1.4836, Generator Loss: 0.5851 D(x): 0.5413, D(G(z)): 0.5664 Epoch: [2/20], Batch Num: [592/600] Discriminator Loss: 1.4486, Generator Loss: 0.6049 D(x): 0.5528, D(G(z)): 0.5622 Epoch: [2/20], Batch Num: [593/600] Discriminator Loss: 1.4284, Generator Loss: 0.5902 D(x): 0.5608, D(G(z)): 0.5625 Epoch: [2/20], Batch Num: [594/600] Discriminator Loss: 1.3661, Generator Loss: 0.6446 D(x): 0.5713, D(G(z)): 0.5417 Epoch: [2/20], Batch Num: [595/600] Discriminator Loss: 1.3383, Generator Loss: 0.6720 D(x): 0.5669, D(G(z)): 0.5280 Epoch: [2/20], Batch Num: [596/600] Discriminator Loss: 1.3352, Generator Loss: 0.6967 D(x): 0.5761, D(G(z)): 0.5342 Epoch: [2/20], Batch Num: [597/600] Discriminator Loss: 1.2331, Generator Loss: 0.7549 D(x): 0.5939, D(G(z)): 0.5009 Epoch: [2/20], Batch Num: [598/600] Discriminator Loss: 1.1910, Generator Loss: 0.7900 D(x): 0.5766, D(G(z)): 0.4650 Epoch: [2/20], Batch Num: [599/600] Discriminator Loss: 1.1565, Generator Loss: 0.8331 D(x): 0.5982, D(G(z)): 0.4651 Epoch: 3, Batch Num: [0/600]
Epoch: [3/20], Batch Num: [0/600] Discriminator Loss: 1.1294, Generator Loss: 0.8685 D(x): 0.5903, D(G(z)): 0.4428 Epoch: [3/20], Batch Num: [1/600] Discriminator Loss: 1.0655, Generator Loss: 0.9874 D(x): 0.5989, D(G(z)): 0.4139 Epoch: [3/20], Batch Num: [2/600] Discriminator Loss: 1.0341, Generator Loss: 1.0876 D(x): 0.5986, D(G(z)): 0.3947 Epoch: [3/20], Batch Num: [3/600] Discriminator Loss: 0.9795, Generator Loss: 1.1210 D(x): 0.6077, D(G(z)): 0.3696 Epoch: [3/20], Batch Num: [4/600] Discriminator Loss: 0.9447, Generator Loss: 1.2584 D(x): 0.5993, D(G(z)): 0.3381 Epoch: [3/20], Batch Num: [5/600] Discriminator Loss: 0.8746, Generator Loss: 1.3311 D(x): 0.6238, D(G(z)): 0.3177 Epoch: [3/20], Batch Num: [6/600] Discriminator Loss: 0.8733, Generator Loss: 1.5905 D(x): 0.6060, D(G(z)): 0.2951 Epoch: [3/20], Batch Num: [7/600] Discriminator Loss: 0.7741, Generator Loss: 1.6693 D(x): 0.6261, D(G(z)): 0.2502 Epoch: [3/20], Batch Num: [8/600] Discriminator Loss: 0.7679, Generator Loss: 1.7163 D(x): 0.6245, D(G(z)): 0.2452 Epoch: [3/20], Batch Num: [9/600] Discriminator Loss: 0.7573, Generator Loss: 1.9245 D(x): 0.6122, D(G(z)): 0.2188 Epoch: [3/20], Batch Num: [10/600] Discriminator Loss: 0.7318, Generator Loss: 2.1972 D(x): 0.6135, D(G(z)): 0.2002 Epoch: [3/20], Batch Num: [11/600] Discriminator Loss: 0.7211, Generator Loss: 2.2038 D(x): 0.6085, D(G(z)): 0.1826 Epoch: [3/20], Batch Num: [12/600] Discriminator Loss: 0.7143, Generator Loss: 2.2826 D(x): 0.6167, D(G(z)): 0.1856 Epoch: [3/20], Batch Num: [13/600] Discriminator Loss: 0.7123, Generator Loss: 2.0849 D(x): 0.6128, D(G(z)): 0.1769 Epoch: [3/20], Batch Num: [14/600] Discriminator Loss: 0.7233, Generator Loss: 2.2976 D(x): 0.6051, D(G(z)): 0.1798 Epoch: [3/20], Batch Num: [15/600] Discriminator Loss: 0.7210, Generator Loss: 2.0877 D(x): 0.6115, D(G(z)): 0.1835 Epoch: [3/20], Batch Num: [16/600] Discriminator Loss: 0.7351, Generator Loss: 1.9035 D(x): 0.6098, D(G(z)): 0.1860 Epoch: [3/20], Batch Num: [17/600] Discriminator Loss: 0.7373, Generator Loss: 1.9597 D(x): 0.6252, D(G(z)): 0.2133 Epoch: [3/20], Batch Num: [18/600] Discriminator Loss: 0.7999, Generator Loss: 1.7046 D(x): 0.6042, D(G(z)): 0.2089 Epoch: [3/20], Batch Num: [19/600] Discriminator Loss: 0.8095, Generator Loss: 1.5888 D(x): 0.6371, D(G(z)): 0.2665 Epoch: [3/20], Batch Num: [20/600] Discriminator Loss: 0.8073, Generator Loss: 1.3806 D(x): 0.6499, D(G(z)): 0.2830 Epoch: [3/20], Batch Num: [21/600] Discriminator Loss: 0.8546, Generator Loss: 1.3959 D(x): 0.6575, D(G(z)): 0.3157 Epoch: [3/20], Batch Num: [22/600] Discriminator Loss: 0.8235, Generator Loss: 1.3814 D(x): 0.6865, D(G(z)): 0.3380 Epoch: [3/20], Batch Num: [23/600] Discriminator Loss: 0.8587, Generator Loss: 1.3391 D(x): 0.6614, D(G(z)): 0.3297 Epoch: [3/20], Batch Num: [24/600] Discriminator Loss: 0.9220, Generator Loss: 1.4201 D(x): 0.6451, D(G(z)): 0.3353 Epoch: [3/20], Batch Num: [25/600] Discriminator Loss: 0.9819, Generator Loss: 1.4864 D(x): 0.5914, D(G(z)): 0.3005 Epoch: [3/20], Batch Num: [26/600] Discriminator Loss: 1.0155, Generator Loss: 1.3032 D(x): 0.6070, D(G(z)): 0.3526 Epoch: [3/20], Batch Num: [27/600] Discriminator Loss: 1.0625, Generator Loss: 1.1443 D(x): 0.6047, D(G(z)): 0.3517 Epoch: [3/20], Batch Num: [28/600] Discriminator Loss: 1.1367, Generator Loss: 1.0261 D(x): 0.6195, D(G(z)): 0.4201 Epoch: [3/20], Batch Num: [29/600] Discriminator Loss: 1.1332, Generator Loss: 0.8763 D(x): 0.6553, D(G(z)): 0.4645 Epoch: [3/20], Batch Num: [30/600] Discriminator Loss: 1.1607, Generator Loss: 0.8438 D(x): 0.6510, D(G(z)): 0.4714 Epoch: [3/20], Batch Num: [31/600] Discriminator Loss: 1.3018, Generator Loss: 0.7836 D(x): 0.6206, D(G(z)): 0.5010 Epoch: [3/20], Batch Num: [32/600] Discriminator Loss: 1.3334, Generator Loss: 0.7034 D(x): 0.5854, D(G(z)): 0.4927 Epoch: [3/20], Batch Num: [33/600] Discriminator Loss: 1.2988, Generator Loss: 0.7045 D(x): 0.6581, D(G(z)): 0.5280 Epoch: [3/20], Batch Num: [34/600] Discriminator Loss: 1.4733, Generator Loss: 0.6905 D(x): 0.6102, D(G(z)): 0.5658 Epoch: [3/20], Batch Num: [35/600] Discriminator Loss: 1.3748, Generator Loss: 0.7278 D(x): 0.6216, D(G(z)): 0.5383 Epoch: [3/20], Batch Num: [36/600] Discriminator Loss: 1.4991, Generator Loss: 0.6290 D(x): 0.5645, D(G(z)): 0.5300 Epoch: [3/20], Batch Num: [37/600] Discriminator Loss: 1.4640, Generator Loss: 0.6331 D(x): 0.5999, D(G(z)): 0.5522 Epoch: [3/20], Batch Num: [38/600] Discriminator Loss: 1.5384, Generator Loss: 0.6515 D(x): 0.6083, D(G(z)): 0.5917 Epoch: [3/20], Batch Num: [39/600] Discriminator Loss: 1.6137, Generator Loss: 0.6218 D(x): 0.5673, D(G(z)): 0.5853 Epoch: [3/20], Batch Num: [40/600] Discriminator Loss: 1.7357, Generator Loss: 0.5718 D(x): 0.5172, D(G(z)): 0.6032 Epoch: [3/20], Batch Num: [41/600] Discriminator Loss: 1.4950, Generator Loss: 0.5509 D(x): 0.6352, D(G(z)): 0.6050 Epoch: [3/20], Batch Num: [42/600] Discriminator Loss: 1.4781, Generator Loss: 0.5476 D(x): 0.6433, D(G(z)): 0.5951 Epoch: [3/20], Batch Num: [43/600] Discriminator Loss: 1.5675, Generator Loss: 0.6573 D(x): 0.5785, D(G(z)): 0.5922 Epoch: [3/20], Batch Num: [44/600] Discriminator Loss: 1.4507, Generator Loss: 0.6780 D(x): 0.6060, D(G(z)): 0.5639 Epoch: [3/20], Batch Num: [45/600] Discriminator Loss: 1.4635, Generator Loss: 0.6706 D(x): 0.5587, D(G(z)): 0.5335 Epoch: [3/20], Batch Num: [46/600] Discriminator Loss: 1.5090, Generator Loss: 0.6954 D(x): 0.5365, D(G(z)): 0.5299 Epoch: [3/20], Batch Num: [47/600] Discriminator Loss: 1.5303, Generator Loss: 0.6492 D(x): 0.5085, D(G(z)): 0.5224 Epoch: [3/20], Batch Num: [48/600] Discriminator Loss: 1.4783, Generator Loss: 0.6516 D(x): 0.5437, D(G(z)): 0.5275 Epoch: [3/20], Batch Num: [49/600] Discriminator Loss: 1.4255, Generator Loss: 0.6754 D(x): 0.5565, D(G(z)): 0.5192 Epoch: [3/20], Batch Num: [50/600] Discriminator Loss: 1.5535, Generator Loss: 0.5911 D(x): 0.5451, D(G(z)): 0.5681 Epoch: [3/20], Batch Num: [51/600] Discriminator Loss: 1.4256, Generator Loss: 0.6036 D(x): 0.6130, D(G(z)): 0.5783 Epoch: [3/20], Batch Num: [52/600] Discriminator Loss: 1.4176, Generator Loss: 0.6371 D(x): 0.6140, D(G(z)): 0.5647 Epoch: [3/20], Batch Num: [53/600] Discriminator Loss: 1.4965, Generator Loss: 0.7148 D(x): 0.5665, D(G(z)): 0.5618 Epoch: [3/20], Batch Num: [54/600] Discriminator Loss: 1.3829, Generator Loss: 0.6816 D(x): 0.5659, D(G(z)): 0.5090 Epoch: [3/20], Batch Num: [55/600] Discriminator Loss: 1.4490, Generator Loss: 0.7461 D(x): 0.5546, D(G(z)): 0.5298 Epoch: [3/20], Batch Num: [56/600] Discriminator Loss: 1.3772, Generator Loss: 0.8246 D(x): 0.5623, D(G(z)): 0.5112 Epoch: [3/20], Batch Num: [57/600] Discriminator Loss: 1.3960, Generator Loss: 0.7903 D(x): 0.5523, D(G(z)): 0.5018 Epoch: [3/20], Batch Num: [58/600] Discriminator Loss: 1.4295, Generator Loss: 0.7671 D(x): 0.5043, D(G(z)): 0.4860 Epoch: [3/20], Batch Num: [59/600] Discriminator Loss: 1.3779, Generator Loss: 0.7566 D(x): 0.5500, D(G(z)): 0.4897 Epoch: [3/20], Batch Num: [60/600] Discriminator Loss: 1.3717, Generator Loss: 0.7200 D(x): 0.5613, D(G(z)): 0.5107 Epoch: [3/20], Batch Num: [61/600] Discriminator Loss: 1.2657, Generator Loss: 0.7243 D(x): 0.5888, D(G(z)): 0.4848 Epoch: [3/20], Batch Num: [62/600] Discriminator Loss: 1.3277, Generator Loss: 0.6959 D(x): 0.5791, D(G(z)): 0.5069 Epoch: [3/20], Batch Num: [63/600] Discriminator Loss: 1.3297, Generator Loss: 0.7015 D(x): 0.5953, D(G(z)): 0.5171 Epoch: [3/20], Batch Num: [64/600] Discriminator Loss: 1.2859, Generator Loss: 0.7514 D(x): 0.6101, D(G(z)): 0.5125 Epoch: [3/20], Batch Num: [65/600] Discriminator Loss: 1.2384, Generator Loss: 0.7534 D(x): 0.6218, D(G(z)): 0.4962 Epoch: [3/20], Batch Num: [66/600] Discriminator Loss: 1.2077, Generator Loss: 0.8452 D(x): 0.6083, D(G(z)): 0.4699 Epoch: [3/20], Batch Num: [67/600] Discriminator Loss: 1.2693, Generator Loss: 0.8390 D(x): 0.5762, D(G(z)): 0.4766 Epoch: [3/20], Batch Num: [68/600] Discriminator Loss: 1.2364, Generator Loss: 0.8839 D(x): 0.5640, D(G(z)): 0.4422 Epoch: [3/20], Batch Num: [69/600] Discriminator Loss: 1.2357, Generator Loss: 0.8349 D(x): 0.5685, D(G(z)): 0.4455 Epoch: [3/20], Batch Num: [70/600] Discriminator Loss: 1.3003, Generator Loss: 0.9294 D(x): 0.5518, D(G(z)): 0.4480 Epoch: [3/20], Batch Num: [71/600] Discriminator Loss: 1.2776, Generator Loss: 0.9067 D(x): 0.5523, D(G(z)): 0.4449 Epoch: [3/20], Batch Num: [72/600] Discriminator Loss: 1.1396, Generator Loss: 0.8303 D(x): 0.6073, D(G(z)): 0.4381 Epoch: [3/20], Batch Num: [73/600] Discriminator Loss: 1.2809, Generator Loss: 0.8382 D(x): 0.5456, D(G(z)): 0.4539 Epoch: [3/20], Batch Num: [74/600] Discriminator Loss: 1.2018, Generator Loss: 0.8098 D(x): 0.5908, D(G(z)): 0.4489 Epoch: [3/20], Batch Num: [75/600] Discriminator Loss: 1.2481, Generator Loss: 0.8465 D(x): 0.6064, D(G(z)): 0.4870 Epoch: [3/20], Batch Num: [76/600] Discriminator Loss: 1.2208, Generator Loss: 0.8673 D(x): 0.6004, D(G(z)): 0.4727 Epoch: [3/20], Batch Num: [77/600] Discriminator Loss: 1.2449, Generator Loss: 0.7820 D(x): 0.5772, D(G(z)): 0.4626 Epoch: [3/20], Batch Num: [78/600] Discriminator Loss: 1.2136, Generator Loss: 0.8706 D(x): 0.5997, D(G(z)): 0.4646 Epoch: [3/20], Batch Num: [79/600] Discriminator Loss: 1.2029, Generator Loss: 0.8421 D(x): 0.5953, D(G(z)): 0.4557 Epoch: [3/20], Batch Num: [80/600] Discriminator Loss: 1.1941, Generator Loss: 0.8964 D(x): 0.5985, D(G(z)): 0.4598 Epoch: [3/20], Batch Num: [81/600] Discriminator Loss: 1.1610, Generator Loss: 0.8643 D(x): 0.5835, D(G(z)): 0.4284 Epoch: [3/20], Batch Num: [82/600] Discriminator Loss: 1.1106, Generator Loss: 0.8933 D(x): 0.6302, D(G(z)): 0.4420 Epoch: [3/20], Batch Num: [83/600] Discriminator Loss: 1.2641, Generator Loss: 0.8621 D(x): 0.5744, D(G(z)): 0.4602 Epoch: [3/20], Batch Num: [84/600] Discriminator Loss: 1.1757, Generator Loss: 0.8617 D(x): 0.6074, D(G(z)): 0.4539 Epoch: [3/20], Batch Num: [85/600] Discriminator Loss: 1.2258, Generator Loss: 0.8763 D(x): 0.6050, D(G(z)): 0.4701 Epoch: [3/20], Batch Num: [86/600] Discriminator Loss: 1.1471, Generator Loss: 0.8635 D(x): 0.6280, D(G(z)): 0.4605 Epoch: [3/20], Batch Num: [87/600] Discriminator Loss: 1.1479, Generator Loss: 0.8778 D(x): 0.5856, D(G(z)): 0.4184 Epoch: [3/20], Batch Num: [88/600] Discriminator Loss: 1.1451, Generator Loss: 0.8561 D(x): 0.6000, D(G(z)): 0.4308 Epoch: [3/20], Batch Num: [89/600] Discriminator Loss: 1.0911, Generator Loss: 0.8506 D(x): 0.6303, D(G(z)): 0.4325 Epoch: [3/20], Batch Num: [90/600] Discriminator Loss: 1.1330, Generator Loss: 0.8877 D(x): 0.6170, D(G(z)): 0.4421 Epoch: [3/20], Batch Num: [91/600] Discriminator Loss: 1.1972, Generator Loss: 0.8686 D(x): 0.5823, D(G(z)): 0.4385 Epoch: [3/20], Batch Num: [92/600] Discriminator Loss: 1.2307, Generator Loss: 0.8737 D(x): 0.6080, D(G(z)): 0.4747 Epoch: [3/20], Batch Num: [93/600] Discriminator Loss: 1.1034, Generator Loss: 0.8701 D(x): 0.6458, D(G(z)): 0.4554 Epoch: [3/20], Batch Num: [94/600] Discriminator Loss: 1.1251, Generator Loss: 0.8543 D(x): 0.6275, D(G(z)): 0.4450 Epoch: [3/20], Batch Num: [95/600] Discriminator Loss: 1.1491, Generator Loss: 0.8574 D(x): 0.6166, D(G(z)): 0.4441 Epoch: [3/20], Batch Num: [96/600] Discriminator Loss: 1.2001, Generator Loss: 0.8123 D(x): 0.5947, D(G(z)): 0.4546 Epoch: [3/20], Batch Num: [97/600] Discriminator Loss: 1.0962, Generator Loss: 0.8568 D(x): 0.6307, D(G(z)): 0.4358 Epoch: [3/20], Batch Num: [98/600] Discriminator Loss: 1.1567, Generator Loss: 0.8781 D(x): 0.6090, D(G(z)): 0.4429 Epoch: [3/20], Batch Num: [99/600] Discriminator Loss: 1.1859, Generator Loss: 0.8543 D(x): 0.5899, D(G(z)): 0.4404 Epoch: 3, Batch Num: [100/600]
Epoch: [3/20], Batch Num: [100/600] Discriminator Loss: 1.1400, Generator Loss: 0.8804 D(x): 0.6210, D(G(z)): 0.4492 Epoch: [3/20], Batch Num: [101/600] Discriminator Loss: 1.2250, Generator Loss: 0.8623 D(x): 0.5832, D(G(z)): 0.4471 Epoch: [3/20], Batch Num: [102/600] Discriminator Loss: 1.1010, Generator Loss: 0.8727 D(x): 0.6369, D(G(z)): 0.4403 Epoch: [3/20], Batch Num: [103/600] Discriminator Loss: 1.1231, Generator Loss: 0.9387 D(x): 0.6284, D(G(z)): 0.4432 Epoch: [3/20], Batch Num: [104/600] Discriminator Loss: 1.1692, Generator Loss: 0.8798 D(x): 0.6039, D(G(z)): 0.4401 Epoch: [3/20], Batch Num: [105/600] Discriminator Loss: 1.1362, Generator Loss: 0.9332 D(x): 0.6048, D(G(z)): 0.4306 Epoch: [3/20], Batch Num: [106/600] Discriminator Loss: 1.1469, Generator Loss: 0.8596 D(x): 0.6200, D(G(z)): 0.4489 Epoch: [3/20], Batch Num: [107/600] Discriminator Loss: 1.1521, Generator Loss: 0.9016 D(x): 0.6201, D(G(z)): 0.4440 Epoch: [3/20], Batch Num: [108/600] Discriminator Loss: 1.1872, Generator Loss: 0.9055 D(x): 0.6093, D(G(z)): 0.4345 Epoch: [3/20], Batch Num: [109/600] Discriminator Loss: 1.1610, Generator Loss: 0.8832 D(x): 0.6015, D(G(z)): 0.4379 Epoch: [3/20], Batch Num: [110/600] Discriminator Loss: 1.2614, Generator Loss: 0.8220 D(x): 0.5784, D(G(z)): 0.4447 Epoch: [3/20], Batch Num: [111/600] Discriminator Loss: 1.1482, Generator Loss: 0.8607 D(x): 0.6229, D(G(z)): 0.4523 Epoch: [3/20], Batch Num: [112/600] Discriminator Loss: 1.1283, Generator Loss: 0.8425 D(x): 0.6381, D(G(z)): 0.4470 Epoch: [3/20], Batch Num: [113/600] Discriminator Loss: 1.1604, Generator Loss: 0.8257 D(x): 0.6311, D(G(z)): 0.4604 Epoch: [3/20], Batch Num: [114/600] Discriminator Loss: 1.1912, Generator Loss: 0.8414 D(x): 0.6309, D(G(z)): 0.4591 Epoch: [3/20], Batch Num: [115/600] Discriminator Loss: 1.1527, Generator Loss: 0.8833 D(x): 0.6341, D(G(z)): 0.4454 Epoch: [3/20], Batch Num: [116/600] Discriminator Loss: 1.1538, Generator Loss: 0.8934 D(x): 0.6235, D(G(z)): 0.4320 Epoch: [3/20], Batch Num: [117/600] Discriminator Loss: 1.1625, Generator Loss: 0.9933 D(x): 0.6226, D(G(z)): 0.4379 Epoch: [3/20], Batch Num: [118/600] Discriminator Loss: 1.1051, Generator Loss: 0.9949 D(x): 0.6343, D(G(z)): 0.4287 Epoch: [3/20], Batch Num: [119/600] Discriminator Loss: 1.2444, Generator Loss: 0.9366 D(x): 0.5485, D(G(z)): 0.4173 Epoch: [3/20], Batch Num: [120/600] Discriminator Loss: 1.0922, Generator Loss: 0.9371 D(x): 0.6290, D(G(z)): 0.4223 Epoch: [3/20], Batch Num: [121/600] Discriminator Loss: 1.1184, Generator Loss: 0.8844 D(x): 0.6500, D(G(z)): 0.4431 Epoch: [3/20], Batch Num: [122/600] Discriminator Loss: 1.1245, Generator Loss: 0.8719 D(x): 0.6393, D(G(z)): 0.4449 Epoch: [3/20], Batch Num: [123/600] Discriminator Loss: 1.1147, Generator Loss: 0.8602 D(x): 0.6543, D(G(z)): 0.4436 Epoch: [3/20], Batch Num: [124/600] Discriminator Loss: 1.1407, Generator Loss: 0.9234 D(x): 0.6173, D(G(z)): 0.4402 Epoch: [3/20], Batch Num: [125/600] Discriminator Loss: 1.1542, Generator Loss: 0.9132 D(x): 0.6267, D(G(z)): 0.4434 Epoch: [3/20], Batch Num: [126/600] Discriminator Loss: 1.1632, Generator Loss: 0.8972 D(x): 0.6188, D(G(z)): 0.4440 Epoch: [3/20], Batch Num: [127/600] Discriminator Loss: 1.1526, Generator Loss: 0.9843 D(x): 0.6099, D(G(z)): 0.4208 Epoch: [3/20], Batch Num: [128/600] Discriminator Loss: 1.0087, Generator Loss: 0.9761 D(x): 0.6437, D(G(z)): 0.3808 Epoch: [3/20], Batch Num: [129/600] Discriminator Loss: 1.1140, Generator Loss: 0.9242 D(x): 0.6022, D(G(z)): 0.3985 Epoch: [3/20], Batch Num: [130/600] Discriminator Loss: 1.0476, Generator Loss: 0.9422 D(x): 0.6767, D(G(z)): 0.4370 Epoch: [3/20], Batch Num: [131/600] Discriminator Loss: 1.1043, Generator Loss: 0.9190 D(x): 0.6303, D(G(z)): 0.4115 Epoch: [3/20], Batch Num: [132/600] Discriminator Loss: 1.0011, Generator Loss: 0.9810 D(x): 0.6690, D(G(z)): 0.4039 Epoch: [3/20], Batch Num: [133/600] Discriminator Loss: 0.9269, Generator Loss: 1.0948 D(x): 0.6937, D(G(z)): 0.3834 Epoch: [3/20], Batch Num: [134/600] Discriminator Loss: 0.9403, Generator Loss: 1.1935 D(x): 0.6703, D(G(z)): 0.3722 Epoch: [3/20], Batch Num: [135/600] Discriminator Loss: 0.8407, Generator Loss: 1.1715 D(x): 0.6920, D(G(z)): 0.3266 Epoch: [3/20], Batch Num: [136/600] Discriminator Loss: 0.8719, Generator Loss: 1.1629 D(x): 0.6728, D(G(z)): 0.3310 Epoch: [3/20], Batch Num: [137/600] Discriminator Loss: 0.8764, Generator Loss: 1.2090 D(x): 0.6896, D(G(z)): 0.3531 Epoch: [3/20], Batch Num: [138/600] Discriminator Loss: 0.7609, Generator Loss: 1.2965 D(x): 0.7457, D(G(z)): 0.3386 Epoch: [3/20], Batch Num: [139/600] Discriminator Loss: 0.7198, Generator Loss: 1.3987 D(x): 0.7590, D(G(z)): 0.3299 Epoch: [3/20], Batch Num: [140/600] Discriminator Loss: 0.7042, Generator Loss: 1.5380 D(x): 0.7467, D(G(z)): 0.2990 Epoch: [3/20], Batch Num: [141/600] Discriminator Loss: 0.6820, Generator Loss: 1.5456 D(x): 0.7409, D(G(z)): 0.2855 Epoch: [3/20], Batch Num: [142/600] Discriminator Loss: 0.6356, Generator Loss: 1.6326 D(x): 0.7478, D(G(z)): 0.2408 Epoch: [3/20], Batch Num: [143/600] Discriminator Loss: 0.6063, Generator Loss: 1.7723 D(x): 0.7582, D(G(z)): 0.2455 Epoch: [3/20], Batch Num: [144/600] Discriminator Loss: 0.5137, Generator Loss: 1.8356 D(x): 0.7941, D(G(z)): 0.2096 Epoch: [3/20], Batch Num: [145/600] Discriminator Loss: 0.4495, Generator Loss: 1.8180 D(x): 0.8307, D(G(z)): 0.1994 Epoch: [3/20], Batch Num: [146/600] Discriminator Loss: 0.4782, Generator Loss: 1.9803 D(x): 0.8173, D(G(z)): 0.2096 Epoch: [3/20], Batch Num: [147/600] Discriminator Loss: 0.4910, Generator Loss: 1.9528 D(x): 0.8076, D(G(z)): 0.2024 Epoch: [3/20], Batch Num: [148/600] Discriminator Loss: 0.4894, Generator Loss: 2.2011 D(x): 0.8059, D(G(z)): 0.2037 Epoch: [3/20], Batch Num: [149/600] Discriminator Loss: 0.4574, Generator Loss: 2.3104 D(x): 0.8178, D(G(z)): 0.1991 Epoch: [3/20], Batch Num: [150/600] Discriminator Loss: 0.4861, Generator Loss: 2.2539 D(x): 0.7913, D(G(z)): 0.1741 Epoch: [3/20], Batch Num: [151/600] Discriminator Loss: 0.4994, Generator Loss: 2.2195 D(x): 0.8268, D(G(z)): 0.2250 Epoch: [3/20], Batch Num: [152/600] Discriminator Loss: 0.5530, Generator Loss: 2.1220 D(x): 0.7609, D(G(z)): 0.1888 Epoch: [3/20], Batch Num: [153/600] Discriminator Loss: 0.5011, Generator Loss: 1.8511 D(x): 0.8173, D(G(z)): 0.2183 Epoch: [3/20], Batch Num: [154/600] Discriminator Loss: 0.7135, Generator Loss: 1.7699 D(x): 0.7824, D(G(z)): 0.2768 Epoch: [3/20], Batch Num: [155/600] Discriminator Loss: 0.6626, Generator Loss: 1.8227 D(x): 0.7842, D(G(z)): 0.2656 Epoch: [3/20], Batch Num: [156/600] Discriminator Loss: 0.9679, Generator Loss: 1.5552 D(x): 0.6951, D(G(z)): 0.3314 Epoch: [3/20], Batch Num: [157/600] Discriminator Loss: 0.8772, Generator Loss: 1.7379 D(x): 0.7482, D(G(z)): 0.3499 Epoch: [3/20], Batch Num: [158/600] Discriminator Loss: 1.1572, Generator Loss: 1.4778 D(x): 0.6430, D(G(z)): 0.3818 Epoch: [3/20], Batch Num: [159/600] Discriminator Loss: 1.1848, Generator Loss: 1.1739 D(x): 0.6459, D(G(z)): 0.3706 Epoch: [3/20], Batch Num: [160/600] Discriminator Loss: 1.3581, Generator Loss: 1.0535 D(x): 0.6330, D(G(z)): 0.4699 Epoch: [3/20], Batch Num: [161/600] Discriminator Loss: 1.5666, Generator Loss: 1.0915 D(x): 0.5762, D(G(z)): 0.4887 Epoch: [3/20], Batch Num: [162/600] Discriminator Loss: 1.7499, Generator Loss: 0.9945 D(x): 0.5153, D(G(z)): 0.4831 Epoch: [3/20], Batch Num: [163/600] Discriminator Loss: 1.9007, Generator Loss: 0.8800 D(x): 0.5181, D(G(z)): 0.5680 Epoch: [3/20], Batch Num: [164/600] Discriminator Loss: 2.1391, Generator Loss: 0.6848 D(x): 0.4900, D(G(z)): 0.5764 Epoch: [3/20], Batch Num: [165/600] Discriminator Loss: 2.2186, Generator Loss: 0.7070 D(x): 0.4621, D(G(z)): 0.6368 Epoch: [3/20], Batch Num: [166/600] Discriminator Loss: 2.5214, Generator Loss: 0.6463 D(x): 0.3954, D(G(z)): 0.6199 Epoch: [3/20], Batch Num: [167/600] Discriminator Loss: 2.5383, Generator Loss: 0.5504 D(x): 0.3845, D(G(z)): 0.6510 Epoch: [3/20], Batch Num: [168/600] Discriminator Loss: 2.3782, Generator Loss: 0.5402 D(x): 0.4222, D(G(z)): 0.6802 Epoch: [3/20], Batch Num: [169/600] Discriminator Loss: 2.3221, Generator Loss: 0.5865 D(x): 0.4338, D(G(z)): 0.6688 Epoch: [3/20], Batch Num: [170/600] Discriminator Loss: 2.3789, Generator Loss: 0.6054 D(x): 0.3756, D(G(z)): 0.6427 Epoch: [3/20], Batch Num: [171/600] Discriminator Loss: 2.0637, Generator Loss: 0.5987 D(x): 0.4424, D(G(z)): 0.6065 Epoch: [3/20], Batch Num: [172/600] Discriminator Loss: 1.9904, Generator Loss: 0.6524 D(x): 0.4312, D(G(z)): 0.5933 Epoch: [3/20], Batch Num: [173/600] Discriminator Loss: 2.1111, Generator Loss: 0.5373 D(x): 0.4027, D(G(z)): 0.6019 Epoch: [3/20], Batch Num: [174/600] Discriminator Loss: 1.9186, Generator Loss: 0.5196 D(x): 0.4568, D(G(z)): 0.5896 Epoch: [3/20], Batch Num: [175/600] Discriminator Loss: 1.7160, Generator Loss: 0.5948 D(x): 0.5120, D(G(z)): 0.5989 Epoch: [3/20], Batch Num: [176/600] Discriminator Loss: 1.7868, Generator Loss: 0.5497 D(x): 0.5157, D(G(z)): 0.6237 Epoch: [3/20], Batch Num: [177/600] Discriminator Loss: 1.6934, Generator Loss: 0.5942 D(x): 0.5131, D(G(z)): 0.5969 Epoch: [3/20], Batch Num: [178/600] Discriminator Loss: 1.5990, Generator Loss: 0.6445 D(x): 0.5063, D(G(z)): 0.5633 Epoch: [3/20], Batch Num: [179/600] Discriminator Loss: 1.5571, Generator Loss: 0.6957 D(x): 0.5206, D(G(z)): 0.5430 Epoch: [3/20], Batch Num: [180/600] Discriminator Loss: 1.5449, Generator Loss: 0.7020 D(x): 0.4984, D(G(z)): 0.5169 Epoch: [3/20], Batch Num: [181/600] Discriminator Loss: 1.5922, Generator Loss: 0.6864 D(x): 0.4839, D(G(z)): 0.5118 Epoch: [3/20], Batch Num: [182/600] Discriminator Loss: 1.6165, Generator Loss: 0.6896 D(x): 0.4693, D(G(z)): 0.5082 Epoch: [3/20], Batch Num: [183/600] Discriminator Loss: 1.5601, Generator Loss: 0.6144 D(x): 0.5015, D(G(z)): 0.5267 Epoch: [3/20], Batch Num: [184/600] Discriminator Loss: 1.4212, Generator Loss: 0.6698 D(x): 0.5611, D(G(z)): 0.5312 Epoch: [3/20], Batch Num: [185/600] Discriminator Loss: 1.4511, Generator Loss: 0.6739 D(x): 0.5532, D(G(z)): 0.5334 Epoch: [3/20], Batch Num: [186/600] Discriminator Loss: 1.4127, Generator Loss: 0.7456 D(x): 0.5808, D(G(z)): 0.5322 Epoch: [3/20], Batch Num: [187/600] Discriminator Loss: 1.3509, Generator Loss: 0.8134 D(x): 0.5810, D(G(z)): 0.5177 Epoch: [3/20], Batch Num: [188/600] Discriminator Loss: 1.3440, Generator Loss: 0.8138 D(x): 0.5612, D(G(z)): 0.4876 Epoch: [3/20], Batch Num: [189/600] Discriminator Loss: 1.3947, Generator Loss: 0.8807 D(x): 0.5332, D(G(z)): 0.4925 Epoch: [3/20], Batch Num: [190/600] Discriminator Loss: 1.4070, Generator Loss: 0.8739 D(x): 0.5033, D(G(z)): 0.4633 Epoch: [3/20], Batch Num: [191/600] Discriminator Loss: 1.3636, Generator Loss: 0.9574 D(x): 0.5087, D(G(z)): 0.4466 Epoch: [3/20], Batch Num: [192/600] Discriminator Loss: 1.2920, Generator Loss: 0.9149 D(x): 0.5381, D(G(z)): 0.4383 Epoch: [3/20], Batch Num: [193/600] Discriminator Loss: 1.1891, Generator Loss: 0.9005 D(x): 0.5412, D(G(z)): 0.3905 Epoch: [3/20], Batch Num: [194/600] Discriminator Loss: 1.1856, Generator Loss: 0.8752 D(x): 0.5648, D(G(z)): 0.4154 Epoch: [3/20], Batch Num: [195/600] Discriminator Loss: 1.1563, Generator Loss: 0.9226 D(x): 0.5695, D(G(z)): 0.4105 Epoch: [3/20], Batch Num: [196/600] Discriminator Loss: 1.1537, Generator Loss: 0.9525 D(x): 0.5991, D(G(z)): 0.4297 Epoch: [3/20], Batch Num: [197/600] Discriminator Loss: 1.0405, Generator Loss: 0.9742 D(x): 0.6311, D(G(z)): 0.4064 Epoch: [3/20], Batch Num: [198/600] Discriminator Loss: 1.0543, Generator Loss: 1.0033 D(x): 0.6301, D(G(z)): 0.4058 Epoch: [3/20], Batch Num: [199/600] Discriminator Loss: 0.9979, Generator Loss: 1.1131 D(x): 0.6366, D(G(z)): 0.3868 Epoch: 3, Batch Num: [200/600]
Epoch: [3/20], Batch Num: [200/600] Discriminator Loss: 1.0064, Generator Loss: 1.1971 D(x): 0.6132, D(G(z)): 0.3614 Epoch: [3/20], Batch Num: [201/600] Discriminator Loss: 0.9805, Generator Loss: 1.1977 D(x): 0.5983, D(G(z)): 0.3261 Epoch: [3/20], Batch Num: [202/600] Discriminator Loss: 0.9730, Generator Loss: 1.2646 D(x): 0.5984, D(G(z)): 0.3239 Epoch: [3/20], Batch Num: [203/600] Discriminator Loss: 0.9294, Generator Loss: 1.3167 D(x): 0.6205, D(G(z)): 0.3210 Epoch: [3/20], Batch Num: [204/600] Discriminator Loss: 0.8712, Generator Loss: 1.4044 D(x): 0.6640, D(G(z)): 0.3311 Epoch: [3/20], Batch Num: [205/600] Discriminator Loss: 0.8060, Generator Loss: 1.3671 D(x): 0.6747, D(G(z)): 0.3028 Epoch: [3/20], Batch Num: [206/600] Discriminator Loss: 0.7815, Generator Loss: 1.4358 D(x): 0.6936, D(G(z)): 0.2946 Epoch: [3/20], Batch Num: [207/600] Discriminator Loss: 0.8357, Generator Loss: 1.4066 D(x): 0.6607, D(G(z)): 0.3046 Epoch: [3/20], Batch Num: [208/600] Discriminator Loss: 0.7718, Generator Loss: 1.4222 D(x): 0.6939, D(G(z)): 0.2896 Epoch: [3/20], Batch Num: [209/600] Discriminator Loss: 0.7775, Generator Loss: 1.4239 D(x): 0.6900, D(G(z)): 0.2864 Epoch: [3/20], Batch Num: [210/600] Discriminator Loss: 0.7360, Generator Loss: 1.5600 D(x): 0.7138, D(G(z)): 0.2842 Epoch: [3/20], Batch Num: [211/600] Discriminator Loss: 0.6644, Generator Loss: 1.6110 D(x): 0.7477, D(G(z)): 0.2737 Epoch: [3/20], Batch Num: [212/600] Discriminator Loss: 0.6514, Generator Loss: 1.5766 D(x): 0.7227, D(G(z)): 0.2334 Epoch: [3/20], Batch Num: [213/600] Discriminator Loss: 0.6880, Generator Loss: 1.5549 D(x): 0.7379, D(G(z)): 0.2603 Epoch: [3/20], Batch Num: [214/600] Discriminator Loss: 0.6184, Generator Loss: 1.6473 D(x): 0.7629, D(G(z)): 0.2454 Epoch: [3/20], Batch Num: [215/600] Discriminator Loss: 0.6783, Generator Loss: 1.6992 D(x): 0.7592, D(G(z)): 0.2795 Epoch: [3/20], Batch Num: [216/600] Discriminator Loss: 0.6028, Generator Loss: 1.7428 D(x): 0.7575, D(G(z)): 0.2400 Epoch: [3/20], Batch Num: [217/600] Discriminator Loss: 0.6240, Generator Loss: 1.4891 D(x): 0.7558, D(G(z)): 0.2436 Epoch: [3/20], Batch Num: [218/600] Discriminator Loss: 0.6267, Generator Loss: 1.6384 D(x): 0.7797, D(G(z)): 0.2736 Epoch: [3/20], Batch Num: [219/600] Discriminator Loss: 0.6066, Generator Loss: 1.7972 D(x): 0.7990, D(G(z)): 0.2683 Epoch: [3/20], Batch Num: [220/600] Discriminator Loss: 0.6318, Generator Loss: 1.8124 D(x): 0.7708, D(G(z)): 0.2630 Epoch: [3/20], Batch Num: [221/600] Discriminator Loss: 0.5196, Generator Loss: 1.8574 D(x): 0.7822, D(G(z)): 0.1913 Epoch: [3/20], Batch Num: [222/600] Discriminator Loss: 0.6955, Generator Loss: 1.7171 D(x): 0.7089, D(G(z)): 0.2152 Epoch: [3/20], Batch Num: [223/600] Discriminator Loss: 0.6431, Generator Loss: 1.6482 D(x): 0.7538, D(G(z)): 0.2313 Epoch: [3/20], Batch Num: [224/600] Discriminator Loss: 0.6475, Generator Loss: 1.4768 D(x): 0.7980, D(G(z)): 0.2786 Epoch: [3/20], Batch Num: [225/600] Discriminator Loss: 0.6583, Generator Loss: 1.5431 D(x): 0.8165, D(G(z)): 0.3095 Epoch: [3/20], Batch Num: [226/600] Discriminator Loss: 0.5939, Generator Loss: 1.6789 D(x): 0.8242, D(G(z)): 0.2976 Epoch: [3/20], Batch Num: [227/600] Discriminator Loss: 0.7094, Generator Loss: 1.7000 D(x): 0.7248, D(G(z)): 0.2522 Epoch: [3/20], Batch Num: [228/600] Discriminator Loss: 0.6153, Generator Loss: 1.6007 D(x): 0.7680, D(G(z)): 0.2528 Epoch: [3/20], Batch Num: [229/600] Discriminator Loss: 0.6102, Generator Loss: 1.7212 D(x): 0.8038, D(G(z)): 0.2785 Epoch: [3/20], Batch Num: [230/600] Discriminator Loss: 0.6256, Generator Loss: 1.7633 D(x): 0.7690, D(G(z)): 0.2507 Epoch: [3/20], Batch Num: [231/600] Discriminator Loss: 0.7272, Generator Loss: 1.5790 D(x): 0.7110, D(G(z)): 0.2404 Epoch: [3/20], Batch Num: [232/600] Discriminator Loss: 0.8098, Generator Loss: 1.2499 D(x): 0.7351, D(G(z)): 0.3027 Epoch: [3/20], Batch Num: [233/600] Discriminator Loss: 0.7833, Generator Loss: 1.3494 D(x): 0.7764, D(G(z)): 0.3437 Epoch: [3/20], Batch Num: [234/600] Discriminator Loss: 0.8234, Generator Loss: 1.2657 D(x): 0.7578, D(G(z)): 0.3429 Epoch: [3/20], Batch Num: [235/600] Discriminator Loss: 0.8424, Generator Loss: 1.3028 D(x): 0.7535, D(G(z)): 0.3392 Epoch: [3/20], Batch Num: [236/600] Discriminator Loss: 0.8187, Generator Loss: 1.3241 D(x): 0.7311, D(G(z)): 0.3320 Epoch: [3/20], Batch Num: [237/600] Discriminator Loss: 1.0691, Generator Loss: 1.1436 D(x): 0.6280, D(G(z)): 0.3344 Epoch: [3/20], Batch Num: [238/600] Discriminator Loss: 0.9060, Generator Loss: 1.1299 D(x): 0.7475, D(G(z)): 0.3900 Epoch: [3/20], Batch Num: [239/600] Discriminator Loss: 1.0876, Generator Loss: 1.0513 D(x): 0.7066, D(G(z)): 0.4425 Epoch: [3/20], Batch Num: [240/600] Discriminator Loss: 1.0684, Generator Loss: 1.0261 D(x): 0.6932, D(G(z)): 0.4103 Epoch: [3/20], Batch Num: [241/600] Discriminator Loss: 1.0299, Generator Loss: 1.0582 D(x): 0.7180, D(G(z)): 0.4336 Epoch: [3/20], Batch Num: [242/600] Discriminator Loss: 1.1071, Generator Loss: 1.0782 D(x): 0.6595, D(G(z)): 0.4278 Epoch: [3/20], Batch Num: [243/600] Discriminator Loss: 1.3148, Generator Loss: 0.9432 D(x): 0.6138, D(G(z)): 0.4291 Epoch: [3/20], Batch Num: [244/600] Discriminator Loss: 1.0612, Generator Loss: 0.9361 D(x): 0.6985, D(G(z)): 0.4488 Epoch: [3/20], Batch Num: [245/600] Discriminator Loss: 1.2024, Generator Loss: 0.8872 D(x): 0.6627, D(G(z)): 0.4671 Epoch: [3/20], Batch Num: [246/600] Discriminator Loss: 1.2084, Generator Loss: 0.8221 D(x): 0.6576, D(G(z)): 0.4764 Epoch: [3/20], Batch Num: [247/600] Discriminator Loss: 1.2504, Generator Loss: 0.7704 D(x): 0.6690, D(G(z)): 0.5114 Epoch: [3/20], Batch Num: [248/600] Discriminator Loss: 1.2349, Generator Loss: 0.7862 D(x): 0.6550, D(G(z)): 0.4829 Epoch: [3/20], Batch Num: [249/600] Discriminator Loss: 1.2915, Generator Loss: 0.7765 D(x): 0.6392, D(G(z)): 0.4896 Epoch: [3/20], Batch Num: [250/600] Discriminator Loss: 1.2261, Generator Loss: 0.7894 D(x): 0.7036, D(G(z)): 0.5274 Epoch: 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Generator Loss: 0.8072 D(x): 0.6168, D(G(z)): 0.5372 Epoch: [3/20], Batch Num: [260/600] Discriminator Loss: 1.3386, Generator Loss: 0.8143 D(x): 0.6139, D(G(z)): 0.5068 Epoch: [3/20], Batch Num: [261/600] Discriminator Loss: 1.2507, Generator Loss: 0.8276 D(x): 0.6308, D(G(z)): 0.4831 Epoch: [3/20], Batch Num: [262/600] Discriminator Loss: 1.3029, Generator Loss: 0.8105 D(x): 0.6047, D(G(z)): 0.4805 Epoch: [3/20], Batch Num: [263/600] Discriminator Loss: 1.1811, Generator Loss: 0.8443 D(x): 0.6590, D(G(z)): 0.4714 Epoch: [3/20], Batch Num: [264/600] Discriminator Loss: 1.3161, Generator Loss: 0.9130 D(x): 0.6149, D(G(z)): 0.4824 Epoch: [3/20], Batch Num: [265/600] Discriminator Loss: 1.1969, Generator Loss: 0.9358 D(x): 0.6395, D(G(z)): 0.4583 Epoch: [3/20], Batch Num: [266/600] Discriminator Loss: 1.1530, Generator Loss: 0.9144 D(x): 0.6475, D(G(z)): 0.4526 Epoch: [3/20], Batch Num: [267/600] Discriminator Loss: 1.1658, Generator Loss: 0.9781 D(x): 0.6420, D(G(z)): 0.4495 Epoch: [3/20], Batch Num: [268/600] Discriminator Loss: 1.1780, Generator Loss: 1.0016 D(x): 0.6143, D(G(z)): 0.4321 Epoch: [3/20], Batch Num: [269/600] Discriminator Loss: 1.0802, Generator Loss: 0.9240 D(x): 0.6748, D(G(z)): 0.4157 Epoch: [3/20], Batch Num: [270/600] Discriminator Loss: 1.0781, Generator Loss: 1.0676 D(x): 0.6705, D(G(z)): 0.4114 Epoch: [3/20], Batch Num: [271/600] Discriminator Loss: 1.0729, Generator Loss: 1.0115 D(x): 0.6324, D(G(z)): 0.3891 Epoch: [3/20], Batch Num: [272/600] Discriminator Loss: 1.1080, Generator Loss: 1.1370 D(x): 0.6476, D(G(z)): 0.4100 Epoch: [3/20], Batch Num: [273/600] Discriminator Loss: 1.1936, Generator Loss: 1.0660 D(x): 0.6187, D(G(z)): 0.4004 Epoch: [3/20], Batch Num: [274/600] Discriminator Loss: 0.9677, Generator Loss: 0.9880 D(x): 0.7076, D(G(z)): 0.3862 Epoch: [3/20], Batch Num: [275/600] Discriminator Loss: 0.9717, Generator Loss: 1.0283 D(x): 0.6943, D(G(z)): 0.3906 Epoch: [3/20], Batch Num: [276/600] Discriminator Loss: 0.9677, Generator Loss: 1.1235 D(x): 0.7078, D(G(z)): 0.4082 Epoch: [3/20], Batch Num: [277/600] Discriminator Loss: 0.9292, Generator Loss: 1.1936 D(x): 0.7098, D(G(z)): 0.3875 Epoch: [3/20], Batch Num: [278/600] Discriminator Loss: 1.0308, Generator Loss: 1.2602 D(x): 0.6699, D(G(z)): 0.3725 Epoch: [3/20], Batch Num: [279/600] Discriminator Loss: 0.8320, Generator Loss: 1.1044 D(x): 0.7252, D(G(z)): 0.3367 Epoch: [3/20], Batch Num: [280/600] Discriminator Loss: 0.8923, Generator Loss: 1.3451 D(x): 0.7128, D(G(z)): 0.3414 Epoch: [3/20], Batch Num: [281/600] Discriminator Loss: 0.7413, Generator Loss: 1.4743 D(x): 0.7479, D(G(z)): 0.3028 Epoch: [3/20], Batch Num: [282/600] Discriminator Loss: 0.8590, Generator Loss: 1.3986 D(x): 0.7142, D(G(z)): 0.3088 Epoch: [3/20], Batch Num: [283/600] Discriminator Loss: 0.8434, Generator Loss: 1.4037 D(x): 0.7096, D(G(z)): 0.3214 Epoch: [3/20], Batch Num: [284/600] Discriminator Loss: 0.7447, Generator Loss: 1.4452 D(x): 0.7439, D(G(z)): 0.2774 Epoch: [3/20], Batch Num: [285/600] Discriminator Loss: 0.7452, Generator Loss: 1.3932 D(x): 0.7537, D(G(z)): 0.3065 Epoch: [3/20], Batch Num: [286/600] Discriminator Loss: 0.7311, Generator Loss: 1.4574 D(x): 0.7647, D(G(z)): 0.3102 Epoch: [3/20], Batch Num: [287/600] Discriminator Loss: 0.7745, Generator Loss: 1.4643 D(x): 0.7834, D(G(z)): 0.3462 Epoch: [3/20], Batch Num: [288/600] Discriminator Loss: 0.8149, Generator Loss: 1.6127 D(x): 0.7136, D(G(z)): 0.2803 Epoch: [3/20], Batch Num: [289/600] Discriminator Loss: 0.7957, Generator Loss: 1.6202 D(x): 0.7254, D(G(z)): 0.2806 Epoch: [3/20], Batch Num: [290/600] Discriminator Loss: 0.8833, Generator Loss: 1.5238 D(x): 0.7231, D(G(z)): 0.3077 Epoch: [3/20], Batch Num: [291/600] Discriminator Loss: 0.7122, Generator Loss: 1.4817 D(x): 0.7967, D(G(z)): 0.3082 Epoch: [3/20], Batch Num: [292/600] Discriminator Loss: 0.6750, Generator Loss: 1.6831 D(x): 0.7993, D(G(z)): 0.2855 Epoch: [3/20], Batch Num: [293/600] Discriminator Loss: 0.7698, Generator Loss: 1.6830 D(x): 0.7678, D(G(z)): 0.2987 Epoch: [3/20], Batch Num: [294/600] Discriminator Loss: 0.8578, Generator Loss: 1.7196 D(x): 0.7534, D(G(z)): 0.3155 Epoch: [3/20], Batch Num: [295/600] Discriminator Loss: 0.9235, Generator Loss: 1.5943 D(x): 0.7030, D(G(z)): 0.3022 Epoch: [3/20], Batch Num: [296/600] Discriminator Loss: 0.8268, Generator Loss: 1.4497 D(x): 0.7631, D(G(z)): 0.3317 Epoch: [3/20], Batch Num: [297/600] Discriminator Loss: 0.7961, Generator Loss: 1.3575 D(x): 0.7967, D(G(z)): 0.3405 Epoch: [3/20], Batch Num: [298/600] Discriminator Loss: 0.8499, Generator Loss: 1.4860 D(x): 0.7608, D(G(z)): 0.3419 Epoch: [3/20], Batch Num: [299/600] Discriminator Loss: 0.8458, Generator Loss: 1.4704 D(x): 0.7916, D(G(z)): 0.3684 Epoch: 3, Batch Num: [300/600]
Epoch: [3/20], Batch Num: [300/600] Discriminator Loss: 0.9302, Generator Loss: 1.5201 D(x): 0.7411, D(G(z)): 0.3525 Epoch: [3/20], Batch Num: [301/600] Discriminator Loss: 0.9451, Generator Loss: 1.5589 D(x): 0.7323, D(G(z)): 0.3256 Epoch: [3/20], Batch Num: [302/600] Discriminator Loss: 0.9975, Generator Loss: 1.3921 D(x): 0.7032, D(G(z)): 0.3412 Epoch: [3/20], Batch Num: [303/600] Discriminator Loss: 1.0231, Generator Loss: 1.2806 D(x): 0.7462, D(G(z)): 0.3999 Epoch: [3/20], Batch Num: [304/600] Discriminator Loss: 1.0938, Generator Loss: 1.0814 D(x): 0.7060, D(G(z)): 0.4007 Epoch: [3/20], Batch Num: [305/600] Discriminator Loss: 1.0892, Generator Loss: 1.1863 D(x): 0.7380, D(G(z)): 0.4165 Epoch: [3/20], Batch Num: [306/600] Discriminator Loss: 0.9947, Generator Loss: 1.2766 D(x): 0.7440, D(G(z)): 0.4172 Epoch: [3/20], Batch Num: [307/600] Discriminator Loss: 1.0212, Generator Loss: 1.3019 D(x): 0.7277, D(G(z)): 0.3963 Epoch: [3/20], Batch Num: [308/600] Discriminator Loss: 1.0288, Generator Loss: 1.2686 D(x): 0.7203, D(G(z)): 0.3615 Epoch: [3/20], Batch Num: [309/600] Discriminator Loss: 1.0720, Generator Loss: 1.2520 D(x): 0.6674, D(G(z)): 0.3553 Epoch: [3/20], Batch Num: [310/600] Discriminator Loss: 1.0116, Generator Loss: 1.1041 D(x): 0.7068, D(G(z)): 0.3980 Epoch: [3/20], Batch Num: [311/600] Discriminator Loss: 0.8694, Generator Loss: 1.1235 D(x): 0.7805, D(G(z)): 0.3974 Epoch: [3/20], Batch Num: [312/600] Discriminator Loss: 0.9413, Generator Loss: 1.1566 D(x): 0.7841, D(G(z)): 0.4204 Epoch: [3/20], Batch Num: [313/600] Discriminator Loss: 1.0014, Generator Loss: 1.4528 D(x): 0.7103, D(G(z)): 0.3831 Epoch: [3/20], Batch Num: [314/600] Discriminator Loss: 0.7664, Generator Loss: 1.6680 D(x): 0.7573, D(G(z)): 0.3028 Epoch: [3/20], Batch Num: [315/600] Discriminator Loss: 0.8926, Generator Loss: 1.6319 D(x): 0.6659, D(G(z)): 0.2860 Epoch: [3/20], Batch Num: [316/600] Discriminator Loss: 0.7302, Generator Loss: 1.6759 D(x): 0.7608, D(G(z)): 0.2919 Epoch: [3/20], Batch Num: [317/600] Discriminator Loss: 0.7946, Generator Loss: 1.7285 D(x): 0.7902, D(G(z)): 0.3172 Epoch: [3/20], Batch Num: [318/600] Discriminator Loss: 0.6349, Generator Loss: 1.8137 D(x): 0.7834, D(G(z)): 0.2534 Epoch: [3/20], Batch Num: [319/600] Discriminator Loss: 0.6397, Generator Loss: 2.0551 D(x): 0.7958, D(G(z)): 0.2488 Epoch: [3/20], Batch Num: [320/600] Discriminator Loss: 0.6224, Generator Loss: 2.4041 D(x): 0.8195, D(G(z)): 0.2564 Epoch: [3/20], Batch Num: [321/600] Discriminator Loss: 0.4418, Generator Loss: 2.9372 D(x): 0.8278, D(G(z)): 0.1620 Epoch: [3/20], Batch Num: [322/600] Discriminator Loss: 0.5418, Generator Loss: 2.6392 D(x): 0.7981, D(G(z)): 0.1601 Epoch: [3/20], Batch Num: [323/600] Discriminator Loss: 0.3586, Generator Loss: 2.6122 D(x): 0.8596, D(G(z)): 0.1401 Epoch: [3/20], Batch Num: [324/600] Discriminator Loss: 0.5037, Generator Loss: 2.4560 D(x): 0.7896, D(G(z)): 0.1577 Epoch: [3/20], Batch Num: [325/600] Discriminator Loss: 0.4026, Generator Loss: 2.3742 D(x): 0.8886, D(G(z)): 0.1836 Epoch: [3/20], Batch Num: [326/600] Discriminator Loss: 0.5775, Generator Loss: 2.6474 D(x): 0.8126, D(G(z)): 0.1823 Epoch: [3/20], Batch Num: [327/600] Discriminator Loss: 0.3590, Generator Loss: 2.3944 D(x): 0.9114, D(G(z)): 0.1879 Epoch: [3/20], Batch Num: [328/600] Discriminator Loss: 0.4154, Generator Loss: 3.2349 D(x): 0.8977, D(G(z)): 0.2086 Epoch: [3/20], Batch Num: [329/600] Discriminator Loss: 0.4816, Generator Loss: 3.6640 D(x): 0.8342, D(G(z)): 0.1632 Epoch: [3/20], Batch Num: [330/600] Discriminator Loss: 0.5682, Generator Loss: 3.3065 D(x): 0.7662, D(G(z)): 0.1171 Epoch: [3/20], Batch Num: [331/600] Discriminator Loss: 0.5981, Generator Loss: 2.2100 D(x): 0.7682, D(G(z)): 0.1219 Epoch: [3/20], Batch Num: [332/600] Discriminator Loss: 0.6974, Generator Loss: 1.8891 D(x): 0.8362, D(G(z)): 0.2746 Epoch: [3/20], Batch Num: [333/600] Discriminator Loss: 0.7544, Generator Loss: 2.3966 D(x): 0.8972, D(G(z)): 0.3545 Epoch: [3/20], Batch Num: [334/600] Discriminator Loss: 0.7646, Generator Loss: 2.7540 D(x): 0.7921, D(G(z)): 0.2681 Epoch: [3/20], Batch Num: [335/600] Discriminator Loss: 0.9551, Generator Loss: 2.4569 D(x): 0.6916, D(G(z)): 0.1990 Epoch: [3/20], Batch Num: [336/600] Discriminator Loss: 0.8833, Generator Loss: 2.1565 D(x): 0.7300, D(G(z)): 0.2281 Epoch: [3/20], Batch Num: [337/600] Discriminator Loss: 0.9891, Generator Loss: 1.7510 D(x): 0.7646, D(G(z)): 0.3215 Epoch: [3/20], Batch Num: [338/600] Discriminator Loss: 1.0932, Generator Loss: 1.9176 D(x): 0.7286, D(G(z)): 0.3144 Epoch: [3/20], Batch Num: [339/600] Discriminator Loss: 1.2709, Generator Loss: 2.0895 D(x): 0.7161, D(G(z)): 0.3943 Epoch: [3/20], Batch Num: [340/600] Discriminator Loss: 1.2461, Generator Loss: 1.7494 D(x): 0.6086, D(G(z)): 0.2804 Epoch: [3/20], Batch Num: [341/600] Discriminator Loss: 1.2924, Generator Loss: 1.4658 D(x): 0.6643, D(G(z)): 0.3348 Epoch: [3/20], Batch Num: [342/600] Discriminator Loss: 1.1671, Generator Loss: 1.6473 D(x): 0.7263, D(G(z)): 0.3997 Epoch: [3/20], Batch Num: [343/600] Discriminator Loss: 1.1515, Generator Loss: 2.5419 D(x): 0.7451, D(G(z)): 0.4035 Epoch: [3/20], Batch Num: [344/600] Discriminator Loss: 1.4541, Generator Loss: 2.2821 D(x): 0.5320, D(G(z)): 0.2513 Epoch: [3/20], Batch Num: [345/600] Discriminator Loss: 0.9810, Generator Loss: 1.7570 D(x): 0.6233, D(G(z)): 0.1712 Epoch: [3/20], Batch Num: [346/600] Discriminator Loss: 0.9629, Generator Loss: 1.3793 D(x): 0.7176, D(G(z)): 0.2668 Epoch: [3/20], Batch Num: [347/600] Discriminator Loss: 0.8260, Generator Loss: 1.8128 D(x): 0.8417, D(G(z)): 0.3689 Epoch: [3/20], Batch Num: [348/600] Discriminator Loss: 0.7031, Generator Loss: 2.8392 D(x): 0.8403, D(G(z)): 0.3103 Epoch: [3/20], Batch Num: [349/600] Discriminator Loss: 0.6126, Generator Loss: 3.5670 D(x): 0.7527, D(G(z)): 0.1548 Epoch: [3/20], Batch Num: [350/600] Discriminator Loss: 0.5210, Generator Loss: 3.5288 D(x): 0.7447, D(G(z)): 0.0803 Epoch: [3/20], Batch Num: [351/600] Discriminator Loss: 0.5817, Generator Loss: 3.0761 D(x): 0.7340, D(G(z)): 0.0846 Epoch: [3/20], Batch Num: [352/600] Discriminator Loss: 0.3911, Generator Loss: 2.5366 D(x): 0.8065, D(G(z)): 0.0840 Epoch: [3/20], Batch Num: [353/600] Discriminator Loss: 0.3667, Generator Loss: 2.4686 D(x): 0.9222, D(G(z)): 0.1899 Epoch: [3/20], Batch Num: [354/600] Discriminator Loss: 0.3953, Generator Loss: 2.9988 D(x): 0.9544, D(G(z)): 0.2421 Epoch: [3/20], Batch Num: [355/600] Discriminator Loss: 0.2956, Generator Loss: 3.5394 D(x): 0.9323, D(G(z)): 0.1611 Epoch: [3/20], Batch Num: [356/600] Discriminator Loss: 0.3200, Generator Loss: 4.4167 D(x): 0.8564, D(G(z)): 0.0963 Epoch: [3/20], Batch Num: [357/600] Discriminator Loss: 0.3246, Generator Loss: 4.3130 D(x): 0.8235, D(G(z)): 0.0503 Epoch: [3/20], Batch Num: [358/600] Discriminator Loss: 0.2528, Generator Loss: 4.0062 D(x): 0.8637, D(G(z)): 0.0421 Epoch: [3/20], Batch Num: [359/600] Discriminator Loss: 0.2941, Generator Loss: 3.1975 D(x): 0.8638, D(G(z)): 0.0619 Epoch: [3/20], Batch Num: [360/600] Discriminator Loss: 0.2547, Generator Loss: 3.1523 D(x): 0.9227, D(G(z)): 0.1060 Epoch: [3/20], Batch Num: [361/600] Discriminator Loss: 0.1978, Generator Loss: 2.8146 D(x): 0.9482, D(G(z)): 0.1177 Epoch: [3/20], Batch Num: [362/600] Discriminator Loss: 0.2736, Generator Loss: 2.8576 D(x): 0.9462, D(G(z)): 0.1607 Epoch: [3/20], Batch Num: [363/600] Discriminator Loss: 0.2946, Generator Loss: 3.0772 D(x): 0.9282, D(G(z)): 0.1583 Epoch: [3/20], Batch Num: [364/600] Discriminator Loss: 0.3076, Generator Loss: 3.4220 D(x): 0.9004, D(G(z)): 0.1199 Epoch: [3/20], Batch Num: [365/600] Discriminator Loss: 0.2644, Generator Loss: 3.4789 D(x): 0.9201, D(G(z)): 0.1291 Epoch: [3/20], Batch Num: [366/600] Discriminator Loss: 0.5190, Generator Loss: 3.0914 D(x): 0.7766, D(G(z)): 0.0910 Epoch: [3/20], Batch Num: [367/600] Discriminator Loss: 0.4157, Generator Loss: 2.6712 D(x): 0.8556, D(G(z)): 0.1336 Epoch: [3/20], Batch Num: [368/600] Discriminator Loss: 0.3935, Generator Loss: 2.4204 D(x): 0.9072, D(G(z)): 0.2070 Epoch: [3/20], Batch Num: [369/600] Discriminator Loss: 0.5737, Generator Loss: 2.1832 D(x): 0.8244, D(G(z)): 0.2013 Epoch: [3/20], Batch Num: [370/600] Discriminator Loss: 0.5149, Generator Loss: 2.3032 D(x): 0.8710, D(G(z)): 0.2550 Epoch: [3/20], Batch Num: [371/600] Discriminator Loss: 0.8350, Generator Loss: 2.3413 D(x): 0.7901, D(G(z)): 0.3061 Epoch: [3/20], Batch Num: [372/600] Discriminator Loss: 0.9966, Generator Loss: 1.9117 D(x): 0.7036, D(G(z)): 0.2732 Epoch: [3/20], Batch Num: [373/600] Discriminator Loss: 1.0406, Generator Loss: 1.5277 D(x): 0.7107, D(G(z)): 0.3094 Epoch: [3/20], Batch Num: [374/600] Discriminator Loss: 1.1371, Generator Loss: 1.1137 D(x): 0.7171, D(G(z)): 0.3743 Epoch: [3/20], Batch Num: [375/600] Discriminator Loss: 1.2710, Generator Loss: 1.3456 D(x): 0.7687, D(G(z)): 0.4885 Epoch: [3/20], Batch Num: [376/600] Discriminator Loss: 1.2022, Generator Loss: 1.6617 D(x): 0.7578, D(G(z)): 0.4374 Epoch: [3/20], Batch Num: [377/600] Discriminator Loss: 1.4932, Generator Loss: 1.4848 D(x): 0.6058, D(G(z)): 0.3912 Epoch: [3/20], Batch Num: [378/600] Discriminator Loss: 1.4784, Generator Loss: 1.2486 D(x): 0.5967, D(G(z)): 0.3916 Epoch: [3/20], Batch Num: [379/600] Discriminator Loss: 1.6723, Generator Loss: 0.8037 D(x): 0.5926, D(G(z)): 0.4569 Epoch: [3/20], Batch Num: [380/600] Discriminator Loss: 1.6210, Generator Loss: 0.7210 D(x): 0.7024, D(G(z)): 0.6107 Epoch: [3/20], Batch Num: [381/600] Discriminator Loss: 1.5484, Generator Loss: 1.1046 D(x): 0.7267, D(G(z)): 0.5884 Epoch: [3/20], Batch Num: [382/600] Discriminator Loss: 1.5492, Generator Loss: 1.4057 D(x): 0.6504, D(G(z)): 0.4958 Epoch: [3/20], Batch Num: [383/600] Discriminator Loss: 1.5471, Generator Loss: 1.2922 D(x): 0.5181, D(G(z)): 0.3513 Epoch: [3/20], Batch Num: [384/600] Discriminator Loss: 1.0572, Generator Loss: 1.0854 D(x): 0.7215, D(G(z)): 0.3676 Epoch: [3/20], Batch Num: [385/600] Discriminator Loss: 1.1691, Generator Loss: 1.1569 D(x): 0.6853, D(G(z)): 0.4079 Epoch: [3/20], Batch Num: [386/600] Discriminator Loss: 1.0135, Generator Loss: 1.3529 D(x): 0.7312, D(G(z)): 0.4023 Epoch: [3/20], Batch Num: [387/600] Discriminator Loss: 0.8540, Generator Loss: 1.6001 D(x): 0.7691, D(G(z)): 0.3460 Epoch: [3/20], Batch Num: [388/600] Discriminator Loss: 0.5620, Generator Loss: 1.8916 D(x): 0.8597, D(G(z)): 0.2712 Epoch: [3/20], Batch Num: [389/600] Discriminator Loss: 0.5945, Generator Loss: 2.2756 D(x): 0.7773, D(G(z)): 0.1958 Epoch: [3/20], Batch Num: [390/600] Discriminator Loss: 0.4282, Generator Loss: 2.4446 D(x): 0.8364, D(G(z)): 0.1560 Epoch: [3/20], Batch Num: [391/600] Discriminator Loss: 0.3917, Generator Loss: 2.8070 D(x): 0.8252, D(G(z)): 0.1170 Epoch: [3/20], Batch Num: [392/600] Discriminator Loss: 0.2374, Generator Loss: 2.6922 D(x): 0.8809, D(G(z)): 0.0847 Epoch: [3/20], Batch Num: [393/600] Discriminator Loss: 0.2532, Generator Loss: 2.7460 D(x): 0.8949, D(G(z)): 0.1050 Epoch: [3/20], Batch Num: [394/600] Discriminator Loss: 0.2563, Generator Loss: 2.6173 D(x): 0.9001, D(G(z)): 0.1136 Epoch: [3/20], Batch Num: [395/600] Discriminator Loss: 0.2257, Generator Loss: 2.6612 D(x): 0.9164, D(G(z)): 0.1104 Epoch: [3/20], Batch Num: [396/600] Discriminator Loss: 0.1748, Generator Loss: 2.7947 D(x): 0.9535, D(G(z)): 0.1112 Epoch: [3/20], Batch Num: [397/600] Discriminator Loss: 0.1665, Generator Loss: 3.0649 D(x): 0.9480, D(G(z)): 0.0995 Epoch: [3/20], Batch Num: [398/600] Discriminator Loss: 0.2402, Generator Loss: 3.1160 D(x): 0.9234, D(G(z)): 0.1114 Epoch: [3/20], Batch Num: [399/600] Discriminator Loss: 0.1822, Generator Loss: 3.1515 D(x): 0.9300, D(G(z)): 0.0816 Epoch: 3, Batch Num: [400/600]
Epoch: [3/20], Batch Num: [400/600] Discriminator Loss: 0.1608, Generator Loss: 3.2403 D(x): 0.9411, D(G(z)): 0.0800 Epoch: [3/20], Batch Num: [401/600] Discriminator Loss: 0.1818, Generator Loss: 2.9349 D(x): 0.9337, D(G(z)): 0.0874 Epoch: [3/20], Batch Num: [402/600] Discriminator Loss: 0.1967, Generator Loss: 3.1465 D(x): 0.9213, D(G(z)): 0.0872 Epoch: [3/20], Batch Num: [403/600] Discriminator Loss: 0.1768, Generator Loss: 2.8488 D(x): 0.9355, D(G(z)): 0.0971 Epoch: [3/20], Batch Num: [404/600] Discriminator Loss: 0.2494, Generator Loss: 2.8692 D(x): 0.9266, D(G(z)): 0.1275 Epoch: [3/20], Batch Num: [405/600] Discriminator Loss: 0.2601, Generator Loss: 2.6095 D(x): 0.9079, D(G(z)): 0.1329 Epoch: [3/20], Batch Num: [406/600] Discriminator Loss: 0.2512, Generator Loss: 2.5498 D(x): 0.9131, D(G(z)): 0.1310 Epoch: [3/20], Batch Num: [407/600] Discriminator Loss: 0.3031, Generator Loss: 2.3644 D(x): 0.8867, D(G(z)): 0.1374 Epoch: [3/20], Batch Num: [408/600] Discriminator Loss: 0.3529, Generator Loss: 2.3288 D(x): 0.9125, D(G(z)): 0.2053 Epoch: [3/20], Batch Num: [409/600] Discriminator Loss: 0.3940, Generator Loss: 1.9575 D(x): 0.8778, D(G(z)): 0.1921 Epoch: [3/20], Batch Num: [410/600] Discriminator Loss: 0.6650, Generator Loss: 1.7757 D(x): 0.8010, D(G(z)): 0.2566 Epoch: [3/20], Batch Num: [411/600] Discriminator Loss: 0.7406, Generator Loss: 1.5178 D(x): 0.8134, D(G(z)): 0.3256 Epoch: [3/20], Batch Num: [412/600] Discriminator Loss: 0.7357, Generator Loss: 1.5644 D(x): 0.8514, D(G(z)): 0.3677 Epoch: [3/20], Batch Num: [413/600] Discriminator Loss: 0.8252, Generator Loss: 1.5036 D(x): 0.8011, D(G(z)): 0.3631 Epoch: [3/20], Batch Num: [414/600] Discriminator Loss: 1.1773, Generator Loss: 1.3871 D(x): 0.7210, D(G(z)): 0.3752 Epoch: [3/20], Batch Num: [415/600] Discriminator Loss: 1.4634, Generator Loss: 1.1227 D(x): 0.6543, D(G(z)): 0.4053 Epoch: [3/20], Batch Num: [416/600] Discriminator Loss: 1.6352, Generator Loss: 0.7545 D(x): 0.7139, D(G(z)): 0.5925 Epoch: [3/20], Batch Num: [417/600] Discriminator Loss: 1.6575, Generator Loss: 0.6227 D(x): 0.7436, D(G(z)): 0.6392 Epoch: [3/20], Batch Num: [418/600] Discriminator Loss: 1.6590, Generator Loss: 0.7558 D(x): 0.7352, D(G(z)): 0.6436 Epoch: [3/20], Batch Num: [419/600] Discriminator Loss: 1.7870, Generator Loss: 0.8046 D(x): 0.6478, D(G(z)): 0.6178 Epoch: [3/20], Batch Num: [420/600] Discriminator Loss: 1.9453, Generator Loss: 0.7373 D(x): 0.5493, D(G(z)): 0.5876 Epoch: [3/20], Batch Num: [421/600] Discriminator Loss: 2.2048, Generator Loss: 0.6210 D(x): 0.5580, D(G(z)): 0.6163 Epoch: [3/20], Batch Num: [422/600] Discriminator Loss: 2.3239, Generator Loss: 0.3864 D(x): 0.5453, D(G(z)): 0.6795 Epoch: [3/20], Batch Num: [423/600] Discriminator Loss: 2.0247, Generator Loss: 0.3152 D(x): 0.6666, D(G(z)): 0.7236 Epoch: [3/20], Batch Num: [424/600] Discriminator Loss: 2.1914, Generator Loss: 0.3109 D(x): 0.6374, D(G(z)): 0.7631 Epoch: [3/20], Batch Num: [425/600] Discriminator Loss: 1.9219, Generator Loss: 0.3337 D(x): 0.7739, D(G(z)): 0.7738 Epoch: [3/20], Batch Num: [426/600] Discriminator Loss: 1.7261, Generator Loss: 0.4598 D(x): 0.7760, D(G(z)): 0.7387 Epoch: [3/20], Batch Num: [427/600] Discriminator Loss: 1.6152, Generator Loss: 0.6122 D(x): 0.7124, D(G(z)): 0.6721 Epoch: [3/20], Batch Num: [428/600] Discriminator Loss: 1.2790, Generator Loss: 0.7689 D(x): 0.7315, D(G(z)): 0.5578 Epoch: [3/20], Batch Num: [429/600] Discriminator Loss: 1.3488, Generator Loss: 0.9638 D(x): 0.6343, D(G(z)): 0.4948 Epoch: [3/20], Batch Num: [430/600] Discriminator Loss: 1.2928, Generator Loss: 1.0164 D(x): 0.6153, D(G(z)): 0.4513 Epoch: [3/20], Batch Num: [431/600] Discriminator Loss: 1.2137, Generator Loss: 1.0077 D(x): 0.6089, D(G(z)): 0.4121 Epoch: [3/20], Batch Num: [432/600] Discriminator Loss: 1.1250, Generator Loss: 0.9321 D(x): 0.6250, D(G(z)): 0.3764 Epoch: [3/20], Batch Num: [433/600] Discriminator Loss: 1.0942, Generator Loss: 0.9425 D(x): 0.6894, D(G(z)): 0.4539 Epoch: [3/20], Batch Num: [434/600] Discriminator Loss: 0.9155, Generator Loss: 0.8757 D(x): 0.7621, D(G(z)): 0.4264 Epoch: [3/20], Batch Num: [435/600] Discriminator Loss: 0.9270, Generator Loss: 0.9689 D(x): 0.7777, D(G(z)): 0.4475 Epoch: [3/20], Batch Num: [436/600] Discriminator Loss: 0.8058, Generator Loss: 1.0687 D(x): 0.8344, D(G(z)): 0.4337 Epoch: [3/20], Batch Num: [437/600] Discriminator Loss: 0.7344, Generator Loss: 1.2797 D(x): 0.8334, D(G(z)): 0.3913 Epoch: [3/20], Batch Num: [438/600] Discriminator Loss: 0.6585, Generator Loss: 1.6277 D(x): 0.8274, D(G(z)): 0.3465 Epoch: [3/20], Batch Num: [439/600] Discriminator Loss: 0.5466, Generator Loss: 2.0015 D(x): 0.8051, D(G(z)): 0.2415 Epoch: [3/20], Batch Num: [440/600] Discriminator Loss: 0.5474, Generator Loss: 2.3687 D(x): 0.7473, D(G(z)): 0.1716 Epoch: [3/20], Batch Num: [441/600] Discriminator Loss: 0.6821, Generator Loss: 2.4461 D(x): 0.6928, D(G(z)): 0.1485 Epoch: [3/20], Batch Num: [442/600] Discriminator Loss: 0.4853, Generator Loss: 2.5373 D(x): 0.7603, D(G(z)): 0.1260 Epoch: [3/20], Batch Num: [443/600] Discriminator Loss: 0.4025, Generator Loss: 2.4405 D(x): 0.8091, D(G(z)): 0.1321 Epoch: [3/20], Batch Num: [444/600] Discriminator Loss: 0.4296, Generator Loss: 2.5466 D(x): 0.8036, D(G(z)): 0.1403 Epoch: [3/20], Batch Num: [445/600] Discriminator Loss: 0.3591, Generator Loss: 2.3073 D(x): 0.8658, D(G(z)): 0.1694 Epoch: [3/20], Batch Num: [446/600] Discriminator Loss: 0.3727, Generator Loss: 2.1920 D(x): 0.8648, D(G(z)): 0.1717 Epoch: [3/20], Batch Num: [447/600] Discriminator Loss: 0.3411, Generator Loss: 2.3967 D(x): 0.8960, D(G(z)): 0.1845 Epoch: [3/20], Batch Num: [448/600] Discriminator Loss: 0.2881, Generator Loss: 2.4763 D(x): 0.8999, D(G(z)): 0.1516 Epoch: [3/20], Batch Num: [449/600] Discriminator Loss: 0.3164, Generator Loss: 2.6663 D(x): 0.8847, D(G(z)): 0.1445 Epoch: [3/20], Batch Num: [450/600] Discriminator Loss: 0.3334, Generator Loss: 2.9837 D(x): 0.8802, D(G(z)): 0.1508 Epoch: [3/20], Batch Num: [451/600] Discriminator Loss: 0.2612, Generator Loss: 2.9985 D(x): 0.8849, D(G(z)): 0.1052 Epoch: [3/20], Batch Num: [452/600] Discriminator Loss: 0.3474, Generator Loss: 3.0135 D(x): 0.8266, D(G(z)): 0.0929 Epoch: [3/20], Batch Num: [453/600] Discriminator Loss: 0.3896, Generator Loss: 2.7267 D(x): 0.8206, D(G(z)): 0.1122 Epoch: [3/20], Batch Num: [454/600] Discriminator Loss: 0.4206, Generator Loss: 2.4335 D(x): 0.8065, D(G(z)): 0.1260 Epoch: [3/20], Batch Num: [455/600] Discriminator Loss: 0.4703, Generator Loss: 2.1162 D(x): 0.8549, D(G(z)): 0.2003 Epoch: [3/20], Batch Num: [456/600] Discriminator Loss: 0.5303, Generator Loss: 1.8390 D(x): 0.8687, D(G(z)): 0.2532 Epoch: [3/20], Batch Num: [457/600] Discriminator Loss: 0.5683, Generator Loss: 2.0289 D(x): 0.8801, D(G(z)): 0.3087 Epoch: [3/20], Batch Num: [458/600] Discriminator Loss: 0.4708, Generator Loss: 2.1289 D(x): 0.8788, D(G(z)): 0.2598 Epoch: [3/20], Batch Num: [459/600] Discriminator Loss: 0.6334, Generator Loss: 2.3785 D(x): 0.7960, D(G(z)): 0.2238 Epoch: [3/20], Batch Num: [460/600] Discriminator Loss: 0.7465, Generator Loss: 2.0566 D(x): 0.7168, D(G(z)): 0.2033 Epoch: [3/20], Batch Num: [461/600] Discriminator Loss: 0.8959, Generator Loss: 1.4718 D(x): 0.7144, D(G(z)): 0.2586 Epoch: [3/20], Batch Num: [462/600] Discriminator Loss: 1.0538, Generator Loss: 1.0854 D(x): 0.7474, D(G(z)): 0.3931 Epoch: [3/20], Batch Num: [463/600] Discriminator Loss: 1.1991, Generator Loss: 0.9259 D(x): 0.7838, D(G(z)): 0.5116 Epoch: [3/20], Batch Num: [464/600] Discriminator Loss: 1.1784, Generator Loss: 1.2285 D(x): 0.7775, D(G(z)): 0.5304 Epoch: [3/20], Batch Num: [465/600] Discriminator Loss: 1.1987, Generator Loss: 1.4576 D(x): 0.7232, D(G(z)): 0.4257 Epoch: [3/20], Batch Num: [466/600] Discriminator Loss: 1.3574, Generator Loss: 1.5922 D(x): 0.6325, D(G(z)): 0.3633 Epoch: [3/20], Batch Num: [467/600] Discriminator Loss: 1.1640, Generator Loss: 1.4310 D(x): 0.6587, D(G(z)): 0.3205 Epoch: [3/20], Batch Num: [468/600] Discriminator Loss: 0.9074, Generator Loss: 1.4340 D(x): 0.7337, D(G(z)): 0.3292 Epoch: [3/20], Batch Num: [469/600] Discriminator Loss: 0.7242, Generator Loss: 1.8711 D(x): 0.8426, D(G(z)): 0.3465 Epoch: [3/20], Batch Num: [470/600] Discriminator Loss: 0.6906, Generator Loss: 2.3769 D(x): 0.8147, D(G(z)): 0.2587 Epoch: [3/20], Batch Num: [471/600] Discriminator Loss: 0.7095, Generator Loss: 2.3209 D(x): 0.7213, D(G(z)): 0.1953 Epoch: [3/20], Batch Num: [472/600] Discriminator Loss: 0.5790, Generator Loss: 2.4460 D(x): 0.7724, D(G(z)): 0.1585 Epoch: [3/20], Batch Num: [473/600] Discriminator Loss: 0.3655, Generator Loss: 3.0551 D(x): 0.8933, D(G(z)): 0.1494 Epoch: [3/20], Batch Num: [474/600] Discriminator Loss: 0.2884, Generator Loss: 3.2353 D(x): 0.8985, D(G(z)): 0.1184 Epoch: [3/20], Batch Num: [475/600] Discriminator Loss: 0.3008, Generator Loss: 3.1442 D(x): 0.8809, D(G(z)): 0.1230 Epoch: [3/20], Batch Num: [476/600] Discriminator Loss: 0.3644, Generator Loss: 3.3112 D(x): 0.8539, D(G(z)): 0.1090 Epoch: [3/20], Batch Num: [477/600] Discriminator Loss: 0.3439, Generator Loss: 3.2066 D(x): 0.8711, D(G(z)): 0.1027 Epoch: [3/20], Batch Num: [478/600] Discriminator Loss: 0.3934, Generator Loss: 3.2035 D(x): 0.8591, D(G(z)): 0.1003 Epoch: [3/20], Batch Num: [479/600] Discriminator Loss: 0.3350, Generator Loss: 3.0448 D(x): 0.8775, D(G(z)): 0.1249 Epoch: [3/20], Batch Num: [480/600] Discriminator Loss: 0.3748, Generator Loss: 2.7403 D(x): 0.8469, D(G(z)): 0.1059 Epoch: [3/20], Batch Num: [481/600] Discriminator Loss: 0.6108, Generator Loss: 2.4082 D(x): 0.7965, D(G(z)): 0.1929 Epoch: [3/20], Batch Num: [482/600] Discriminator Loss: 0.5274, Generator Loss: 2.3835 D(x): 0.8241, D(G(z)): 0.1911 Epoch: [3/20], Batch Num: [483/600] Discriminator Loss: 0.5723, Generator Loss: 2.5495 D(x): 0.8402, D(G(z)): 0.2509 Epoch: [3/20], Batch Num: [484/600] Discriminator Loss: 0.5739, Generator Loss: 2.6888 D(x): 0.8262, D(G(z)): 0.2124 Epoch: [3/20], Batch Num: [485/600] Discriminator Loss: 0.7876, Generator Loss: 2.2970 D(x): 0.7286, D(G(z)): 0.2020 Epoch: [3/20], Batch Num: [486/600] Discriminator Loss: 1.2029, Generator Loss: 1.8380 D(x): 0.6512, D(G(z)): 0.2677 Epoch: [3/20], Batch Num: [487/600] Discriminator Loss: 1.1067, Generator Loss: 1.2100 D(x): 0.7297, D(G(z)): 0.3697 Epoch: [3/20], Batch Num: [488/600] Discriminator Loss: 1.5453, Generator Loss: 1.2746 D(x): 0.6696, D(G(z)): 0.4836 Epoch: [3/20], Batch Num: [489/600] Discriminator Loss: 1.5692, Generator Loss: 1.8788 D(x): 0.6949, D(G(z)): 0.5039 Epoch: [3/20], Batch Num: [490/600] Discriminator Loss: 1.7295, Generator Loss: 1.9555 D(x): 0.5713, D(G(z)): 0.3945 Epoch: [3/20], Batch Num: [491/600] Discriminator Loss: 2.0399, Generator Loss: 1.1543 D(x): 0.4351, D(G(z)): 0.2783 Epoch: [3/20], Batch Num: [492/600] Discriminator Loss: 2.2605, Generator Loss: 0.7762 D(x): 0.5636, D(G(z)): 0.5577 Epoch: [3/20], Batch Num: [493/600] Discriminator Loss: 2.3106, Generator Loss: 0.7541 D(x): 0.5536, D(G(z)): 0.5901 Epoch: [3/20], Batch Num: [494/600] Discriminator Loss: 2.2376, Generator Loss: 0.8953 D(x): 0.6036, D(G(z)): 0.6309 Epoch: [3/20], Batch Num: [495/600] Discriminator Loss: 2.4942, Generator Loss: 1.2070 D(x): 0.4631, D(G(z)): 0.5220 Epoch: [3/20], Batch Num: [496/600] Discriminator Loss: 2.4780, Generator Loss: 1.0843 D(x): 0.4435, D(G(z)): 0.4487 Epoch: [3/20], Batch Num: [497/600] Discriminator Loss: 2.1236, Generator Loss: 0.8070 D(x): 0.5230, D(G(z)): 0.4759 Epoch: [3/20], Batch Num: [498/600] Discriminator Loss: 1.9861, Generator Loss: 0.7145 D(x): 0.5321, D(G(z)): 0.5225 Epoch: [3/20], Batch Num: [499/600] Discriminator Loss: 1.7042, Generator Loss: 0.7620 D(x): 0.5850, D(G(z)): 0.5157 Epoch: 3, Batch Num: [500/600]
Epoch: [3/20], Batch Num: [500/600] Discriminator Loss: 1.6079, Generator Loss: 0.8807 D(x): 0.6508, D(G(z)): 0.5389 Epoch: [3/20], Batch Num: [501/600] Discriminator Loss: 1.3683, Generator Loss: 1.0046 D(x): 0.6661, D(G(z)): 0.4877 Epoch: [3/20], Batch Num: [502/600] Discriminator Loss: 1.1391, Generator Loss: 1.3360 D(x): 0.6704, D(G(z)): 0.4046 Epoch: [3/20], Batch Num: [503/600] Discriminator Loss: 1.0845, Generator Loss: 1.4771 D(x): 0.6606, D(G(z)): 0.3330 Epoch: [3/20], Batch Num: [504/600] Discriminator Loss: 0.9799, Generator Loss: 1.7270 D(x): 0.6268, D(G(z)): 0.2447 Epoch: [3/20], Batch Num: [505/600] Discriminator Loss: 0.8007, Generator Loss: 1.7561 D(x): 0.6847, D(G(z)): 0.2326 Epoch: [3/20], Batch Num: [506/600] Discriminator Loss: 0.6766, Generator Loss: 1.9916 D(x): 0.7267, D(G(z)): 0.2085 Epoch: [3/20], Batch Num: [507/600] Discriminator Loss: 0.6079, Generator Loss: 2.0023 D(x): 0.7397, D(G(z)): 0.1714 Epoch: [3/20], Batch Num: [508/600] Discriminator Loss: 0.5200, Generator Loss: 2.0018 D(x): 0.7701, D(G(z)): 0.1664 Epoch: [3/20], Batch Num: [509/600] Discriminator Loss: 0.4312, Generator Loss: 2.1094 D(x): 0.8232, D(G(z)): 0.1552 Epoch: [3/20], Batch Num: [510/600] Discriminator Loss: 0.4297, Generator Loss: 2.3095 D(x): 0.8255, D(G(z)): 0.1636 Epoch: [3/20], Batch Num: [511/600] Discriminator Loss: 0.4087, Generator Loss: 2.4512 D(x): 0.8428, D(G(z)): 0.1645 Epoch: [3/20], Batch Num: [512/600] Discriminator Loss: 0.3738, Generator Loss: 2.4926 D(x): 0.8551, D(G(z)): 0.1621 Epoch: [3/20], Batch Num: [513/600] Discriminator Loss: 0.2951, Generator Loss: 2.6741 D(x): 0.8912, D(G(z)): 0.1451 Epoch: [3/20], Batch Num: [514/600] Discriminator Loss: 0.3427, Generator Loss: 2.7820 D(x): 0.8427, D(G(z)): 0.1094 Epoch: [3/20], Batch Num: [515/600] Discriminator Loss: 0.2528, Generator Loss: 2.7715 D(x): 0.8861, D(G(z)): 0.1001 Epoch: [3/20], Batch Num: [516/600] Discriminator Loss: 0.3053, Generator Loss: 2.8549 D(x): 0.8699, D(G(z)): 0.1081 Epoch: [3/20], Batch Num: [517/600] Discriminator Loss: 0.3267, Generator Loss: 2.9171 D(x): 0.8486, D(G(z)): 0.1125 Epoch: [3/20], Batch Num: [518/600] Discriminator Loss: 0.3131, Generator Loss: 2.5373 D(x): 0.8487, D(G(z)): 0.1062 Epoch: [3/20], Batch Num: [519/600] Discriminator Loss: 0.3001, Generator Loss: 2.5800 D(x): 0.8794, D(G(z)): 0.1352 Epoch: [3/20], Batch Num: [520/600] Discriminator Loss: 0.3269, Generator Loss: 2.3409 D(x): 0.8520, D(G(z)): 0.1224 Epoch: [3/20], Batch Num: [521/600] Discriminator Loss: 0.2840, Generator Loss: 2.4878 D(x): 0.9009, D(G(z)): 0.1477 Epoch: [3/20], Batch Num: [522/600] Discriminator Loss: 0.3533, Generator Loss: 2.3975 D(x): 0.8736, D(G(z)): 0.1695 Epoch: [3/20], Batch Num: [523/600] Discriminator Loss: 0.3721, Generator Loss: 2.3802 D(x): 0.8716, D(G(z)): 0.1682 Epoch: [3/20], Batch Num: [524/600] Discriminator Loss: 0.4024, Generator Loss: 2.2721 D(x): 0.8387, D(G(z)): 0.1638 Epoch: [3/20], Batch Num: [525/600] Discriminator Loss: 0.3366, Generator Loss: 2.4093 D(x): 0.8728, D(G(z)): 0.1490 Epoch: [3/20], Batch Num: [526/600] Discriminator Loss: 0.5215, Generator Loss: 2.3159 D(x): 0.7870, D(G(z)): 0.1779 Epoch: [3/20], Batch Num: [527/600] Discriminator Loss: 0.5506, Generator Loss: 2.1231 D(x): 0.8034, D(G(z)): 0.2013 Epoch: [3/20], Batch Num: [528/600] Discriminator Loss: 0.5275, Generator Loss: 1.9573 D(x): 0.8189, D(G(z)): 0.2285 Epoch: [3/20], Batch Num: [529/600] Discriminator Loss: 0.5000, Generator Loss: 1.9161 D(x): 0.8381, D(G(z)): 0.2289 Epoch: [3/20], Batch Num: [530/600] Discriminator Loss: 0.6359, Generator Loss: 1.8192 D(x): 0.8059, D(G(z)): 0.2525 Epoch: [3/20], Batch Num: [531/600] Discriminator Loss: 0.6426, Generator Loss: 1.6927 D(x): 0.7908, D(G(z)): 0.2511 Epoch: [3/20], Batch Num: [532/600] Discriminator Loss: 0.9610, Generator Loss: 1.5857 D(x): 0.7193, D(G(z)): 0.3074 Epoch: [3/20], Batch Num: [533/600] Discriminator Loss: 0.8730, Generator Loss: 1.5837 D(x): 0.7503, D(G(z)): 0.3441 Epoch: [3/20], Batch Num: [534/600] Discriminator Loss: 0.8470, Generator Loss: 1.4452 D(x): 0.7647, D(G(z)): 0.3287 Epoch: [3/20], Batch Num: [535/600] Discriminator Loss: 0.8668, Generator Loss: 1.4131 D(x): 0.7345, D(G(z)): 0.3047 Epoch: [3/20], Batch Num: [536/600] Discriminator Loss: 1.1033, Generator Loss: 1.2133 D(x): 0.6766, D(G(z)): 0.3618 Epoch: [3/20], Batch Num: [537/600] Discriminator Loss: 1.0658, Generator Loss: 1.2004 D(x): 0.6939, D(G(z)): 0.3569 Epoch: [3/20], Batch Num: [538/600] Discriminator Loss: 1.2782, Generator Loss: 1.0368 D(x): 0.6622, D(G(z)): 0.3900 Epoch: [3/20], Batch Num: [539/600] Discriminator Loss: 1.1464, Generator Loss: 1.2233 D(x): 0.7262, D(G(z)): 0.4244 Epoch: [3/20], Batch Num: [540/600] Discriminator Loss: 1.4079, Generator Loss: 1.2912 D(x): 0.6157, D(G(z)): 0.4186 Epoch: [3/20], Batch Num: [541/600] Discriminator Loss: 1.3020, Generator Loss: 1.2947 D(x): 0.6307, D(G(z)): 0.3954 Epoch: [3/20], Batch Num: [542/600] Discriminator Loss: 1.3694, Generator Loss: 1.3770 D(x): 0.6543, D(G(z)): 0.4305 Epoch: [3/20], Batch Num: [543/600] Discriminator Loss: 1.3038, Generator Loss: 1.1787 D(x): 0.6007, D(G(z)): 0.3400 Epoch: [3/20], Batch Num: [544/600] Discriminator Loss: 1.2885, Generator Loss: 1.0464 D(x): 0.6759, D(G(z)): 0.3918 Epoch: [3/20], Batch Num: [545/600] Discriminator Loss: 1.3592, Generator Loss: 1.1195 D(x): 0.6763, D(G(z)): 0.4762 Epoch: [3/20], Batch Num: [546/600] Discriminator Loss: 1.2487, Generator Loss: 1.2692 D(x): 0.6870, D(G(z)): 0.4281 Epoch: [3/20], Batch Num: [547/600] Discriminator Loss: 1.1157, Generator Loss: 1.4884 D(x): 0.6655, D(G(z)): 0.3409 Epoch: [3/20], Batch Num: [548/600] Discriminator Loss: 1.3853, Generator Loss: 1.3385 D(x): 0.5717, D(G(z)): 0.3457 Epoch: [3/20], Batch Num: [549/600] Discriminator Loss: 1.3971, Generator Loss: 1.1265 D(x): 0.5751, D(G(z)): 0.3246 Epoch: [3/20], Batch Num: [550/600] Discriminator Loss: 1.1217, Generator Loss: 1.1189 D(x): 0.6975, D(G(z)): 0.4198 Epoch: [3/20], Batch Num: [551/600] Discriminator Loss: 1.0746, Generator Loss: 1.1288 D(x): 0.6965, D(G(z)): 0.4076 Epoch: [3/20], Batch Num: [552/600] Discriminator Loss: 1.0810, Generator Loss: 1.2885 D(x): 0.6784, D(G(z)): 0.4109 Epoch: [3/20], Batch Num: [553/600] Discriminator Loss: 0.9263, Generator Loss: 1.3941 D(x): 0.7166, D(G(z)): 0.3323 Epoch: [3/20], Batch Num: [554/600] Discriminator Loss: 0.9891, Generator Loss: 1.5965 D(x): 0.6857, D(G(z)): 0.3388 Epoch: [3/20], Batch Num: [555/600] Discriminator Loss: 1.0248, Generator Loss: 1.8133 D(x): 0.6424, D(G(z)): 0.2846 Epoch: [3/20], Batch Num: [556/600] Discriminator Loss: 0.8386, Generator Loss: 1.8096 D(x): 0.6907, D(G(z)): 0.2414 Epoch: [3/20], Batch Num: [557/600] Discriminator Loss: 0.7414, Generator Loss: 1.8292 D(x): 0.7569, D(G(z)): 0.2233 Epoch: [3/20], Batch Num: [558/600] Discriminator Loss: 0.8862, Generator Loss: 1.7419 D(x): 0.6902, D(G(z)): 0.2371 Epoch: [3/20], Batch Num: [559/600] Discriminator Loss: 0.7229, Generator Loss: 1.7041 D(x): 0.7705, D(G(z)): 0.2610 Epoch: [3/20], Batch Num: [560/600] Discriminator Loss: 0.6135, Generator Loss: 1.9027 D(x): 0.8233, D(G(z)): 0.2754 Epoch: [3/20], Batch Num: [561/600] Discriminator Loss: 0.6198, Generator Loss: 2.1497 D(x): 0.8164, D(G(z)): 0.2484 Epoch: [3/20], Batch Num: [562/600] Discriminator Loss: 0.5312, Generator Loss: 2.4136 D(x): 0.8191, D(G(z)): 0.1967 Epoch: [3/20], Batch Num: [563/600] Discriminator Loss: 0.4787, Generator Loss: 2.8236 D(x): 0.8367, D(G(z)): 0.1828 Epoch: [3/20], Batch Num: [564/600] Discriminator Loss: 0.5221, Generator Loss: 2.8735 D(x): 0.7780, D(G(z)): 0.1187 Epoch: [3/20], Batch Num: [565/600] Discriminator Loss: 0.4333, Generator Loss: 2.7719 D(x): 0.8042, D(G(z)): 0.1276 Epoch: [3/20], Batch Num: [566/600] Discriminator Loss: 0.4363, Generator Loss: 2.6311 D(x): 0.8025, D(G(z)): 0.1125 Epoch: [3/20], Batch Num: [567/600] Discriminator Loss: 0.4825, Generator Loss: 2.4418 D(x): 0.8389, D(G(z)): 0.1672 Epoch: [3/20], Batch Num: [568/600] Discriminator Loss: 0.4631, Generator Loss: 2.0675 D(x): 0.8161, D(G(z)): 0.1572 Epoch: [3/20], Batch Num: [569/600] Discriminator Loss: 0.4736, Generator Loss: 2.1163 D(x): 0.8774, D(G(z)): 0.2080 Epoch: [3/20], Batch Num: [570/600] Discriminator Loss: 0.5009, Generator Loss: 2.1772 D(x): 0.8641, D(G(z)): 0.2361 Epoch: [3/20], Batch Num: [571/600] Discriminator Loss: 0.4383, Generator Loss: 2.4598 D(x): 0.8694, D(G(z)): 0.1966 Epoch: [3/20], Batch Num: [572/600] Discriminator Loss: 0.4920, Generator Loss: 2.9517 D(x): 0.8434, D(G(z)): 0.1937 Epoch: [3/20], Batch Num: [573/600] Discriminator Loss: 0.5249, Generator Loss: 2.8128 D(x): 0.8043, D(G(z)): 0.1572 Epoch: [3/20], Batch Num: [574/600] Discriminator Loss: 0.4464, Generator Loss: 2.8586 D(x): 0.8256, D(G(z)): 0.1108 Epoch: [3/20], Batch Num: [575/600] Discriminator Loss: 0.5156, Generator Loss: 2.5066 D(x): 0.8262, D(G(z)): 0.1387 Epoch: [3/20], Batch Num: [576/600] Discriminator Loss: 0.3219, Generator Loss: 2.3109 D(x): 0.8935, D(G(z)): 0.1492 Epoch: [3/20], Batch Num: [577/600] Discriminator Loss: 0.4264, Generator Loss: 2.3593 D(x): 0.8821, D(G(z)): 0.1932 Epoch: [3/20], Batch Num: [578/600] Discriminator Loss: 0.5582, Generator Loss: 2.0637 D(x): 0.8469, D(G(z)): 0.2361 Epoch: [3/20], Batch Num: [579/600] Discriminator Loss: 0.4189, Generator Loss: 2.6209 D(x): 0.8975, D(G(z)): 0.2071 Epoch: [3/20], Batch Num: [580/600] Discriminator Loss: 0.4480, Generator Loss: 2.4934 D(x): 0.8795, D(G(z)): 0.1612 Epoch: [3/20], Batch Num: [581/600] Discriminator Loss: 0.4181, Generator Loss: 2.7143 D(x): 0.8652, D(G(z)): 0.1486 Epoch: [3/20], Batch Num: [582/600] Discriminator Loss: 0.4933, Generator Loss: 2.5059 D(x): 0.8165, D(G(z)): 0.1254 Epoch: [3/20], Batch Num: [583/600] Discriminator Loss: 0.4229, Generator Loss: 2.6113 D(x): 0.8835, D(G(z)): 0.1779 Epoch: [3/20], Batch Num: [584/600] Discriminator Loss: 0.4064, Generator Loss: 2.3356 D(x): 0.8564, D(G(z)): 0.1431 Epoch: [3/20], Batch Num: [585/600] Discriminator Loss: 0.4078, Generator Loss: 2.3424 D(x): 0.8636, D(G(z)): 0.1491 Epoch: [3/20], Batch Num: [586/600] Discriminator Loss: 0.5026, Generator Loss: 2.2270 D(x): 0.8684, D(G(z)): 0.2111 Epoch: [3/20], Batch Num: [587/600] Discriminator Loss: 0.5229, Generator Loss: 2.1509 D(x): 0.8808, D(G(z)): 0.2242 Epoch: [3/20], Batch Num: [588/600] Discriminator Loss: 0.3924, Generator Loss: 2.4170 D(x): 0.8904, D(G(z)): 0.1905 Epoch: [3/20], Batch Num: [589/600] Discriminator Loss: 0.5117, Generator Loss: 2.5263 D(x): 0.8400, D(G(z)): 0.1727 Epoch: [3/20], Batch Num: [590/600] Discriminator Loss: 0.4568, Generator Loss: 2.7059 D(x): 0.8426, D(G(z)): 0.1630 Epoch: [3/20], Batch Num: [591/600] Discriminator Loss: 0.3575, Generator Loss: 2.6809 D(x): 0.8935, D(G(z)): 0.1296 Epoch: [3/20], Batch Num: [592/600] Discriminator Loss: 0.4898, Generator Loss: 2.3115 D(x): 0.8701, D(G(z)): 0.1945 Epoch: [3/20], Batch Num: [593/600] Discriminator Loss: 0.4982, Generator Loss: 2.4024 D(x): 0.8538, D(G(z)): 0.1843 Epoch: [3/20], Batch Num: [594/600] Discriminator Loss: 0.3590, Generator Loss: 2.5136 D(x): 0.8954, D(G(z)): 0.1615 Epoch: [3/20], Batch Num: [595/600] Discriminator Loss: 0.3777, Generator Loss: 2.4947 D(x): 0.8933, D(G(z)): 0.1689 Epoch: [3/20], Batch Num: [596/600] Discriminator Loss: 0.3802, Generator Loss: 2.8424 D(x): 0.8876, D(G(z)): 0.1560 Epoch: [3/20], Batch Num: [597/600] Discriminator Loss: 0.4442, Generator Loss: 2.8341 D(x): 0.8543, D(G(z)): 0.1622 Epoch: [3/20], Batch Num: [598/600] Discriminator Loss: 0.2471, Generator Loss: 2.6380 D(x): 0.9131, D(G(z)): 0.1134 Epoch: [3/20], Batch Num: [599/600] Discriminator Loss: 0.3303, Generator Loss: 2.8134 D(x): 0.9145, D(G(z)): 0.1348 Epoch: 4, Batch Num: [0/600]
Epoch: [4/20], Batch Num: [0/600] Discriminator Loss: 0.4060, Generator Loss: 2.5524 D(x): 0.8747, D(G(z)): 0.1370 Epoch: [4/20], Batch Num: [1/600] Discriminator Loss: 0.4005, Generator Loss: 2.8931 D(x): 0.8957, D(G(z)): 0.1601 Epoch: [4/20], Batch Num: [2/600] Discriminator Loss: 0.3188, Generator Loss: 2.8174 D(x): 0.9344, D(G(z)): 0.1717 Epoch: [4/20], Batch Num: [3/600] Discriminator Loss: 0.3292, Generator Loss: 2.8893 D(x): 0.9049, D(G(z)): 0.1620 Epoch: [4/20], Batch Num: [4/600] Discriminator Loss: 0.4939, Generator Loss: 2.9301 D(x): 0.8408, D(G(z)): 0.1343 Epoch: [4/20], Batch Num: [5/600] Discriminator Loss: 0.4142, Generator Loss: 2.5989 D(x): 0.8399, D(G(z)): 0.1310 Epoch: [4/20], Batch Num: [6/600] Discriminator Loss: 0.3689, Generator Loss: 2.6180 D(x): 0.9166, D(G(z)): 0.1715 Epoch: [4/20], Batch Num: [7/600] Discriminator Loss: 0.5670, Generator Loss: 2.4005 D(x): 0.8348, D(G(z)): 0.1867 Epoch: [4/20], Batch Num: [8/600] Discriminator Loss: 0.5991, Generator Loss: 2.0083 D(x): 0.8232, D(G(z)): 0.2110 Epoch: [4/20], Batch Num: [9/600] Discriminator Loss: 0.4858, Generator Loss: 2.2023 D(x): 0.8817, D(G(z)): 0.2126 Epoch: [4/20], Batch Num: [10/600] Discriminator Loss: 0.5467, Generator Loss: 2.4343 D(x): 0.8864, D(G(z)): 0.2561 Epoch: [4/20], Batch Num: [11/600] Discriminator Loss: 0.6960, Generator Loss: 2.5981 D(x): 0.8002, D(G(z)): 0.2109 Epoch: [4/20], Batch Num: [12/600] Discriminator Loss: 0.7080, Generator Loss: 2.4357 D(x): 0.7892, D(G(z)): 0.1929 Epoch: [4/20], Batch Num: [13/600] Discriminator Loss: 0.6076, Generator Loss: 2.3207 D(x): 0.8096, D(G(z)): 0.1881 Epoch: [4/20], Batch Num: [14/600] Discriminator Loss: 0.8869, Generator Loss: 1.9288 D(x): 0.7753, D(G(z)): 0.2696 Epoch: [4/20], Batch Num: [15/600] Discriminator Loss: 0.6496, Generator Loss: 2.0200 D(x): 0.8265, D(G(z)): 0.2553 Epoch: [4/20], Batch Num: [16/600] Discriminator Loss: 0.9054, Generator Loss: 2.0551 D(x): 0.7959, D(G(z)): 0.3572 Epoch: [4/20], Batch Num: [17/600] Discriminator Loss: 0.9113, Generator Loss: 2.3876 D(x): 0.7702, D(G(z)): 0.2827 Epoch: [4/20], Batch Num: [18/600] Discriminator Loss: 0.9166, Generator Loss: 2.3574 D(x): 0.7549, D(G(z)): 0.2144 Epoch: [4/20], Batch Num: [19/600] Discriminator Loss: 0.8287, Generator Loss: 2.1857 D(x): 0.7543, D(G(z)): 0.2291 Epoch: [4/20], Batch Num: [20/600] Discriminator Loss: 0.9822, Generator Loss: 1.7439 D(x): 0.7458, D(G(z)): 0.2911 Epoch: [4/20], Batch Num: [21/600] Discriminator Loss: 0.8953, Generator Loss: 1.8658 D(x): 0.8249, D(G(z)): 0.3607 Epoch: [4/20], Batch Num: [22/600] Discriminator Loss: 0.9584, Generator Loss: 2.1011 D(x): 0.7442, D(G(z)): 0.3069 Epoch: [4/20], Batch Num: [23/600] Discriminator Loss: 0.8820, Generator Loss: 2.1118 D(x): 0.7533, D(G(z)): 0.2253 Epoch: [4/20], Batch Num: [24/600] Discriminator Loss: 0.8309, Generator Loss: 1.7859 D(x): 0.7630, D(G(z)): 0.2160 Epoch: [4/20], Batch Num: [25/600] Discriminator Loss: 0.9380, Generator Loss: 1.8391 D(x): 0.7727, D(G(z)): 0.2796 Epoch: [4/20], Batch Num: [26/600] Discriminator Loss: 0.6189, Generator Loss: 2.0211 D(x): 0.8518, D(G(z)): 0.2620 Epoch: [4/20], Batch Num: [27/600] Discriminator Loss: 0.5453, Generator Loss: 2.5463 D(x): 0.8414, D(G(z)): 0.1801 Epoch: [4/20], Batch Num: [28/600] Discriminator Loss: 0.4709, Generator Loss: 2.3146 D(x): 0.8762, D(G(z)): 0.1905 Epoch: [4/20], Batch Num: [29/600] Discriminator Loss: 0.5348, Generator Loss: 3.1495 D(x): 0.8482, D(G(z)): 0.2012 Epoch: [4/20], Batch Num: [30/600] Discriminator Loss: 0.4687, Generator Loss: 3.2118 D(x): 0.8318, D(G(z)): 0.1418 Epoch: [4/20], Batch Num: [31/600] Discriminator Loss: 0.5241, Generator Loss: 3.3031 D(x): 0.8299, D(G(z)): 0.1745 Epoch: [4/20], Batch Num: [32/600] Discriminator Loss: 0.4251, Generator Loss: 2.8917 D(x): 0.8817, D(G(z)): 0.1444 Epoch: [4/20], Batch Num: [33/600] Discriminator Loss: 0.4778, Generator Loss: 2.9496 D(x): 0.8745, D(G(z)): 0.1664 Epoch: [4/20], Batch Num: [34/600] Discriminator Loss: 0.5435, Generator Loss: 2.6186 D(x): 0.8936, D(G(z)): 0.2244 Epoch: [4/20], Batch Num: [35/600] Discriminator Loss: 0.5906, Generator Loss: 2.9062 D(x): 0.8343, D(G(z)): 0.1934 Epoch: [4/20], Batch Num: [36/600] Discriminator Loss: 0.4723, Generator Loss: 3.4450 D(x): 0.9144, D(G(z)): 0.2263 Epoch: [4/20], Batch Num: [37/600] Discriminator Loss: 0.4924, Generator Loss: 3.2847 D(x): 0.8784, D(G(z)): 0.1905 Epoch: [4/20], Batch Num: [38/600] Discriminator Loss: 0.5437, Generator Loss: 4.0449 D(x): 0.8878, D(G(z)): 0.1965 Epoch: [4/20], Batch Num: [39/600] Discriminator Loss: 0.6109, Generator Loss: 3.8794 D(x): 0.8292, D(G(z)): 0.2007 Epoch: [4/20], Batch Num: [40/600] Discriminator Loss: 0.6776, Generator Loss: 3.7801 D(x): 0.8027, D(G(z)): 0.1910 Epoch: [4/20], Batch Num: [41/600] Discriminator Loss: 0.6639, Generator Loss: 3.5049 D(x): 0.8418, D(G(z)): 0.2263 Epoch: [4/20], Batch Num: [42/600] Discriminator Loss: 0.6292, Generator Loss: 3.5813 D(x): 0.8592, D(G(z)): 0.2491 Epoch: 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1.7646 D(x): 0.6970, D(G(z)): 0.3785 Epoch: [4/20], Batch Num: [52/600] Discriminator Loss: 1.2647, Generator Loss: 2.1498 D(x): 0.7008, D(G(z)): 0.3888 Epoch: [4/20], Batch Num: [53/600] Discriminator Loss: 1.3669, Generator Loss: 2.4520 D(x): 0.6636, D(G(z)): 0.3456 Epoch: [4/20], Batch Num: [54/600] Discriminator Loss: 1.1242, Generator Loss: 2.4190 D(x): 0.6716, D(G(z)): 0.3014 Epoch: [4/20], Batch Num: [55/600] Discriminator Loss: 1.1088, Generator Loss: 2.3754 D(x): 0.6754, D(G(z)): 0.2662 Epoch: [4/20], Batch Num: [56/600] Discriminator Loss: 1.6154, Generator Loss: 1.8335 D(x): 0.5809, D(G(z)): 0.2630 Epoch: [4/20], Batch Num: [57/600] Discriminator Loss: 1.2033, Generator Loss: 1.5776 D(x): 0.6611, D(G(z)): 0.3243 Epoch: [4/20], Batch Num: [58/600] Discriminator Loss: 1.1196, Generator Loss: 1.7349 D(x): 0.7121, D(G(z)): 0.3526 Epoch: [4/20], Batch Num: [59/600] Discriminator Loss: 0.9003, Generator Loss: 2.2330 D(x): 0.8165, D(G(z)): 0.3689 Epoch: [4/20], Batch Num: [60/600] Discriminator Loss: 1.0585, Generator Loss: 2.8074 D(x): 0.7676, D(G(z)): 0.3086 Epoch: [4/20], Batch Num: [61/600] Discriminator Loss: 0.7240, Generator Loss: 2.8988 D(x): 0.7257, D(G(z)): 0.1331 Epoch: [4/20], Batch Num: [62/600] Discriminator Loss: 0.7907, Generator Loss: 3.2224 D(x): 0.7010, D(G(z)): 0.1446 Epoch: [4/20], Batch Num: [63/600] Discriminator Loss: 0.8263, Generator Loss: 2.8006 D(x): 0.6753, D(G(z)): 0.1416 Epoch: [4/20], Batch Num: [64/600] Discriminator Loss: 0.6115, Generator Loss: 2.2523 D(x): 0.7922, D(G(z)): 0.1665 Epoch: [4/20], Batch Num: [65/600] Discriminator Loss: 0.6237, Generator Loss: 2.2516 D(x): 0.7866, D(G(z)): 0.2035 Epoch: [4/20], Batch Num: [66/600] Discriminator Loss: 0.6341, Generator Loss: 2.3118 D(x): 0.8036, D(G(z)): 0.2299 Epoch: [4/20], Batch Num: [67/600] Discriminator Loss: 0.5855, Generator Loss: 2.1777 D(x): 0.8249, D(G(z)): 0.2409 Epoch: [4/20], Batch Num: [68/600] Discriminator Loss: 0.5469, Generator Loss: 2.1291 D(x): 0.8505, D(G(z)): 0.2448 Epoch: [4/20], Batch Num: [69/600] Discriminator Loss: 0.4966, Generator Loss: 2.8380 D(x): 0.8583, D(G(z)): 0.2072 Epoch: [4/20], Batch Num: [70/600] Discriminator Loss: 0.4030, Generator Loss: 2.6841 D(x): 0.8389, D(G(z)): 0.1513 Epoch: [4/20], Batch Num: [71/600] Discriminator Loss: 0.5494, Generator Loss: 2.7189 D(x): 0.7770, D(G(z)): 0.1321 Epoch: [4/20], Batch Num: [72/600] Discriminator Loss: 0.5551, Generator Loss: 2.5982 D(x): 0.8095, D(G(z)): 0.1656 Epoch: [4/20], Batch Num: [73/600] Discriminator Loss: 0.6499, Generator Loss: 2.4173 D(x): 0.7340, D(G(z)): 0.1520 Epoch: [4/20], Batch Num: [74/600] Discriminator Loss: 0.6650, Generator Loss: 1.9708 D(x): 0.7553, D(G(z)): 0.2030 Epoch: [4/20], Batch Num: [75/600] Discriminator Loss: 0.7329, Generator Loss: 1.7806 D(x): 0.7929, D(G(z)): 0.2780 Epoch: [4/20], Batch Num: [76/600] Discriminator Loss: 0.8439, Generator Loss: 1.7758 D(x): 0.7987, D(G(z)): 0.3474 Epoch: [4/20], Batch Num: [77/600] Discriminator Loss: 0.7301, Generator Loss: 2.0220 D(x): 0.8357, D(G(z)): 0.3384 Epoch: [4/20], Batch Num: [78/600] Discriminator Loss: 0.7964, Generator Loss: 2.4395 D(x): 0.7545, D(G(z)): 0.2351 Epoch: [4/20], Batch Num: [79/600] Discriminator Loss: 0.7949, Generator Loss: 2.2324 D(x): 0.7334, D(G(z)): 0.1813 Epoch: [4/20], Batch Num: [80/600] Discriminator Loss: 0.8938, Generator Loss: 2.0442 D(x): 0.6934, D(G(z)): 0.2070 Epoch: [4/20], Batch Num: [81/600] Discriminator Loss: 0.8193, Generator Loss: 1.7248 D(x): 0.7550, D(G(z)): 0.2570 Epoch: [4/20], Batch Num: [82/600] Discriminator Loss: 0.8544, Generator Loss: 1.6753 D(x): 0.7360, D(G(z)): 0.2781 Epoch: [4/20], Batch Num: [83/600] Discriminator Loss: 1.1570, Generator Loss: 1.6190 D(x): 0.7028, D(G(z)): 0.3209 Epoch: [4/20], Batch Num: [84/600] Discriminator Loss: 1.1277, Generator Loss: 1.4959 D(x): 0.7000, D(G(z)): 0.3398 Epoch: [4/20], Batch Num: [85/600] Discriminator Loss: 1.1168, Generator Loss: 1.6413 D(x): 0.7225, D(G(z)): 0.3614 Epoch: [4/20], Batch Num: [86/600] Discriminator Loss: 1.0778, Generator Loss: 1.8064 D(x): 0.7190, D(G(z)): 0.3214 Epoch: [4/20], Batch Num: [87/600] Discriminator Loss: 1.3657, Generator Loss: 1.6999 D(x): 0.6101, D(G(z)): 0.2911 Epoch: [4/20], Batch Num: [88/600] Discriminator Loss: 1.1532, Generator Loss: 1.5292 D(x): 0.6693, D(G(z)): 0.2940 Epoch: [4/20], Batch Num: [89/600] Discriminator Loss: 1.1041, Generator Loss: 1.6227 D(x): 0.7024, D(G(z)): 0.3371 Epoch: [4/20], Batch Num: [90/600] Discriminator Loss: 1.1751, Generator Loss: 1.5221 D(x): 0.6996, D(G(z)): 0.3742 Epoch: [4/20], Batch Num: [91/600] Discriminator Loss: 1.2925, Generator Loss: 1.5048 D(x): 0.6320, D(G(z)): 0.3318 Epoch: [4/20], Batch Num: [92/600] Discriminator Loss: 1.1627, Generator Loss: 1.5077 D(x): 0.6546, D(G(z)): 0.3122 Epoch: [4/20], Batch Num: [93/600] Discriminator Loss: 0.9019, Generator Loss: 1.6068 D(x): 0.7797, D(G(z)): 0.3315 Epoch: [4/20], Batch Num: [94/600] Discriminator Loss: 0.9630, Generator Loss: 1.6847 D(x): 0.7364, D(G(z)): 0.3380 Epoch: [4/20], Batch Num: [95/600] Discriminator Loss: 0.8208, Generator Loss: 1.9319 D(x): 0.7198, D(G(z)): 0.2531 Epoch: [4/20], Batch Num: [96/600] Discriminator Loss: 0.8741, Generator Loss: 2.0525 D(x): 0.6997, D(G(z)): 0.2276 Epoch: [4/20], Batch Num: [97/600] Discriminator Loss: 0.6461, Generator Loss: 2.0544 D(x): 0.7828, D(G(z)): 0.2069 Epoch: [4/20], Batch Num: [98/600] Discriminator Loss: 0.7573, Generator Loss: 2.1430 D(x): 0.7518, D(G(z)): 0.2256 Epoch: [4/20], Batch Num: [99/600] Discriminator Loss: 0.7445, Generator Loss: 2.0152 D(x): 0.7408, D(G(z)): 0.2137 Epoch: 4, Batch Num: [100/600]
Epoch: [4/20], Batch Num: [100/600] Discriminator Loss: 0.5353, Generator Loss: 2.0367 D(x): 0.8476, D(G(z)): 0.2245 Epoch: [4/20], Batch Num: [101/600] Discriminator Loss: 0.6495, Generator Loss: 2.1879 D(x): 0.8133, D(G(z)): 0.2505 Epoch: [4/20], Batch Num: [102/600] Discriminator Loss: 0.5685, Generator Loss: 2.1324 D(x): 0.8268, D(G(z)): 0.2269 Epoch: [4/20], Batch Num: [103/600] Discriminator Loss: 0.5470, Generator Loss: 2.3026 D(x): 0.8257, D(G(z)): 0.1876 Epoch: [4/20], Batch Num: [104/600] Discriminator Loss: 0.5208, Generator Loss: 2.2978 D(x): 0.8126, D(G(z)): 0.1614 Epoch: [4/20], Batch Num: [105/600] Discriminator Loss: 0.5772, Generator Loss: 2.3150 D(x): 0.8349, D(G(z)): 0.1856 Epoch: [4/20], Batch Num: [106/600] Discriminator Loss: 0.5748, Generator Loss: 2.4676 D(x): 0.7882, D(G(z)): 0.1658 Epoch: [4/20], Batch Num: [107/600] Discriminator Loss: 0.5520, Generator Loss: 2.4102 D(x): 0.8087, D(G(z)): 0.1707 Epoch: [4/20], Batch Num: [108/600] Discriminator Loss: 0.4689, Generator Loss: 2.1225 D(x): 0.8451, D(G(z)): 0.1783 Epoch: [4/20], Batch Num: [109/600] Discriminator Loss: 0.4854, Generator Loss: 2.1683 D(x): 0.8585, D(G(z)): 0.1779 Epoch: [4/20], Batch Num: [110/600] Discriminator Loss: 0.6116, Generator Loss: 2.0760 D(x): 0.7742, D(G(z)): 0.1746 Epoch: [4/20], Batch Num: [111/600] Discriminator Loss: 0.6864, Generator Loss: 1.9597 D(x): 0.8033, D(G(z)): 0.2216 Epoch: [4/20], Batch Num: [112/600] Discriminator Loss: 0.5757, Generator Loss: 1.9623 D(x): 0.8509, D(G(z)): 0.2448 Epoch: [4/20], Batch Num: [113/600] Discriminator Loss: 0.5465, Generator Loss: 2.1217 D(x): 0.8782, D(G(z)): 0.2734 Epoch: [4/20], Batch Num: [114/600] Discriminator Loss: 0.5871, Generator Loss: 2.2186 D(x): 0.8267, D(G(z)): 0.2262 Epoch: [4/20], Batch Num: [115/600] Discriminator Loss: 0.4191, Generator Loss: 2.4645 D(x): 0.8906, D(G(z)): 0.2011 Epoch: [4/20], Batch Num: [116/600] Discriminator Loss: 0.4664, Generator Loss: 2.7811 D(x): 0.8356, D(G(z)): 0.1332 Epoch: [4/20], Batch Num: [117/600] Discriminator Loss: 0.3782, Generator Loss: 2.7524 D(x): 0.8518, D(G(z)): 0.1024 Epoch: [4/20], Batch Num: [118/600] Discriminator Loss: 0.4281, Generator Loss: 2.6413 D(x): 0.8473, D(G(z)): 0.1232 Epoch: [4/20], Batch Num: [119/600] Discriminator Loss: 0.5104, Generator Loss: 2.6894 D(x): 0.8247, D(G(z)): 0.1549 Epoch: [4/20], Batch Num: [120/600] Discriminator Loss: 0.3325, Generator Loss: 2.3191 D(x): 0.8992, D(G(z)): 0.1480 Epoch: [4/20], Batch Num: [121/600] Discriminator Loss: 0.3839, Generator Loss: 2.5137 D(x): 0.8866, D(G(z)): 0.1531 Epoch: [4/20], Batch Num: [122/600] Discriminator Loss: 0.4256, Generator Loss: 2.4550 D(x): 0.8824, D(G(z)): 0.1748 Epoch: [4/20], Batch Num: [123/600] Discriminator Loss: 0.5010, Generator Loss: 2.6115 D(x): 0.8909, D(G(z)): 0.2332 Epoch: [4/20], Batch Num: [124/600] Discriminator Loss: 0.3653, Generator Loss: 2.6082 D(x): 0.9084, D(G(z)): 0.1735 Epoch: [4/20], Batch Num: [125/600] Discriminator Loss: 0.4490, Generator Loss: 2.8387 D(x): 0.8813, D(G(z)): 0.2045 Epoch: [4/20], Batch Num: [126/600] Discriminator Loss: 0.4041, Generator Loss: 3.2363 D(x): 0.8752, D(G(z)): 0.1526 Epoch: [4/20], Batch Num: [127/600] Discriminator Loss: 0.5296, Generator Loss: 3.0431 D(x): 0.8205, D(G(z)): 0.1498 Epoch: [4/20], Batch Num: [128/600] Discriminator Loss: 0.4584, Generator Loss: 2.8624 D(x): 0.8744, D(G(z)): 0.1607 Epoch: [4/20], Batch Num: [129/600] Discriminator Loss: 0.5350, Generator Loss: 2.6755 D(x): 0.8302, D(G(z)): 0.1554 Epoch: [4/20], Batch Num: [130/600] Discriminator Loss: 0.4599, Generator Loss: 2.7584 D(x): 0.8392, D(G(z)): 0.1436 Epoch: [4/20], Batch Num: [131/600] Discriminator Loss: 0.5134, Generator Loss: 2.2034 D(x): 0.8295, D(G(z)): 0.1687 Epoch: [4/20], Batch Num: [132/600] Discriminator Loss: 0.5917, Generator Loss: 2.0570 D(x): 0.8542, D(G(z)): 0.2225 Epoch: [4/20], Batch Num: [133/600] Discriminator Loss: 0.4278, Generator Loss: 2.0602 D(x): 0.9290, D(G(z)): 0.2489 Epoch: [4/20], Batch Num: [134/600] Discriminator Loss: 0.6368, Generator Loss: 2.1749 D(x): 0.8579, D(G(z)): 0.2610 Epoch: [4/20], Batch Num: [135/600] Discriminator Loss: 0.6903, Generator Loss: 2.4526 D(x): 0.8326, D(G(z)): 0.2562 Epoch: [4/20], Batch Num: [136/600] Discriminator Loss: 0.7028, Generator Loss: 2.4300 D(x): 0.7936, D(G(z)): 0.2021 Epoch: [4/20], Batch Num: [137/600] Discriminator Loss: 0.5747, Generator Loss: 2.3913 D(x): 0.8120, D(G(z)): 0.1821 Epoch: [4/20], Batch Num: [138/600] Discriminator Loss: 0.5317, Generator Loss: 2.2775 D(x): 0.8380, D(G(z)): 0.1959 Epoch: [4/20], Batch Num: [139/600] Discriminator Loss: 0.7524, Generator Loss: 1.8715 D(x): 0.7669, D(G(z)): 0.2151 Epoch: [4/20], Batch Num: [140/600] Discriminator Loss: 0.6250, Generator Loss: 1.9637 D(x): 0.8605, D(G(z)): 0.2537 Epoch: [4/20], Batch Num: [141/600] Discriminator Loss: 0.6310, Generator Loss: 1.6493 D(x): 0.8389, D(G(z)): 0.2611 Epoch: [4/20], Batch Num: [142/600] Discriminator Loss: 0.5819, Generator Loss: 1.9594 D(x): 0.8669, D(G(z)): 0.2677 Epoch: [4/20], Batch Num: [143/600] Discriminator Loss: 0.5348, Generator Loss: 2.2844 D(x): 0.8739, D(G(z)): 0.2562 Epoch: [4/20], Batch Num: [144/600] Discriminator Loss: 0.6084, Generator Loss: 2.3673 D(x): 0.8106, D(G(z)): 0.1883 Epoch: [4/20], Batch Num: [145/600] Discriminator Loss: 0.8858, Generator Loss: 2.1949 D(x): 0.6940, D(G(z)): 0.1828 Epoch: [4/20], Batch Num: [146/600] Discriminator Loss: 0.5965, Generator Loss: 1.7365 D(x): 0.8210, D(G(z)): 0.1982 Epoch: [4/20], Batch Num: [147/600] Discriminator Loss: 0.6035, Generator Loss: 1.7473 D(x): 0.8709, D(G(z)): 0.2586 Epoch: [4/20], Batch Num: [148/600] Discriminator Loss: 0.7371, Generator Loss: 1.6417 D(x): 0.8102, D(G(z)): 0.2671 Epoch: [4/20], Batch Num: [149/600] Discriminator Loss: 0.5687, Generator Loss: 1.6668 D(x): 0.8618, D(G(z)): 0.2511 Epoch: [4/20], Batch Num: [150/600] Discriminator Loss: 0.7725, Generator Loss: 1.7915 D(x): 0.7805, D(G(z)): 0.2693 Epoch: [4/20], Batch Num: [151/600] Discriminator Loss: 0.5722, Generator Loss: 2.0210 D(x): 0.8641, D(G(z)): 0.2606 Epoch: [4/20], Batch Num: [152/600] Discriminator Loss: 0.4814, Generator Loss: 2.1347 D(x): 0.8760, D(G(z)): 0.2195 Epoch: [4/20], Batch Num: [153/600] Discriminator Loss: 0.5591, Generator Loss: 2.1563 D(x): 0.8369, D(G(z)): 0.1832 Epoch: [4/20], Batch Num: [154/600] Discriminator Loss: 0.6564, Generator Loss: 2.0649 D(x): 0.7990, D(G(z)): 0.1697 Epoch: [4/20], Batch Num: [155/600] Discriminator Loss: 0.6275, Generator Loss: 1.7977 D(x): 0.8032, D(G(z)): 0.1634 Epoch: [4/20], Batch Num: [156/600] Discriminator Loss: 0.5727, Generator Loss: 1.6616 D(x): 0.8484, D(G(z)): 0.2122 Epoch: [4/20], Batch Num: [157/600] Discriminator Loss: 0.8216, Generator Loss: 1.4746 D(x): 0.7683, D(G(z)): 0.2765 Epoch: [4/20], Batch Num: [158/600] Discriminator Loss: 0.7157, Generator Loss: 1.4628 D(x): 0.8483, D(G(z)): 0.3030 Epoch: [4/20], Batch Num: [159/600] Discriminator Loss: 0.6046, Generator Loss: 2.0432 D(x): 0.9043, D(G(z)): 0.3278 Epoch: [4/20], Batch Num: [160/600] Discriminator Loss: 0.5895, Generator Loss: 2.4259 D(x): 0.8415, D(G(z)): 0.2189 Epoch: [4/20], Batch Num: [161/600] Discriminator Loss: 0.6661, Generator Loss: 2.7241 D(x): 0.8250, D(G(z)): 0.1497 Epoch: [4/20], Batch Num: [162/600] Discriminator Loss: 0.8017, Generator Loss: 2.5841 D(x): 0.7372, D(G(z)): 0.1355 Epoch: [4/20], Batch Num: [163/600] Discriminator Loss: 0.8714, Generator Loss: 2.1747 D(x): 0.7026, D(G(z)): 0.1556 Epoch: [4/20], Batch Num: [164/600] Discriminator Loss: 0.7139, Generator Loss: 1.4481 D(x): 0.7600, D(G(z)): 0.1785 Epoch: [4/20], Batch Num: [165/600] Discriminator Loss: 0.7627, Generator Loss: 1.2305 D(x): 0.8540, D(G(z)): 0.3135 Epoch: [4/20], Batch Num: [166/600] Discriminator Loss: 0.8618, Generator Loss: 1.7235 D(x): 0.8694, D(G(z)): 0.3844 Epoch: [4/20], Batch Num: [167/600] Discriminator Loss: 0.8407, Generator Loss: 2.2605 D(x): 0.8573, D(G(z)): 0.3219 Epoch: [4/20], Batch Num: [168/600] Discriminator Loss: 0.7631, Generator Loss: 2.4893 D(x): 0.7692, D(G(z)): 0.1689 Epoch: [4/20], Batch Num: [169/600] Discriminator Loss: 0.7646, Generator Loss: 2.6068 D(x): 0.7241, D(G(z)): 0.1417 Epoch: [4/20], Batch Num: [170/600] Discriminator Loss: 0.7477, Generator Loss: 2.3811 D(x): 0.7258, D(G(z)): 0.1282 Epoch: [4/20], Batch Num: [171/600] Discriminator Loss: 0.6111, Generator Loss: 2.0542 D(x): 0.8254, D(G(z)): 0.1888 Epoch: [4/20], Batch Num: [172/600] Discriminator Loss: 0.7299, Generator Loss: 1.6571 D(x): 0.7694, D(G(z)): 0.2176 Epoch: [4/20], Batch Num: [173/600] Discriminator Loss: 0.6923, Generator Loss: 1.4876 D(x): 0.8058, D(G(z)): 0.2440 Epoch: [4/20], Batch Num: [174/600] Discriminator Loss: 0.6572, Generator Loss: 1.5752 D(x): 0.9153, D(G(z)): 0.3414 Epoch: [4/20], Batch Num: [175/600] Discriminator Loss: 0.7194, Generator Loss: 1.7747 D(x): 0.8651, D(G(z)): 0.3150 Epoch: [4/20], Batch Num: [176/600] Discriminator Loss: 0.6546, Generator Loss: 2.0626 D(x): 0.8597, D(G(z)): 0.2677 Epoch: [4/20], Batch Num: [177/600] Discriminator Loss: 0.5279, Generator Loss: 2.4704 D(x): 0.8389, D(G(z)): 0.1710 Epoch: [4/20], Batch Num: [178/600] Discriminator Loss: 0.5834, Generator Loss: 2.4160 D(x): 0.7913, D(G(z)): 0.1524 Epoch: [4/20], Batch Num: [179/600] Discriminator Loss: 0.4184, Generator Loss: 2.5613 D(x): 0.8739, D(G(z)): 0.1447 Epoch: [4/20], Batch Num: [180/600] Discriminator Loss: 0.4711, Generator Loss: 2.3905 D(x): 0.8415, D(G(z)): 0.1577 Epoch: [4/20], Batch Num: [181/600] Discriminator Loss: 0.4664, Generator Loss: 2.1435 D(x): 0.8539, D(G(z)): 0.1368 Epoch: [4/20], Batch Num: [182/600] Discriminator Loss: 0.3615, Generator Loss: 2.1071 D(x): 0.9014, D(G(z)): 0.1634 Epoch: [4/20], Batch Num: [183/600] Discriminator Loss: 0.3650, Generator Loss: 2.0975 D(x): 0.9325, D(G(z)): 0.1996 Epoch: [4/20], Batch Num: [184/600] Discriminator Loss: 0.3722, Generator Loss: 1.9595 D(x): 0.9059, D(G(z)): 0.1764 Epoch: [4/20], Batch Num: [185/600] Discriminator Loss: 0.3922, Generator Loss: 2.2727 D(x): 0.9260, D(G(z)): 0.2004 Epoch: [4/20], Batch Num: [186/600] Discriminator Loss: 0.3733, Generator Loss: 2.3029 D(x): 0.9370, D(G(z)): 0.2026 Epoch: [4/20], Batch Num: [187/600] Discriminator Loss: 0.4191, Generator Loss: 2.5430 D(x): 0.9009, D(G(z)): 0.1835 Epoch: [4/20], Batch Num: [188/600] Discriminator Loss: 0.3600, Generator Loss: 3.0321 D(x): 0.8830, D(G(z)): 0.1336 Epoch: [4/20], Batch Num: [189/600] Discriminator Loss: 0.3015, Generator Loss: 3.2118 D(x): 0.8949, D(G(z)): 0.1139 Epoch: [4/20], Batch Num: [190/600] Discriminator Loss: 0.3288, Generator Loss: 3.4060 D(x): 0.8864, D(G(z)): 0.1060 Epoch: [4/20], Batch Num: [191/600] Discriminator Loss: 0.3473, Generator Loss: 3.4819 D(x): 0.8882, D(G(z)): 0.1043 Epoch: [4/20], Batch Num: [192/600] Discriminator Loss: 0.4054, Generator Loss: 3.1180 D(x): 0.8822, D(G(z)): 0.1264 Epoch: [4/20], Batch Num: [193/600] Discriminator Loss: 0.1712, Generator Loss: 3.2598 D(x): 0.9503, D(G(z)): 0.0858 Epoch: [4/20], Batch Num: [194/600] Discriminator Loss: 0.2612, Generator Loss: 3.3709 D(x): 0.9376, D(G(z)): 0.1110 Epoch: [4/20], Batch Num: [195/600] Discriminator Loss: 0.2691, Generator Loss: 3.2847 D(x): 0.9294, D(G(z)): 0.1119 Epoch: [4/20], Batch Num: [196/600] Discriminator Loss: 0.2615, Generator Loss: 3.3738 D(x): 0.9276, D(G(z)): 0.1162 Epoch: [4/20], Batch Num: [197/600] Discriminator Loss: 0.2023, Generator Loss: 3.2981 D(x): 0.9449, D(G(z)): 0.1008 Epoch: [4/20], Batch Num: [198/600] Discriminator Loss: 0.2188, Generator Loss: 3.5744 D(x): 0.9573, D(G(z)): 0.1316 Epoch: [4/20], Batch Num: [199/600] Discriminator Loss: 0.2097, Generator Loss: 3.6835 D(x): 0.9609, D(G(z)): 0.1235 Epoch: 4, Batch Num: [200/600]
Epoch: [4/20], Batch Num: [200/600] Discriminator Loss: 0.1494, Generator Loss: 3.7953 D(x): 0.9596, D(G(z)): 0.0828 Epoch: [4/20], Batch Num: [201/600] Discriminator Loss: 0.2596, Generator Loss: 4.0962 D(x): 0.9139, D(G(z)): 0.0936 Epoch: [4/20], Batch Num: [202/600] Discriminator Loss: 0.2862, Generator Loss: 4.1589 D(x): 0.9017, D(G(z)): 0.0782 Epoch: [4/20], Batch Num: [203/600] Discriminator Loss: 0.1413, Generator Loss: 3.9781 D(x): 0.9600, D(G(z)): 0.0762 Epoch: [4/20], Batch Num: [204/600] Discriminator Loss: 0.2785, Generator Loss: 3.9526 D(x): 0.9059, D(G(z)): 0.0734 Epoch: [4/20], Batch Num: [205/600] Discriminator Loss: 0.2195, Generator Loss: 3.4561 D(x): 0.9436, D(G(z)): 0.0905 Epoch: [4/20], Batch Num: [206/600] Discriminator Loss: 0.1482, Generator Loss: 3.6127 D(x): 0.9616, D(G(z)): 0.0839 Epoch: [4/20], Batch Num: [207/600] Discriminator Loss: 0.3103, Generator Loss: 3.3224 D(x): 0.9242, D(G(z)): 0.1140 Epoch: [4/20], Batch Num: [208/600] Discriminator Loss: 0.1709, Generator Loss: 3.6202 D(x): 0.9748, D(G(z)): 0.1188 Epoch: [4/20], Batch Num: [209/600] Discriminator Loss: 0.2332, Generator Loss: 3.4184 D(x): 0.9390, D(G(z)): 0.0909 Epoch: [4/20], Batch Num: [210/600] Discriminator Loss: 0.3285, Generator Loss: 3.4677 D(x): 0.9061, D(G(z)): 0.1123 Epoch: [4/20], Batch Num: [211/600] Discriminator Loss: 0.2583, Generator Loss: 3.7842 D(x): 0.9478, D(G(z)): 0.1393 Epoch: [4/20], Batch Num: [212/600] Discriminator Loss: 0.2729, Generator Loss: 3.8552 D(x): 0.9417, D(G(z)): 0.1407 Epoch: [4/20], Batch Num: [213/600] Discriminator Loss: 0.3007, Generator Loss: 3.7963 D(x): 0.9445, D(G(z)): 0.1592 Epoch: [4/20], Batch Num: [214/600] Discriminator Loss: 0.3342, Generator Loss: 3.8099 D(x): 0.9245, D(G(z)): 0.1351 Epoch: [4/20], Batch Num: [215/600] Discriminator Loss: 0.6233, Generator Loss: 3.2289 D(x): 0.8639, D(G(z)): 0.1963 Epoch: [4/20], Batch Num: [216/600] Discriminator Loss: 0.4684, Generator Loss: 3.1504 D(x): 0.8890, D(G(z)): 0.1869 Epoch: [4/20], Batch Num: [217/600] Discriminator Loss: 0.8934, Generator Loss: 2.4507 D(x): 0.8575, D(G(z)): 0.3045 Epoch: [4/20], Batch Num: [218/600] Discriminator Loss: 0.7931, Generator Loss: 2.7251 D(x): 0.9149, D(G(z)): 0.3383 Epoch: [4/20], Batch Num: [219/600] Discriminator Loss: 0.5959, Generator Loss: 3.3568 D(x): 0.9318, D(G(z)): 0.2708 Epoch: [4/20], Batch Num: [220/600] Discriminator Loss: 0.9202, Generator Loss: 3.1131 D(x): 0.7750, D(G(z)): 0.2855 Epoch: [4/20], Batch Num: [221/600] Discriminator Loss: 0.8738, Generator Loss: 2.5220 D(x): 0.7819, D(G(z)): 0.2464 Epoch: [4/20], Batch Num: [222/600] Discriminator Loss: 0.9131, Generator Loss: 2.2916 D(x): 0.8389, D(G(z)): 0.3057 Epoch: [4/20], Batch Num: [223/600] Discriminator Loss: 1.0076, Generator Loss: 2.1588 D(x): 0.7963, D(G(z)): 0.2920 Epoch: [4/20], Batch Num: [224/600] Discriminator Loss: 0.7911, Generator Loss: 2.4000 D(x): 0.8260, D(G(z)): 0.2817 Epoch: [4/20], Batch Num: [225/600] Discriminator Loss: 0.9034, Generator Loss: 2.5385 D(x): 0.7671, D(G(z)): 0.2666 Epoch: [4/20], Batch Num: [226/600] Discriminator Loss: 0.8270, Generator Loss: 2.1412 D(x): 0.7893, D(G(z)): 0.2306 Epoch: [4/20], Batch Num: [227/600] Discriminator Loss: 0.7200, Generator Loss: 2.1952 D(x): 0.7960, D(G(z)): 0.2187 Epoch: [4/20], Batch Num: [228/600] Discriminator Loss: 0.6635, Generator Loss: 2.2231 D(x): 0.8114, D(G(z)): 0.2199 Epoch: [4/20], Batch Num: [229/600] Discriminator Loss: 0.8285, Generator Loss: 2.2247 D(x): 0.7915, D(G(z)): 0.2824 Epoch: [4/20], Batch Num: [230/600] Discriminator Loss: 0.6833, Generator Loss: 2.5030 D(x): 0.7936, D(G(z)): 0.2235 Epoch: [4/20], Batch Num: [231/600] Discriminator Loss: 0.6393, Generator Loss: 2.0583 D(x): 0.7688, D(G(z)): 0.1718 Epoch: [4/20], Batch Num: [232/600] Discriminator Loss: 0.6656, Generator Loss: 1.7046 D(x): 0.7832, D(G(z)): 0.1878 Epoch: [4/20], Batch Num: [233/600] Discriminator Loss: 0.5613, Generator Loss: 1.8604 D(x): 0.8871, D(G(z)): 0.2898 Epoch: [4/20], Batch Num: [234/600] Discriminator Loss: 0.7125, Generator Loss: 2.2000 D(x): 0.8907, D(G(z)): 0.3443 Epoch: [4/20], Batch Num: [235/600] Discriminator Loss: 0.5853, Generator Loss: 2.7973 D(x): 0.8563, D(G(z)): 0.2392 Epoch: [4/20], Batch Num: [236/600] Discriminator Loss: 0.5539, Generator Loss: 3.1805 D(x): 0.8303, D(G(z)): 0.1899 Epoch: [4/20], Batch Num: [237/600] Discriminator Loss: 0.6221, Generator Loss: 3.2868 D(x): 0.7466, D(G(z)): 0.1206 Epoch: [4/20], Batch Num: [238/600] Discriminator Loss: 0.6669, Generator Loss: 2.9335 D(x): 0.7767, D(G(z)): 0.1781 Epoch: [4/20], Batch Num: [239/600] Discriminator Loss: 0.5545, Generator Loss: 2.6635 D(x): 0.8876, D(G(z)): 0.2468 Epoch: [4/20], Batch Num: [240/600] Discriminator Loss: 0.6239, Generator Loss: 2.6788 D(x): 0.8780, D(G(z)): 0.2717 Epoch: [4/20], Batch Num: [241/600] Discriminator Loss: 0.5922, Generator Loss: 3.3478 D(x): 0.8721, D(G(z)): 0.2476 Epoch: [4/20], Batch Num: [242/600] Discriminator Loss: 0.6305, Generator Loss: 3.1887 D(x): 0.8318, D(G(z)): 0.1905 Epoch: [4/20], Batch Num: [243/600] Discriminator Loss: 0.7785, Generator Loss: 3.2483 D(x): 0.7644, D(G(z)): 0.1680 Epoch: [4/20], Batch Num: [244/600] Discriminator Loss: 0.8425, Generator Loss: 2.7428 D(x): 0.7637, D(G(z)): 0.1768 Epoch: [4/20], Batch Num: [245/600] Discriminator Loss: 0.9844, Generator Loss: 2.4248 D(x): 0.8032, D(G(z)): 0.3078 Epoch: [4/20], Batch Num: [246/600] Discriminator Loss: 0.9751, Generator Loss: 2.3906 D(x): 0.7749, D(G(z)): 0.3084 Epoch: [4/20], Batch Num: [247/600] Discriminator Loss: 1.1637, Generator Loss: 2.2088 D(x): 0.7635, D(G(z)): 0.3434 Epoch: [4/20], Batch Num: [248/600] Discriminator Loss: 1.1464, Generator Loss: 2.0754 D(x): 0.7352, D(G(z)): 0.3454 Epoch: [4/20], Batch Num: [249/600] Discriminator Loss: 1.0805, Generator Loss: 2.0435 D(x): 0.7504, D(G(z)): 0.3055 Epoch: [4/20], Batch Num: [250/600] Discriminator Loss: 1.3142, Generator Loss: 1.7733 D(x): 0.6957, D(G(z)): 0.3388 Epoch: [4/20], Batch Num: [251/600] Discriminator Loss: 1.4152, Generator Loss: 1.8049 D(x): 0.6753, D(G(z)): 0.3653 Epoch: [4/20], Batch Num: [252/600] Discriminator Loss: 1.3618, Generator Loss: 1.9036 D(x): 0.7282, D(G(z)): 0.4030 Epoch: [4/20], Batch Num: [253/600] Discriminator Loss: 1.4963, Generator Loss: 1.7318 D(x): 0.5845, D(G(z)): 0.3007 Epoch: [4/20], Batch Num: [254/600] Discriminator Loss: 1.3863, Generator Loss: 1.3901 D(x): 0.6283, D(G(z)): 0.3316 Epoch: [4/20], Batch Num: [255/600] Discriminator Loss: 1.4322, Generator Loss: 1.4458 D(x): 0.6968, D(G(z)): 0.4034 Epoch: [4/20], Batch Num: [256/600] Discriminator Loss: 1.4045, Generator Loss: 1.8044 D(x): 0.7020, D(G(z)): 0.4015 Epoch: [4/20], Batch Num: [257/600] Discriminator Loss: 1.4247, Generator Loss: 1.8850 D(x): 0.6520, D(G(z)): 0.3574 Epoch: [4/20], Batch Num: [258/600] Discriminator Loss: 0.9649, Generator Loss: 2.1860 D(x): 0.7004, D(G(z)): 0.2461 Epoch: [4/20], Batch Num: [259/600] Discriminator Loss: 0.9932, Generator Loss: 1.9594 D(x): 0.6720, D(G(z)): 0.2253 Epoch: [4/20], Batch Num: [260/600] Discriminator Loss: 0.8596, Generator Loss: 2.0524 D(x): 0.6939, D(G(z)): 0.2145 Epoch: [4/20], Batch Num: [261/600] Discriminator Loss: 0.9179, Generator Loss: 1.8591 D(x): 0.6886, D(G(z)): 0.2394 Epoch: [4/20], Batch Num: [262/600] Discriminator Loss: 0.8887, Generator Loss: 1.9391 D(x): 0.7033, D(G(z)): 0.2292 Epoch: [4/20], Batch Num: [263/600] Discriminator Loss: 0.8564, Generator Loss: 1.8659 D(x): 0.7486, D(G(z)): 0.2909 Epoch: [4/20], Batch Num: [264/600] Discriminator Loss: 0.8560, Generator Loss: 1.8757 D(x): 0.7418, D(G(z)): 0.2654 Epoch: [4/20], Batch Num: [265/600] Discriminator Loss: 0.9574, Generator Loss: 2.0252 D(x): 0.7471, D(G(z)): 0.3043 Epoch: [4/20], Batch Num: [266/600] Discriminator Loss: 0.9616, Generator Loss: 2.1662 D(x): 0.6861, D(G(z)): 0.2161 Epoch: [4/20], Batch Num: [267/600] Discriminator Loss: 0.9604, Generator Loss: 2.2492 D(x): 0.6961, D(G(z)): 0.2474 Epoch: [4/20], Batch Num: [268/600] Discriminator Loss: 0.9184, Generator Loss: 2.2810 D(x): 0.6857, D(G(z)): 0.2267 Epoch: [4/20], Batch Num: [269/600] Discriminator Loss: 0.7984, Generator Loss: 2.1018 D(x): 0.7384, D(G(z)): 0.2216 Epoch: [4/20], Batch Num: [270/600] Discriminator Loss: 0.7874, Generator Loss: 2.3204 D(x): 0.7469, D(G(z)): 0.2261 Epoch: [4/20], Batch Num: [271/600] Discriminator Loss: 0.8304, Generator Loss: 2.0973 D(x): 0.7095, D(G(z)): 0.2007 Epoch: [4/20], Batch Num: [272/600] Discriminator Loss: 0.9132, Generator Loss: 2.1144 D(x): 0.6882, D(G(z)): 0.2254 Epoch: [4/20], Batch Num: [273/600] Discriminator Loss: 0.7844, Generator Loss: 2.0565 D(x): 0.7742, D(G(z)): 0.2361 Epoch: [4/20], Batch Num: [274/600] Discriminator Loss: 0.7459, Generator Loss: 1.8763 D(x): 0.7577, D(G(z)): 0.2267 Epoch: [4/20], Batch Num: [275/600] Discriminator Loss: 0.8017, Generator Loss: 1.9746 D(x): 0.7755, D(G(z)): 0.2706 Epoch: [4/20], Batch Num: [276/600] Discriminator Loss: 0.8192, Generator Loss: 1.9479 D(x): 0.7580, D(G(z)): 0.2251 Epoch: [4/20], Batch Num: [277/600] Discriminator Loss: 0.9205, Generator Loss: 1.6876 D(x): 0.7129, D(G(z)): 0.2493 Epoch: [4/20], Batch Num: [278/600] Discriminator Loss: 0.6378, Generator Loss: 1.9553 D(x): 0.8362, D(G(z)): 0.2551 Epoch: [4/20], Batch Num: [279/600] Discriminator Loss: 0.7818, Generator Loss: 2.0457 D(x): 0.7417, D(G(z)): 0.2441 Epoch: [4/20], Batch Num: [280/600] Discriminator Loss: 0.6839, Generator Loss: 2.0988 D(x): 0.7848, D(G(z)): 0.2312 Epoch: [4/20], Batch Num: [281/600] Discriminator Loss: 0.6898, Generator Loss: 2.0105 D(x): 0.7881, D(G(z)): 0.2418 Epoch: [4/20], Batch Num: [282/600] Discriminator Loss: 0.6602, Generator Loss: 2.0981 D(x): 0.7691, D(G(z)): 0.2118 Epoch: [4/20], Batch Num: [283/600] Discriminator Loss: 0.6147, Generator Loss: 2.1069 D(x): 0.8436, D(G(z)): 0.2364 Epoch: [4/20], Batch Num: [284/600] Discriminator Loss: 0.7419, Generator Loss: 1.9998 D(x): 0.7442, D(G(z)): 0.2099 Epoch: [4/20], Batch Num: [285/600] Discriminator Loss: 0.7508, Generator Loss: 1.6254 D(x): 0.7822, D(G(z)): 0.2377 Epoch: [4/20], Batch Num: [286/600] Discriminator Loss: 0.6510, Generator Loss: 1.6797 D(x): 0.8102, D(G(z)): 0.2503 Epoch: [4/20], Batch Num: [287/600] Discriminator Loss: 0.7873, Generator Loss: 1.7454 D(x): 0.7806, D(G(z)): 0.2444 Epoch: [4/20], Batch Num: [288/600] Discriminator Loss: 0.7687, Generator Loss: 1.7321 D(x): 0.7971, D(G(z)): 0.3047 Epoch: [4/20], Batch Num: [289/600] Discriminator Loss: 0.6993, Generator Loss: 1.7383 D(x): 0.8383, D(G(z)): 0.2735 Epoch: [4/20], Batch Num: [290/600] Discriminator Loss: 0.7932, Generator Loss: 1.9629 D(x): 0.7624, D(G(z)): 0.2634 Epoch: [4/20], Batch Num: [291/600] Discriminator Loss: 0.5427, Generator Loss: 2.0280 D(x): 0.8414, D(G(z)): 0.2201 Epoch: [4/20], Batch Num: [292/600] Discriminator Loss: 0.7633, Generator Loss: 1.6948 D(x): 0.7350, D(G(z)): 0.2165 Epoch: [4/20], Batch Num: [293/600] Discriminator Loss: 0.5622, Generator Loss: 1.5974 D(x): 0.8492, D(G(z)): 0.2432 Epoch: [4/20], Batch Num: [294/600] Discriminator Loss: 0.7646, Generator Loss: 1.7102 D(x): 0.8175, D(G(z)): 0.3076 Epoch: [4/20], Batch Num: [295/600] Discriminator Loss: 0.8820, Generator Loss: 1.5967 D(x): 0.7773, D(G(z)): 0.2740 Epoch: [4/20], Batch Num: [296/600] Discriminator Loss: 0.7205, Generator Loss: 1.6529 D(x): 0.8122, D(G(z)): 0.2586 Epoch: [4/20], Batch Num: [297/600] Discriminator Loss: 0.7498, Generator Loss: 1.6273 D(x): 0.8033, D(G(z)): 0.2653 Epoch: [4/20], Batch Num: [298/600] Discriminator Loss: 0.6806, Generator Loss: 1.8160 D(x): 0.8043, D(G(z)): 0.2555 Epoch: [4/20], Batch Num: [299/600] Discriminator Loss: 0.6789, Generator Loss: 1.8051 D(x): 0.8244, D(G(z)): 0.2703 Epoch: 4, Batch Num: [300/600]
Epoch: [4/20], Batch Num: [300/600] Discriminator Loss: 0.7676, Generator Loss: 1.8864 D(x): 0.7888, D(G(z)): 0.2491 Epoch: [4/20], Batch Num: [301/600] Discriminator Loss: 0.6020, Generator Loss: 1.9384 D(x): 0.8680, D(G(z)): 0.2742 Epoch: [4/20], Batch Num: [302/600] Discriminator Loss: 0.6475, Generator Loss: 2.0322 D(x): 0.8111, D(G(z)): 0.2184 Epoch: [4/20], Batch Num: [303/600] Discriminator Loss: 0.5732, Generator Loss: 2.3202 D(x): 0.8312, D(G(z)): 0.1958 Epoch: [4/20], Batch Num: [304/600] Discriminator Loss: 0.5685, Generator Loss: 1.8872 D(x): 0.8000, D(G(z)): 0.1717 Epoch: [4/20], Batch Num: [305/600] Discriminator Loss: 0.5769, Generator Loss: 2.0611 D(x): 0.8328, D(G(z)): 0.2134 Epoch: [4/20], Batch Num: [306/600] Discriminator Loss: 0.4627, Generator Loss: 2.1373 D(x): 0.8642, D(G(z)): 0.1918 Epoch: [4/20], Batch Num: [307/600] Discriminator Loss: 0.4180, Generator Loss: 2.1140 D(x): 0.9037, D(G(z)): 0.2046 Epoch: [4/20], Batch Num: [308/600] Discriminator Loss: 0.4358, Generator Loss: 2.1135 D(x): 0.8776, D(G(z)): 0.1964 Epoch: [4/20], Batch Num: [309/600] Discriminator Loss: 0.4476, Generator Loss: 2.3735 D(x): 0.8696, D(G(z)): 0.1901 Epoch: [4/20], Batch Num: [310/600] Discriminator Loss: 0.5026, Generator Loss: 2.2876 D(x): 0.8544, D(G(z)): 0.1592 Epoch: [4/20], Batch Num: [311/600] Discriminator Loss: 0.5662, Generator Loss: 2.3063 D(x): 0.8146, D(G(z)): 0.1534 Epoch: [4/20], Batch Num: [312/600] Discriminator Loss: 0.5038, Generator Loss: 2.3810 D(x): 0.8351, D(G(z)): 0.1630 Epoch: [4/20], Batch Num: [313/600] Discriminator Loss: 0.4689, Generator Loss: 2.3193 D(x): 0.8525, D(G(z)): 0.1708 Epoch: [4/20], Batch Num: [314/600] Discriminator Loss: 0.3559, Generator Loss: 2.2233 D(x): 0.8983, D(G(z)): 0.1609 Epoch: [4/20], Batch Num: [315/600] Discriminator Loss: 0.3615, Generator Loss: 2.1973 D(x): 0.9102, D(G(z)): 0.1736 Epoch: [4/20], Batch Num: [316/600] Discriminator Loss: 0.3774, Generator Loss: 2.2331 D(x): 0.9061, D(G(z)): 0.1741 Epoch: [4/20], Batch Num: [317/600] Discriminator Loss: 0.3331, Generator Loss: 2.2248 D(x): 0.9248, D(G(z)): 0.1779 Epoch: [4/20], Batch Num: [318/600] Discriminator Loss: 0.2683, Generator Loss: 2.4551 D(x): 0.9260, D(G(z)): 0.1430 Epoch: [4/20], Batch Num: [319/600] Discriminator Loss: 0.4922, Generator Loss: 2.5120 D(x): 0.8283, D(G(z)): 0.1352 Epoch: [4/20], Batch Num: [320/600] Discriminator Loss: 0.2748, Generator Loss: 2.5156 D(x): 0.9235, D(G(z)): 0.1296 Epoch: [4/20], Batch Num: [321/600] Discriminator Loss: 0.3754, Generator Loss: 2.5447 D(x): 0.8749, D(G(z)): 0.1301 Epoch: [4/20], Batch Num: [322/600] Discriminator Loss: 0.3602, Generator Loss: 2.6520 D(x): 0.8856, D(G(z)): 0.1270 Epoch: [4/20], Batch Num: [323/600] Discriminator Loss: 0.4487, Generator Loss: 2.3126 D(x): 0.8425, D(G(z)): 0.1232 Epoch: [4/20], Batch Num: [324/600] Discriminator Loss: 0.4702, Generator Loss: 2.1632 D(x): 0.8286, D(G(z)): 0.1404 Epoch: [4/20], Batch Num: [325/600] Discriminator Loss: 0.4646, Generator Loss: 1.9898 D(x): 0.8715, D(G(z)): 0.2002 Epoch: [4/20], Batch Num: [326/600] Discriminator Loss: 0.5353, Generator Loss: 1.8097 D(x): 0.8701, D(G(z)): 0.2316 Epoch: [4/20], Batch Num: [327/600] Discriminator Loss: 0.3720, Generator Loss: 2.0531 D(x): 0.9434, D(G(z)): 0.2363 Epoch: [4/20], Batch Num: [328/600] Discriminator Loss: 0.4189, Generator Loss: 2.5137 D(x): 0.9120, D(G(z)): 0.1958 Epoch: [4/20], Batch Num: [329/600] Discriminator Loss: 0.3796, Generator Loss: 2.7362 D(x): 0.8914, D(G(z)): 0.1513 Epoch: [4/20], Batch Num: [330/600] Discriminator Loss: 0.4849, Generator Loss: 2.7833 D(x): 0.8238, D(G(z)): 0.1130 Epoch: [4/20], Batch Num: [331/600] Discriminator Loss: 0.6433, Generator Loss: 2.4882 D(x): 0.7731, D(G(z)): 0.0957 Epoch: [4/20], Batch Num: [332/600] Discriminator Loss: 0.5036, Generator Loss: 2.2132 D(x): 0.8317, D(G(z)): 0.1472 Epoch: [4/20], Batch Num: [333/600] Discriminator Loss: 0.4716, Generator Loss: 1.8849 D(x): 0.8755, D(G(z)): 0.1796 Epoch: [4/20], Batch Num: [334/600] Discriminator Loss: 0.5439, Generator Loss: 1.7809 D(x): 0.8472, D(G(z)): 0.2012 Epoch: [4/20], Batch Num: [335/600] Discriminator Loss: 0.5708, Generator Loss: 1.9575 D(x): 0.8696, D(G(z)): 0.2267 Epoch: [4/20], Batch Num: [336/600] Discriminator Loss: 0.4556, Generator Loss: 2.1127 D(x): 0.9322, D(G(z)): 0.2568 Epoch: [4/20], Batch Num: [337/600] Discriminator Loss: 0.6684, Generator Loss: 2.3536 D(x): 0.8117, D(G(z)): 0.1806 Epoch: [4/20], Batch Num: [338/600] Discriminator Loss: 0.4502, Generator Loss: 2.5267 D(x): 0.8350, D(G(z)): 0.1269 Epoch: [4/20], Batch Num: [339/600] Discriminator Loss: 0.7778, Generator Loss: 2.2894 D(x): 0.7691, D(G(z)): 0.1565 Epoch: [4/20], Batch Num: [340/600] Discriminator Loss: 0.8637, Generator Loss: 2.0453 D(x): 0.7537, D(G(z)): 0.1614 Epoch: [4/20], Batch Num: [341/600] Discriminator Loss: 0.8981, Generator Loss: 1.7013 D(x): 0.7468, D(G(z)): 0.2213 Epoch: [4/20], Batch Num: [342/600] Discriminator Loss: 0.9631, Generator Loss: 1.6292 D(x): 0.8135, D(G(z)): 0.3010 Epoch: [4/20], Batch Num: [343/600] Discriminator Loss: 0.6914, Generator Loss: 1.8159 D(x): 0.8469, D(G(z)): 0.2881 Epoch: [4/20], Batch Num: [344/600] Discriminator Loss: 0.7074, Generator Loss: 2.2750 D(x): 0.8908, D(G(z)): 0.3119 Epoch: [4/20], Batch Num: [345/600] Discriminator Loss: 0.8374, Generator Loss: 2.4956 D(x): 0.7815, D(G(z)): 0.2079 Epoch: [4/20], Batch Num: [346/600] Discriminator Loss: 1.0588, Generator Loss: 2.3950 D(x): 0.6784, D(G(z)): 0.1363 Epoch: [4/20], Batch Num: [347/600] Discriminator Loss: 0.7469, Generator Loss: 2.1172 D(x): 0.7817, D(G(z)): 0.1435 Epoch: [4/20], Batch Num: [348/600] Discriminator Loss: 0.8131, Generator Loss: 1.6593 D(x): 0.7341, D(G(z)): 0.1709 Epoch: [4/20], Batch Num: [349/600] Discriminator Loss: 0.7562, Generator Loss: 1.5670 D(x): 0.8303, D(G(z)): 0.2665 Epoch: [4/20], Batch Num: [350/600] Discriminator Loss: 0.7539, Generator Loss: 1.4814 D(x): 0.8154, D(G(z)): 0.2885 Epoch: [4/20], Batch Num: [351/600] Discriminator Loss: 0.9708, Generator Loss: 1.6363 D(x): 0.8481, D(G(z)): 0.3860 Epoch: [4/20], Batch Num: [352/600] Discriminator Loss: 0.7244, Generator Loss: 2.0324 D(x): 0.8423, D(G(z)): 0.2796 Epoch: [4/20], Batch Num: [353/600] Discriminator Loss: 0.6351, Generator Loss: 2.1356 D(x): 0.8005, D(G(z)): 0.1931 Epoch: [4/20], Batch Num: [354/600] Discriminator Loss: 0.5820, Generator Loss: 2.2268 D(x): 0.8255, D(G(z)): 0.1828 Epoch: [4/20], Batch Num: [355/600] Discriminator Loss: 0.5993, Generator Loss: 2.2793 D(x): 0.8185, D(G(z)): 0.1499 Epoch: [4/20], Batch Num: [356/600] Discriminator Loss: 0.8996, Generator Loss: 2.0531 D(x): 0.7156, D(G(z)): 0.1502 Epoch: [4/20], Batch Num: [357/600] Discriminator Loss: 0.7955, Generator Loss: 1.8808 D(x): 0.7234, D(G(z)): 0.1656 Epoch: [4/20], Batch Num: [358/600] Discriminator Loss: 0.6499, Generator Loss: 1.7974 D(x): 0.8294, D(G(z)): 0.2449 Epoch: [4/20], Batch Num: [359/600] Discriminator Loss: 0.6998, Generator Loss: 1.6933 D(x): 0.8676, D(G(z)): 0.2701 Epoch: [4/20], Batch Num: [360/600] Discriminator Loss: 0.6042, Generator Loss: 1.9199 D(x): 0.8764, D(G(z)): 0.2490 Epoch: [4/20], Batch Num: [361/600] Discriminator Loss: 0.5256, Generator Loss: 2.1701 D(x): 0.8556, D(G(z)): 0.2135 Epoch: [4/20], Batch Num: [362/600] Discriminator Loss: 0.5952, Generator Loss: 2.1286 D(x): 0.8359, D(G(z)): 0.1854 Epoch: [4/20], Batch Num: [363/600] Discriminator Loss: 0.6548, Generator Loss: 2.2503 D(x): 0.8308, D(G(z)): 0.1818 Epoch: [4/20], Batch Num: [364/600] Discriminator Loss: 0.5244, Generator Loss: 2.3120 D(x): 0.8400, D(G(z)): 0.1581 Epoch: [4/20], Batch Num: [365/600] Discriminator Loss: 0.5873, Generator Loss: 2.3247 D(x): 0.7758, D(G(z)): 0.1396 Epoch: [4/20], Batch Num: [366/600] Discriminator Loss: 0.5359, Generator Loss: 2.2223 D(x): 0.8137, D(G(z)): 0.1549 Epoch: [4/20], Batch Num: [367/600] Discriminator Loss: 0.4368, Generator Loss: 2.3362 D(x): 0.8984, D(G(z)): 0.1836 Epoch: [4/20], Batch Num: [368/600] Discriminator Loss: 0.4540, Generator Loss: 2.1124 D(x): 0.8796, D(G(z)): 0.1855 Epoch: [4/20], Batch Num: [369/600] Discriminator Loss: 0.4996, Generator Loss: 2.2653 D(x): 0.8717, D(G(z)): 0.1840 Epoch: [4/20], Batch Num: [370/600] Discriminator Loss: 0.6328, Generator Loss: 2.2649 D(x): 0.8423, D(G(z)): 0.2424 Epoch: [4/20], Batch Num: [371/600] Discriminator Loss: 0.4605, Generator Loss: 2.5936 D(x): 0.8767, D(G(z)): 0.1704 Epoch: [4/20], Batch Num: [372/600] Discriminator Loss: 0.5564, Generator Loss: 2.5631 D(x): 0.8368, D(G(z)): 0.1745 Epoch: [4/20], Batch Num: [373/600] Discriminator Loss: 0.4875, Generator Loss: 2.5326 D(x): 0.8563, D(G(z)): 0.1654 Epoch: [4/20], Batch Num: [374/600] Discriminator Loss: 0.5365, Generator Loss: 2.7518 D(x): 0.8228, D(G(z)): 0.1496 Epoch: [4/20], Batch Num: [375/600] Discriminator Loss: 0.6134, Generator Loss: 2.2820 D(x): 0.7986, D(G(z)): 0.1326 Epoch: [4/20], Batch Num: [376/600] Discriminator Loss: 0.3769, Generator Loss: 2.3941 D(x): 0.8979, D(G(z)): 0.1504 Epoch: [4/20], Batch Num: [377/600] Discriminator Loss: 0.4725, Generator Loss: 2.1940 D(x): 0.8535, D(G(z)): 0.1536 Epoch: [4/20], Batch Num: [378/600] Discriminator Loss: 0.6329, Generator Loss: 2.1546 D(x): 0.8503, D(G(z)): 0.2314 Epoch: [4/20], Batch Num: [379/600] Discriminator Loss: 0.6517, Generator Loss: 2.3884 D(x): 0.8685, D(G(z)): 0.2139 Epoch: [4/20], Batch Num: [380/600] Discriminator Loss: 0.4894, Generator Loss: 2.7612 D(x): 0.8986, D(G(z)): 0.2022 Epoch: [4/20], Batch Num: [381/600] Discriminator Loss: 0.3801, Generator Loss: 2.7432 D(x): 0.8883, D(G(z)): 0.1592 Epoch: [4/20], Batch Num: [382/600] Discriminator Loss: 0.6221, Generator Loss: 2.8833 D(x): 0.8110, D(G(z)): 0.1632 Epoch: [4/20], Batch Num: [383/600] Discriminator Loss: 0.4410, Generator Loss: 2.8583 D(x): 0.8477, D(G(z)): 0.1249 Epoch: [4/20], Batch Num: [384/600] Discriminator Loss: 0.4856, Generator Loss: 2.7360 D(x): 0.8480, D(G(z)): 0.1580 Epoch: [4/20], Batch Num: [385/600] Discriminator Loss: 0.6918, Generator Loss: 2.7264 D(x): 0.7652, D(G(z)): 0.1215 Epoch: [4/20], Batch Num: [386/600] Discriminator Loss: 0.5435, Generator Loss: 2.1875 D(x): 0.8320, D(G(z)): 0.1695 Epoch: [4/20], Batch Num: [387/600] Discriminator Loss: 0.5543, Generator Loss: 2.3651 D(x): 0.8860, D(G(z)): 0.2339 Epoch: [4/20], Batch Num: [388/600] Discriminator Loss: 0.5667, Generator Loss: 2.1606 D(x): 0.9022, D(G(z)): 0.2502 Epoch: [4/20], Batch Num: [389/600] Discriminator Loss: 0.4903, Generator Loss: 2.6804 D(x): 0.8817, D(G(z)): 0.2148 Epoch: [4/20], Batch Num: [390/600] Discriminator Loss: 0.4288, Generator Loss: 2.9049 D(x): 0.8978, D(G(z)): 0.1821 Epoch: [4/20], Batch Num: [391/600] Discriminator Loss: 0.4994, Generator Loss: 3.2318 D(x): 0.8347, D(G(z)): 0.1543 Epoch: [4/20], Batch Num: [392/600] Discriminator Loss: 0.5036, Generator Loss: 3.0512 D(x): 0.8122, D(G(z)): 0.1270 Epoch: [4/20], Batch Num: [393/600] Discriminator Loss: 0.5874, Generator Loss: 2.9264 D(x): 0.7570, D(G(z)): 0.1254 Epoch: [4/20], Batch Num: [394/600] Discriminator Loss: 0.4261, Generator Loss: 2.2606 D(x): 0.8678, D(G(z)): 0.1619 Epoch: [4/20], Batch Num: [395/600] Discriminator Loss: 0.5454, Generator Loss: 2.0931 D(x): 0.8861, D(G(z)): 0.2718 Epoch: [4/20], Batch Num: [396/600] Discriminator Loss: 0.6716, Generator Loss: 2.0591 D(x): 0.8729, D(G(z)): 0.2835 Epoch: [4/20], Batch Num: [397/600] Discriminator Loss: 0.6617, Generator Loss: 2.1550 D(x): 0.8654, D(G(z)): 0.2925 Epoch: [4/20], Batch Num: [398/600] Discriminator Loss: 0.6389, Generator Loss: 2.2387 D(x): 0.8004, D(G(z)): 0.2512 Epoch: [4/20], Batch Num: [399/600] Discriminator Loss: 1.0733, Generator Loss: 2.0555 D(x): 0.7073, D(G(z)): 0.2150 Epoch: 4, Batch Num: [400/600]
Epoch: [4/20], Batch Num: [400/600] Discriminator Loss: 0.9489, Generator Loss: 1.9235 D(x): 0.7359, D(G(z)): 0.2464 Epoch: [4/20], Batch Num: [401/600] Discriminator Loss: 0.7612, Generator Loss: 1.8729 D(x): 0.7812, D(G(z)): 0.2803 Epoch: [4/20], Batch Num: [402/600] Discriminator Loss: 0.8535, Generator Loss: 1.4715 D(x): 0.7860, D(G(z)): 0.3291 Epoch: [4/20], Batch Num: [403/600] Discriminator Loss: 0.9627, Generator Loss: 1.6908 D(x): 0.7847, D(G(z)): 0.3500 Epoch: [4/20], Batch Num: [404/600] Discriminator Loss: 1.0622, Generator Loss: 1.9610 D(x): 0.7172, D(G(z)): 0.3322 Epoch: [4/20], Batch Num: [405/600] Discriminator Loss: 0.8987, Generator Loss: 1.9776 D(x): 0.7342, D(G(z)): 0.2620 Epoch: [4/20], Batch Num: [406/600] Discriminator Loss: 0.9622, Generator Loss: 1.7850 D(x): 0.7271, D(G(z)): 0.2907 Epoch: [4/20], Batch Num: [407/600] Discriminator Loss: 0.9171, Generator Loss: 1.8612 D(x): 0.7366, D(G(z)): 0.2787 Epoch: [4/20], Batch Num: [408/600] Discriminator Loss: 1.0273, Generator Loss: 1.8217 D(x): 0.7179, D(G(z)): 0.3313 Epoch: [4/20], Batch Num: [409/600] Discriminator Loss: 0.8726, Generator Loss: 1.5240 D(x): 0.7544, D(G(z)): 0.3055 Epoch: [4/20], Batch Num: [410/600] Discriminator Loss: 0.7666, Generator Loss: 1.7656 D(x): 0.8276, D(G(z)): 0.3351 Epoch: [4/20], Batch Num: [411/600] Discriminator Loss: 0.6774, Generator Loss: 1.8855 D(x): 0.7825, D(G(z)): 0.2644 Epoch: [4/20], Batch Num: [412/600] Discriminator Loss: 0.7336, Generator Loss: 2.0564 D(x): 0.7398, D(G(z)): 0.2312 Epoch: [4/20], Batch Num: [413/600] Discriminator Loss: 0.9288, Generator Loss: 1.7291 D(x): 0.7062, D(G(z)): 0.2828 Epoch: [4/20], Batch Num: [414/600] Discriminator Loss: 0.7050, Generator Loss: 1.9626 D(x): 0.8100, D(G(z)): 0.2899 Epoch: [4/20], Batch Num: [415/600] Discriminator Loss: 0.6984, Generator Loss: 1.9013 D(x): 0.8119, D(G(z)): 0.3051 Epoch: [4/20], Batch Num: [416/600] Discriminator Loss: 0.7222, Generator Loss: 2.1803 D(x): 0.7877, D(G(z)): 0.2583 Epoch: [4/20], Batch Num: [417/600] Discriminator Loss: 0.7029, Generator Loss: 2.0678 D(x): 0.7847, D(G(z)): 0.2402 Epoch: [4/20], Batch Num: [418/600] Discriminator Loss: 0.6555, Generator Loss: 2.0260 D(x): 0.7847, D(G(z)): 0.2150 Epoch: [4/20], Batch Num: [419/600] Discriminator Loss: 0.5466, Generator Loss: 2.4159 D(x): 0.8647, D(G(z)): 0.2470 Epoch: [4/20], Batch Num: [420/600] Discriminator Loss: 0.5086, Generator Loss: 2.5478 D(x): 0.8407, D(G(z)): 0.2065 Epoch: [4/20], Batch Num: [421/600] Discriminator Loss: 0.5858, Generator Loss: 2.4754 D(x): 0.8065, D(G(z)): 0.1796 Epoch: [4/20], Batch Num: [422/600] Discriminator Loss: 0.5863, Generator Loss: 2.3105 D(x): 0.8048, D(G(z)): 0.1819 Epoch: [4/20], Batch Num: [423/600] Discriminator Loss: 0.6653, Generator Loss: 2.0956 D(x): 0.8019, D(G(z)): 0.2034 Epoch: [4/20], Batch Num: [424/600] Discriminator Loss: 0.6645, Generator Loss: 2.2514 D(x): 0.8104, D(G(z)): 0.2349 Epoch: [4/20], Batch Num: [425/600] Discriminator Loss: 0.4612, Generator Loss: 2.0300 D(x): 0.8944, D(G(z)): 0.2341 Epoch: [4/20], Batch Num: [426/600] Discriminator Loss: 0.6479, Generator Loss: 2.1352 D(x): 0.7897, D(G(z)): 0.2036 Epoch: [4/20], Batch Num: [427/600] Discriminator Loss: 0.6754, Generator Loss: 2.1747 D(x): 0.8643, D(G(z)): 0.2506 Epoch: [4/20], Batch Num: [428/600] Discriminator Loss: 0.5435, Generator Loss: 2.3533 D(x): 0.8513, D(G(z)): 0.2217 Epoch: [4/20], Batch Num: [429/600] Discriminator Loss: 0.5800, Generator Loss: 2.3009 D(x): 0.8038, D(G(z)): 0.1856 Epoch: [4/20], Batch Num: [430/600] Discriminator Loss: 0.6575, Generator Loss: 1.9536 D(x): 0.7663, D(G(z)): 0.1786 Epoch: [4/20], Batch Num: [431/600] Discriminator Loss: 0.6423, Generator Loss: 1.7649 D(x): 0.7947, D(G(z)): 0.2053 Epoch: [4/20], Batch Num: [432/600] Discriminator Loss: 0.7635, Generator Loss: 1.6996 D(x): 0.8210, D(G(z)): 0.2867 Epoch: [4/20], Batch Num: [433/600] Discriminator Loss: 0.6494, Generator Loss: 1.9735 D(x): 0.8450, D(G(z)): 0.2698 Epoch: [4/20], Batch Num: [434/600] Discriminator Loss: 0.4556, Generator Loss: 2.2350 D(x): 0.8878, D(G(z)): 0.2293 Epoch: [4/20], Batch Num: [435/600] Discriminator Loss: 0.6164, Generator Loss: 2.3254 D(x): 0.8105, D(G(z)): 0.1630 Epoch: [4/20], Batch Num: [436/600] Discriminator Loss: 0.6400, Generator Loss: 2.2857 D(x): 0.7839, D(G(z)): 0.1504 Epoch: [4/20], Batch Num: [437/600] Discriminator Loss: 0.6498, Generator Loss: 1.8840 D(x): 0.7651, D(G(z)): 0.1534 Epoch: [4/20], Batch Num: [438/600] Discriminator Loss: 0.4712, Generator Loss: 1.7295 D(x): 0.8575, D(G(z)): 0.1809 Epoch: [4/20], Batch Num: [439/600] Discriminator Loss: 0.5651, Generator Loss: 1.8219 D(x): 0.8877, D(G(z)): 0.2848 Epoch: [4/20], Batch Num: [440/600] Discriminator Loss: 0.6539, Generator Loss: 2.1007 D(x): 0.8550, D(G(z)): 0.2723 Epoch: [4/20], Batch Num: [441/600] Discriminator Loss: 0.5602, Generator Loss: 2.2764 D(x): 0.8392, D(G(z)): 0.2191 Epoch: [4/20], Batch Num: [442/600] Discriminator Loss: 0.6231, Generator Loss: 2.3370 D(x): 0.7904, D(G(z)): 0.1666 Epoch: [4/20], Batch Num: [443/600] Discriminator Loss: 0.4770, Generator Loss: 2.5056 D(x): 0.8488, D(G(z)): 0.1549 Epoch: [4/20], Batch Num: [444/600] Discriminator Loss: 0.6662, Generator Loss: 2.3797 D(x): 0.7817, D(G(z)): 0.1735 Epoch: [4/20], Batch Num: [445/600] Discriminator Loss: 0.5419, Generator Loss: 2.0204 D(x): 0.8099, D(G(z)): 0.1467 Epoch: [4/20], Batch Num: [446/600] Discriminator Loss: 0.2924, Generator Loss: 2.2739 D(x): 0.9402, D(G(z)): 0.1765 Epoch: [4/20], Batch Num: [447/600] Discriminator Loss: 0.5395, Generator Loss: 2.3729 D(x): 0.8558, D(G(z)): 0.2211 Epoch: [4/20], Batch Num: [448/600] Discriminator Loss: 0.4003, Generator Loss: 2.4071 D(x): 0.8600, D(G(z)): 0.1608 Epoch: [4/20], Batch Num: [449/600] Discriminator Loss: 0.5089, Generator Loss: 2.6501 D(x): 0.8448, D(G(z)): 0.1871 Epoch: [4/20], Batch Num: [450/600] Discriminator Loss: 0.5153, Generator Loss: 2.8022 D(x): 0.8387, D(G(z)): 0.1534 Epoch: [4/20], Batch Num: [451/600] Discriminator Loss: 0.5634, Generator Loss: 2.5836 D(x): 0.8152, D(G(z)): 0.1332 Epoch: [4/20], Batch Num: [452/600] Discriminator Loss: 0.4553, Generator Loss: 2.7801 D(x): 0.8825, D(G(z)): 0.1636 Epoch: [4/20], Batch Num: [453/600] Discriminator Loss: 0.5306, Generator Loss: 2.9577 D(x): 0.8496, D(G(z)): 0.1579 Epoch: [4/20], Batch Num: [454/600] Discriminator Loss: 0.4048, Generator Loss: 2.5488 D(x): 0.8458, D(G(z)): 0.1108 Epoch: [4/20], Batch Num: [455/600] Discriminator Loss: 0.4984, Generator Loss: 2.7767 D(x): 0.8761, D(G(z)): 0.1746 Epoch: [4/20], Batch Num: [456/600] Discriminator Loss: 0.3997, Generator Loss: 3.0276 D(x): 0.8797, D(G(z)): 0.1562 Epoch: [4/20], Batch Num: [457/600] Discriminator Loss: 0.4552, Generator Loss: 3.1271 D(x): 0.8562, D(G(z)): 0.1524 Epoch: [4/20], Batch Num: [458/600] Discriminator Loss: 0.4527, Generator Loss: 3.0652 D(x): 0.8360, D(G(z)): 0.1094 Epoch: [4/20], Batch Num: [459/600] Discriminator Loss: 0.5715, Generator Loss: 2.9899 D(x): 0.8354, D(G(z)): 0.1514 Epoch: [4/20], Batch Num: [460/600] Discriminator Loss: 0.5313, Generator Loss: 2.6965 D(x): 0.8484, D(G(z)): 0.1794 Epoch: [4/20], Batch Num: [461/600] Discriminator Loss: 0.5605, Generator Loss: 3.1539 D(x): 0.8654, D(G(z)): 0.1777 Epoch: [4/20], Batch Num: [462/600] Discriminator Loss: 0.5039, Generator Loss: 3.4491 D(x): 0.8611, D(G(z)): 0.1504 Epoch: [4/20], Batch Num: [463/600] Discriminator Loss: 0.4822, Generator Loss: 3.6742 D(x): 0.8458, D(G(z)): 0.1202 Epoch: [4/20], Batch Num: [464/600] Discriminator Loss: 0.5175, Generator Loss: 3.0186 D(x): 0.8098, D(G(z)): 0.1035 Epoch: [4/20], Batch Num: [465/600] Discriminator Loss: 0.6565, Generator Loss: 2.6479 D(x): 0.8181, D(G(z)): 0.1285 Epoch: [4/20], Batch Num: [466/600] Discriminator Loss: 0.5232, Generator Loss: 2.5182 D(x): 0.9192, D(G(z)): 0.2431 Epoch: [4/20], Batch Num: [467/600] Discriminator Loss: 0.4418, Generator Loss: 3.2950 D(x): 0.8925, D(G(z)): 0.1466 Epoch: [4/20], Batch Num: [468/600] Discriminator Loss: 0.6475, Generator Loss: 3.3869 D(x): 0.8409, D(G(z)): 0.1528 Epoch: [4/20], Batch Num: [469/600] Discriminator Loss: 0.6407, Generator Loss: 3.2321 D(x): 0.8252, D(G(z)): 0.1343 Epoch: [4/20], Batch Num: [470/600] Discriminator Loss: 0.6635, Generator Loss: 2.8813 D(x): 0.8171, D(G(z)): 0.1455 Epoch: [4/20], Batch Num: [471/600] Discriminator Loss: 0.7428, Generator Loss: 2.4795 D(x): 0.8239, D(G(z)): 0.1749 Epoch: [4/20], Batch Num: [472/600] Discriminator Loss: 0.7950, Generator Loss: 2.4716 D(x): 0.8191, D(G(z)): 0.2691 Epoch: [4/20], Batch Num: [473/600] Discriminator Loss: 0.8939, Generator Loss: 2.4713 D(x): 0.7980, D(G(z)): 0.2618 Epoch: [4/20], Batch Num: [474/600] Discriminator Loss: 0.6795, Generator Loss: 2.9661 D(x): 0.8199, D(G(z)): 0.2209 Epoch: [4/20], Batch Num: [475/600] Discriminator Loss: 0.5749, Generator Loss: 3.1435 D(x): 0.8471, D(G(z)): 0.1798 Epoch: [4/20], Batch Num: [476/600] Discriminator Loss: 0.8990, Generator Loss: 2.7266 D(x): 0.7323, D(G(z)): 0.1461 Epoch: [4/20], Batch Num: [477/600] Discriminator Loss: 0.5455, Generator Loss: 2.4208 D(x): 0.8418, D(G(z)): 0.1790 Epoch: [4/20], Batch Num: [478/600] Discriminator Loss: 0.7441, Generator Loss: 2.3182 D(x): 0.8161, D(G(z)): 0.1936 Epoch: [4/20], Batch Num: [479/600] Discriminator Loss: 0.5808, Generator Loss: 2.2877 D(x): 0.8647, D(G(z)): 0.2366 Epoch: [4/20], Batch Num: [480/600] Discriminator Loss: 0.6571, Generator Loss: 2.7361 D(x): 0.8297, D(G(z)): 0.2267 Epoch: [4/20], Batch Num: [481/600] Discriminator Loss: 0.4617, Generator Loss: 3.0743 D(x): 0.8720, D(G(z)): 0.1628 Epoch: [4/20], Batch Num: [482/600] Discriminator Loss: 0.5112, Generator Loss: 3.4299 D(x): 0.8367, D(G(z)): 0.1282 Epoch: [4/20], Batch Num: [483/600] Discriminator Loss: 0.5709, Generator Loss: 3.3237 D(x): 0.7905, D(G(z)): 0.0919 Epoch: [4/20], Batch Num: [484/600] Discriminator Loss: 0.5018, Generator Loss: 2.6839 D(x): 0.8245, D(G(z)): 0.1173 Epoch: [4/20], Batch Num: [485/600] Discriminator Loss: 0.4139, Generator Loss: 2.9619 D(x): 0.8907, D(G(z)): 0.1662 Epoch: [4/20], Batch Num: [486/600] Discriminator Loss: 0.4369, Generator Loss: 2.8644 D(x): 0.8789, D(G(z)): 0.1617 Epoch: [4/20], Batch Num: [487/600] Discriminator Loss: 0.3666, Generator Loss: 2.7004 D(x): 0.9022, D(G(z)): 0.1562 Epoch: [4/20], Batch Num: [488/600] Discriminator Loss: 0.3794, Generator Loss: 3.0230 D(x): 0.9035, D(G(z)): 0.1521 Epoch: [4/20], Batch Num: [489/600] Discriminator Loss: 0.4111, Generator Loss: 3.2110 D(x): 0.8677, D(G(z)): 0.1209 Epoch: [4/20], Batch Num: [490/600] Discriminator Loss: 0.3715, Generator Loss: 3.1172 D(x): 0.8610, D(G(z)): 0.1113 Epoch: [4/20], Batch Num: [491/600] Discriminator Loss: 0.3145, Generator Loss: 3.2030 D(x): 0.8918, D(G(z)): 0.1220 Epoch: [4/20], Batch Num: [492/600] Discriminator Loss: 0.3020, Generator Loss: 3.3704 D(x): 0.9224, D(G(z)): 0.1461 Epoch: [4/20], Batch Num: [493/600] Discriminator Loss: 0.4696, Generator Loss: 3.0111 D(x): 0.8206, D(G(z)): 0.0981 Epoch: [4/20], Batch Num: [494/600] Discriminator Loss: 0.4236, Generator Loss: 3.1504 D(x): 0.8648, D(G(z)): 0.1243 Epoch: [4/20], Batch Num: [495/600] Discriminator Loss: 0.4327, Generator Loss: 2.8191 D(x): 0.8634, D(G(z)): 0.1461 Epoch: [4/20], Batch Num: [496/600] Discriminator Loss: 0.4008, Generator Loss: 2.8399 D(x): 0.9152, D(G(z)): 0.1815 Epoch: [4/20], Batch Num: [497/600] Discriminator Loss: 0.4083, Generator Loss: 2.8666 D(x): 0.8932, D(G(z)): 0.1500 Epoch: [4/20], Batch Num: [498/600] Discriminator Loss: 0.6197, Generator Loss: 2.8938 D(x): 0.8120, D(G(z)): 0.1709 Epoch: [4/20], Batch Num: [499/600] Discriminator Loss: 0.4732, Generator Loss: 3.0599 D(x): 0.8638, D(G(z)): 0.1474 Epoch: 4, Batch Num: [500/600]
Epoch: [4/20], Batch Num: [500/600] Discriminator Loss: 0.6642, Generator Loss: 3.1603 D(x): 0.8142, D(G(z)): 0.1644 Epoch: [4/20], Batch Num: [501/600] Discriminator Loss: 0.6735, Generator Loss: 2.8174 D(x): 0.8201, D(G(z)): 0.1731 Epoch: [4/20], Batch Num: [502/600] Discriminator Loss: 0.5986, Generator Loss: 2.8425 D(x): 0.8296, D(G(z)): 0.1757 Epoch: [4/20], Batch Num: [503/600] Discriminator Loss: 0.5887, Generator Loss: 3.0972 D(x): 0.8536, D(G(z)): 0.1905 Epoch: [4/20], Batch Num: [504/600] Discriminator Loss: 0.4192, Generator Loss: 3.0423 D(x): 0.8877, D(G(z)): 0.1386 Epoch: [4/20], Batch Num: [505/600] Discriminator Loss: 0.6166, Generator Loss: 2.9143 D(x): 0.8400, D(G(z)): 0.1402 Epoch: [4/20], Batch Num: [506/600] Discriminator Loss: 0.5971, Generator Loss: 3.2017 D(x): 0.8364, D(G(z)): 0.1667 Epoch: [4/20], Batch Num: [507/600] Discriminator Loss: 0.8743, Generator Loss: 2.5988 D(x): 0.7654, D(G(z)): 0.1920 Epoch: [4/20], Batch Num: [508/600] Discriminator Loss: 0.7134, Generator Loss: 2.5918 D(x): 0.8212, D(G(z)): 0.1917 Epoch: [4/20], Batch Num: [509/600] Discriminator Loss: 0.6525, Generator Loss: 2.5569 D(x): 0.8917, D(G(z)): 0.2677 Epoch: [4/20], Batch Num: [510/600] Discriminator Loss: 0.7937, Generator Loss: 2.7541 D(x): 0.8200, D(G(z)): 0.2219 Epoch: [4/20], Batch Num: [511/600] Discriminator Loss: 0.7329, Generator Loss: 2.5209 D(x): 0.7940, D(G(z)): 0.1539 Epoch: [4/20], Batch Num: [512/600] Discriminator Loss: 0.7131, Generator Loss: 2.7237 D(x): 0.8331, D(G(z)): 0.1810 Epoch: [4/20], Batch Num: [513/600] Discriminator Loss: 1.0393, Generator Loss: 2.3039 D(x): 0.7367, D(G(z)): 0.1873 Epoch: [4/20], Batch Num: [514/600] Discriminator Loss: 0.7644, Generator Loss: 2.0373 D(x): 0.7954, D(G(z)): 0.2079 Epoch: [4/20], Batch Num: [515/600] Discriminator Loss: 0.6255, Generator Loss: 2.1914 D(x): 0.8518, D(G(z)): 0.2420 Epoch: [4/20], Batch Num: [516/600] Discriminator Loss: 0.8425, Generator Loss: 2.1740 D(x): 0.8157, D(G(z)): 0.2730 Epoch: [4/20], Batch Num: [517/600] Discriminator Loss: 0.6003, Generator Loss: 2.4435 D(x): 0.8567, D(G(z)): 0.2075 Epoch: [4/20], Batch Num: [518/600] Discriminator Loss: 0.4828, Generator Loss: 2.7038 D(x): 0.8692, D(G(z)): 0.1757 Epoch: [4/20], Batch Num: [519/600] Discriminator Loss: 0.6094, Generator Loss: 2.6481 D(x): 0.8467, D(G(z)): 0.1409 Epoch: [4/20], Batch Num: [520/600] Discriminator Loss: 0.5438, Generator Loss: 2.4795 D(x): 0.8228, D(G(z)): 0.1208 Epoch: [4/20], Batch Num: [521/600] Discriminator Loss: 0.6046, Generator Loss: 2.2963 D(x): 0.8087, D(G(z)): 0.1250 Epoch: [4/20], Batch Num: [522/600] Discriminator Loss: 0.6417, Generator Loss: 1.8811 D(x): 0.8086, D(G(z)): 0.1875 Epoch: [4/20], Batch Num: [523/600] Discriminator Loss: 0.4634, Generator Loss: 2.0430 D(x): 0.9198, D(G(z)): 0.2501 Epoch: [4/20], Batch Num: [524/600] Discriminator Loss: 0.4535, Generator Loss: 2.2041 D(x): 0.9050, D(G(z)): 0.2331 Epoch: [4/20], Batch Num: [525/600] Discriminator Loss: 0.4997, Generator Loss: 2.4147 D(x): 0.8575, D(G(z)): 0.1862 Epoch: [4/20], Batch Num: [526/600] Discriminator Loss: 0.4374, Generator Loss: 2.6934 D(x): 0.8761, D(G(z)): 0.1677 Epoch: [4/20], Batch Num: [527/600] Discriminator Loss: 0.4605, Generator Loss: 2.5787 D(x): 0.8710, D(G(z)): 0.1422 Epoch: [4/20], Batch Num: [528/600] Discriminator Loss: 0.4943, Generator Loss: 2.6808 D(x): 0.8289, D(G(z)): 0.1094 Epoch: [4/20], Batch Num: [529/600] Discriminator Loss: 0.4769, Generator Loss: 2.5339 D(x): 0.8465, D(G(z)): 0.1259 Epoch: [4/20], Batch Num: [530/600] Discriminator Loss: 0.3725, Generator Loss: 2.4950 D(x): 0.8944, D(G(z)): 0.1508 Epoch: [4/20], Batch Num: [531/600] Discriminator Loss: 0.5584, Generator Loss: 2.3687 D(x): 0.8524, D(G(z)): 0.1797 Epoch: [4/20], Batch Num: [532/600] Discriminator Loss: 0.4735, Generator Loss: 2.3727 D(x): 0.8567, D(G(z)): 0.1579 Epoch: [4/20], Batch Num: [533/600] Discriminator Loss: 0.5227, Generator Loss: 2.4410 D(x): 0.8496, D(G(z)): 0.1681 Epoch: [4/20], Batch Num: [534/600] Discriminator Loss: 0.4011, Generator Loss: 2.4088 D(x): 0.9008, D(G(z)): 0.1868 Epoch: [4/20], Batch Num: [535/600] Discriminator Loss: 0.4066, Generator Loss: 2.5442 D(x): 0.9014, D(G(z)): 0.1910 Epoch: [4/20], Batch Num: [536/600] Discriminator Loss: 0.4341, Generator Loss: 2.7390 D(x): 0.9022, D(G(z)): 0.1715 Epoch: [4/20], Batch Num: [537/600] Discriminator Loss: 0.4907, Generator Loss: 2.8603 D(x): 0.8693, D(G(z)): 0.1401 Epoch: [4/20], Batch Num: [538/600] Discriminator Loss: 0.5307, Generator Loss: 2.9903 D(x): 0.8404, D(G(z)): 0.1136 Epoch: [4/20], Batch Num: [539/600] Discriminator Loss: 0.4036, Generator Loss: 2.7765 D(x): 0.8616, D(G(z)): 0.1157 Epoch: [4/20], Batch Num: [540/600] Discriminator Loss: 0.3714, Generator Loss: 2.7405 D(x): 0.8650, D(G(z)): 0.0913 Epoch: [4/20], Batch Num: [541/600] Discriminator Loss: 0.6031, Generator Loss: 2.4693 D(x): 0.8227, D(G(z)): 0.1538 Epoch: [4/20], Batch Num: [542/600] Discriminator Loss: 0.4518, Generator Loss: 2.1873 D(x): 0.9076, D(G(z)): 0.1953 Epoch: [4/20], Batch Num: [543/600] Discriminator Loss: 0.6369, Generator Loss: 2.2066 D(x): 0.8724, D(G(z)): 0.2241 Epoch: [4/20], Batch Num: [544/600] Discriminator Loss: 0.5996, Generator Loss: 2.0625 D(x): 0.8643, D(G(z)): 0.2177 Epoch: [4/20], Batch Num: [545/600] Discriminator Loss: 0.6485, Generator Loss: 2.1938 D(x): 0.8411, D(G(z)): 0.2225 Epoch: [4/20], Batch Num: [546/600] Discriminator Loss: 0.4651, Generator Loss: 2.2444 D(x): 0.8983, D(G(z)): 0.1980 Epoch: [4/20], Batch Num: [547/600] Discriminator Loss: 0.3916, Generator Loss: 2.5352 D(x): 0.9209, D(G(z)): 0.1938 Epoch: [4/20], Batch Num: [548/600] Discriminator Loss: 0.7213, Generator Loss: 2.7418 D(x): 0.7853, D(G(z)): 0.1623 Epoch: [4/20], Batch Num: [549/600] Discriminator Loss: 0.5989, Generator Loss: 2.3621 D(x): 0.8304, D(G(z)): 0.1519 Epoch: [4/20], Batch Num: [550/600] Discriminator Loss: 0.7512, Generator Loss: 2.4612 D(x): 0.7933, D(G(z)): 0.1645 Epoch: [4/20], Batch Num: [551/600] Discriminator Loss: 0.4071, Generator Loss: 2.2077 D(x): 0.9048, D(G(z)): 0.1787 Epoch: [4/20], Batch Num: [552/600] Discriminator Loss: 0.6006, Generator Loss: 1.9761 D(x): 0.8426, D(G(z)): 0.1721 Epoch: [4/20], Batch Num: [553/600] Discriminator Loss: 0.6682, Generator Loss: 1.6559 D(x): 0.8299, D(G(z)): 0.2243 Epoch: [4/20], Batch Num: [554/600] Discriminator Loss: 0.6369, Generator Loss: 1.7997 D(x): 0.8663, D(G(z)): 0.2505 Epoch: [4/20], Batch Num: [555/600] Discriminator Loss: 0.5539, Generator Loss: 1.8255 D(x): 0.8694, D(G(z)): 0.2609 Epoch: [4/20], Batch Num: [556/600] Discriminator Loss: 0.5705, Generator Loss: 2.1581 D(x): 0.8803, D(G(z)): 0.2484 Epoch: [4/20], Batch Num: [557/600] Discriminator Loss: 0.4975, Generator Loss: 2.0900 D(x): 0.8899, D(G(z)): 0.2192 Epoch: [4/20], Batch Num: [558/600] Discriminator Loss: 0.5333, Generator Loss: 2.5916 D(x): 0.8781, D(G(z)): 0.2082 Epoch: [4/20], Batch Num: [559/600] Discriminator Loss: 0.3874, Generator Loss: 2.5123 D(x): 0.8772, D(G(z)): 0.1209 Epoch: [4/20], Batch Num: [560/600] Discriminator Loss: 0.6282, Generator Loss: 2.4779 D(x): 0.7947, D(G(z)): 0.1460 Epoch: [4/20], Batch Num: [561/600] Discriminator Loss: 0.6326, Generator Loss: 2.4421 D(x): 0.8073, D(G(z)): 0.1364 Epoch: [4/20], Batch Num: [562/600] Discriminator Loss: 0.6384, Generator Loss: 2.0137 D(x): 0.8100, D(G(z)): 0.1515 Epoch: [4/20], Batch Num: [563/600] Discriminator Loss: 0.5586, Generator Loss: 1.9802 D(x): 0.8573, D(G(z)): 0.2169 Epoch: [4/20], Batch Num: [564/600] Discriminator Loss: 0.7227, Generator Loss: 1.9003 D(x): 0.8833, D(G(z)): 0.3099 Epoch: [4/20], Batch Num: [565/600] Discriminator Loss: 0.6282, Generator Loss: 1.9875 D(x): 0.8649, D(G(z)): 0.2550 Epoch: [4/20], Batch Num: [566/600] Discriminator Loss: 0.8824, Generator Loss: 2.1370 D(x): 0.7989, D(G(z)): 0.2791 Epoch: [4/20], Batch Num: [567/600] Discriminator Loss: 0.5267, Generator Loss: 2.3868 D(x): 0.8568, D(G(z)): 0.1906 Epoch: [4/20], Batch Num: [568/600] Discriminator Loss: 0.6335, Generator Loss: 2.4990 D(x): 0.8020, D(G(z)): 0.1576 Epoch: [4/20], Batch Num: [569/600] Discriminator Loss: 0.5978, Generator Loss: 2.3424 D(x): 0.8157, D(G(z)): 0.1840 Epoch: [4/20], Batch Num: [570/600] Discriminator Loss: 0.4853, Generator Loss: 2.2556 D(x): 0.8739, D(G(z)): 0.1823 Epoch: [4/20], Batch Num: [571/600] Discriminator Loss: 0.5635, Generator Loss: 2.0578 D(x): 0.8588, D(G(z)): 0.1873 Epoch: [4/20], Batch Num: [572/600] Discriminator Loss: 0.7225, Generator Loss: 2.2467 D(x): 0.8104, D(G(z)): 0.2276 Epoch: [4/20], Batch Num: [573/600] Discriminator Loss: 0.7143, Generator Loss: 2.0353 D(x): 0.8164, D(G(z)): 0.2604 Epoch: [4/20], Batch Num: [574/600] Discriminator Loss: 0.5397, Generator Loss: 1.7531 D(x): 0.8660, D(G(z)): 0.2250 Epoch: [4/20], Batch Num: [575/600] Discriminator Loss: 0.6073, Generator Loss: 1.9491 D(x): 0.8652, D(G(z)): 0.2350 Epoch: [4/20], Batch Num: [576/600] Discriminator Loss: 0.5096, Generator Loss: 1.9419 D(x): 0.8872, D(G(z)): 0.2148 Epoch: [4/20], Batch Num: [577/600] Discriminator Loss: 0.5601, Generator Loss: 2.1098 D(x): 0.8574, D(G(z)): 0.1941 Epoch: [4/20], Batch Num: [578/600] Discriminator Loss: 0.6203, Generator Loss: 2.1363 D(x): 0.8234, D(G(z)): 0.2063 Epoch: [4/20], Batch Num: [579/600] Discriminator Loss: 0.6246, Generator Loss: 2.1476 D(x): 0.8284, D(G(z)): 0.1860 Epoch: [4/20], Batch Num: [580/600] Discriminator Loss: 0.6474, Generator Loss: 1.8952 D(x): 0.8153, D(G(z)): 0.2044 Epoch: [4/20], Batch Num: [581/600] Discriminator Loss: 0.5954, Generator Loss: 1.8375 D(x): 0.8348, D(G(z)): 0.2080 Epoch: [4/20], Batch Num: [582/600] Discriminator Loss: 0.5170, Generator Loss: 1.7605 D(x): 0.8522, D(G(z)): 0.1925 Epoch: [4/20], Batch Num: [583/600] Discriminator Loss: 0.6059, Generator Loss: 1.8680 D(x): 0.8497, D(G(z)): 0.2301 Epoch: [4/20], Batch Num: [584/600] Discriminator Loss: 0.5078, Generator Loss: 1.8180 D(x): 0.8798, D(G(z)): 0.2136 Epoch: [4/20], Batch Num: [585/600] Discriminator Loss: 0.6789, Generator Loss: 1.6792 D(x): 0.8035, D(G(z)): 0.2236 Epoch: [4/20], Batch Num: [586/600] Discriminator Loss: 0.5559, Generator Loss: 1.6985 D(x): 0.8736, D(G(z)): 0.2331 Epoch: [4/20], Batch Num: [587/600] Discriminator Loss: 0.5398, Generator Loss: 2.0532 D(x): 0.8811, D(G(z)): 0.2427 Epoch: [4/20], Batch Num: [588/600] Discriminator Loss: 0.4869, Generator Loss: 2.0766 D(x): 0.8739, D(G(z)): 0.1997 Epoch: [4/20], Batch Num: [589/600] Discriminator Loss: 0.5473, Generator Loss: 2.2321 D(x): 0.8534, D(G(z)): 0.1880 Epoch: [4/20], Batch Num: [590/600] Discriminator Loss: 0.6813, Generator Loss: 2.3590 D(x): 0.8111, D(G(z)): 0.1766 Epoch: [4/20], Batch Num: [591/600] Discriminator Loss: 0.6189, Generator Loss: 2.2979 D(x): 0.8007, D(G(z)): 0.1498 Epoch: [4/20], Batch Num: [592/600] Discriminator Loss: 0.6633, Generator Loss: 1.9988 D(x): 0.7978, D(G(z)): 0.1456 Epoch: [4/20], Batch Num: [593/600] Discriminator Loss: 0.6034, Generator Loss: 1.8523 D(x): 0.8132, D(G(z)): 0.1651 Epoch: [4/20], Batch Num: [594/600] Discriminator Loss: 0.5270, Generator Loss: 1.7349 D(x): 0.8888, D(G(z)): 0.2370 Epoch: [4/20], Batch Num: [595/600] Discriminator Loss: 0.5445, Generator Loss: 1.6937 D(x): 0.9039, D(G(z)): 0.2526 Epoch: [4/20], Batch Num: [596/600] Discriminator Loss: 0.3412, Generator Loss: 1.9594 D(x): 0.9446, D(G(z)): 0.2187 Epoch: [4/20], Batch Num: [597/600] Discriminator Loss: 0.3925, Generator Loss: 2.1131 D(x): 0.9173, D(G(z)): 0.1994 Epoch: [4/20], Batch Num: [598/600] Discriminator Loss: 0.4710, Generator Loss: 2.2659 D(x): 0.8726, D(G(z)): 0.1887 Epoch: [4/20], Batch Num: [599/600] Discriminator Loss: 0.4335, Generator Loss: 2.5567 D(x): 0.8744, D(G(z)): 0.1462 Epoch: 5, Batch Num: [0/600]
Epoch: [5/20], Batch Num: [0/600] Discriminator Loss: 0.4650, Generator Loss: 2.5329 D(x): 0.8676, D(G(z)): 0.1484 Epoch: [5/20], Batch Num: [1/600] Discriminator Loss: 0.3792, Generator Loss: 2.7025 D(x): 0.8741, D(G(z)): 0.1184 Epoch: [5/20], Batch Num: [2/600] Discriminator Loss: 0.4554, Generator Loss: 2.4232 D(x): 0.8333, D(G(z)): 0.1181 Epoch: [5/20], Batch Num: [3/600] Discriminator Loss: 0.3947, Generator Loss: 2.1708 D(x): 0.8771, D(G(z)): 0.1216 Epoch: [5/20], Batch Num: [4/600] Discriminator Loss: 0.2591, Generator Loss: 2.0857 D(x): 0.9416, D(G(z)): 0.1311 Epoch: [5/20], Batch Num: [5/600] Discriminator Loss: 0.3107, Generator Loss: 2.1738 D(x): 0.8977, D(G(z)): 0.1338 Epoch: [5/20], Batch Num: [6/600] Discriminator Loss: 0.3713, Generator Loss: 2.0106 D(x): 0.9049, D(G(z)): 0.1641 Epoch: [5/20], Batch Num: [7/600] Discriminator Loss: 0.3729, Generator Loss: 2.1349 D(x): 0.9248, D(G(z)): 0.1965 Epoch: [5/20], Batch Num: [8/600] Discriminator Loss: 0.3083, Generator Loss: 2.3339 D(x): 0.9371, D(G(z)): 0.1680 Epoch: [5/20], Batch Num: [9/600] Discriminator Loss: 0.2518, Generator Loss: 2.4801 D(x): 0.9472, D(G(z)): 0.1360 Epoch: [5/20], Batch Num: [10/600] Discriminator Loss: 0.3128, Generator Loss: 2.6258 D(x): 0.9114, D(G(z)): 0.1273 Epoch: [5/20], Batch Num: [11/600] Discriminator Loss: 0.2188, Generator Loss: 2.7800 D(x): 0.9404, D(G(z)): 0.1042 Epoch: [5/20], Batch Num: [12/600] Discriminator Loss: 0.2673, Generator Loss: 2.9804 D(x): 0.9108, D(G(z)): 0.0908 Epoch: [5/20], Batch Num: [13/600] Discriminator Loss: 0.2512, Generator Loss: 2.9681 D(x): 0.9099, D(G(z)): 0.0791 Epoch: [5/20], Batch Num: [14/600] Discriminator Loss: 0.4092, Generator Loss: 2.9456 D(x): 0.8647, D(G(z)): 0.0756 Epoch: [5/20], Batch Num: [15/600] Discriminator Loss: 0.2318, Generator Loss: 2.7452 D(x): 0.9221, D(G(z)): 0.0947 Epoch: [5/20], Batch Num: [16/600] Discriminator Loss: 0.3300, Generator Loss: 2.6193 D(x): 0.8963, D(G(z)): 0.0978 Epoch: [5/20], Batch Num: [17/600] Discriminator Loss: 0.2283, Generator Loss: 2.4956 D(x): 0.9407, D(G(z)): 0.1079 Epoch: [5/20], Batch Num: [18/600] Discriminator Loss: 0.2670, Generator Loss: 2.4553 D(x): 0.9304, D(G(z)): 0.1298 Epoch: [5/20], Batch Num: [19/600] Discriminator Loss: 0.3008, Generator Loss: 2.5723 D(x): 0.9294, D(G(z)): 0.1438 Epoch: [5/20], Batch Num: [20/600] Discriminator Loss: 0.2817, Generator Loss: 2.6245 D(x): 0.9399, D(G(z)): 0.1383 Epoch: [5/20], Batch Num: [21/600] Discriminator Loss: 0.2575, Generator Loss: 2.6746 D(x): 0.9228, D(G(z)): 0.1137 Epoch: [5/20], Batch Num: [22/600] Discriminator Loss: 0.3395, Generator Loss: 2.8034 D(x): 0.8987, D(G(z)): 0.1033 Epoch: [5/20], Batch Num: [23/600] Discriminator Loss: 0.2466, Generator Loss: 2.8282 D(x): 0.9205, D(G(z)): 0.1033 Epoch: [5/20], Batch Num: [24/600] Discriminator Loss: 0.2747, Generator Loss: 2.7968 D(x): 0.9280, D(G(z)): 0.1179 Epoch: [5/20], Batch Num: [25/600] Discriminator Loss: 0.2978, Generator Loss: 2.8218 D(x): 0.9037, D(G(z)): 0.1007 Epoch: [5/20], Batch Num: [26/600] Discriminator Loss: 0.2736, Generator Loss: 2.7701 D(x): 0.9200, D(G(z)): 0.1062 Epoch: [5/20], Batch Num: [27/600] Discriminator Loss: 0.2859, Generator Loss: 2.6832 D(x): 0.9277, D(G(z)): 0.0856 Epoch: [5/20], Batch Num: [28/600] Discriminator Loss: 0.2793, Generator Loss: 2.9372 D(x): 0.9301, D(G(z)): 0.1252 Epoch: [5/20], Batch Num: [29/600] Discriminator Loss: 0.3025, Generator Loss: 3.0702 D(x): 0.9198, D(G(z)): 0.1152 Epoch: [5/20], Batch Num: [30/600] Discriminator Loss: 0.3068, Generator Loss: 3.1433 D(x): 0.9166, D(G(z)): 0.1119 Epoch: [5/20], Batch Num: [31/600] Discriminator Loss: 0.3509, Generator Loss: 3.1601 D(x): 0.8901, D(G(z)): 0.0999 Epoch: [5/20], Batch Num: [32/600] Discriminator Loss: 0.4361, Generator Loss: 3.1369 D(x): 0.8598, D(G(z)): 0.0784 Epoch: [5/20], Batch Num: [33/600] Discriminator Loss: 0.3194, Generator Loss: 2.8846 D(x): 0.9029, D(G(z)): 0.1060 Epoch: [5/20], Batch Num: [34/600] Discriminator Loss: 0.3556, Generator Loss: 2.7663 D(x): 0.8854, D(G(z)): 0.0877 Epoch: [5/20], Batch Num: [35/600] Discriminator Loss: 0.4314, Generator Loss: 2.6913 D(x): 0.8686, D(G(z)): 0.1087 Epoch: [5/20], Batch Num: [36/600] Discriminator Loss: 0.3447, Generator Loss: 2.5000 D(x): 0.9256, D(G(z)): 0.1268 Epoch: [5/20], Batch Num: [37/600] Discriminator Loss: 0.3892, Generator Loss: 2.6294 D(x): 0.9019, D(G(z)): 0.1583 Epoch: [5/20], Batch Num: [38/600] Discriminator Loss: 0.4787, Generator Loss: 2.8654 D(x): 0.8578, D(G(z)): 0.1517 Epoch: [5/20], Batch Num: [39/600] Discriminator Loss: 0.6206, Generator Loss: 2.7902 D(x): 0.8392, D(G(z)): 0.1288 Epoch: [5/20], Batch Num: [40/600] Discriminator Loss: 0.4091, Generator Loss: 2.8520 D(x): 0.9090, D(G(z)): 0.1441 Epoch: [5/20], Batch Num: [41/600] Discriminator Loss: 0.3430, Generator Loss: 3.0286 D(x): 0.9200, D(G(z)): 0.1174 Epoch: [5/20], Batch Num: [42/600] Discriminator Loss: 0.4639, Generator Loss: 3.2025 D(x): 0.9035, D(G(z)): 0.1174 Epoch: [5/20], Batch Num: [43/600] Discriminator Loss: 0.3097, Generator Loss: 3.2212 D(x): 0.8917, D(G(z)): 0.0814 Epoch: [5/20], Batch Num: [44/600] Discriminator Loss: 0.4113, Generator Loss: 3.1395 D(x): 0.8562, D(G(z)): 0.0849 Epoch: [5/20], Batch Num: [45/600] Discriminator Loss: 0.4268, Generator Loss: 2.9409 D(x): 0.8848, D(G(z)): 0.1351 Epoch: [5/20], Batch Num: [46/600] Discriminator Loss: 0.3552, Generator Loss: 2.9882 D(x): 0.8903, D(G(z)): 0.0995 Epoch: [5/20], Batch Num: [47/600] Discriminator Loss: 0.5427, Generator Loss: 2.7123 D(x): 0.8678, D(G(z)): 0.1243 Epoch: [5/20], Batch Num: [48/600] Discriminator Loss: 0.3773, Generator Loss: 2.6667 D(x): 0.9076, D(G(z)): 0.1599 Epoch: [5/20], Batch Num: [49/600] Discriminator Loss: 0.5613, Generator Loss: 3.0704 D(x): 0.8772, D(G(z)): 0.1860 Epoch: [5/20], Batch Num: [50/600] Discriminator Loss: 0.4643, Generator Loss: 2.9122 D(x): 0.8835, D(G(z)): 0.1387 Epoch: [5/20], Batch Num: [51/600] Discriminator Loss: 0.6854, Generator Loss: 3.1182 D(x): 0.8217, D(G(z)): 0.1185 Epoch: [5/20], Batch Num: [52/600] Discriminator Loss: 0.4682, Generator Loss: 3.2249 D(x): 0.8710, D(G(z)): 0.1207 Epoch: [5/20], Batch Num: [53/600] Discriminator Loss: 0.3711, Generator Loss: 3.1023 D(x): 0.8583, D(G(z)): 0.0832 Epoch: [5/20], Batch Num: [54/600] Discriminator Loss: 0.3453, Generator Loss: 2.7944 D(x): 0.8931, D(G(z)): 0.1069 Epoch: [5/20], Batch Num: [55/600] Discriminator Loss: 0.5402, Generator Loss: 2.6337 D(x): 0.8698, D(G(z)): 0.1457 Epoch: [5/20], Batch Num: [56/600] Discriminator Loss: 0.4131, Generator Loss: 2.5747 D(x): 0.8758, D(G(z)): 0.1188 Epoch: [5/20], Batch Num: [57/600] Discriminator Loss: 0.3936, Generator Loss: 2.4338 D(x): 0.9272, D(G(z)): 0.1447 Epoch: [5/20], Batch Num: [58/600] Discriminator Loss: 0.3510, Generator Loss: 2.8613 D(x): 0.9127, D(G(z)): 0.1180 Epoch: [5/20], Batch Num: [59/600] Discriminator Loss: 0.5096, Generator Loss: 2.8851 D(x): 0.8718, D(G(z)): 0.1579 Epoch: [5/20], Batch Num: [60/600] Discriminator Loss: 0.5960, Generator Loss: 2.9593 D(x): 0.8219, D(G(z)): 0.1161 Epoch: [5/20], Batch Num: [61/600] Discriminator Loss: 0.4436, Generator Loss: 2.9033 D(x): 0.8589, D(G(z)): 0.0966 Epoch: [5/20], Batch Num: [62/600] Discriminator Loss: 0.4278, Generator Loss: 2.6500 D(x): 0.8578, D(G(z)): 0.1148 Epoch: [5/20], Batch Num: [63/600] Discriminator Loss: 0.5389, Generator Loss: 2.5053 D(x): 0.8296, D(G(z)): 0.1092 Epoch: [5/20], Batch Num: [64/600] Discriminator Loss: 0.3522, Generator Loss: 2.3561 D(x): 0.9105, D(G(z)): 0.1348 Epoch: [5/20], Batch Num: [65/600] Discriminator Loss: 0.3424, Generator Loss: 2.5650 D(x): 0.9015, D(G(z)): 0.1517 Epoch: [5/20], Batch Num: [66/600] Discriminator Loss: 0.3266, Generator Loss: 2.6929 D(x): 0.9194, D(G(z)): 0.1538 Epoch: [5/20], Batch Num: [67/600] Discriminator Loss: 0.3751, Generator Loss: 2.7186 D(x): 0.8715, D(G(z)): 0.1073 Epoch: [5/20], Batch Num: [68/600] Discriminator Loss: 0.3231, Generator Loss: 2.9753 D(x): 0.9128, D(G(z)): 0.1171 Epoch: [5/20], Batch Num: [69/600] Discriminator Loss: 0.3590, Generator Loss: 3.0694 D(x): 0.8883, D(G(z)): 0.0815 Epoch: [5/20], Batch Num: [70/600] Discriminator Loss: 0.2557, Generator Loss: 3.3108 D(x): 0.9187, D(G(z)): 0.0782 Epoch: [5/20], Batch Num: [71/600] Discriminator Loss: 0.2908, Generator Loss: 3.2854 D(x): 0.8976, D(G(z)): 0.0566 Epoch: [5/20], Batch Num: [72/600] Discriminator Loss: 0.2544, Generator Loss: 3.2287 D(x): 0.9223, D(G(z)): 0.0667 Epoch: [5/20], Batch Num: [73/600] Discriminator Loss: 0.3087, Generator Loss: 3.0537 D(x): 0.8965, D(G(z)): 0.0619 Epoch: [5/20], Batch Num: [74/600] Discriminator Loss: 0.2706, Generator Loss: 2.9426 D(x): 0.9205, D(G(z)): 0.0973 Epoch: [5/20], Batch Num: [75/600] Discriminator Loss: 0.1694, Generator Loss: 3.0602 D(x): 0.9597, D(G(z)): 0.0976 Epoch: [5/20], Batch Num: [76/600] Discriminator Loss: 0.1352, Generator Loss: 3.0588 D(x): 0.9596, D(G(z)): 0.0655 Epoch: [5/20], Batch Num: [77/600] Discriminator Loss: 0.2281, Generator Loss: 3.0628 D(x): 0.9349, D(G(z)): 0.0867 Epoch: [5/20], Batch Num: [78/600] Discriminator Loss: 0.1419, Generator Loss: 3.1593 D(x): 0.9651, D(G(z)): 0.0833 Epoch: [5/20], Batch Num: [79/600] Discriminator Loss: 0.1535, Generator Loss: 3.4850 D(x): 0.9731, D(G(z)): 0.0846 Epoch: [5/20], Batch Num: [80/600] Discriminator Loss: 0.2009, Generator Loss: 3.4034 D(x): 0.9337, D(G(z)): 0.0722 Epoch: [5/20], Batch Num: [81/600] Discriminator Loss: 0.2054, Generator Loss: 3.5513 D(x): 0.9231, D(G(z)): 0.0516 Epoch: [5/20], Batch Num: [82/600] Discriminator Loss: 0.2727, Generator Loss: 3.6300 D(x): 0.9051, D(G(z)): 0.0463 Epoch: [5/20], Batch Num: [83/600] Discriminator Loss: 0.2252, Generator Loss: 3.0671 D(x): 0.9197, D(G(z)): 0.0594 Epoch: [5/20], Batch Num: [84/600] Discriminator Loss: 0.2692, Generator Loss: 2.8102 D(x): 0.9235, D(G(z)): 0.0825 Epoch: [5/20], Batch Num: [85/600] Discriminator Loss: 0.1511, Generator Loss: 2.6701 D(x): 0.9781, D(G(z)): 0.1050 Epoch: [5/20], Batch Num: [86/600] Discriminator Loss: 0.3293, Generator Loss: 2.8134 D(x): 0.9353, D(G(z)): 0.1499 Epoch: [5/20], Batch Num: [87/600] Discriminator Loss: 0.2916, Generator Loss: 2.8954 D(x): 0.9335, D(G(z)): 0.1274 Epoch: [5/20], Batch Num: [88/600] Discriminator Loss: 0.1924, Generator Loss: 3.2930 D(x): 0.9496, D(G(z)): 0.0899 Epoch: [5/20], Batch Num: [89/600] Discriminator Loss: 0.3679, Generator Loss: 2.9854 D(x): 0.8838, D(G(z)): 0.0766 Epoch: [5/20], Batch Num: [90/600] Discriminator Loss: 0.4069, Generator Loss: 2.8399 D(x): 0.8847, D(G(z)): 0.0922 Epoch: [5/20], Batch Num: [91/600] Discriminator Loss: 0.3513, Generator Loss: 2.5245 D(x): 0.8770, D(G(z)): 0.1077 Epoch: [5/20], Batch Num: [92/600] Discriminator Loss: 0.2965, Generator Loss: 2.3631 D(x): 0.9352, D(G(z)): 0.1254 Epoch: [5/20], Batch Num: [93/600] Discriminator Loss: 0.4498, Generator Loss: 2.0789 D(x): 0.8955, D(G(z)): 0.1848 Epoch: [5/20], Batch Num: [94/600] Discriminator Loss: 0.4767, Generator Loss: 2.4037 D(x): 0.9126, D(G(z)): 0.1711 Epoch: [5/20], Batch Num: [95/600] Discriminator Loss: 0.3286, Generator Loss: 2.3967 D(x): 0.9108, D(G(z)): 0.1337 Epoch: [5/20], Batch Num: [96/600] Discriminator Loss: 0.4997, Generator Loss: 2.3265 D(x): 0.8839, D(G(z)): 0.1741 Epoch: [5/20], Batch Num: [97/600] Discriminator Loss: 0.6240, Generator Loss: 2.5423 D(x): 0.8450, D(G(z)): 0.1486 Epoch: [5/20], Batch Num: [98/600] Discriminator Loss: 0.6791, Generator Loss: 2.6627 D(x): 0.8641, D(G(z)): 0.1872 Epoch: [5/20], Batch Num: [99/600] Discriminator Loss: 0.4945, Generator Loss: 2.4350 D(x): 0.8656, D(G(z)): 0.1370 Epoch: 5, Batch Num: [100/600]
Epoch: [5/20], Batch Num: [100/600] Discriminator Loss: 0.5534, Generator Loss: 2.4913 D(x): 0.8454, D(G(z)): 0.1347 Epoch: [5/20], Batch Num: [101/600] Discriminator Loss: 0.4993, Generator Loss: 2.1995 D(x): 0.8665, D(G(z)): 0.1565 Epoch: [5/20], Batch Num: [102/600] Discriminator Loss: 0.5129, Generator Loss: 2.2040 D(x): 0.8955, D(G(z)): 0.1875 Epoch: [5/20], Batch Num: [103/600] Discriminator Loss: 0.6486, Generator Loss: 2.0342 D(x): 0.8195, D(G(z)): 0.1688 Epoch: [5/20], Batch Num: [104/600] Discriminator Loss: 0.5681, Generator Loss: 2.1152 D(x): 0.8664, D(G(z)): 0.1973 Epoch: [5/20], Batch Num: [105/600] Discriminator Loss: 0.5304, Generator Loss: 2.2705 D(x): 0.8840, D(G(z)): 0.1741 Epoch: [5/20], Batch Num: [106/600] Discriminator Loss: 0.7130, Generator Loss: 2.1901 D(x): 0.8063, D(G(z)): 0.1822 Epoch: [5/20], Batch Num: [107/600] Discriminator Loss: 0.6316, Generator Loss: 2.1023 D(x): 0.8182, D(G(z)): 0.1646 Epoch: [5/20], Batch Num: [108/600] Discriminator Loss: 0.5499, Generator Loss: 2.0283 D(x): 0.8660, D(G(z)): 0.1938 Epoch: [5/20], Batch Num: [109/600] Discriminator Loss: 0.5001, Generator Loss: 2.0533 D(x): 0.8531, D(G(z)): 0.1738 Epoch: [5/20], Batch Num: [110/600] Discriminator Loss: 0.7589, Generator Loss: 1.9630 D(x): 0.8088, D(G(z)): 0.2082 Epoch: [5/20], Batch Num: [111/600] Discriminator Loss: 0.4596, Generator Loss: 2.1413 D(x): 0.8792, D(G(z)): 0.1820 Epoch: [5/20], Batch Num: [112/600] Discriminator Loss: 0.4416, Generator Loss: 2.2920 D(x): 0.8711, D(G(z)): 0.1560 Epoch: [5/20], Batch Num: [113/600] Discriminator Loss: 0.5954, Generator Loss: 2.4931 D(x): 0.8570, D(G(z)): 0.1874 Epoch: [5/20], Batch Num: [114/600] Discriminator Loss: 0.3895, Generator Loss: 2.8355 D(x): 0.8733, D(G(z)): 0.1252 Epoch: [5/20], Batch Num: [115/600] Discriminator Loss: 0.4421, Generator Loss: 2.7973 D(x): 0.8334, D(G(z)): 0.1081 Epoch: [5/20], Batch Num: [116/600] Discriminator Loss: 0.3009, Generator Loss: 2.3234 D(x): 0.8946, D(G(z)): 0.1145 Epoch: [5/20], Batch Num: [117/600] Discriminator Loss: 0.3402, Generator Loss: 2.6031 D(x): 0.9260, D(G(z)): 0.1569 Epoch: [5/20], Batch Num: [118/600] Discriminator Loss: 0.3188, Generator Loss: 2.8382 D(x): 0.9310, D(G(z)): 0.1482 Epoch: [5/20], Batch Num: [119/600] Discriminator Loss: 0.2583, Generator Loss: 3.0694 D(x): 0.9289, D(G(z)): 0.1082 Epoch: [5/20], Batch Num: [120/600] Discriminator Loss: 0.4125, Generator Loss: 2.9707 D(x): 0.9057, D(G(z)): 0.1310 Epoch: [5/20], Batch Num: [121/600] Discriminator Loss: 0.5507, Generator Loss: 3.0063 D(x): 0.8189, D(G(z)): 0.0985 Epoch: [5/20], Batch Num: [122/600] Discriminator Loss: 0.2727, Generator Loss: 2.9193 D(x): 0.9131, D(G(z)): 0.0875 Epoch: [5/20], Batch Num: [123/600] Discriminator Loss: 0.4509, Generator Loss: 3.0572 D(x): 0.8574, D(G(z)): 0.1071 Epoch: [5/20], Batch Num: [124/600] Discriminator Loss: 0.3981, Generator Loss: 2.6171 D(x): 0.8564, D(G(z)): 0.0978 Epoch: [5/20], Batch Num: [125/600] Discriminator Loss: 0.5663, Generator Loss: 2.6002 D(x): 0.8650, D(G(z)): 0.1729 Epoch: [5/20], Batch Num: [126/600] Discriminator Loss: 0.4169, Generator Loss: 2.7814 D(x): 0.8726, D(G(z)): 0.1292 Epoch: [5/20], Batch Num: [127/600] Discriminator Loss: 0.4001, Generator Loss: 2.9988 D(x): 0.9204, D(G(z)): 0.1385 Epoch: [5/20], Batch Num: [128/600] Discriminator Loss: 0.4376, Generator Loss: 3.2870 D(x): 0.8921, D(G(z)): 0.1364 Epoch: [5/20], Batch Num: [129/600] Discriminator Loss: 0.6582, Generator Loss: 3.4166 D(x): 0.8187, D(G(z)): 0.1190 Epoch: [5/20], Batch Num: [130/600] Discriminator Loss: 0.9312, Generator Loss: 3.3503 D(x): 0.7761, D(G(z)): 0.1340 Epoch: [5/20], Batch Num: [131/600] Discriminator Loss: 0.7926, Generator Loss: 2.9023 D(x): 0.7546, D(G(z)): 0.0922 Epoch: [5/20], Batch Num: [132/600] Discriminator Loss: 0.9326, Generator Loss: 2.5644 D(x): 0.7576, D(G(z)): 0.1856 Epoch: [5/20], Batch Num: [133/600] Discriminator Loss: 1.0190, Generator Loss: 2.2534 D(x): 0.7803, D(G(z)): 0.2563 Epoch: [5/20], Batch Num: [134/600] Discriminator Loss: 1.0942, Generator Loss: 2.4944 D(x): 0.8054, D(G(z)): 0.2564 Epoch: [5/20], Batch Num: [135/600] Discriminator Loss: 1.0072, Generator Loss: 2.8412 D(x): 0.7694, D(G(z)): 0.2384 Epoch: [5/20], Batch Num: [136/600] Discriminator Loss: 1.3333, Generator Loss: 2.7509 D(x): 0.6550, D(G(z)): 0.1946 Epoch: [5/20], Batch Num: [137/600] Discriminator Loss: 1.3813, Generator Loss: 2.8979 D(x): 0.6843, D(G(z)): 0.2285 Epoch: [5/20], Batch Num: [138/600] Discriminator Loss: 1.0396, Generator Loss: 2.9715 D(x): 0.7342, D(G(z)): 0.2009 Epoch: [5/20], Batch Num: [139/600] Discriminator Loss: 0.6438, Generator Loss: 2.6446 D(x): 0.8198, D(G(z)): 0.1573 Epoch: [5/20], Batch Num: [140/600] Discriminator Loss: 0.6169, Generator Loss: 2.3533 D(x): 0.7973, D(G(z)): 0.1398 Epoch: [5/20], Batch Num: [141/600] Discriminator Loss: 0.7593, Generator Loss: 1.9176 D(x): 0.7530, D(G(z)): 0.1736 Epoch: [5/20], Batch Num: [142/600] Discriminator Loss: 0.8503, Generator Loss: 1.9562 D(x): 0.7956, D(G(z)): 0.2521 Epoch: [5/20], Batch Num: [143/600] Discriminator Loss: 0.6471, Generator Loss: 2.4337 D(x): 0.8643, D(G(z)): 0.2621 Epoch: [5/20], Batch Num: [144/600] Discriminator Loss: 0.2974, Generator Loss: 3.1165 D(x): 0.9185, D(G(z)): 0.1522 Epoch: [5/20], Batch Num: [145/600] Discriminator Loss: 0.3905, Generator Loss: 3.6942 D(x): 0.8698, D(G(z)): 0.1163 Epoch: [5/20], Batch Num: [146/600] Discriminator Loss: 0.4736, Generator Loss: 3.9817 D(x): 0.8220, D(G(z)): 0.0901 Epoch: [5/20], Batch Num: [147/600] Discriminator Loss: 0.3383, Generator Loss: 3.4091 D(x): 0.8780, D(G(z)): 0.1046 Epoch: [5/20], Batch Num: [148/600] Discriminator Loss: 0.3915, Generator Loss: 2.9469 D(x): 0.8402, D(G(z)): 0.0846 Epoch: [5/20], Batch Num: [149/600] Discriminator Loss: 0.3184, Generator Loss: 2.8590 D(x): 0.8988, D(G(z)): 0.1334 Epoch: [5/20], Batch Num: [150/600] Discriminator Loss: 0.3444, Generator Loss: 2.6831 D(x): 0.9184, D(G(z)): 0.1625 Epoch: [5/20], Batch Num: [151/600] Discriminator Loss: 0.4183, Generator Loss: 2.8840 D(x): 0.8702, D(G(z)): 0.1639 Epoch: [5/20], Batch Num: [152/600] Discriminator Loss: 0.5825, Generator Loss: 2.5769 D(x): 0.8585, D(G(z)): 0.1991 Epoch: [5/20], Batch Num: [153/600] Discriminator Loss: 0.4882, Generator Loss: 2.8758 D(x): 0.8843, D(G(z)): 0.2069 Epoch: [5/20], Batch Num: [154/600] Discriminator Loss: 0.4498, Generator Loss: 2.9266 D(x): 0.8769, D(G(z)): 0.1802 Epoch: [5/20], Batch Num: [155/600] Discriminator Loss: 0.7264, Generator Loss: 2.8772 D(x): 0.7833, D(G(z)): 0.1809 Epoch: [5/20], Batch Num: [156/600] Discriminator Loss: 0.5184, Generator Loss: 2.6616 D(x): 0.8482, D(G(z)): 0.1747 Epoch: [5/20], Batch Num: [157/600] Discriminator Loss: 0.5813, Generator Loss: 2.3157 D(x): 0.8224, D(G(z)): 0.1785 Epoch: [5/20], Batch Num: [158/600] Discriminator Loss: 0.6559, Generator Loss: 2.0600 D(x): 0.7946, D(G(z)): 0.1934 Epoch: [5/20], Batch Num: [159/600] Discriminator Loss: 0.7060, Generator Loss: 1.8461 D(x): 0.8196, D(G(z)): 0.2565 Epoch: [5/20], Batch Num: [160/600] Discriminator Loss: 0.5671, Generator Loss: 2.0201 D(x): 0.8530, D(G(z)): 0.2399 Epoch: [5/20], Batch Num: [161/600] Discriminator Loss: 0.5315, Generator Loss: 2.3378 D(x): 0.8656, D(G(z)): 0.2350 Epoch: [5/20], Batch Num: [162/600] Discriminator Loss: 0.4757, Generator Loss: 2.5791 D(x): 0.8539, D(G(z)): 0.1877 Epoch: [5/20], Batch Num: [163/600] Discriminator Loss: 0.4889, Generator Loss: 2.6181 D(x): 0.8218, D(G(z)): 0.1497 Epoch: [5/20], Batch Num: [164/600] Discriminator Loss: 0.5406, Generator Loss: 2.2565 D(x): 0.8212, D(G(z)): 0.1750 Epoch: [5/20], Batch Num: [165/600] Discriminator Loss: 0.6942, Generator Loss: 2.3881 D(x): 0.8170, D(G(z)): 0.1985 Epoch: [5/20], Batch Num: [166/600] Discriminator Loss: 0.4826, Generator Loss: 2.0461 D(x): 0.8774, D(G(z)): 0.2049 Epoch: [5/20], Batch Num: [167/600] Discriminator Loss: 0.4635, Generator Loss: 2.2615 D(x): 0.8946, D(G(z)): 0.2012 Epoch: [5/20], Batch Num: [168/600] Discriminator Loss: 0.4489, Generator Loss: 2.3240 D(x): 0.8885, D(G(z)): 0.1900 Epoch: [5/20], Batch Num: [169/600] Discriminator Loss: 0.5123, Generator Loss: 2.6474 D(x): 0.8673, D(G(z)): 0.1884 Epoch: [5/20], Batch Num: [170/600] Discriminator Loss: 0.4910, Generator Loss: 2.6360 D(x): 0.8488, D(G(z)): 0.1571 Epoch: [5/20], Batch Num: [171/600] Discriminator Loss: 0.3928, Generator Loss: 2.7992 D(x): 0.8658, D(G(z)): 0.1282 Epoch: [5/20], Batch Num: [172/600] Discriminator Loss: 0.5371, Generator Loss: 2.4654 D(x): 0.8063, D(G(z)): 0.1376 Epoch: [5/20], Batch Num: [173/600] Discriminator Loss: 0.4975, Generator Loss: 2.0950 D(x): 0.8370, D(G(z)): 0.1594 Epoch: [5/20], Batch Num: [174/600] Discriminator Loss: 0.3300, Generator Loss: 2.1464 D(x): 0.9536, D(G(z)): 0.2161 Epoch: [5/20], Batch Num: [175/600] Discriminator Loss: 0.3839, Generator Loss: 2.5160 D(x): 0.9053, D(G(z)): 0.1873 Epoch: [5/20], Batch Num: [176/600] Discriminator Loss: 0.3497, Generator Loss: 2.7869 D(x): 0.9159, D(G(z)): 0.1768 Epoch: [5/20], Batch Num: [177/600] Discriminator Loss: 0.3968, Generator Loss: 3.1673 D(x): 0.8900, D(G(z)): 0.1376 Epoch: [5/20], Batch Num: [178/600] Discriminator Loss: 0.5030, Generator Loss: 2.8321 D(x): 0.8169, D(G(z)): 0.1272 Epoch: [5/20], Batch Num: [179/600] Discriminator Loss: 0.4281, Generator Loss: 2.4349 D(x): 0.8412, D(G(z)): 0.1050 Epoch: [5/20], Batch Num: [180/600] Discriminator Loss: 0.3372, Generator Loss: 2.6663 D(x): 0.9301, D(G(z)): 0.1754 Epoch: [5/20], Batch Num: [181/600] Discriminator Loss: 0.3903, Generator Loss: 2.3809 D(x): 0.8900, D(G(z)): 0.1409 Epoch: [5/20], Batch Num: [182/600] Discriminator Loss: 0.4023, Generator Loss: 2.5153 D(x): 0.8984, D(G(z)): 0.1692 Epoch: [5/20], Batch Num: [183/600] Discriminator Loss: 0.4122, Generator Loss: 2.7285 D(x): 0.8946, D(G(z)): 0.1688 Epoch: [5/20], Batch Num: [184/600] Discriminator Loss: 0.2865, Generator Loss: 2.8108 D(x): 0.9464, D(G(z)): 0.1508 Epoch: [5/20], Batch Num: [185/600] Discriminator Loss: 0.4666, Generator Loss: 2.8899 D(x): 0.8798, D(G(z)): 0.1690 Epoch: [5/20], Batch Num: [186/600] Discriminator Loss: 0.4023, Generator Loss: 3.0820 D(x): 0.8769, D(G(z)): 0.1240 Epoch: [5/20], Batch Num: [187/600] Discriminator Loss: 0.5568, Generator Loss: 2.7486 D(x): 0.7901, D(G(z)): 0.0879 Epoch: [5/20], Batch Num: [188/600] Discriminator Loss: 0.6679, Generator Loss: 2.3463 D(x): 0.8505, D(G(z)): 0.2073 Epoch: [5/20], Batch Num: [189/600] Discriminator Loss: 0.5118, Generator Loss: 2.4670 D(x): 0.8988, D(G(z)): 0.1784 Epoch: [5/20], Batch Num: [190/600] Discriminator Loss: 0.4286, Generator Loss: 2.4768 D(x): 0.9124, D(G(z)): 0.2248 Epoch: [5/20], Batch Num: [191/600] Discriminator Loss: 0.5207, Generator Loss: 2.6841 D(x): 0.8855, D(G(z)): 0.2053 Epoch: [5/20], Batch Num: [192/600] Discriminator Loss: 0.4136, Generator Loss: 2.6850 D(x): 0.8784, D(G(z)): 0.1468 Epoch: [5/20], Batch Num: [193/600] Discriminator Loss: 0.6578, Generator Loss: 2.7803 D(x): 0.8245, D(G(z)): 0.1821 Epoch: [5/20], Batch Num: [194/600] Discriminator Loss: 0.7235, Generator Loss: 2.7810 D(x): 0.7933, D(G(z)): 0.1987 Epoch: [5/20], Batch Num: [195/600] Discriminator Loss: 0.7353, Generator Loss: 2.1873 D(x): 0.8038, D(G(z)): 0.1736 Epoch: [5/20], Batch Num: [196/600] Discriminator Loss: 0.4809, Generator Loss: 1.9973 D(x): 0.8929, D(G(z)): 0.2029 Epoch: [5/20], Batch Num: [197/600] Discriminator Loss: 0.6519, Generator Loss: 2.0973 D(x): 0.8417, D(G(z)): 0.2175 Epoch: [5/20], Batch Num: [198/600] Discriminator Loss: 0.6546, Generator Loss: 2.2193 D(x): 0.8552, D(G(z)): 0.2330 Epoch: [5/20], Batch Num: [199/600] Discriminator Loss: 0.6541, Generator Loss: 1.9846 D(x): 0.7951, D(G(z)): 0.2074 Epoch: 5, Batch Num: [200/600]
Epoch: [5/20], Batch Num: [200/600] Discriminator Loss: 0.7595, Generator Loss: 1.9853 D(x): 0.8135, D(G(z)): 0.2672 Epoch: [5/20], Batch Num: [201/600] Discriminator Loss: 0.6142, Generator Loss: 2.1680 D(x): 0.8522, D(G(z)): 0.2281 Epoch: [5/20], Batch Num: [202/600] Discriminator Loss: 0.8092, Generator Loss: 1.8549 D(x): 0.7427, D(G(z)): 0.1956 Epoch: [5/20], Batch Num: [203/600] Discriminator Loss: 0.7089, Generator Loss: 1.8754 D(x): 0.8270, D(G(z)): 0.2573 Epoch: [5/20], Batch Num: [204/600] Discriminator Loss: 0.5595, Generator Loss: 2.0601 D(x): 0.8868, D(G(z)): 0.2361 Epoch: [5/20], Batch Num: [205/600] Discriminator Loss: 0.8365, Generator Loss: 1.9762 D(x): 0.7863, D(G(z)): 0.2363 Epoch: [5/20], Batch Num: [206/600] Discriminator Loss: 0.8088, Generator Loss: 2.1839 D(x): 0.7924, D(G(z)): 0.2452 Epoch: [5/20], Batch Num: [207/600] Discriminator Loss: 0.7161, Generator Loss: 2.4522 D(x): 0.7997, D(G(z)): 0.2058 Epoch: [5/20], Batch Num: [208/600] Discriminator Loss: 0.3904, Generator Loss: 2.6132 D(x): 0.8786, D(G(z)): 0.1668 Epoch: [5/20], Batch Num: [209/600] Discriminator Loss: 0.4511, Generator Loss: 2.7744 D(x): 0.8750, D(G(z)): 0.1486 Epoch: [5/20], Batch Num: [210/600] Discriminator Loss: 0.4480, Generator Loss: 2.8690 D(x): 0.8487, D(G(z)): 0.1341 Epoch: [5/20], Batch Num: [211/600] Discriminator Loss: 0.3571, Generator Loss: 2.9955 D(x): 0.8777, D(G(z)): 0.1145 Epoch: [5/20], Batch Num: [212/600] Discriminator Loss: 0.4047, Generator Loss: 3.0276 D(x): 0.8886, D(G(z)): 0.1525 Epoch: [5/20], Batch Num: [213/600] Discriminator Loss: 0.3017, Generator Loss: 3.3838 D(x): 0.8971, D(G(z)): 0.1183 Epoch: [5/20], Batch Num: [214/600] Discriminator Loss: 0.3749, Generator Loss: 3.2174 D(x): 0.8717, D(G(z)): 0.1313 Epoch: [5/20], Batch Num: [215/600] Discriminator Loss: 0.3167, Generator Loss: 3.2120 D(x): 0.8929, D(G(z)): 0.1175 Epoch: [5/20], Batch Num: [216/600] Discriminator Loss: 0.3470, Generator Loss: 3.2502 D(x): 0.9130, D(G(z)): 0.1575 Epoch: [5/20], Batch Num: [217/600] Discriminator Loss: 0.4142, Generator Loss: 3.2983 D(x): 0.8935, D(G(z)): 0.1317 Epoch: [5/20], Batch Num: [218/600] Discriminator Loss: 0.3050, Generator Loss: 3.5171 D(x): 0.8910, D(G(z)): 0.0859 Epoch: [5/20], Batch Num: [219/600] Discriminator Loss: 0.2659, Generator Loss: 3.4175 D(x): 0.9077, D(G(z)): 0.0943 Epoch: [5/20], Batch Num: [220/600] Discriminator Loss: 0.3335, Generator Loss: 3.4486 D(x): 0.8981, D(G(z)): 0.0988 Epoch: [5/20], Batch Num: [221/600] Discriminator Loss: 0.3748, Generator Loss: 3.3515 D(x): 0.8825, D(G(z)): 0.1300 Epoch: [5/20], Batch Num: [222/600] Discriminator Loss: 0.4948, Generator Loss: 3.6646 D(x): 0.8608, D(G(z)): 0.1469 Epoch: [5/20], Batch Num: [223/600] Discriminator Loss: 0.3968, Generator Loss: 3.8251 D(x): 0.8893, D(G(z)): 0.1155 Epoch: [5/20], Batch Num: [224/600] Discriminator Loss: 0.4480, Generator Loss: 3.6853 D(x): 0.8683, D(G(z)): 0.0954 Epoch: [5/20], Batch Num: [225/600] Discriminator Loss: 0.5137, Generator Loss: 3.5059 D(x): 0.8398, D(G(z)): 0.0981 Epoch: [5/20], Batch Num: [226/600] Discriminator Loss: 0.5247, Generator Loss: 3.3435 D(x): 0.8776, D(G(z)): 0.1228 Epoch: [5/20], Batch Num: [227/600] Discriminator Loss: 0.6444, Generator Loss: 3.6185 D(x): 0.8630, D(G(z)): 0.1630 Epoch: [5/20], Batch Num: [228/600] Discriminator Loss: 0.6351, Generator Loss: 4.2031 D(x): 0.8711, D(G(z)): 0.2281 Epoch: [5/20], Batch Num: [229/600] Discriminator Loss: 0.7909, Generator Loss: 4.0415 D(x): 0.7641, D(G(z)): 0.0989 Epoch: [5/20], Batch Num: [230/600] Discriminator Loss: 0.9955, Generator Loss: 3.1342 D(x): 0.7104, D(G(z)): 0.1184 Epoch: [5/20], Batch Num: [231/600] Discriminator Loss: 0.9560, Generator Loss: 2.4674 D(x): 0.7756, D(G(z)): 0.1898 Epoch: [5/20], Batch Num: [232/600] Discriminator Loss: 0.9028, Generator Loss: 2.9764 D(x): 0.9011, D(G(z)): 0.3602 Epoch: [5/20], Batch Num: [233/600] Discriminator Loss: 0.7460, Generator Loss: 3.8169 D(x): 0.8481, D(G(z)): 0.2264 Epoch: [5/20], Batch Num: [234/600] Discriminator Loss: 0.7267, Generator Loss: 3.9127 D(x): 0.7750, D(G(z)): 0.1186 Epoch: [5/20], Batch Num: [235/600] Discriminator Loss: 1.2082, Generator Loss: 2.9563 D(x): 0.6390, D(G(z)): 0.1012 Epoch: [5/20], Batch Num: [236/600] Discriminator Loss: 1.1898, Generator Loss: 1.8070 D(x): 0.6943, D(G(z)): 0.1429 Epoch: [5/20], Batch Num: [237/600] Discriminator Loss: 1.0998, Generator Loss: 1.7944 D(x): 0.8473, D(G(z)): 0.3430 Epoch: [5/20], Batch Num: [238/600] Discriminator Loss: 0.9704, Generator Loss: 2.5178 D(x): 0.8843, D(G(z)): 0.3750 Epoch: [5/20], Batch Num: [239/600] Discriminator Loss: 0.8555, Generator Loss: 3.4013 D(x): 0.7917, D(G(z)): 0.2112 Epoch: [5/20], Batch Num: [240/600] Discriminator Loss: 0.9588, Generator Loss: 3.3993 D(x): 0.7037, D(G(z)): 0.1161 Epoch: [5/20], Batch Num: [241/600] Discriminator Loss: 0.9280, Generator Loss: 2.8215 D(x): 0.7031, D(G(z)): 0.0887 Epoch: [5/20], Batch Num: [242/600] Discriminator Loss: 0.7234, Generator Loss: 2.1329 D(x): 0.7815, D(G(z)): 0.1737 Epoch: [5/20], Batch Num: [243/600] Discriminator Loss: 0.8272, Generator Loss: 1.8110 D(x): 0.8215, D(G(z)): 0.2937 Epoch: [5/20], Batch Num: [244/600] Discriminator Loss: 0.7880, Generator Loss: 2.3630 D(x): 0.8531, D(G(z)): 0.3044 Epoch: [5/20], Batch Num: [245/600] Discriminator Loss: 0.5890, Generator Loss: 2.8517 D(x): 0.8657, D(G(z)): 0.2298 Epoch: [5/20], Batch Num: [246/600] Discriminator Loss: 0.5510, Generator Loss: 2.9213 D(x): 0.8050, D(G(z)): 0.1312 Epoch: [5/20], Batch Num: [247/600] Discriminator Loss: 0.6058, Generator Loss: 2.9266 D(x): 0.7680, D(G(z)): 0.1006 Epoch: [5/20], Batch Num: [248/600] Discriminator Loss: 0.6204, Generator Loss: 2.7462 D(x): 0.7927, D(G(z)): 0.1364 Epoch: [5/20], Batch Num: [249/600] Discriminator Loss: 0.5800, Generator Loss: 2.3179 D(x): 0.8083, D(G(z)): 0.1462 Epoch: [5/20], Batch Num: [250/600] Discriminator Loss: 0.4235, Generator Loss: 2.1596 D(x): 0.9174, D(G(z)): 0.2248 Epoch: [5/20], Batch Num: [251/600] Discriminator Loss: 0.4412, Generator Loss: 2.4302 D(x): 0.9091, D(G(z)): 0.2223 Epoch: [5/20], Batch Num: [252/600] Discriminator Loss: 0.2644, Generator Loss: 2.8521 D(x): 0.9380, D(G(z)): 0.1457 Epoch: [5/20], Batch Num: [253/600] Discriminator Loss: 0.3271, Generator Loss: 3.1538 D(x): 0.9277, D(G(z)): 0.1479 Epoch: [5/20], Batch Num: [254/600] Discriminator Loss: 0.3695, Generator Loss: 3.2727 D(x): 0.8531, D(G(z)): 0.0889 Epoch: [5/20], Batch Num: [255/600] Discriminator Loss: 0.3638, Generator Loss: 3.3174 D(x): 0.8724, D(G(z)): 0.0752 Epoch: [5/20], Batch Num: [256/600] Discriminator Loss: 0.6080, Generator Loss: 2.9572 D(x): 0.7874, D(G(z)): 0.1015 Epoch: [5/20], Batch Num: [257/600] Discriminator Loss: 0.2966, Generator Loss: 2.9131 D(x): 0.8778, D(G(z)): 0.0811 Epoch: [5/20], Batch Num: [258/600] Discriminator Loss: 0.4236, Generator Loss: 2.1699 D(x): 0.8623, D(G(z)): 0.1250 Epoch: [5/20], Batch Num: [259/600] Discriminator Loss: 0.3500, Generator Loss: 1.9476 D(x): 0.9178, D(G(z)): 0.1827 Epoch: [5/20], Batch Num: [260/600] Discriminator Loss: 0.3550, Generator Loss: 2.0500 D(x): 0.9530, D(G(z)): 0.2089 Epoch: [5/20], Batch Num: [261/600] Discriminator Loss: 0.4412, Generator Loss: 2.6267 D(x): 0.9136, D(G(z)): 0.2251 Epoch: [5/20], Batch Num: [262/600] Discriminator Loss: 0.2649, Generator Loss: 3.0728 D(x): 0.9459, D(G(z)): 0.1327 Epoch: [5/20], Batch Num: [263/600] Discriminator Loss: 0.2511, Generator Loss: 3.2780 D(x): 0.9052, D(G(z)): 0.0927 Epoch: [5/20], Batch Num: [264/600] Discriminator Loss: 0.3738, Generator Loss: 3.5389 D(x): 0.8733, D(G(z)): 0.0866 Epoch: [5/20], Batch Num: [265/600] Discriminator Loss: 0.5623, Generator Loss: 3.0742 D(x): 0.8152, D(G(z)): 0.0800 Epoch: [5/20], Batch Num: [266/600] Discriminator Loss: 0.6616, Generator Loss: 2.7871 D(x): 0.7788, D(G(z)): 0.1019 Epoch: [5/20], Batch Num: [267/600] Discriminator Loss: 0.3730, Generator Loss: 2.2971 D(x): 0.8732, D(G(z)): 0.1109 Epoch: [5/20], Batch Num: [268/600] Discriminator Loss: 0.4173, Generator Loss: 1.9146 D(x): 0.9191, D(G(z)): 0.1963 Epoch: [5/20], Batch Num: [269/600] Discriminator Loss: 0.4741, Generator Loss: 1.8960 D(x): 0.9143, D(G(z)): 0.2263 Epoch: [5/20], Batch Num: [270/600] Discriminator Loss: 0.3968, Generator Loss: 2.0720 D(x): 0.9183, D(G(z)): 0.2085 Epoch: [5/20], Batch Num: [271/600] Discriminator Loss: 0.4453, Generator Loss: 2.5610 D(x): 0.8799, D(G(z)): 0.1791 Epoch: [5/20], Batch Num: [272/600] Discriminator Loss: 0.4930, Generator Loss: 2.6010 D(x): 0.9020, D(G(z)): 0.1982 Epoch: [5/20], Batch Num: [273/600] Discriminator Loss: 0.5329, Generator Loss: 2.7638 D(x): 0.8058, D(G(z)): 0.1120 Epoch: [5/20], Batch Num: [274/600] Discriminator Loss: 0.5063, Generator Loss: 2.6284 D(x): 0.8296, D(G(z)): 0.1150 Epoch: [5/20], Batch Num: [275/600] Discriminator Loss: 0.6056, Generator Loss: 2.6432 D(x): 0.8194, D(G(z)): 0.1448 Epoch: [5/20], Batch Num: [276/600] Discriminator Loss: 0.5418, Generator Loss: 2.2428 D(x): 0.8249, D(G(z)): 0.1329 Epoch: [5/20], Batch Num: [277/600] Discriminator Loss: 0.7817, Generator Loss: 2.2533 D(x): 0.8204, D(G(z)): 0.2171 Epoch: [5/20], Batch Num: [278/600] Discriminator Loss: 0.5787, Generator Loss: 2.1784 D(x): 0.8491, D(G(z)): 0.1861 Epoch: [5/20], Batch Num: [279/600] Discriminator Loss: 0.4721, Generator Loss: 2.3698 D(x): 0.9073, D(G(z)): 0.2028 Epoch: [5/20], Batch Num: [280/600] Discriminator Loss: 0.7035, Generator Loss: 2.4959 D(x): 0.8084, D(G(z)): 0.1977 Epoch: [5/20], Batch Num: [281/600] Discriminator Loss: 0.5239, Generator Loss: 2.3481 D(x): 0.8341, D(G(z)): 0.1411 Epoch: [5/20], Batch Num: [282/600] Discriminator Loss: 0.5411, Generator Loss: 2.5429 D(x): 0.8477, D(G(z)): 0.1595 Epoch: [5/20], Batch Num: [283/600] Discriminator Loss: 0.7332, Generator Loss: 2.2632 D(x): 0.7219, D(G(z)): 0.1412 Epoch: [5/20], Batch Num: [284/600] Discriminator Loss: 0.5191, Generator Loss: 2.0499 D(x): 0.8768, D(G(z)): 0.1781 Epoch: [5/20], Batch Num: [285/600] Discriminator Loss: 0.6525, Generator Loss: 1.9906 D(x): 0.8394, D(G(z)): 0.2022 Epoch: [5/20], Batch Num: [286/600] Discriminator Loss: 0.5580, Generator Loss: 1.9680 D(x): 0.8840, D(G(z)): 0.2168 Epoch: [5/20], Batch Num: [287/600] Discriminator Loss: 0.7007, Generator Loss: 2.1898 D(x): 0.8146, D(G(z)): 0.2029 Epoch: [5/20], Batch Num: [288/600] Discriminator Loss: 0.5256, Generator Loss: 2.2039 D(x): 0.8351, D(G(z)): 0.1690 Epoch: [5/20], Batch Num: [289/600] Discriminator Loss: 0.4840, Generator Loss: 2.4969 D(x): 0.9017, D(G(z)): 0.1838 Epoch: [5/20], Batch Num: [290/600] Discriminator Loss: 0.4807, Generator Loss: 2.4807 D(x): 0.8302, D(G(z)): 0.1301 Epoch: [5/20], Batch Num: [291/600] Discriminator Loss: 0.4834, Generator Loss: 2.5365 D(x): 0.8457, D(G(z)): 0.1327 Epoch: [5/20], Batch Num: [292/600] Discriminator Loss: 0.4418, Generator Loss: 2.4021 D(x): 0.8621, D(G(z)): 0.1348 Epoch: [5/20], Batch Num: [293/600] Discriminator Loss: 0.3715, Generator Loss: 2.3200 D(x): 0.8856, D(G(z)): 0.1367 Epoch: [5/20], Batch Num: [294/600] Discriminator Loss: 0.5508, Generator Loss: 2.4718 D(x): 0.8450, D(G(z)): 0.1793 Epoch: [5/20], Batch Num: [295/600] Discriminator Loss: 0.4977, Generator Loss: 2.5597 D(x): 0.8778, D(G(z)): 0.1819 Epoch: [5/20], Batch Num: [296/600] Discriminator Loss: 0.3703, Generator Loss: 2.5915 D(x): 0.9129, D(G(z)): 0.1725 Epoch: [5/20], Batch Num: [297/600] Discriminator Loss: 0.4567, Generator Loss: 2.8149 D(x): 0.8665, D(G(z)): 0.1259 Epoch: [5/20], Batch Num: [298/600] Discriminator Loss: 0.5398, Generator Loss: 2.7681 D(x): 0.8310, D(G(z)): 0.1336 Epoch: [5/20], Batch Num: [299/600] Discriminator Loss: 0.4861, Generator Loss: 2.7619 D(x): 0.8666, D(G(z)): 0.1404 Epoch: 5, Batch Num: [300/600]
Epoch: [5/20], Batch Num: [300/600] Discriminator Loss: 0.5822, Generator Loss: 2.5947 D(x): 0.8498, D(G(z)): 0.1632 Epoch: [5/20], Batch Num: [301/600] Discriminator Loss: 0.5589, Generator Loss: 2.5788 D(x): 0.8080, D(G(z)): 0.1238 Epoch: [5/20], Batch Num: [302/600] Discriminator Loss: 0.4767, Generator Loss: 2.3989 D(x): 0.8309, D(G(z)): 0.1426 Epoch: [5/20], Batch Num: [303/600] Discriminator Loss: 0.3709, Generator Loss: 2.2450 D(x): 0.8861, D(G(z)): 0.1436 Epoch: [5/20], Batch Num: [304/600] Discriminator Loss: 0.4352, Generator Loss: 2.2554 D(x): 0.8947, D(G(z)): 0.1828 Epoch: [5/20], Batch Num: [305/600] Discriminator Loss: 0.6605, Generator Loss: 2.4192 D(x): 0.8799, D(G(z)): 0.2247 Epoch: [5/20], Batch Num: [306/600] Discriminator Loss: 0.5084, Generator Loss: 2.8602 D(x): 0.8750, D(G(z)): 0.1816 Epoch: [5/20], Batch Num: [307/600] Discriminator Loss: 0.2472, Generator Loss: 2.8721 D(x): 0.9316, D(G(z)): 0.1317 Epoch: [5/20], Batch Num: [308/600] Discriminator Loss: 0.4019, Generator Loss: 3.0182 D(x): 0.8353, D(G(z)): 0.0730 Epoch: [5/20], Batch Num: [309/600] Discriminator Loss: 0.6637, Generator Loss: 2.6290 D(x): 0.7515, D(G(z)): 0.0897 Epoch: [5/20], Batch Num: [310/600] Discriminator Loss: 0.4144, Generator Loss: 2.3091 D(x): 0.8673, D(G(z)): 0.1449 Epoch: [5/20], Batch Num: [311/600] Discriminator Loss: 0.5181, Generator Loss: 2.2096 D(x): 0.8310, D(G(z)): 0.1789 Epoch: [5/20], Batch Num: [312/600] Discriminator Loss: 0.5578, Generator Loss: 1.9879 D(x): 0.8714, D(G(z)): 0.2261 Epoch: [5/20], Batch Num: [313/600] Discriminator Loss: 0.5568, Generator Loss: 1.8969 D(x): 0.8978, D(G(z)): 0.2493 Epoch: [5/20], Batch Num: [314/600] Discriminator Loss: 0.6108, Generator Loss: 2.3125 D(x): 0.8478, D(G(z)): 0.2315 Epoch: [5/20], Batch Num: [315/600] Discriminator Loss: 0.4675, Generator Loss: 2.5329 D(x): 0.9080, D(G(z)): 0.2145 Epoch: [5/20], Batch Num: [316/600] Discriminator Loss: 0.5459, Generator Loss: 2.6766 D(x): 0.7975, D(G(z)): 0.1340 Epoch: [5/20], Batch Num: [317/600] Discriminator Loss: 0.7066, Generator Loss: 2.5293 D(x): 0.7804, D(G(z)): 0.1705 Epoch: [5/20], Batch Num: [318/600] Discriminator Loss: 0.5081, Generator Loss: 2.1776 D(x): 0.8256, D(G(z)): 0.1155 Epoch: [5/20], Batch Num: [319/600] Discriminator Loss: 0.4798, Generator Loss: 2.2048 D(x): 0.8510, D(G(z)): 0.1592 Epoch: [5/20], Batch Num: [320/600] Discriminator Loss: 0.4727, Generator Loss: 2.1498 D(x): 0.8934, D(G(z)): 0.2183 Epoch: [5/20], Batch Num: [321/600] Discriminator Loss: 0.6063, Generator Loss: 2.0504 D(x): 0.8197, D(G(z)): 0.2131 Epoch: [5/20], Batch Num: [322/600] Discriminator Loss: 0.6641, Generator Loss: 2.0799 D(x): 0.8309, D(G(z)): 0.2234 Epoch: [5/20], Batch Num: [323/600] Discriminator Loss: 0.6591, Generator Loss: 2.1577 D(x): 0.8290, D(G(z)): 0.2322 Epoch: [5/20], Batch Num: [324/600] Discriminator Loss: 0.5105, Generator Loss: 2.4214 D(x): 0.8521, D(G(z)): 0.1812 Epoch: [5/20], Batch Num: [325/600] Discriminator Loss: 0.5722, Generator Loss: 2.4395 D(x): 0.8127, D(G(z)): 0.1684 Epoch: [5/20], Batch Num: [326/600] Discriminator Loss: 0.4875, Generator Loss: 2.3555 D(x): 0.8481, D(G(z)): 0.1769 Epoch: [5/20], Batch Num: [327/600] Discriminator Loss: 0.5725, Generator Loss: 2.3566 D(x): 0.8194, D(G(z)): 0.1760 Epoch: [5/20], Batch Num: [328/600] Discriminator Loss: 0.4435, Generator Loss: 2.2116 D(x): 0.8473, D(G(z)): 0.1567 Epoch: [5/20], Batch Num: [329/600] Discriminator Loss: 0.5336, Generator Loss: 2.0572 D(x): 0.8469, D(G(z)): 0.1937 Epoch: [5/20], Batch Num: [330/600] Discriminator Loss: 0.5130, Generator Loss: 2.0011 D(x): 0.8640, D(G(z)): 0.1931 Epoch: [5/20], Batch Num: [331/600] Discriminator Loss: 0.7223, Generator Loss: 2.1813 D(x): 0.8200, D(G(z)): 0.2448 Epoch: [5/20], Batch Num: [332/600] Discriminator Loss: 0.5050, Generator Loss: 2.0432 D(x): 0.8714, D(G(z)): 0.2147 Epoch: [5/20], Batch Num: [333/600] Discriminator Loss: 0.4895, Generator Loss: 2.1909 D(x): 0.8557, D(G(z)): 0.1937 Epoch: [5/20], Batch Num: [334/600] Discriminator Loss: 0.6537, Generator Loss: 2.1347 D(x): 0.7760, D(G(z)): 0.1874 Epoch: [5/20], Batch Num: [335/600] Discriminator Loss: 0.4819, Generator Loss: 2.2359 D(x): 0.8658, D(G(z)): 0.1741 Epoch: [5/20], Batch Num: [336/600] Discriminator Loss: 0.6448, Generator Loss: 2.0791 D(x): 0.7872, D(G(z)): 0.1936 Epoch: [5/20], Batch Num: [337/600] Discriminator Loss: 0.4594, Generator Loss: 2.1533 D(x): 0.8777, D(G(z)): 0.1899 Epoch: [5/20], Batch Num: [338/600] Discriminator Loss: 0.5473, Generator Loss: 2.0954 D(x): 0.8322, D(G(z)): 0.1977 Epoch: [5/20], Batch Num: [339/600] Discriminator Loss: 0.5128, Generator Loss: 2.1689 D(x): 0.8496, D(G(z)): 0.2166 Epoch: [5/20], Batch Num: [340/600] Discriminator Loss: 0.6093, Generator Loss: 2.4139 D(x): 0.8660, D(G(z)): 0.2344 Epoch: [5/20], Batch Num: [341/600] Discriminator Loss: 0.5071, Generator Loss: 2.3850 D(x): 0.8453, D(G(z)): 0.1746 Epoch: [5/20], Batch Num: [342/600] Discriminator Loss: 0.5546, Generator Loss: 2.5049 D(x): 0.8338, D(G(z)): 0.1821 Epoch: [5/20], Batch Num: [343/600] Discriminator Loss: 0.3729, Generator Loss: 2.3811 D(x): 0.8828, D(G(z)): 0.1431 Epoch: [5/20], Batch Num: [344/600] Discriminator Loss: 0.5365, Generator Loss: 2.3278 D(x): 0.8290, D(G(z)): 0.1562 Epoch: [5/20], Batch Num: [345/600] Discriminator Loss: 0.5082, Generator Loss: 2.3397 D(x): 0.8371, D(G(z)): 0.1855 Epoch: [5/20], Batch Num: [346/600] Discriminator Loss: 0.6855, Generator Loss: 2.0706 D(x): 0.8055, D(G(z)): 0.1973 Epoch: [5/20], Batch Num: [347/600] Discriminator Loss: 0.6913, Generator Loss: 2.1968 D(x): 0.8216, D(G(z)): 0.2261 Epoch: [5/20], Batch Num: [348/600] Discriminator Loss: 0.5243, Generator Loss: 2.0892 D(x): 0.8810, D(G(z)): 0.2233 Epoch: [5/20], Batch Num: [349/600] Discriminator Loss: 0.6296, Generator Loss: 2.2283 D(x): 0.8097, D(G(z)): 0.1944 Epoch: [5/20], Batch Num: [350/600] Discriminator Loss: 0.6507, Generator Loss: 2.1576 D(x): 0.8236, D(G(z)): 0.1949 Epoch: [5/20], Batch Num: [351/600] Discriminator Loss: 0.6727, Generator Loss: 1.9400 D(x): 0.8290, D(G(z)): 0.2257 Epoch: [5/20], Batch Num: [352/600] Discriminator Loss: 0.6555, Generator Loss: 2.1589 D(x): 0.8313, D(G(z)): 0.2390 Epoch: [5/20], Batch Num: [353/600] Discriminator Loss: 0.5011, Generator Loss: 2.4104 D(x): 0.8490, D(G(z)): 0.1976 Epoch: [5/20], Batch Num: [354/600] Discriminator Loss: 0.7052, Generator Loss: 2.2262 D(x): 0.7863, D(G(z)): 0.1895 Epoch: [5/20], Batch Num: [355/600] Discriminator Loss: 0.8361, Generator Loss: 2.1001 D(x): 0.7503, D(G(z)): 0.2107 Epoch: [5/20], Batch Num: [356/600] Discriminator Loss: 0.4764, Generator Loss: 2.3388 D(x): 0.9117, D(G(z)): 0.2574 Epoch: [5/20], Batch Num: [357/600] Discriminator Loss: 0.6022, Generator Loss: 2.3220 D(x): 0.8473, D(G(z)): 0.2140 Epoch: [5/20], Batch Num: [358/600] Discriminator Loss: 0.5219, Generator Loss: 2.5999 D(x): 0.8667, D(G(z)): 0.2164 Epoch: [5/20], Batch Num: [359/600] Discriminator Loss: 0.7922, Generator Loss: 2.6396 D(x): 0.7626, D(G(z)): 0.1564 Epoch: [5/20], Batch Num: [360/600] Discriminator Loss: 0.4736, Generator Loss: 2.6642 D(x): 0.8603, D(G(z)): 0.1423 Epoch: [5/20], Batch Num: [361/600] Discriminator Loss: 0.5735, Generator Loss: 2.6244 D(x): 0.8278, D(G(z)): 0.1787 Epoch: [5/20], Batch Num: [362/600] Discriminator Loss: 0.5628, Generator Loss: 2.4398 D(x): 0.8225, D(G(z)): 0.1783 Epoch: [5/20], Batch Num: [363/600] Discriminator Loss: 0.5501, Generator Loss: 2.3830 D(x): 0.8695, D(G(z)): 0.2161 Epoch: [5/20], Batch Num: [364/600] Discriminator Loss: 0.4989, Generator Loss: 2.3919 D(x): 0.8834, D(G(z)): 0.2086 Epoch: [5/20], Batch Num: [365/600] Discriminator Loss: 0.4529, Generator Loss: 2.6064 D(x): 0.8759, D(G(z)): 0.1931 Epoch: [5/20], Batch Num: [366/600] Discriminator Loss: 0.5740, Generator Loss: 2.7027 D(x): 0.8633, D(G(z)): 0.2071 Epoch: [5/20], Batch Num: [367/600] Discriminator Loss: 0.4627, Generator Loss: 2.7824 D(x): 0.8428, D(G(z)): 0.1309 Epoch: [5/20], Batch Num: [368/600] Discriminator Loss: 0.4003, Generator Loss: 2.3528 D(x): 0.8473, D(G(z)): 0.1178 Epoch: [5/20], Batch Num: [369/600] Discriminator Loss: 0.4588, Generator Loss: 2.0408 D(x): 0.8586, D(G(z)): 0.1675 Epoch: [5/20], Batch Num: [370/600] Discriminator Loss: 0.3498, Generator Loss: 2.1943 D(x): 0.9206, D(G(z)): 0.1730 Epoch: [5/20], Batch Num: [371/600] Discriminator Loss: 0.4274, Generator Loss: 2.4112 D(x): 0.9246, D(G(z)): 0.2349 Epoch: [5/20], Batch Num: [372/600] Discriminator Loss: 0.3380, Generator Loss: 2.5454 D(x): 0.9059, D(G(z)): 0.1460 Epoch: [5/20], Batch Num: [373/600] Discriminator Loss: 0.2743, Generator Loss: 2.7606 D(x): 0.9220, D(G(z)): 0.1349 Epoch: [5/20], Batch Num: [374/600] Discriminator Loss: 0.3365, Generator Loss: 2.7712 D(x): 0.9003, D(G(z)): 0.1269 Epoch: [5/20], Batch Num: [375/600] Discriminator Loss: 0.3667, Generator Loss: 2.9547 D(x): 0.8916, D(G(z)): 0.1189 Epoch: [5/20], Batch Num: [376/600] Discriminator Loss: 0.3805, Generator Loss: 2.6264 D(x): 0.8697, D(G(z)): 0.1086 Epoch: [5/20], Batch Num: [377/600] Discriminator Loss: 0.2994, Generator Loss: 2.6157 D(x): 0.9287, D(G(z)): 0.1366 Epoch: [5/20], Batch Num: [378/600] Discriminator Loss: 0.4117, Generator Loss: 2.3083 D(x): 0.8831, D(G(z)): 0.1589 Epoch: [5/20], Batch Num: [379/600] Discriminator Loss: 0.2690, Generator Loss: 2.4013 D(x): 0.9501, D(G(z)): 0.1613 Epoch: [5/20], Batch Num: [380/600] Discriminator Loss: 0.3965, Generator Loss: 2.3728 D(x): 0.9071, D(G(z)): 0.1662 Epoch: [5/20], Batch Num: [381/600] Discriminator Loss: 0.2728, Generator Loss: 2.7907 D(x): 0.9413, D(G(z)): 0.1460 Epoch: [5/20], Batch Num: [382/600] Discriminator Loss: 0.4135, Generator Loss: 2.7104 D(x): 0.8785, D(G(z)): 0.1218 Epoch: [5/20], Batch Num: [383/600] Discriminator Loss: 0.4643, Generator Loss: 2.5545 D(x): 0.8833, D(G(z)): 0.1672 Epoch: [5/20], Batch Num: [384/600] Discriminator Loss: 0.3252, Generator Loss: 2.6090 D(x): 0.9119, D(G(z)): 0.1391 Epoch: [5/20], Batch Num: [385/600] Discriminator Loss: 0.5147, Generator Loss: 2.1694 D(x): 0.8440, D(G(z)): 0.1697 Epoch: [5/20], Batch Num: [386/600] Discriminator Loss: 0.4265, Generator Loss: 2.0385 D(x): 0.9062, D(G(z)): 0.1668 Epoch: [5/20], Batch Num: [387/600] Discriminator Loss: 0.4768, Generator Loss: 2.1945 D(x): 0.9147, D(G(z)): 0.2242 Epoch: [5/20], Batch Num: [388/600] Discriminator Loss: 0.5597, Generator Loss: 2.1771 D(x): 0.8791, D(G(z)): 0.2101 Epoch: [5/20], Batch Num: [389/600] Discriminator Loss: 0.4016, Generator Loss: 2.5709 D(x): 0.9035, D(G(z)): 0.1825 Epoch: [5/20], Batch Num: [390/600] Discriminator Loss: 0.5924, Generator Loss: 2.4708 D(x): 0.8040, D(G(z)): 0.1336 Epoch: [5/20], Batch Num: [391/600] Discriminator Loss: 0.4890, Generator Loss: 2.0581 D(x): 0.8243, D(G(z)): 0.1366 Epoch: [5/20], Batch Num: [392/600] Discriminator Loss: 0.6380, Generator Loss: 1.8674 D(x): 0.8310, D(G(z)): 0.1939 Epoch: [5/20], Batch Num: [393/600] Discriminator Loss: 0.7801, Generator Loss: 1.6555 D(x): 0.8375, D(G(z)): 0.2556 Epoch: [5/20], Batch Num: [394/600] Discriminator Loss: 0.6136, Generator Loss: 1.5828 D(x): 0.9036, D(G(z)): 0.2817 Epoch: [5/20], Batch Num: [395/600] Discriminator Loss: 0.6303, Generator Loss: 1.7869 D(x): 0.8630, D(G(z)): 0.2595 Epoch: [5/20], Batch Num: [396/600] Discriminator Loss: 0.6183, Generator Loss: 2.2754 D(x): 0.8567, D(G(z)): 0.2207 Epoch: [5/20], Batch Num: [397/600] Discriminator Loss: 0.5872, Generator Loss: 2.3685 D(x): 0.8588, D(G(z)): 0.1722 Epoch: [5/20], Batch Num: [398/600] Discriminator Loss: 0.7004, Generator Loss: 2.1552 D(x): 0.7933, D(G(z)): 0.1617 Epoch: [5/20], Batch Num: [399/600] Discriminator Loss: 0.5297, Generator Loss: 1.9264 D(x): 0.8451, D(G(z)): 0.1542 Epoch: 5, Batch Num: [400/600]
Epoch: [5/20], Batch Num: [400/600] Discriminator Loss: 0.6211, Generator Loss: 1.8879 D(x): 0.8271, D(G(z)): 0.2094 Epoch: [5/20], Batch Num: [401/600] Discriminator Loss: 0.6682, Generator Loss: 1.7942 D(x): 0.8560, D(G(z)): 0.2429 Epoch: [5/20], Batch Num: [402/600] Discriminator Loss: 0.5801, Generator Loss: 1.8933 D(x): 0.8740, D(G(z)): 0.2609 Epoch: [5/20], Batch Num: [403/600] Discriminator Loss: 0.8045, Generator Loss: 1.9186 D(x): 0.8051, D(G(z)): 0.2333 Epoch: [5/20], Batch Num: [404/600] Discriminator Loss: 0.7438, Generator Loss: 1.9023 D(x): 0.7898, D(G(z)): 0.2289 Epoch: [5/20], Batch Num: [405/600] Discriminator Loss: 0.6767, Generator Loss: 1.9013 D(x): 0.8043, D(G(z)): 0.2121 Epoch: [5/20], Batch Num: [406/600] Discriminator Loss: 0.5494, Generator Loss: 1.9071 D(x): 0.8612, D(G(z)): 0.2346 Epoch: [5/20], Batch Num: [407/600] Discriminator Loss: 0.6451, Generator Loss: 2.0617 D(x): 0.8432, D(G(z)): 0.2187 Epoch: [5/20], Batch Num: [408/600] Discriminator Loss: 0.6400, Generator Loss: 2.1485 D(x): 0.8182, D(G(z)): 0.2193 Epoch: [5/20], Batch Num: [409/600] Discriminator Loss: 0.8225, Generator Loss: 2.1850 D(x): 0.7510, D(G(z)): 0.2197 Epoch: [5/20], Batch Num: [410/600] Discriminator Loss: 0.4829, Generator Loss: 2.0084 D(x): 0.8710, D(G(z)): 0.1919 Epoch: [5/20], Batch Num: [411/600] Discriminator Loss: 0.5239, Generator Loss: 2.0435 D(x): 0.8585, D(G(z)): 0.2085 Epoch: [5/20], Batch Num: [412/600] Discriminator Loss: 0.7129, Generator Loss: 1.8914 D(x): 0.7707, D(G(z)): 0.1734 Epoch: [5/20], Batch Num: [413/600] Discriminator Loss: 0.5006, Generator Loss: 1.8604 D(x): 0.8898, D(G(z)): 0.2284 Epoch: [5/20], Batch Num: [414/600] Discriminator Loss: 0.5280, Generator Loss: 1.8910 D(x): 0.8435, D(G(z)): 0.1920 Epoch: [5/20], Batch Num: [415/600] Discriminator Loss: 0.4661, Generator Loss: 1.9974 D(x): 0.8658, D(G(z)): 0.1939 Epoch: [5/20], Batch Num: [416/600] Discriminator Loss: 0.3157, Generator Loss: 2.2543 D(x): 0.9290, D(G(z)): 0.1722 Epoch: [5/20], Batch Num: [417/600] Discriminator Loss: 0.4458, Generator Loss: 2.3749 D(x): 0.8738, D(G(z)): 0.1646 Epoch: [5/20], Batch Num: [418/600] Discriminator Loss: 0.3987, Generator Loss: 2.5327 D(x): 0.8633, D(G(z)): 0.1419 Epoch: [5/20], Batch Num: [419/600] Discriminator Loss: 0.2629, Generator Loss: 2.6066 D(x): 0.9311, D(G(z)): 0.1240 Epoch: [5/20], Batch Num: [420/600] Discriminator Loss: 0.4501, Generator Loss: 2.5507 D(x): 0.8523, D(G(z)): 0.1377 Epoch: [5/20], Batch Num: [421/600] Discriminator Loss: 0.2757, Generator Loss: 2.6146 D(x): 0.8999, D(G(z)): 0.0989 Epoch: [5/20], Batch Num: [422/600] Discriminator Loss: 0.3102, Generator Loss: 2.6496 D(x): 0.9295, D(G(z)): 0.1360 Epoch: [5/20], Batch Num: [423/600] Discriminator Loss: 0.3320, Generator Loss: 2.6962 D(x): 0.9017, D(G(z)): 0.1250 Epoch: [5/20], Batch Num: [424/600] Discriminator Loss: 0.3244, Generator Loss: 2.8054 D(x): 0.8985, D(G(z)): 0.1194 Epoch: [5/20], Batch Num: [425/600] Discriminator Loss: 0.2218, Generator Loss: 2.7133 D(x): 0.9500, D(G(z)): 0.1230 Epoch: [5/20], Batch Num: [426/600] Discriminator Loss: 0.3095, Generator Loss: 2.7261 D(x): 0.9010, D(G(z)): 0.1009 Epoch: [5/20], Batch Num: [427/600] Discriminator Loss: 0.2822, Generator Loss: 2.8529 D(x): 0.9085, D(G(z)): 0.1115 Epoch: [5/20], Batch Num: [428/600] Discriminator Loss: 0.3441, Generator Loss: 3.0142 D(x): 0.9347, D(G(z)): 0.1579 Epoch: [5/20], Batch Num: [429/600] Discriminator Loss: 0.3434, Generator Loss: 3.3161 D(x): 0.9175, D(G(z)): 0.1082 Epoch: [5/20], Batch Num: [430/600] Discriminator Loss: 0.2719, Generator Loss: 3.4086 D(x): 0.9057, D(G(z)): 0.0856 Epoch: [5/20], Batch Num: [431/600] Discriminator Loss: 0.2155, Generator Loss: 3.4427 D(x): 0.9324, D(G(z)): 0.0824 Epoch: [5/20], Batch Num: [432/600] Discriminator Loss: 0.3838, Generator Loss: 3.1496 D(x): 0.8860, D(G(z)): 0.0923 Epoch: [5/20], Batch Num: [433/600] Discriminator Loss: 0.2486, Generator Loss: 3.1914 D(x): 0.9374, D(G(z)): 0.0968 Epoch: [5/20], Batch Num: [434/600] Discriminator Loss: 0.2577, Generator Loss: 3.0951 D(x): 0.9300, D(G(z)): 0.0986 Epoch: [5/20], Batch Num: [435/600] Discriminator Loss: 0.3825, Generator Loss: 3.2421 D(x): 0.9111, D(G(z)): 0.1279 Epoch: [5/20], Batch Num: [436/600] Discriminator Loss: 0.4042, Generator Loss: 3.2700 D(x): 0.8675, D(G(z)): 0.0961 Epoch: [5/20], Batch Num: [437/600] Discriminator Loss: 0.3596, Generator Loss: 3.0626 D(x): 0.8978, D(G(z)): 0.1173 Epoch: [5/20], Batch Num: [438/600] Discriminator Loss: 0.5196, Generator Loss: 2.7325 D(x): 0.8380, D(G(z)): 0.0986 Epoch: [5/20], Batch Num: [439/600] Discriminator Loss: 0.6985, Generator Loss: 2.4572 D(x): 0.8493, D(G(z)): 0.1838 Epoch: [5/20], Batch Num: [440/600] Discriminator Loss: 0.4678, Generator Loss: 2.8442 D(x): 0.9119, D(G(z)): 0.2097 Epoch: [5/20], Batch Num: [441/600] Discriminator Loss: 0.4916, Generator Loss: 3.2237 D(x): 0.8805, D(G(z)): 0.1265 Epoch: [5/20], Batch Num: [442/600] Discriminator Loss: 0.6824, Generator Loss: 2.9285 D(x): 0.7927, D(G(z)): 0.1058 Epoch: [5/20], Batch Num: [443/600] Discriminator Loss: 0.5233, Generator Loss: 2.8090 D(x): 0.8792, D(G(z)): 0.1638 Epoch: [5/20], Batch Num: [444/600] Discriminator Loss: 0.8781, Generator Loss: 2.6341 D(x): 0.8041, D(G(z)): 0.1839 Epoch: [5/20], Batch Num: [445/600] Discriminator Loss: 0.7565, Generator Loss: 2.6958 D(x): 0.8674, D(G(z)): 0.2211 Epoch: [5/20], Batch Num: [446/600] Discriminator Loss: 0.8338, Generator Loss: 2.9441 D(x): 0.8238, D(G(z)): 0.1978 Epoch: [5/20], Batch Num: [447/600] Discriminator Loss: 0.9857, Generator Loss: 2.8648 D(x): 0.7181, D(G(z)): 0.1594 Epoch: [5/20], Batch Num: [448/600] Discriminator Loss: 0.8107, Generator Loss: 2.6211 D(x): 0.7877, D(G(z)): 0.1767 Epoch: [5/20], Batch Num: [449/600] Discriminator Loss: 0.9480, Generator Loss: 2.7042 D(x): 0.7563, D(G(z)): 0.2085 Epoch: [5/20], Batch Num: [450/600] Discriminator Loss: 0.8703, Generator Loss: 2.4684 D(x): 0.7725, D(G(z)): 0.1982 Epoch: [5/20], Batch Num: [451/600] Discriminator Loss: 0.5513, Generator Loss: 2.4725 D(x): 0.8466, D(G(z)): 0.1728 Epoch: [5/20], Batch Num: [452/600] Discriminator Loss: 0.8800, Generator Loss: 2.4320 D(x): 0.7871, D(G(z)): 0.2031 Epoch: [5/20], Batch Num: [453/600] Discriminator Loss: 0.8674, Generator Loss: 2.5980 D(x): 0.7907, D(G(z)): 0.2083 Epoch: [5/20], Batch Num: [454/600] Discriminator Loss: 1.0176, Generator Loss: 2.5922 D(x): 0.7565, D(G(z)): 0.2330 Epoch: [5/20], Batch Num: [455/600] Discriminator Loss: 0.7533, Generator Loss: 2.7414 D(x): 0.7851, D(G(z)): 0.1893 Epoch: [5/20], Batch Num: [456/600] Discriminator Loss: 0.5390, Generator Loss: 2.8741 D(x): 0.8837, D(G(z)): 0.2115 Epoch: [5/20], Batch Num: [457/600] Discriminator Loss: 0.7259, Generator Loss: 3.2695 D(x): 0.7866, D(G(z)): 0.1677 Epoch: [5/20], Batch Num: [458/600] Discriminator Loss: 0.6208, Generator Loss: 2.9510 D(x): 0.8465, D(G(z)): 0.1686 Epoch: [5/20], Batch Num: [459/600] Discriminator Loss: 0.7092, Generator Loss: 3.1185 D(x): 0.7404, D(G(z)): 0.1248 Epoch: [5/20], Batch Num: [460/600] Discriminator Loss: 0.6703, Generator Loss: 2.3577 D(x): 0.7880, D(G(z)): 0.1745 Epoch: [5/20], Batch Num: [461/600] Discriminator Loss: 0.7615, Generator Loss: 2.1715 D(x): 0.8318, D(G(z)): 0.2384 Epoch: [5/20], Batch Num: [462/600] Discriminator Loss: 0.6534, Generator Loss: 2.2910 D(x): 0.8288, D(G(z)): 0.2101 Epoch: [5/20], Batch Num: [463/600] Discriminator Loss: 0.6213, Generator Loss: 2.6873 D(x): 0.8497, D(G(z)): 0.2052 Epoch: [5/20], Batch Num: [464/600] Discriminator Loss: 0.5739, Generator Loss: 2.6881 D(x): 0.8142, D(G(z)): 0.1723 Epoch: [5/20], Batch Num: [465/600] Discriminator Loss: 0.8273, Generator Loss: 2.5666 D(x): 0.7709, D(G(z)): 0.1927 Epoch: [5/20], Batch Num: [466/600] Discriminator Loss: 0.6088, Generator Loss: 2.5386 D(x): 0.7802, D(G(z)): 0.1337 Epoch: [5/20], Batch Num: [467/600] Discriminator Loss: 0.7682, Generator Loss: 2.6329 D(x): 0.8040, D(G(z)): 0.2172 Epoch: [5/20], Batch Num: [468/600] Discriminator Loss: 0.4880, Generator Loss: 2.4495 D(x): 0.8731, D(G(z)): 0.2038 Epoch: [5/20], Batch Num: [469/600] Discriminator Loss: 0.7542, Generator Loss: 2.5348 D(x): 0.7485, D(G(z)): 0.1910 Epoch: [5/20], Batch Num: [470/600] Discriminator Loss: 0.5397, Generator Loss: 2.6647 D(x): 0.8379, D(G(z)): 0.1636 Epoch: [5/20], Batch Num: [471/600] Discriminator Loss: 0.6873, Generator Loss: 2.7054 D(x): 0.7789, D(G(z)): 0.1746 Epoch: [5/20], Batch Num: [472/600] Discriminator Loss: 0.5404, Generator Loss: 2.5131 D(x): 0.8093, D(G(z)): 0.1334 Epoch: [5/20], Batch Num: [473/600] Discriminator Loss: 0.4126, Generator Loss: 2.4987 D(x): 0.8791, D(G(z)): 0.1586 Epoch: [5/20], Batch Num: [474/600] Discriminator Loss: 0.6654, Generator Loss: 2.4253 D(x): 0.8514, D(G(z)): 0.2212 Epoch: [5/20], Batch Num: [475/600] Discriminator Loss: 0.3810, Generator Loss: 2.5949 D(x): 0.8862, D(G(z)): 0.1524 Epoch: [5/20], Batch Num: [476/600] Discriminator Loss: 0.4429, Generator Loss: 2.3740 D(x): 0.8649, D(G(z)): 0.1608 Epoch: [5/20], Batch Num: [477/600] Discriminator Loss: 0.4824, Generator Loss: 2.8239 D(x): 0.8476, D(G(z)): 0.1298 Epoch: [5/20], Batch Num: [478/600] Discriminator Loss: 0.3103, Generator Loss: 2.5901 D(x): 0.9088, D(G(z)): 0.1119 Epoch: [5/20], Batch Num: [479/600] Discriminator Loss: 0.5591, Generator Loss: 2.5470 D(x): 0.8484, D(G(z)): 0.1507 Epoch: [5/20], Batch Num: [480/600] Discriminator Loss: 0.5311, Generator Loss: 2.5750 D(x): 0.8552, D(G(z)): 0.1568 Epoch: [5/20], Batch Num: [481/600] Discriminator Loss: 0.4492, Generator Loss: 2.5420 D(x): 0.8818, D(G(z)): 0.1611 Epoch: [5/20], Batch Num: [482/600] Discriminator Loss: 0.2963, Generator Loss: 2.4570 D(x): 0.9310, D(G(z)): 0.1418 Epoch: [5/20], Batch Num: [483/600] Discriminator Loss: 0.4824, Generator Loss: 2.4931 D(x): 0.8807, D(G(z)): 0.1875 Epoch: [5/20], Batch Num: [484/600] Discriminator Loss: 0.6235, Generator Loss: 2.6144 D(x): 0.8110, D(G(z)): 0.1534 Epoch: [5/20], Batch Num: [485/600] Discriminator Loss: 0.5556, Generator Loss: 2.4553 D(x): 0.8303, D(G(z)): 0.1382 Epoch: [5/20], Batch Num: [486/600] Discriminator Loss: 0.3616, Generator Loss: 2.4260 D(x): 0.8914, D(G(z)): 0.1447 Epoch: [5/20], Batch Num: [487/600] Discriminator Loss: 0.5455, Generator Loss: 2.0424 D(x): 0.8284, D(G(z)): 0.1540 Epoch: [5/20], Batch Num: [488/600] Discriminator Loss: 0.6036, Generator Loss: 2.1509 D(x): 0.8547, D(G(z)): 0.2156 Epoch: [5/20], Batch Num: [489/600] Discriminator Loss: 0.6009, Generator Loss: 2.2781 D(x): 0.8610, D(G(z)): 0.2362 Epoch: [5/20], Batch Num: [490/600] Discriminator Loss: 0.5981, Generator Loss: 2.2928 D(x): 0.8289, D(G(z)): 0.1763 Epoch: [5/20], Batch Num: [491/600] Discriminator Loss: 0.5535, Generator Loss: 2.2839 D(x): 0.8792, D(G(z)): 0.2048 Epoch: [5/20], Batch Num: [492/600] Discriminator Loss: 0.6667, Generator Loss: 2.5013 D(x): 0.8034, D(G(z)): 0.1777 Epoch: [5/20], Batch Num: [493/600] Discriminator Loss: 0.5607, Generator Loss: 2.4920 D(x): 0.8305, D(G(z)): 0.1525 Epoch: [5/20], Batch Num: [494/600] Discriminator Loss: 0.5135, Generator Loss: 2.3455 D(x): 0.8555, D(G(z)): 0.1361 Epoch: [5/20], Batch Num: [495/600] Discriminator Loss: 0.5497, Generator Loss: 2.2124 D(x): 0.8473, D(G(z)): 0.1689 Epoch: [5/20], Batch Num: [496/600] Discriminator Loss: 0.6624, Generator Loss: 2.2810 D(x): 0.8876, D(G(z)): 0.2255 Epoch: [5/20], Batch Num: [497/600] Discriminator Loss: 0.5210, Generator Loss: 2.5694 D(x): 0.8804, D(G(z)): 0.2065 Epoch: [5/20], Batch Num: [498/600] Discriminator Loss: 0.4846, Generator Loss: 2.6206 D(x): 0.8380, D(G(z)): 0.1327 Epoch: [5/20], Batch Num: [499/600] Discriminator Loss: 0.5098, Generator Loss: 2.6077 D(x): 0.8312, D(G(z)): 0.1063 Epoch: 5, Batch Num: [500/600]
Epoch: [5/20], Batch Num: [500/600] Discriminator Loss: 0.4909, Generator Loss: 2.0547 D(x): 0.8470, D(G(z)): 0.1407 Epoch: [5/20], Batch Num: [501/600] Discriminator Loss: 0.4860, Generator Loss: 1.8852 D(x): 0.8637, D(G(z)): 0.1843 Epoch: [5/20], Batch Num: [502/600] Discriminator Loss: 0.6726, Generator Loss: 1.9086 D(x): 0.8412, D(G(z)): 0.2158 Epoch: [5/20], Batch Num: [503/600] Discriminator Loss: 0.7961, Generator Loss: 1.7752 D(x): 0.8439, D(G(z)): 0.2782 Epoch: [5/20], Batch Num: [504/600] Discriminator Loss: 0.7190, Generator Loss: 2.0268 D(x): 0.8784, D(G(z)): 0.2738 Epoch: [5/20], Batch Num: [505/600] Discriminator Loss: 0.5789, Generator Loss: 2.4202 D(x): 0.8548, D(G(z)): 0.1939 Epoch: [5/20], Batch Num: [506/600] Discriminator Loss: 0.3942, Generator Loss: 2.6661 D(x): 0.8951, D(G(z)): 0.1292 Epoch: [5/20], Batch Num: [507/600] Discriminator Loss: 0.4959, Generator Loss: 2.7746 D(x): 0.8326, D(G(z)): 0.1078 Epoch: [5/20], Batch Num: [508/600] Discriminator Loss: 0.4659, Generator Loss: 2.7723 D(x): 0.8485, D(G(z)): 0.0939 Epoch: [5/20], Batch Num: [509/600] Discriminator Loss: 0.5105, Generator Loss: 2.5607 D(x): 0.8462, D(G(z)): 0.1327 Epoch: [5/20], Batch Num: [510/600] Discriminator Loss: 0.5176, Generator Loss: 2.4765 D(x): 0.8470, D(G(z)): 0.1296 Epoch: [5/20], Batch Num: [511/600] Discriminator Loss: 0.4471, Generator Loss: 2.1464 D(x): 0.8544, D(G(z)): 0.1382 Epoch: [5/20], Batch Num: [512/600] Discriminator Loss: 0.4193, Generator Loss: 2.1959 D(x): 0.9332, D(G(z)): 0.2043 Epoch: [5/20], Batch Num: [513/600] Discriminator Loss: 0.3745, Generator Loss: 2.2879 D(x): 0.9330, D(G(z)): 0.1976 Epoch: [5/20], Batch Num: [514/600] Discriminator Loss: 0.2980, Generator Loss: 2.6606 D(x): 0.9606, D(G(z)): 0.1621 Epoch: [5/20], Batch Num: [515/600] Discriminator Loss: 0.2381, Generator Loss: 3.1043 D(x): 0.9413, D(G(z)): 0.1198 Epoch: [5/20], Batch Num: [516/600] Discriminator Loss: 0.2923, Generator Loss: 3.0778 D(x): 0.8941, D(G(z)): 0.0820 Epoch: [5/20], Batch Num: [517/600] Discriminator Loss: 0.2205, Generator Loss: 3.3436 D(x): 0.9226, D(G(z)): 0.0795 Epoch: [5/20], Batch Num: [518/600] Discriminator Loss: 0.2833, Generator Loss: 3.3904 D(x): 0.8989, D(G(z)): 0.0616 Epoch: [5/20], Batch Num: [519/600] Discriminator Loss: 0.3090, Generator Loss: 3.2916 D(x): 0.8905, D(G(z)): 0.0746 Epoch: [5/20], Batch Num: [520/600] Discriminator Loss: 0.2259, Generator Loss: 3.0380 D(x): 0.9332, D(G(z)): 0.0912 Epoch: [5/20], Batch Num: [521/600] Discriminator Loss: 0.2142, Generator Loss: 2.8800 D(x): 0.9485, D(G(z)): 0.1008 Epoch: [5/20], Batch Num: [522/600] Discriminator Loss: 0.2692, Generator Loss: 2.8279 D(x): 0.9294, D(G(z)): 0.1171 Epoch: [5/20], Batch Num: [523/600] Discriminator Loss: 0.2894, Generator Loss: 2.7321 D(x): 0.9379, D(G(z)): 0.1163 Epoch: [5/20], Batch Num: [524/600] Discriminator Loss: 0.3608, Generator Loss: 2.7481 D(x): 0.9325, D(G(z)): 0.1535 Epoch: [5/20], Batch Num: [525/600] Discriminator Loss: 0.1975, Generator Loss: 3.1434 D(x): 0.9777, D(G(z)): 0.1277 Epoch: [5/20], Batch Num: [526/600] Discriminator Loss: 0.3726, Generator Loss: 3.2630 D(x): 0.9299, D(G(z)): 0.1143 Epoch: [5/20], Batch Num: [527/600] Discriminator Loss: 0.3249, Generator Loss: 3.1794 D(x): 0.8963, D(G(z)): 0.1039 Epoch: [5/20], Batch Num: [528/600] Discriminator Loss: 0.2399, Generator Loss: 3.2257 D(x): 0.9155, D(G(z)): 0.0684 Epoch: [5/20], Batch Num: [529/600] Discriminator Loss: 0.3723, Generator Loss: 3.1031 D(x): 0.8842, D(G(z)): 0.0910 Epoch: [5/20], Batch Num: [530/600] Discriminator Loss: 0.2318, Generator Loss: 2.8602 D(x): 0.9412, D(G(z)): 0.1116 Epoch: [5/20], Batch Num: [531/600] Discriminator Loss: 0.4630, Generator Loss: 2.9228 D(x): 0.8945, D(G(z)): 0.1503 Epoch: [5/20], Batch Num: [532/600] Discriminator Loss: 0.5443, Generator Loss: 2.6370 D(x): 0.8917, D(G(z)): 0.1654 Epoch: [5/20], Batch Num: [533/600] Discriminator Loss: 0.4531, Generator Loss: 2.7530 D(x): 0.9107, D(G(z)): 0.1791 Epoch: [5/20], Batch Num: [534/600] Discriminator Loss: 0.6653, Generator Loss: 2.7078 D(x): 0.8595, D(G(z)): 0.1888 Epoch: [5/20], Batch Num: [535/600] Discriminator Loss: 0.5797, Generator Loss: 2.5734 D(x): 0.8751, D(G(z)): 0.1927 Epoch: [5/20], Batch Num: [536/600] Discriminator Loss: 0.5781, Generator Loss: 2.8135 D(x): 0.8861, D(G(z)): 0.1811 Epoch: [5/20], Batch Num: [537/600] Discriminator Loss: 0.6911, Generator Loss: 3.0765 D(x): 0.8712, D(G(z)): 0.2041 Epoch: [5/20], Batch Num: [538/600] Discriminator Loss: 0.8397, Generator Loss: 2.8057 D(x): 0.7961, D(G(z)): 0.1585 Epoch: [5/20], Batch Num: [539/600] Discriminator Loss: 0.8409, Generator Loss: 2.6670 D(x): 0.7633, D(G(z)): 0.1509 Epoch: [5/20], Batch Num: [540/600] Discriminator Loss: 0.5364, Generator Loss: 2.3087 D(x): 0.8816, D(G(z)): 0.1732 Epoch: [5/20], Batch Num: [541/600] Discriminator Loss: 0.7784, Generator Loss: 2.4013 D(x): 0.8764, D(G(z)): 0.2697 Epoch: [5/20], Batch Num: [542/600] Discriminator Loss: 0.9432, Generator Loss: 2.1587 D(x): 0.8879, D(G(z)): 0.3015 Epoch: [5/20], Batch Num: [543/600] Discriminator Loss: 0.7238, Generator Loss: 2.4958 D(x): 0.8687, D(G(z)): 0.2567 Epoch: [5/20], Batch Num: [544/600] Discriminator Loss: 0.7826, Generator Loss: 2.5563 D(x): 0.8051, D(G(z)): 0.2225 Epoch: [5/20], Batch Num: [545/600] Discriminator Loss: 0.7682, Generator Loss: 2.4850 D(x): 0.7859, D(G(z)): 0.1969 Epoch: [5/20], Batch Num: [546/600] Discriminator Loss: 0.6222, Generator Loss: 2.5672 D(x): 0.8216, D(G(z)): 0.1795 Epoch: [5/20], Batch Num: [547/600] Discriminator Loss: 0.5085, Generator Loss: 2.1126 D(x): 0.8434, D(G(z)): 0.1558 Epoch: [5/20], Batch Num: [548/600] Discriminator Loss: 0.6042, Generator Loss: 2.2090 D(x): 0.8586, D(G(z)): 0.2417 Epoch: [5/20], Batch Num: [549/600] Discriminator Loss: 0.6006, Generator Loss: 2.3993 D(x): 0.8430, D(G(z)): 0.2274 Epoch: [5/20], Batch Num: [550/600] Discriminator Loss: 0.5529, Generator Loss: 2.2862 D(x): 0.8350, D(G(z)): 0.1916 Epoch: [5/20], Batch Num: [551/600] Discriminator Loss: 0.6055, Generator Loss: 2.0455 D(x): 0.8472, D(G(z)): 0.2066 Epoch: [5/20], Batch Num: [552/600] Discriminator Loss: 0.4958, Generator Loss: 2.3719 D(x): 0.8324, D(G(z)): 0.1673 Epoch: [5/20], Batch Num: [553/600] Discriminator Loss: 0.4287, Generator Loss: 2.2864 D(x): 0.8700, D(G(z)): 0.1800 Epoch: [5/20], Batch Num: [554/600] Discriminator Loss: 0.4824, Generator Loss: 2.1619 D(x): 0.8657, D(G(z)): 0.1979 Epoch: [5/20], Batch Num: [555/600] Discriminator Loss: 0.6423, Generator Loss: 2.2605 D(x): 0.7967, D(G(z)): 0.1966 Epoch: [5/20], Batch Num: [556/600] Discriminator Loss: 0.6243, Generator Loss: 2.3880 D(x): 0.7885, D(G(z)): 0.1876 Epoch: [5/20], Batch Num: [557/600] Discriminator Loss: 0.5626, Generator Loss: 2.2411 D(x): 0.8257, D(G(z)): 0.1965 Epoch: [5/20], Batch Num: [558/600] Discriminator Loss: 0.5181, Generator Loss: 1.9773 D(x): 0.8429, D(G(z)): 0.2009 Epoch: [5/20], Batch Num: [559/600] Discriminator Loss: 0.5277, Generator Loss: 1.9736 D(x): 0.8779, D(G(z)): 0.2364 Epoch: [5/20], Batch Num: [560/600] Discriminator Loss: 0.6589, Generator Loss: 2.2236 D(x): 0.8343, D(G(z)): 0.2488 Epoch: [5/20], Batch Num: [561/600] Discriminator Loss: 0.4315, Generator Loss: 2.3662 D(x): 0.9029, D(G(z)): 0.2076 Epoch: [5/20], Batch Num: [562/600] Discriminator Loss: 0.6234, Generator Loss: 2.3273 D(x): 0.7809, D(G(z)): 0.1707 Epoch: [5/20], Batch Num: [563/600] Discriminator Loss: 0.7313, Generator Loss: 2.3976 D(x): 0.7550, D(G(z)): 0.1753 Epoch: [5/20], Batch Num: [564/600] Discriminator Loss: 0.5981, Generator Loss: 2.1234 D(x): 0.8390, D(G(z)): 0.2195 Epoch: [5/20], Batch Num: [565/600] Discriminator Loss: 0.6250, Generator Loss: 1.9112 D(x): 0.8217, D(G(z)): 0.2230 Epoch: [5/20], Batch Num: [566/600] Discriminator Loss: 0.6535, Generator Loss: 2.3183 D(x): 0.7992, D(G(z)): 0.1895 Epoch: [5/20], Batch Num: [567/600] Discriminator Loss: 0.5439, Generator Loss: 2.1859 D(x): 0.8352, D(G(z)): 0.2043 Epoch: [5/20], Batch Num: [568/600] Discriminator Loss: 0.6677, Generator Loss: 2.4925 D(x): 0.8328, D(G(z)): 0.2370 Epoch: [5/20], Batch Num: [569/600] Discriminator Loss: 0.5666, Generator Loss: 2.6233 D(x): 0.7932, D(G(z)): 0.1742 Epoch: [5/20], Batch Num: [570/600] Discriminator Loss: 0.7139, Generator Loss: 2.3291 D(x): 0.7887, D(G(z)): 0.1889 Epoch: [5/20], Batch Num: [571/600] Discriminator Loss: 0.8306, Generator Loss: 2.0621 D(x): 0.7476, D(G(z)): 0.1940 Epoch: [5/20], Batch Num: [572/600] Discriminator Loss: 0.7063, Generator Loss: 2.0081 D(x): 0.8297, D(G(z)): 0.2428 Epoch: [5/20], Batch Num: [573/600] Discriminator Loss: 0.8113, Generator Loss: 2.1491 D(x): 0.7758, D(G(z)): 0.2118 Epoch: [5/20], Batch Num: [574/600] Discriminator Loss: 0.9626, Generator Loss: 1.8777 D(x): 0.7896, D(G(z)): 0.2720 Epoch: [5/20], Batch Num: [575/600] Discriminator Loss: 0.9705, Generator Loss: 2.1060 D(x): 0.7657, D(G(z)): 0.3080 Epoch: [5/20], Batch Num: [576/600] Discriminator Loss: 0.9335, Generator Loss: 2.2039 D(x): 0.7833, D(G(z)): 0.2623 Epoch: [5/20], Batch Num: [577/600] Discriminator Loss: 0.7112, Generator Loss: 2.1003 D(x): 0.8217, D(G(z)): 0.2313 Epoch: [5/20], Batch Num: [578/600] Discriminator Loss: 1.0221, Generator Loss: 2.3096 D(x): 0.7052, D(G(z)): 0.2206 Epoch: [5/20], Batch Num: [579/600] Discriminator Loss: 0.7570, Generator Loss: 2.0719 D(x): 0.7617, D(G(z)): 0.2183 Epoch: [5/20], Batch Num: [580/600] Discriminator Loss: 0.9256, Generator Loss: 1.9159 D(x): 0.7859, D(G(z)): 0.2656 Epoch: [5/20], Batch Num: [581/600] Discriminator Loss: 0.9271, Generator Loss: 1.7557 D(x): 0.7444, D(G(z)): 0.2403 Epoch: [5/20], Batch Num: [582/600] Discriminator Loss: 0.9447, Generator Loss: 1.7210 D(x): 0.7545, D(G(z)): 0.2554 Epoch: [5/20], Batch Num: [583/600] Discriminator Loss: 0.7581, Generator Loss: 2.1276 D(x): 0.8511, D(G(z)): 0.3184 Epoch: [5/20], Batch Num: [584/600] Discriminator Loss: 0.7131, Generator Loss: 2.0534 D(x): 0.8026, D(G(z)): 0.2455 Epoch: [5/20], Batch Num: [585/600] Discriminator Loss: 0.8631, Generator Loss: 1.9846 D(x): 0.7325, D(G(z)): 0.1860 Epoch: [5/20], Batch Num: [586/600] Discriminator Loss: 0.9803, Generator Loss: 2.1006 D(x): 0.6741, D(G(z)): 0.1874 Epoch: [5/20], Batch Num: [587/600] Discriminator Loss: 0.9764, Generator Loss: 1.8383 D(x): 0.7165, D(G(z)): 0.2505 Epoch: [5/20], Batch Num: [588/600] Discriminator Loss: 0.9216, Generator Loss: 1.7589 D(x): 0.8083, D(G(z)): 0.3182 Epoch: [5/20], Batch Num: [589/600] Discriminator Loss: 0.8778, Generator Loss: 1.7800 D(x): 0.7695, D(G(z)): 0.3173 Epoch: [5/20], Batch Num: [590/600] Discriminator Loss: 0.8525, Generator Loss: 1.7449 D(x): 0.7587, D(G(z)): 0.2790 Epoch: [5/20], Batch Num: [591/600] Discriminator Loss: 0.8759, Generator Loss: 1.9025 D(x): 0.7496, D(G(z)): 0.2743 Epoch: [5/20], Batch Num: [592/600] Discriminator Loss: 0.9093, Generator Loss: 1.6907 D(x): 0.7150, D(G(z)): 0.2808 Epoch: [5/20], Batch Num: [593/600] Discriminator Loss: 0.8414, Generator Loss: 1.8017 D(x): 0.7400, D(G(z)): 0.2510 Epoch: [5/20], Batch Num: [594/600] Discriminator Loss: 0.7907, Generator Loss: 1.6447 D(x): 0.7356, D(G(z)): 0.2583 Epoch: [5/20], Batch Num: [595/600] Discriminator Loss: 0.8615, Generator Loss: 1.6440 D(x): 0.7539, D(G(z)): 0.2749 Epoch: [5/20], Batch Num: [596/600] Discriminator Loss: 0.9179, Generator Loss: 1.7241 D(x): 0.7986, D(G(z)): 0.3295 Epoch: [5/20], Batch Num: [597/600] Discriminator Loss: 0.7687, Generator Loss: 1.9284 D(x): 0.7887, D(G(z)): 0.2831 Epoch: [5/20], Batch Num: [598/600] Discriminator Loss: 0.8452, Generator Loss: 2.0318 D(x): 0.7293, D(G(z)): 0.2257 Epoch: [5/20], Batch Num: [599/600] Discriminator Loss: 0.8427, Generator Loss: 2.0306 D(x): 0.7314, D(G(z)): 0.2238 Epoch: 6, Batch Num: [0/600]
Epoch: [6/20], Batch Num: [0/600] Discriminator Loss: 0.7458, Generator Loss: 2.1053 D(x): 0.7186, D(G(z)): 0.1803 Epoch: [6/20], Batch Num: [1/600] Discriminator Loss: 0.8689, Generator Loss: 2.0317 D(x): 0.6958, D(G(z)): 0.1990 Epoch: [6/20], Batch Num: [2/600] Discriminator Loss: 0.7488, Generator Loss: 1.6947 D(x): 0.7274, D(G(z)): 0.2081 Epoch: [6/20], Batch Num: [3/600] Discriminator Loss: 0.8363, Generator Loss: 1.4972 D(x): 0.7695, D(G(z)): 0.2712 Epoch: [6/20], Batch Num: [4/600] Discriminator Loss: 0.8681, Generator Loss: 1.5039 D(x): 0.8077, D(G(z)): 0.3375 Epoch: [6/20], Batch Num: [5/600] Discriminator Loss: 0.5739, Generator Loss: 1.7465 D(x): 0.8586, D(G(z)): 0.2603 Epoch: [6/20], Batch Num: [6/600] Discriminator Loss: 0.7461, Generator Loss: 1.9441 D(x): 0.7991, D(G(z)): 0.2695 Epoch: [6/20], Batch Num: [7/600] Discriminator Loss: 0.7702, Generator Loss: 1.8800 D(x): 0.7061, D(G(z)): 0.1893 Epoch: [6/20], Batch Num: [8/600] Discriminator Loss: 0.7670, Generator Loss: 2.0393 D(x): 0.7577, D(G(z)): 0.2342 Epoch: [6/20], Batch Num: [9/600] Discriminator Loss: 0.6964, Generator Loss: 1.9519 D(x): 0.7522, D(G(z)): 0.1971 Epoch: [6/20], Batch Num: [10/600] Discriminator Loss: 0.8617, Generator Loss: 1.5882 D(x): 0.6989, D(G(z)): 0.2236 Epoch: [6/20], Batch Num: [11/600] Discriminator Loss: 0.8572, Generator Loss: 1.2872 D(x): 0.7285, D(G(z)): 0.2611 Epoch: [6/20], Batch Num: [12/600] Discriminator Loss: 0.7619, Generator Loss: 1.5702 D(x): 0.8500, D(G(z)): 0.3557 Epoch: [6/20], Batch Num: [13/600] Discriminator Loss: 0.7349, Generator Loss: 1.6391 D(x): 0.8075, D(G(z)): 0.2762 Epoch: [6/20], Batch Num: [14/600] Discriminator Loss: 0.7047, Generator Loss: 1.7621 D(x): 0.8103, D(G(z)): 0.2701 Epoch: [6/20], Batch Num: [15/600] Discriminator Loss: 0.6463, Generator Loss: 2.0247 D(x): 0.7739, D(G(z)): 0.2046 Epoch: [6/20], Batch Num: [16/600] Discriminator Loss: 0.7269, Generator Loss: 1.8022 D(x): 0.7536, D(G(z)): 0.1997 Epoch: [6/20], Batch Num: [17/600] Discriminator Loss: 0.7524, Generator Loss: 1.7028 D(x): 0.7211, D(G(z)): 0.2117 Epoch: [6/20], Batch Num: [18/600] Discriminator Loss: 0.5207, Generator Loss: 1.4662 D(x): 0.8321, D(G(z)): 0.2180 Epoch: [6/20], Batch Num: [19/600] Discriminator Loss: 0.6448, Generator Loss: 1.5949 D(x): 0.8394, D(G(z)): 0.2952 Epoch: [6/20], Batch Num: [20/600] Discriminator Loss: 0.6881, Generator Loss: 1.6009 D(x): 0.8146, D(G(z)): 0.2975 Epoch: [6/20], Batch Num: [21/600] Discriminator Loss: 0.7982, Generator Loss: 1.6701 D(x): 0.8058, D(G(z)): 0.3161 Epoch: [6/20], Batch Num: [22/600] Discriminator Loss: 0.8440, Generator Loss: 1.7710 D(x): 0.7277, D(G(z)): 0.2654 Epoch: [6/20], Batch Num: [23/600] Discriminator Loss: 0.8477, Generator Loss: 1.7098 D(x): 0.7045, D(G(z)): 0.2089 Epoch: [6/20], Batch Num: [24/600] Discriminator Loss: 0.6736, Generator Loss: 1.6690 D(x): 0.7844, D(G(z)): 0.2380 Epoch: [6/20], Batch Num: [25/600] Discriminator Loss: 0.6532, Generator Loss: 1.3828 D(x): 0.8065, D(G(z)): 0.2504 Epoch: [6/20], Batch Num: [26/600] Discriminator Loss: 0.8188, Generator Loss: 1.4220 D(x): 0.7861, D(G(z)): 0.3056 Epoch: [6/20], Batch Num: [27/600] Discriminator Loss: 0.8561, Generator Loss: 1.3183 D(x): 0.7364, D(G(z)): 0.3079 Epoch: [6/20], Batch Num: [28/600] Discriminator Loss: 1.0081, Generator Loss: 1.5119 D(x): 0.7848, D(G(z)): 0.3691 Epoch: [6/20], Batch Num: [29/600] Discriminator Loss: 0.8693, Generator Loss: 1.6605 D(x): 0.7564, D(G(z)): 0.2991 Epoch: [6/20], Batch Num: [30/600] Discriminator Loss: 0.9110, Generator Loss: 1.7497 D(x): 0.7389, D(G(z)): 0.2779 Epoch: [6/20], Batch Num: [31/600] Discriminator Loss: 0.9874, Generator Loss: 1.7430 D(x): 0.6393, D(G(z)): 0.2291 Epoch: [6/20], Batch Num: [32/600] Discriminator Loss: 0.8225, Generator Loss: 1.4054 D(x): 0.7057, D(G(z)): 0.2279 Epoch: [6/20], Batch Num: [33/600] Discriminator Loss: 0.8130, Generator Loss: 1.5626 D(x): 0.7985, D(G(z)): 0.3136 Epoch: [6/20], Batch Num: [34/600] Discriminator Loss: 1.1348, Generator Loss: 1.4893 D(x): 0.7039, D(G(z)): 0.3483 Epoch: [6/20], Batch Num: [35/600] Discriminator Loss: 0.9873, Generator Loss: 1.5229 D(x): 0.7829, D(G(z)): 0.3435 Epoch: [6/20], Batch Num: [36/600] Discriminator Loss: 0.9119, Generator Loss: 1.6759 D(x): 0.7612, D(G(z)): 0.3058 Epoch: [6/20], Batch Num: [37/600] Discriminator Loss: 0.9773, Generator Loss: 1.7459 D(x): 0.7050, D(G(z)): 0.2799 Epoch: [6/20], Batch Num: [38/600] Discriminator Loss: 0.9411, Generator Loss: 1.7107 D(x): 0.6588, D(G(z)): 0.2120 Epoch: [6/20], Batch Num: [39/600] Discriminator Loss: 0.8452, Generator Loss: 1.5543 D(x): 0.7526, D(G(z)): 0.2829 Epoch: [6/20], Batch Num: [40/600] Discriminator Loss: 0.8103, Generator Loss: 1.4365 D(x): 0.7070, D(G(z)): 0.2422 Epoch: [6/20], Batch Num: [41/600] Discriminator Loss: 0.9174, Generator Loss: 1.3853 D(x): 0.7473, D(G(z)): 0.3225 Epoch: [6/20], Batch Num: [42/600] Discriminator Loss: 1.0225, Generator Loss: 1.4122 D(x): 0.7371, D(G(z)): 0.3593 Epoch: 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1.4937 D(x): 0.7441, D(G(z)): 0.2935 Epoch: [6/20], Batch Num: [52/600] Discriminator Loss: 0.7864, Generator Loss: 1.4054 D(x): 0.7575, D(G(z)): 0.2863 Epoch: [6/20], Batch Num: [53/600] Discriminator Loss: 0.8551, Generator Loss: 1.4366 D(x): 0.7565, D(G(z)): 0.3157 Epoch: [6/20], Batch Num: [54/600] Discriminator Loss: 0.8198, Generator Loss: 1.4325 D(x): 0.7769, D(G(z)): 0.2991 Epoch: [6/20], Batch Num: [55/600] Discriminator Loss: 0.8353, Generator Loss: 1.5044 D(x): 0.7627, D(G(z)): 0.3037 Epoch: [6/20], Batch Num: [56/600] Discriminator Loss: 0.8595, Generator Loss: 1.4707 D(x): 0.7103, D(G(z)): 0.2763 Epoch: [6/20], Batch Num: [57/600] Discriminator Loss: 0.9239, Generator Loss: 1.4676 D(x): 0.6800, D(G(z)): 0.2713 Epoch: [6/20], Batch Num: [58/600] Discriminator Loss: 0.7761, Generator Loss: 1.2283 D(x): 0.7770, D(G(z)): 0.3078 Epoch: [6/20], Batch Num: [59/600] Discriminator Loss: 0.8867, Generator Loss: 1.3285 D(x): 0.7702, D(G(z)): 0.3653 Epoch: [6/20], Batch Num: [60/600] Discriminator Loss: 0.7324, Generator Loss: 1.3827 D(x): 0.8313, D(G(z)): 0.3323 Epoch: [6/20], Batch Num: [61/600] Discriminator Loss: 0.6751, Generator Loss: 1.4271 D(x): 0.8089, D(G(z)): 0.2943 Epoch: [6/20], Batch Num: [62/600] Discriminator Loss: 0.7381, Generator Loss: 1.6109 D(x): 0.8131, D(G(z)): 0.3243 Epoch: [6/20], Batch Num: [63/600] Discriminator Loss: 0.6923, Generator Loss: 1.6505 D(x): 0.7306, D(G(z)): 0.2225 Epoch: [6/20], Batch Num: [64/600] Discriminator Loss: 0.6757, Generator Loss: 1.6213 D(x): 0.7590, D(G(z)): 0.2264 Epoch: [6/20], Batch Num: [65/600] Discriminator Loss: 0.6699, Generator Loss: 1.4613 D(x): 0.7870, D(G(z)): 0.2533 Epoch: [6/20], Batch Num: [66/600] Discriminator Loss: 0.6165, Generator Loss: 1.4298 D(x): 0.8265, D(G(z)): 0.2673 Epoch: [6/20], Batch Num: [67/600] Discriminator Loss: 0.7119, Generator Loss: 1.3751 D(x): 0.8073, D(G(z)): 0.3036 Epoch: [6/20], Batch Num: [68/600] Discriminator Loss: 0.7686, Generator Loss: 1.5071 D(x): 0.8762, D(G(z)): 0.3816 Epoch: [6/20], Batch Num: [69/600] Discriminator Loss: 0.6939, Generator Loss: 1.7324 D(x): 0.7941, D(G(z)): 0.2813 Epoch: [6/20], Batch Num: [70/600] Discriminator Loss: 0.6059, Generator Loss: 1.8040 D(x): 0.7735, D(G(z)): 0.1979 Epoch: [6/20], Batch Num: [71/600] Discriminator Loss: 0.7253, Generator Loss: 1.7087 D(x): 0.7356, D(G(z)): 0.2114 Epoch: [6/20], Batch Num: [72/600] Discriminator Loss: 0.7286, Generator Loss: 1.5534 D(x): 0.7472, D(G(z)): 0.2357 Epoch: [6/20], Batch Num: [73/600] Discriminator Loss: 0.6180, Generator Loss: 1.2758 D(x): 0.8081, D(G(z)): 0.2608 Epoch: [6/20], Batch Num: [74/600] Discriminator Loss: 0.7104, Generator Loss: 1.3905 D(x): 0.8518, D(G(z)): 0.3274 Epoch: [6/20], Batch Num: [75/600] Discriminator Loss: 0.7010, Generator Loss: 1.4898 D(x): 0.8511, D(G(z)): 0.3331 Epoch: [6/20], Batch Num: [76/600] Discriminator Loss: 0.6873, Generator Loss: 1.5765 D(x): 0.8403, D(G(z)): 0.3248 Epoch: [6/20], Batch Num: [77/600] Discriminator Loss: 0.6648, Generator Loss: 1.7660 D(x): 0.8197, D(G(z)): 0.2534 Epoch: [6/20], Batch Num: [78/600] Discriminator Loss: 0.6909, Generator Loss: 1.7463 D(x): 0.7543, D(G(z)): 0.2235 Epoch: [6/20], Batch Num: [79/600] Discriminator Loss: 0.7037, Generator Loss: 1.9540 D(x): 0.7537, D(G(z)): 0.2434 Epoch: [6/20], Batch Num: [80/600] Discriminator Loss: 0.6306, Generator Loss: 1.6677 D(x): 0.8078, D(G(z)): 0.2421 Epoch: [6/20], Batch Num: [81/600] Discriminator Loss: 0.5524, Generator Loss: 1.6669 D(x): 0.8524, D(G(z)): 0.2331 Epoch: [6/20], Batch Num: [82/600] Discriminator Loss: 0.6023, Generator Loss: 1.6689 D(x): 0.8268, D(G(z)): 0.2588 Epoch: [6/20], Batch Num: [83/600] Discriminator Loss: 0.6764, Generator Loss: 1.7044 D(x): 0.8414, D(G(z)): 0.3048 Epoch: [6/20], Batch Num: [84/600] Discriminator Loss: 0.7063, Generator Loss: 1.8119 D(x): 0.8187, D(G(z)): 0.2871 Epoch: [6/20], Batch Num: [85/600] Discriminator Loss: 0.6022, Generator Loss: 2.0116 D(x): 0.8107, D(G(z)): 0.2283 Epoch: [6/20], Batch Num: [86/600] Discriminator Loss: 0.6599, Generator Loss: 2.1907 D(x): 0.7557, D(G(z)): 0.2130 Epoch: [6/20], Batch Num: [87/600] Discriminator Loss: 0.5554, Generator Loss: 1.9471 D(x): 0.8335, D(G(z)): 0.2211 Epoch: [6/20], Batch Num: [88/600] Discriminator Loss: 0.7255, Generator Loss: 1.6903 D(x): 0.7593, D(G(z)): 0.2262 Epoch: [6/20], Batch Num: [89/600] Discriminator Loss: 0.6367, Generator Loss: 1.5739 D(x): 0.8312, D(G(z)): 0.2816 Epoch: [6/20], Batch Num: [90/600] Discriminator Loss: 0.6570, Generator Loss: 1.7071 D(x): 0.8543, D(G(z)): 0.2986 Epoch: [6/20], Batch Num: [91/600] Discriminator Loss: 0.6664, Generator Loss: 1.9500 D(x): 0.8381, D(G(z)): 0.2715 Epoch: [6/20], Batch Num: [92/600] Discriminator Loss: 0.7946, Generator Loss: 1.9684 D(x): 0.7156, D(G(z)): 0.2223 Epoch: [6/20], Batch Num: [93/600] Discriminator Loss: 0.7623, Generator Loss: 1.7745 D(x): 0.7424, D(G(z)): 0.2217 Epoch: [6/20], Batch Num: [94/600] Discriminator Loss: 0.7084, Generator Loss: 1.4625 D(x): 0.7825, D(G(z)): 0.2387 Epoch: [6/20], Batch Num: [95/600] Discriminator Loss: 0.8831, Generator Loss: 1.2799 D(x): 0.8339, D(G(z)): 0.3431 Epoch: [6/20], Batch Num: [96/600] Discriminator Loss: 0.7754, Generator Loss: 1.8603 D(x): 0.8957, D(G(z)): 0.3867 Epoch: [6/20], Batch Num: [97/600] Discriminator Loss: 0.6782, Generator Loss: 2.1623 D(x): 0.8202, D(G(z)): 0.2536 Epoch: [6/20], Batch Num: [98/600] Discriminator Loss: 0.7635, Generator Loss: 2.5411 D(x): 0.7115, D(G(z)): 0.1702 Epoch: [6/20], Batch Num: [99/600] Discriminator Loss: 0.8488, Generator Loss: 1.8471 D(x): 0.6928, D(G(z)): 0.2028 Epoch: 6, Batch Num: [100/600]
Epoch: [6/20], Batch Num: [100/600] Discriminator Loss: 0.7633, Generator Loss: 1.6331 D(x): 0.7743, D(G(z)): 0.2564 Epoch: [6/20], Batch Num: [101/600] Discriminator Loss: 0.7976, Generator Loss: 1.2263 D(x): 0.8070, D(G(z)): 0.3257 Epoch: [6/20], Batch Num: [102/600] Discriminator Loss: 0.8490, Generator Loss: 1.7143 D(x): 0.8378, D(G(z)): 0.3814 Epoch: [6/20], Batch Num: [103/600] Discriminator Loss: 0.8194, Generator Loss: 1.9472 D(x): 0.7756, D(G(z)): 0.2861 Epoch: [6/20], Batch Num: [104/600] Discriminator Loss: 0.7348, Generator Loss: 2.1230 D(x): 0.7636, D(G(z)): 0.2259 Epoch: [6/20], Batch Num: [105/600] Discriminator Loss: 0.8340, Generator Loss: 1.9222 D(x): 0.7041, D(G(z)): 0.1907 Epoch: [6/20], Batch Num: [106/600] Discriminator Loss: 0.9500, Generator Loss: 1.7909 D(x): 0.6639, D(G(z)): 0.1832 Epoch: [6/20], Batch Num: [107/600] Discriminator Loss: 0.7229, Generator Loss: 1.4051 D(x): 0.7779, D(G(z)): 0.2250 Epoch: [6/20], Batch Num: [108/600] Discriminator Loss: 0.9375, Generator Loss: 1.3645 D(x): 0.7723, D(G(z)): 0.3364 Epoch: [6/20], Batch Num: [109/600] Discriminator Loss: 0.8865, Generator Loss: 1.4566 D(x): 0.8166, D(G(z)): 0.3927 Epoch: [6/20], Batch Num: [110/600] Discriminator Loss: 0.7467, Generator Loss: 1.7823 D(x): 0.8054, D(G(z)): 0.2935 Epoch: [6/20], Batch Num: [111/600] Discriminator Loss: 0.6827, Generator Loss: 1.9376 D(x): 0.8012, D(G(z)): 0.2519 Epoch: [6/20], Batch Num: [112/600] Discriminator Loss: 0.6572, Generator Loss: 2.1204 D(x): 0.7726, D(G(z)): 0.1938 Epoch: [6/20], Batch Num: [113/600] Discriminator Loss: 0.9029, Generator Loss: 1.8351 D(x): 0.7040, D(G(z)): 0.2095 Epoch: [6/20], Batch Num: [114/600] Discriminator Loss: 0.7051, Generator Loss: 1.5790 D(x): 0.7668, D(G(z)): 0.2122 Epoch: [6/20], Batch Num: [115/600] Discriminator Loss: 0.6333, Generator Loss: 1.4781 D(x): 0.8326, D(G(z)): 0.2587 Epoch: [6/20], Batch Num: [116/600] Discriminator Loss: 0.6035, Generator Loss: 1.5093 D(x): 0.8847, D(G(z)): 0.3105 Epoch: [6/20], Batch Num: [117/600] Discriminator Loss: 0.8025, Generator Loss: 1.6875 D(x): 0.8000, D(G(z)): 0.3105 Epoch: [6/20], Batch Num: [118/600] Discriminator Loss: 0.8072, Generator Loss: 2.1399 D(x): 0.7619, D(G(z)): 0.2486 Epoch: [6/20], Batch Num: [119/600] Discriminator Loss: 0.6895, Generator Loss: 2.0727 D(x): 0.7909, D(G(z)): 0.2135 Epoch: [6/20], Batch Num: [120/600] Discriminator Loss: 0.7617, Generator Loss: 2.0746 D(x): 0.7134, D(G(z)): 0.1620 Epoch: [6/20], Batch Num: [121/600] Discriminator Loss: 0.5498, Generator Loss: 1.6734 D(x): 0.8254, D(G(z)): 0.1728 Epoch: [6/20], Batch Num: [122/600] Discriminator Loss: 0.7023, Generator Loss: 1.6358 D(x): 0.8181, D(G(z)): 0.2425 Epoch: [6/20], Batch Num: [123/600] Discriminator Loss: 0.6134, Generator Loss: 1.6260 D(x): 0.8265, D(G(z)): 0.2553 Epoch: [6/20], Batch Num: [124/600] Discriminator Loss: 0.6532, Generator Loss: 1.7163 D(x): 0.8356, D(G(z)): 0.2904 Epoch: [6/20], Batch Num: [125/600] Discriminator Loss: 0.5700, Generator Loss: 1.8645 D(x): 0.8615, D(G(z)): 0.2448 Epoch: [6/20], Batch Num: [126/600] Discriminator Loss: 0.5517, Generator Loss: 2.0548 D(x): 0.8098, D(G(z)): 0.1862 Epoch: [6/20], Batch Num: [127/600] Discriminator Loss: 0.6916, Generator Loss: 2.0993 D(x): 0.7588, D(G(z)): 0.1779 Epoch: [6/20], Batch Num: [128/600] Discriminator Loss: 0.4514, Generator Loss: 2.0160 D(x): 0.8609, D(G(z)): 0.1706 Epoch: [6/20], Batch Num: [129/600] Discriminator Loss: 0.6837, Generator Loss: 1.6922 D(x): 0.7937, D(G(z)): 0.2214 Epoch: [6/20], Batch Num: [130/600] Discriminator Loss: 0.6133, Generator Loss: 1.5595 D(x): 0.8357, D(G(z)): 0.2392 Epoch: [6/20], Batch Num: [131/600] Discriminator Loss: 0.6029, Generator Loss: 1.8712 D(x): 0.8751, D(G(z)): 0.2838 Epoch: [6/20], Batch Num: [132/600] Discriminator Loss: 0.6173, Generator Loss: 1.7859 D(x): 0.8317, D(G(z)): 0.2409 Epoch: [6/20], Batch Num: [133/600] Discriminator Loss: 0.5629, Generator Loss: 2.1104 D(x): 0.8441, D(G(z)): 0.2201 Epoch: [6/20], Batch Num: [134/600] Discriminator Loss: 0.5819, Generator Loss: 2.1382 D(x): 0.8182, D(G(z)): 0.1756 Epoch: [6/20], Batch Num: [135/600] Discriminator Loss: 0.6368, Generator Loss: 1.9761 D(x): 0.7794, D(G(z)): 0.1568 Epoch: [6/20], Batch Num: [136/600] Discriminator Loss: 0.5424, Generator Loss: 2.0870 D(x): 0.8814, D(G(z)): 0.1988 Epoch: [6/20], Batch Num: [137/600] Discriminator Loss: 0.6505, Generator Loss: 2.0343 D(x): 0.7889, D(G(z)): 0.1927 Epoch: [6/20], Batch Num: [138/600] Discriminator Loss: 0.6425, Generator Loss: 1.6428 D(x): 0.7986, D(G(z)): 0.1756 Epoch: [6/20], Batch Num: [139/600] Discriminator Loss: 0.6074, Generator Loss: 1.8664 D(x): 0.8427, D(G(z)): 0.2361 Epoch: [6/20], Batch Num: [140/600] Discriminator Loss: 0.4968, Generator Loss: 1.8270 D(x): 0.8976, D(G(z)): 0.2484 Epoch: [6/20], Batch Num: [141/600] Discriminator Loss: 0.5557, Generator Loss: 1.9673 D(x): 0.8325, D(G(z)): 0.2107 Epoch: [6/20], Batch Num: [142/600] Discriminator Loss: 0.4270, Generator Loss: 2.0761 D(x): 0.8698, D(G(z)): 0.1779 Epoch: [6/20], Batch Num: [143/600] Discriminator Loss: 0.7455, Generator Loss: 1.8846 D(x): 0.7508, D(G(z)): 0.1858 Epoch: [6/20], Batch Num: [144/600] Discriminator Loss: 0.5568, Generator Loss: 1.6330 D(x): 0.8027, D(G(z)): 0.1820 Epoch: [6/20], Batch Num: [145/600] Discriminator Loss: 0.6865, Generator Loss: 1.6743 D(x): 0.8366, D(G(z)): 0.2730 Epoch: [6/20], Batch Num: [146/600] Discriminator Loss: 0.4930, Generator Loss: 1.7066 D(x): 0.8972, D(G(z)): 0.2478 Epoch: [6/20], Batch Num: [147/600] Discriminator Loss: 0.6881, Generator Loss: 2.1100 D(x): 0.8525, D(G(z)): 0.2393 Epoch: [6/20], Batch Num: [148/600] Discriminator Loss: 0.5861, Generator Loss: 2.2177 D(x): 0.8062, D(G(z)): 0.1604 Epoch: [6/20], Batch Num: [149/600] Discriminator Loss: 0.5276, Generator Loss: 2.1773 D(x): 0.8089, D(G(z)): 0.1609 Epoch: [6/20], Batch Num: [150/600] Discriminator Loss: 0.4538, Generator Loss: 2.0418 D(x): 0.8633, D(G(z)): 0.1835 Epoch: [6/20], Batch Num: [151/600] Discriminator Loss: 0.4725, Generator Loss: 1.9011 D(x): 0.8650, D(G(z)): 0.1819 Epoch: [6/20], Batch Num: [152/600] Discriminator Loss: 0.5494, Generator Loss: 1.8501 D(x): 0.8678, D(G(z)): 0.2274 Epoch: [6/20], Batch Num: [153/600] Discriminator Loss: 0.4650, Generator Loss: 2.1375 D(x): 0.8844, D(G(z)): 0.1978 Epoch: [6/20], Batch Num: [154/600] Discriminator Loss: 0.4596, Generator Loss: 2.0856 D(x): 0.8232, D(G(z)): 0.1565 Epoch: [6/20], Batch Num: [155/600] Discriminator Loss: 0.4998, Generator Loss: 1.9339 D(x): 0.8347, D(G(z)): 0.1415 Epoch: [6/20], Batch Num: [156/600] Discriminator Loss: 0.5225, Generator Loss: 2.1187 D(x): 0.8588, D(G(z)): 0.1692 Epoch: [6/20], Batch Num: [157/600] Discriminator Loss: 0.4461, Generator Loss: 2.1644 D(x): 0.9095, D(G(z)): 0.2107 Epoch: [6/20], Batch Num: [158/600] Discriminator Loss: 0.6355, Generator Loss: 2.3100 D(x): 0.8876, D(G(z)): 0.2213 Epoch: [6/20], Batch Num: [159/600] Discriminator Loss: 0.4128, Generator Loss: 2.3669 D(x): 0.8303, D(G(z)): 0.1127 Epoch: [6/20], Batch Num: [160/600] Discriminator Loss: 0.5270, Generator Loss: 2.4085 D(x): 0.8279, D(G(z)): 0.1291 Epoch: [6/20], Batch Num: [161/600] Discriminator Loss: 0.4185, Generator Loss: 2.0489 D(x): 0.8616, D(G(z)): 0.1315 Epoch: [6/20], Batch Num: [162/600] Discriminator Loss: 0.4984, Generator Loss: 1.9041 D(x): 0.8530, D(G(z)): 0.1562 Epoch: [6/20], Batch Num: [163/600] Discriminator Loss: 0.6033, Generator Loss: 2.0969 D(x): 0.8835, D(G(z)): 0.2385 Epoch: [6/20], Batch Num: [164/600] Discriminator Loss: 0.4723, Generator Loss: 2.2543 D(x): 0.8989, D(G(z)): 0.2013 Epoch: [6/20], Batch Num: [165/600] Discriminator Loss: 0.5083, Generator Loss: 2.3218 D(x): 0.8254, D(G(z)): 0.1470 Epoch: [6/20], Batch Num: [166/600] Discriminator Loss: 0.4922, Generator Loss: 2.3299 D(x): 0.8349, D(G(z)): 0.1355 Epoch: [6/20], Batch Num: [167/600] Discriminator Loss: 0.6067, Generator Loss: 2.2196 D(x): 0.8325, D(G(z)): 0.1997 Epoch: [6/20], Batch Num: [168/600] Discriminator Loss: 0.4332, Generator Loss: 2.3759 D(x): 0.8703, D(G(z)): 0.1680 Epoch: [6/20], Batch Num: [169/600] Discriminator Loss: 0.4958, Generator Loss: 2.4872 D(x): 0.8581, D(G(z)): 0.1601 Epoch: [6/20], Batch Num: [170/600] Discriminator Loss: 0.6727, Generator Loss: 2.4035 D(x): 0.8134, D(G(z)): 0.1785 Epoch: [6/20], Batch Num: [171/600] Discriminator Loss: 0.4027, Generator Loss: 2.3560 D(x): 0.8763, D(G(z)): 0.1503 Epoch: [6/20], Batch Num: [172/600] Discriminator Loss: 0.5591, Generator Loss: 2.3028 D(x): 0.8430, D(G(z)): 0.1891 Epoch: [6/20], Batch Num: [173/600] Discriminator Loss: 0.7848, Generator Loss: 2.0659 D(x): 0.7841, D(G(z)): 0.1810 Epoch: [6/20], Batch Num: [174/600] Discriminator Loss: 0.7474, Generator Loss: 1.8390 D(x): 0.8244, D(G(z)): 0.2453 Epoch: [6/20], Batch Num: [175/600] Discriminator Loss: 0.6362, Generator Loss: 1.8378 D(x): 0.8133, D(G(z)): 0.2221 Epoch: [6/20], Batch Num: [176/600] Discriminator Loss: 0.5814, Generator Loss: 1.9279 D(x): 0.8523, D(G(z)): 0.2266 Epoch: [6/20], Batch Num: [177/600] Discriminator Loss: 0.7631, Generator Loss: 2.0714 D(x): 0.8005, D(G(z)): 0.2387 Epoch: [6/20], Batch Num: [178/600] Discriminator Loss: 0.8123, Generator Loss: 2.0397 D(x): 0.7442, D(G(z)): 0.1877 Epoch: [6/20], Batch Num: [179/600] Discriminator Loss: 0.8756, Generator Loss: 2.0078 D(x): 0.8165, D(G(z)): 0.2994 Epoch: [6/20], Batch Num: [180/600] Discriminator Loss: 0.8880, Generator Loss: 2.0741 D(x): 0.7943, D(G(z)): 0.2702 Epoch: [6/20], Batch Num: [181/600] Discriminator Loss: 0.7983, Generator Loss: 2.0512 D(x): 0.7407, D(G(z)): 0.2079 Epoch: [6/20], Batch Num: [182/600] Discriminator Loss: 0.8681, Generator Loss: 1.8664 D(x): 0.7444, D(G(z)): 0.2277 Epoch: [6/20], Batch Num: [183/600] Discriminator Loss: 0.9464, Generator Loss: 1.8323 D(x): 0.7808, D(G(z)): 0.2794 Epoch: [6/20], Batch Num: [184/600] Discriminator Loss: 0.9583, Generator Loss: 1.8791 D(x): 0.7795, D(G(z)): 0.2867 Epoch: [6/20], Batch Num: [185/600] Discriminator Loss: 0.6981, Generator Loss: 2.0416 D(x): 0.8108, D(G(z)): 0.2421 Epoch: [6/20], Batch Num: [186/600] Discriminator Loss: 0.9020, Generator Loss: 1.9923 D(x): 0.7120, D(G(z)): 0.2248 Epoch: [6/20], Batch Num: [187/600] Discriminator Loss: 0.8807, Generator Loss: 1.6628 D(x): 0.7116, D(G(z)): 0.2023 Epoch: [6/20], Batch Num: [188/600] Discriminator Loss: 0.7086, Generator Loss: 1.4673 D(x): 0.8022, D(G(z)): 0.2523 Epoch: [6/20], Batch Num: [189/600] Discriminator Loss: 0.8441, Generator Loss: 1.6368 D(x): 0.8575, D(G(z)): 0.3426 Epoch: [6/20], Batch Num: [190/600] Discriminator Loss: 0.7233, Generator Loss: 2.0837 D(x): 0.8423, D(G(z)): 0.2878 Epoch: [6/20], Batch Num: [191/600] Discriminator Loss: 0.7270, Generator Loss: 2.2159 D(x): 0.7505, D(G(z)): 0.1896 Epoch: [6/20], Batch Num: [192/600] Discriminator Loss: 0.5498, Generator Loss: 2.0112 D(x): 0.7686, D(G(z)): 0.1372 Epoch: [6/20], Batch Num: [193/600] Discriminator Loss: 0.6600, Generator Loss: 1.8771 D(x): 0.7856, D(G(z)): 0.1977 Epoch: [6/20], Batch Num: [194/600] Discriminator Loss: 0.6169, Generator Loss: 1.4943 D(x): 0.8216, D(G(z)): 0.2350 Epoch: [6/20], Batch Num: [195/600] Discriminator Loss: 0.7357, Generator Loss: 1.6925 D(x): 0.8698, D(G(z)): 0.3176 Epoch: [6/20], Batch Num: [196/600] Discriminator Loss: 0.6655, Generator Loss: 1.9405 D(x): 0.8440, D(G(z)): 0.2663 Epoch: [6/20], Batch Num: [197/600] Discriminator Loss: 0.5446, Generator Loss: 2.2329 D(x): 0.8650, D(G(z)): 0.2048 Epoch: [6/20], Batch Num: [198/600] Discriminator Loss: 0.5679, Generator Loss: 2.1961 D(x): 0.8321, D(G(z)): 0.1766 Epoch: [6/20], Batch Num: [199/600] Discriminator Loss: 0.6181, Generator Loss: 2.4212 D(x): 0.8280, D(G(z)): 0.1963 Epoch: 6, Batch Num: [200/600]
Epoch: [6/20], Batch Num: [200/600] Discriminator Loss: 0.5636, Generator Loss: 2.1215 D(x): 0.8104, D(G(z)): 0.1497 Epoch: [6/20], Batch Num: [201/600] Discriminator Loss: 0.6407, Generator Loss: 2.0958 D(x): 0.8112, D(G(z)): 0.2123 Epoch: [6/20], Batch Num: [202/600] Discriminator Loss: 0.7647, Generator Loss: 1.9770 D(x): 0.8301, D(G(z)): 0.2648 Epoch: [6/20], Batch Num: [203/600] Discriminator Loss: 0.6230, Generator Loss: 2.2716 D(x): 0.8832, D(G(z)): 0.2418 Epoch: [6/20], Batch Num: [204/600] Discriminator Loss: 0.6183, Generator Loss: 2.3110 D(x): 0.8259, D(G(z)): 0.1937 Epoch: [6/20], Batch Num: [205/600] Discriminator Loss: 0.5990, Generator Loss: 2.1442 D(x): 0.8011, D(G(z)): 0.1773 Epoch: [6/20], Batch Num: [206/600] Discriminator Loss: 0.5530, Generator Loss: 2.3148 D(x): 0.8631, D(G(z)): 0.2150 Epoch: [6/20], Batch Num: [207/600] Discriminator Loss: 0.5251, Generator Loss: 2.2929 D(x): 0.8495, D(G(z)): 0.1934 Epoch: [6/20], Batch Num: [208/600] Discriminator Loss: 0.3710, Generator Loss: 2.3401 D(x): 0.8660, D(G(z)): 0.1412 Epoch: [6/20], Batch Num: [209/600] Discriminator Loss: 0.4948, Generator Loss: 2.1109 D(x): 0.8412, D(G(z)): 0.1844 Epoch: [6/20], Batch Num: [210/600] Discriminator Loss: 0.4688, Generator Loss: 2.1561 D(x): 0.8484, D(G(z)): 0.1810 Epoch: [6/20], Batch Num: [211/600] Discriminator Loss: 0.5252, Generator Loss: 2.1427 D(x): 0.8398, D(G(z)): 0.1953 Epoch: [6/20], Batch Num: [212/600] Discriminator Loss: 0.4173, Generator Loss: 2.2200 D(x): 0.9141, D(G(z)): 0.2348 Epoch: [6/20], Batch Num: [213/600] Discriminator Loss: 0.4233, Generator Loss: 2.2429 D(x): 0.8638, D(G(z)): 0.1784 Epoch: [6/20], Batch Num: [214/600] Discriminator Loss: 0.3941, Generator Loss: 2.4066 D(x): 0.8788, D(G(z)): 0.1724 Epoch: [6/20], Batch Num: [215/600] Discriminator Loss: 0.5164, Generator Loss: 2.4113 D(x): 0.7972, D(G(z)): 0.1640 Epoch: [6/20], Batch Num: [216/600] Discriminator Loss: 0.4029, Generator Loss: 2.1510 D(x): 0.8590, D(G(z)): 0.1614 Epoch: [6/20], Batch Num: [217/600] Discriminator Loss: 0.4451, Generator Loss: 1.9686 D(x): 0.8910, D(G(z)): 0.2025 Epoch: [6/20], Batch Num: [218/600] Discriminator Loss: 0.4431, Generator Loss: 2.2114 D(x): 0.8963, D(G(z)): 0.2346 Epoch: [6/20], Batch Num: [219/600] Discriminator Loss: 0.3147, Generator Loss: 2.4194 D(x): 0.9174, D(G(z)): 0.1721 Epoch: [6/20], Batch Num: [220/600] Discriminator Loss: 0.3524, Generator Loss: 2.6170 D(x): 0.8771, D(G(z)): 0.1420 Epoch: [6/20], Batch Num: [221/600] Discriminator Loss: 0.4284, Generator Loss: 2.5945 D(x): 0.8407, D(G(z)): 0.1198 Epoch: [6/20], Batch Num: [222/600] Discriminator Loss: 0.3343, Generator Loss: 2.3878 D(x): 0.8766, D(G(z)): 0.1385 Epoch: [6/20], Batch Num: [223/600] Discriminator Loss: 0.2661, Generator Loss: 2.3371 D(x): 0.9316, D(G(z)): 0.1460 Epoch: [6/20], Batch Num: [224/600] Discriminator Loss: 0.3260, Generator Loss: 2.1253 D(x): 0.9125, D(G(z)): 0.1789 Epoch: [6/20], Batch Num: [225/600] Discriminator Loss: 0.3619, Generator Loss: 2.4039 D(x): 0.9010, D(G(z)): 0.1710 Epoch: [6/20], Batch Num: [226/600] Discriminator Loss: 0.2532, Generator Loss: 2.6243 D(x): 0.9544, D(G(z)): 0.1636 Epoch: [6/20], Batch Num: [227/600] Discriminator Loss: 0.3365, Generator Loss: 2.6916 D(x): 0.8859, D(G(z)): 0.1356 Epoch: [6/20], Batch Num: [228/600] Discriminator Loss: 0.3840, Generator Loss: 2.4685 D(x): 0.8398, D(G(z)): 0.0982 Epoch: [6/20], Batch Num: [229/600] Discriminator Loss: 0.3677, Generator Loss: 2.5164 D(x): 0.8780, D(G(z)): 0.1318 Epoch: [6/20], Batch Num: [230/600] Discriminator Loss: 0.2297, Generator Loss: 2.2107 D(x): 0.9449, D(G(z)): 0.1403 Epoch: [6/20], Batch Num: [231/600] Discriminator Loss: 0.2598, Generator Loss: 2.3596 D(x): 0.9424, D(G(z)): 0.1566 Epoch: [6/20], Batch Num: [232/600] Discriminator Loss: 0.3563, Generator Loss: 2.8403 D(x): 0.9358, D(G(z)): 0.2057 Epoch: [6/20], Batch Num: [233/600] Discriminator Loss: 0.3347, Generator Loss: 3.0865 D(x): 0.8781, D(G(z)): 0.1193 Epoch: [6/20], Batch Num: [234/600] Discriminator Loss: 0.3754, Generator Loss: 2.9096 D(x): 0.8577, D(G(z)): 0.0939 Epoch: [6/20], Batch Num: [235/600] Discriminator Loss: 0.3815, Generator Loss: 2.7971 D(x): 0.8799, D(G(z)): 0.1557 Epoch: [6/20], Batch Num: [236/600] Discriminator Loss: 0.3776, Generator Loss: 2.5188 D(x): 0.8707, D(G(z)): 0.1301 Epoch: [6/20], Batch Num: [237/600] Discriminator Loss: 0.3222, Generator Loss: 2.6755 D(x): 0.9362, D(G(z)): 0.1715 Epoch: [6/20], Batch Num: [238/600] Discriminator Loss: 0.2775, Generator Loss: 2.6023 D(x): 0.9242, D(G(z)): 0.1431 Epoch: [6/20], Batch Num: [239/600] Discriminator Loss: 0.3532, Generator Loss: 2.6193 D(x): 0.8827, D(G(z)): 0.1397 Epoch: [6/20], Batch Num: [240/600] Discriminator Loss: 0.3846, Generator Loss: 2.5832 D(x): 0.8702, D(G(z)): 0.1266 Epoch: [6/20], Batch Num: [241/600] Discriminator Loss: 0.4467, Generator Loss: 2.4558 D(x): 0.8851, D(G(z)): 0.1700 Epoch: [6/20], Batch Num: [242/600] Discriminator Loss: 0.3707, Generator Loss: 2.5639 D(x): 0.9090, D(G(z)): 0.1738 Epoch: [6/20], Batch Num: [243/600] Discriminator Loss: 0.2938, Generator Loss: 2.5866 D(x): 0.9018, D(G(z)): 0.1233 Epoch: [6/20], Batch Num: [244/600] Discriminator Loss: 0.4331, Generator Loss: 2.7473 D(x): 0.8517, D(G(z)): 0.1458 Epoch: [6/20], Batch Num: [245/600] Discriminator Loss: 0.4633, Generator Loss: 2.6667 D(x): 0.8636, D(G(z)): 0.1596 Epoch: [6/20], Batch Num: [246/600] Discriminator Loss: 0.4454, Generator Loss: 2.4599 D(x): 0.8709, D(G(z)): 0.1820 Epoch: [6/20], Batch Num: [247/600] Discriminator Loss: 0.6148, Generator Loss: 2.6262 D(x): 0.8666, D(G(z)): 0.2322 Epoch: [6/20], Batch Num: [248/600] Discriminator Loss: 0.5174, Generator Loss: 2.6848 D(x): 0.8280, D(G(z)): 0.1529 Epoch: [6/20], Batch Num: [249/600] Discriminator Loss: 0.5654, Generator Loss: 2.2244 D(x): 0.8328, D(G(z)): 0.1579 Epoch: [6/20], Batch Num: [250/600] Discriminator Loss: 0.7126, Generator Loss: 1.9729 D(x): 0.8086, D(G(z)): 0.2236 Epoch: [6/20], Batch Num: [251/600] Discriminator Loss: 0.4931, Generator Loss: 2.6890 D(x): 0.9147, D(G(z)): 0.2514 Epoch: [6/20], Batch Num: [252/600] Discriminator Loss: 0.6145, Generator Loss: 2.6542 D(x): 0.7975, D(G(z)): 0.1572 Epoch: [6/20], Batch Num: [253/600] Discriminator Loss: 0.6481, Generator Loss: 2.4267 D(x): 0.7768, D(G(z)): 0.1631 Epoch: [6/20], Batch Num: [254/600] Discriminator Loss: 0.6907, Generator Loss: 1.9625 D(x): 0.8119, D(G(z)): 0.2120 Epoch: [6/20], Batch Num: [255/600] Discriminator Loss: 0.5939, Generator Loss: 2.5821 D(x): 0.8833, D(G(z)): 0.2654 Epoch: [6/20], Batch Num: [256/600] Discriminator Loss: 0.6485, Generator Loss: 2.8972 D(x): 0.8219, D(G(z)): 0.1343 Epoch: [6/20], Batch Num: [257/600] Discriminator Loss: 0.8527, Generator Loss: 2.1044 D(x): 0.7099, D(G(z)): 0.1221 Epoch: [6/20], Batch Num: [258/600] Discriminator Loss: 0.5336, Generator Loss: 2.0471 D(x): 0.8881, D(G(z)): 0.2352 Epoch: [6/20], Batch Num: [259/600] Discriminator Loss: 0.6306, Generator Loss: 2.4611 D(x): 0.8735, D(G(z)): 0.2601 Epoch: [6/20], Batch Num: [260/600] Discriminator Loss: 0.5068, Generator Loss: 3.1550 D(x): 0.8850, D(G(z)): 0.1931 Epoch: [6/20], Batch Num: [261/600] Discriminator Loss: 0.6666, Generator Loss: 3.0028 D(x): 0.7559, D(G(z)): 0.1104 Epoch: [6/20], Batch Num: [262/600] Discriminator Loss: 0.6601, Generator Loss: 2.3003 D(x): 0.7695, D(G(z)): 0.0857 Epoch: [6/20], Batch Num: [263/600] Discriminator Loss: 0.6380, Generator Loss: 1.8092 D(x): 0.8706, D(G(z)): 0.2318 Epoch: [6/20], Batch Num: [264/600] Discriminator Loss: 0.5558, Generator Loss: 2.6087 D(x): 0.9136, D(G(z)): 0.2603 Epoch: [6/20], Batch Num: [265/600] Discriminator Loss: 0.5107, Generator Loss: 3.1456 D(x): 0.8679, D(G(z)): 0.1791 Epoch: [6/20], Batch Num: [266/600] Discriminator Loss: 0.4646, Generator Loss: 3.2085 D(x): 0.8257, D(G(z)): 0.0997 Epoch: [6/20], Batch Num: [267/600] Discriminator Loss: 0.7412, Generator Loss: 2.3543 D(x): 0.7488, D(G(z)): 0.0945 Epoch: [6/20], Batch Num: [268/600] Discriminator Loss: 0.5928, Generator Loss: 2.1144 D(x): 0.8724, D(G(z)): 0.2266 Epoch: [6/20], Batch Num: [269/600] Discriminator Loss: 0.7767, Generator Loss: 2.7248 D(x): 0.8831, D(G(z)): 0.2828 Epoch: [6/20], Batch Num: [270/600] Discriminator Loss: 0.5509, Generator Loss: 3.0181 D(x): 0.8581, D(G(z)): 0.1727 Epoch: [6/20], Batch Num: [271/600] Discriminator Loss: 0.5408, Generator Loss: 2.9272 D(x): 0.8101, D(G(z)): 0.1241 Epoch: [6/20], Batch Num: [272/600] Discriminator Loss: 0.5927, Generator Loss: 2.5517 D(x): 0.8109, D(G(z)): 0.1232 Epoch: [6/20], Batch Num: [273/600] Discriminator Loss: 0.3253, Generator Loss: 2.4840 D(x): 0.9023, D(G(z)): 0.1388 Epoch: [6/20], Batch Num: [274/600] Discriminator Loss: 0.5263, Generator Loss: 2.3571 D(x): 0.8483, D(G(z)): 0.1656 Epoch: [6/20], Batch Num: [275/600] Discriminator Loss: 0.4404, Generator Loss: 2.8821 D(x): 0.9178, D(G(z)): 0.2077 Epoch: [6/20], Batch Num: [276/600] Discriminator Loss: 0.5202, Generator Loss: 3.2753 D(x): 0.8607, D(G(z)): 0.1544 Epoch: [6/20], Batch Num: [277/600] Discriminator Loss: 0.4926, Generator Loss: 3.1924 D(x): 0.8288, D(G(z)): 0.0841 Epoch: [6/20], Batch Num: [278/600] Discriminator Loss: 0.5401, Generator Loss: 2.9288 D(x): 0.8300, D(G(z)): 0.1294 Epoch: [6/20], Batch Num: [279/600] Discriminator Loss: 0.3991, Generator Loss: 2.3591 D(x): 0.8466, D(G(z)): 0.1073 Epoch: [6/20], Batch Num: [280/600] Discriminator Loss: 0.3162, Generator Loss: 2.5702 D(x): 0.9686, D(G(z)): 0.1984 Epoch: [6/20], Batch Num: [281/600] Discriminator Loss: 0.2927, Generator Loss: 2.8718 D(x): 0.9235, D(G(z)): 0.1114 Epoch: [6/20], Batch Num: [282/600] Discriminator Loss: 0.3285, Generator Loss: 3.1226 D(x): 0.9044, D(G(z)): 0.0986 Epoch: [6/20], Batch Num: [283/600] Discriminator Loss: 0.2174, Generator Loss: 3.8646 D(x): 0.9462, D(G(z)): 0.0996 Epoch: [6/20], Batch Num: [284/600] Discriminator Loss: 0.2146, Generator Loss: 3.6422 D(x): 0.9209, D(G(z)): 0.0704 Epoch: [6/20], Batch Num: [285/600] Discriminator Loss: 0.2763, Generator Loss: 3.7581 D(x): 0.8945, D(G(z)): 0.0619 Epoch: [6/20], Batch Num: [286/600] Discriminator Loss: 0.2874, Generator Loss: 3.5624 D(x): 0.8990, D(G(z)): 0.0858 Epoch: [6/20], Batch Num: [287/600] Discriminator Loss: 0.2079, Generator Loss: 3.2956 D(x): 0.9253, D(G(z)): 0.0721 Epoch: [6/20], Batch Num: [288/600] Discriminator Loss: 0.2582, Generator Loss: 2.8755 D(x): 0.9288, D(G(z)): 0.0929 Epoch: [6/20], Batch Num: [289/600] Discriminator Loss: 0.2732, Generator Loss: 3.2148 D(x): 0.9513, D(G(z)): 0.1245 Epoch: [6/20], Batch Num: [290/600] Discriminator Loss: 0.2887, Generator Loss: 3.1624 D(x): 0.9586, D(G(z)): 0.1582 Epoch: [6/20], Batch Num: [291/600] Discriminator Loss: 0.2608, Generator Loss: 3.5718 D(x): 0.9358, D(G(z)): 0.1161 Epoch: [6/20], Batch Num: [292/600] Discriminator Loss: 0.2612, Generator Loss: 3.7994 D(x): 0.8946, D(G(z)): 0.0728 Epoch: [6/20], Batch Num: [293/600] Discriminator Loss: 0.1738, Generator Loss: 3.9777 D(x): 0.9321, D(G(z)): 0.0579 Epoch: [6/20], Batch Num: [294/600] Discriminator Loss: 0.2032, Generator Loss: 3.6411 D(x): 0.9017, D(G(z)): 0.0448 Epoch: [6/20], Batch Num: [295/600] Discriminator Loss: 0.3032, Generator Loss: 2.6892 D(x): 0.8989, D(G(z)): 0.0850 Epoch: [6/20], Batch Num: [296/600] Discriminator Loss: 0.2767, Generator Loss: 2.8690 D(x): 0.9558, D(G(z)): 0.1516 Epoch: [6/20], Batch Num: [297/600] Discriminator Loss: 0.4133, Generator Loss: 2.8285 D(x): 0.9148, D(G(z)): 0.1461 Epoch: [6/20], Batch Num: [298/600] Discriminator Loss: 0.3842, Generator Loss: 3.1612 D(x): 0.8989, D(G(z)): 0.1214 Epoch: [6/20], Batch Num: [299/600] Discriminator Loss: 0.4261, Generator Loss: 3.3667 D(x): 0.8899, D(G(z)): 0.1257 Epoch: 6, Batch Num: [300/600]
Epoch: [6/20], Batch Num: [300/600] Discriminator Loss: 0.6353, Generator Loss: 2.8430 D(x): 0.8040, D(G(z)): 0.1432 Epoch: [6/20], Batch Num: [301/600] Discriminator Loss: 0.6144, Generator Loss: 2.7299 D(x): 0.8351, D(G(z)): 0.1894 Epoch: [6/20], Batch Num: [302/600] Discriminator Loss: 0.5377, Generator Loss: 2.6053 D(x): 0.8923, D(G(z)): 0.1763 Epoch: [6/20], Batch Num: [303/600] Discriminator Loss: 0.4198, Generator Loss: 2.5419 D(x): 0.8796, D(G(z)): 0.1520 Epoch: [6/20], Batch Num: [304/600] Discriminator Loss: 0.7332, Generator Loss: 2.3980 D(x): 0.8124, D(G(z)): 0.1594 Epoch: [6/20], Batch Num: [305/600] Discriminator Loss: 0.7612, Generator Loss: 2.2493 D(x): 0.8301, D(G(z)): 0.2546 Epoch: [6/20], Batch Num: [306/600] Discriminator Loss: 0.6237, Generator Loss: 2.6806 D(x): 0.8647, D(G(z)): 0.2195 Epoch: [6/20], Batch Num: [307/600] Discriminator Loss: 0.6930, Generator Loss: 2.7798 D(x): 0.8336, D(G(z)): 0.1788 Epoch: [6/20], Batch Num: [308/600] Discriminator Loss: 1.0239, Generator Loss: 2.3495 D(x): 0.6956, D(G(z)): 0.1761 Epoch: [6/20], Batch Num: [309/600] Discriminator Loss: 0.9072, Generator Loss: 2.0162 D(x): 0.7458, D(G(z)): 0.1575 Epoch: [6/20], Batch Num: [310/600] Discriminator Loss: 0.6076, Generator Loss: 1.5931 D(x): 0.8534, D(G(z)): 0.2464 Epoch: [6/20], Batch Num: [311/600] Discriminator Loss: 0.8929, Generator Loss: 2.0418 D(x): 0.8431, D(G(z)): 0.3289 Epoch: [6/20], Batch Num: [312/600] Discriminator Loss: 0.6270, Generator Loss: 2.3410 D(x): 0.8489, D(G(z)): 0.2392 Epoch: [6/20], Batch Num: [313/600] Discriminator Loss: 0.9670, Generator Loss: 2.5086 D(x): 0.7026, D(G(z)): 0.1945 Epoch: [6/20], Batch Num: [314/600] Discriminator Loss: 0.8022, Generator Loss: 2.3049 D(x): 0.7268, D(G(z)): 0.1657 Epoch: [6/20], Batch Num: [315/600] Discriminator Loss: 0.8936, Generator Loss: 2.0871 D(x): 0.7258, D(G(z)): 0.2051 Epoch: [6/20], Batch Num: [316/600] Discriminator Loss: 0.7824, Generator Loss: 1.8629 D(x): 0.8222, D(G(z)): 0.2575 Epoch: [6/20], Batch Num: [317/600] Discriminator Loss: 0.7750, Generator Loss: 2.0220 D(x): 0.8074, D(G(z)): 0.2530 Epoch: [6/20], Batch Num: [318/600] Discriminator Loss: 0.8650, Generator Loss: 2.0275 D(x): 0.7711, D(G(z)): 0.2634 Epoch: [6/20], Batch Num: [319/600] Discriminator Loss: 0.6868, Generator Loss: 2.2763 D(x): 0.8198, D(G(z)): 0.2114 Epoch: [6/20], Batch Num: [320/600] Discriminator Loss: 0.6104, Generator Loss: 2.5855 D(x): 0.8521, D(G(z)): 0.2145 Epoch: [6/20], Batch Num: [321/600] Discriminator Loss: 0.6993, Generator Loss: 2.5584 D(x): 0.7457, D(G(z)): 0.1420 Epoch: [6/20], Batch Num: [322/600] Discriminator Loss: 0.5061, Generator Loss: 2.6812 D(x): 0.8188, D(G(z)): 0.1632 Epoch: [6/20], Batch Num: [323/600] Discriminator Loss: 0.7674, Generator Loss: 2.3769 D(x): 0.7649, D(G(z)): 0.1814 Epoch: [6/20], Batch Num: [324/600] Discriminator Loss: 0.6676, Generator Loss: 2.5198 D(x): 0.8153, D(G(z)): 0.2330 Epoch: [6/20], Batch Num: [325/600] Discriminator Loss: 0.8715, Generator Loss: 2.2574 D(x): 0.7570, D(G(z)): 0.2290 Epoch: [6/20], Batch Num: [326/600] Discriminator Loss: 0.7520, Generator Loss: 2.2785 D(x): 0.7764, D(G(z)): 0.2123 Epoch: [6/20], Batch Num: [327/600] Discriminator Loss: 0.8880, Generator Loss: 2.2472 D(x): 0.7303, D(G(z)): 0.2383 Epoch: [6/20], Batch Num: [328/600] Discriminator Loss: 0.6414, Generator Loss: 2.3834 D(x): 0.8446, D(G(z)): 0.2590 Epoch: [6/20], Batch Num: [329/600] Discriminator Loss: 0.7747, Generator Loss: 2.9346 D(x): 0.8131, D(G(z)): 0.2364 Epoch: [6/20], Batch Num: [330/600] Discriminator Loss: 0.6607, Generator Loss: 3.1498 D(x): 0.7694, D(G(z)): 0.1557 Epoch: [6/20], Batch Num: [331/600] Discriminator Loss: 0.9447, Generator Loss: 2.7825 D(x): 0.6467, D(G(z)): 0.1118 Epoch: [6/20], Batch Num: [332/600] Discriminator Loss: 0.6901, Generator Loss: 2.1920 D(x): 0.7770, D(G(z)): 0.1754 Epoch: [6/20], Batch Num: [333/600] Discriminator Loss: 0.7274, Generator Loss: 2.1116 D(x): 0.8235, D(G(z)): 0.2609 Epoch: [6/20], Batch Num: [334/600] Discriminator Loss: 0.7023, Generator Loss: 2.6998 D(x): 0.8594, D(G(z)): 0.2775 Epoch: [6/20], Batch Num: [335/600] Discriminator Loss: 0.6275, Generator Loss: 3.2630 D(x): 0.7831, D(G(z)): 0.1510 Epoch: [6/20], Batch Num: [336/600] Discriminator Loss: 0.6618, Generator Loss: 3.0538 D(x): 0.7422, D(G(z)): 0.0963 Epoch: [6/20], Batch Num: [337/600] Discriminator Loss: 0.9479, Generator Loss: 2.4600 D(x): 0.7156, D(G(z)): 0.1924 Epoch: [6/20], Batch Num: [338/600] Discriminator Loss: 0.5964, Generator Loss: 2.7668 D(x): 0.8446, D(G(z)): 0.2165 Epoch: [6/20], Batch Num: [339/600] Discriminator Loss: 0.5195, Generator Loss: 2.4497 D(x): 0.8455, D(G(z)): 0.1938 Epoch: [6/20], Batch Num: [340/600] Discriminator Loss: 0.7115, Generator Loss: 2.8335 D(x): 0.7914, D(G(z)): 0.2017 Epoch: [6/20], Batch Num: [341/600] Discriminator Loss: 0.5897, Generator Loss: 2.9266 D(x): 0.8277, D(G(z)): 0.1861 Epoch: [6/20], Batch Num: [342/600] Discriminator Loss: 0.5768, Generator Loss: 2.7191 D(x): 0.7830, D(G(z)): 0.1331 Epoch: [6/20], Batch Num: [343/600] Discriminator Loss: 0.7637, Generator Loss: 2.0420 D(x): 0.7695, D(G(z)): 0.1911 Epoch: [6/20], Batch Num: [344/600] Discriminator Loss: 0.9128, Generator Loss: 2.3159 D(x): 0.7956, D(G(z)): 0.2962 Epoch: [6/20], Batch Num: [345/600] Discriminator Loss: 0.8433, Generator Loss: 3.0101 D(x): 0.8330, D(G(z)): 0.3039 Epoch: [6/20], Batch Num: [346/600] Discriminator Loss: 0.8147, Generator Loss: 2.9352 D(x): 0.7420, D(G(z)): 0.1341 Epoch: [6/20], Batch Num: [347/600] Discriminator Loss: 1.0118, Generator Loss: 2.0992 D(x): 0.6784, D(G(z)): 0.1365 Epoch: [6/20], Batch Num: [348/600] Discriminator Loss: 0.7292, Generator Loss: 1.8980 D(x): 0.8361, D(G(z)): 0.2809 Epoch: [6/20], Batch Num: [349/600] Discriminator Loss: 0.7185, Generator Loss: 2.0833 D(x): 0.8512, D(G(z)): 0.2701 Epoch: [6/20], Batch Num: [350/600] Discriminator Loss: 0.9108, Generator Loss: 2.3974 D(x): 0.7802, D(G(z)): 0.2480 Epoch: [6/20], Batch Num: [351/600] Discriminator Loss: 0.6162, Generator Loss: 2.4645 D(x): 0.8396, D(G(z)): 0.2228 Epoch: [6/20], Batch Num: [352/600] Discriminator Loss: 0.7434, Generator Loss: 2.7001 D(x): 0.8039, D(G(z)): 0.2124 Epoch: [6/20], Batch Num: [353/600] Discriminator Loss: 0.9608, Generator Loss: 2.5616 D(x): 0.6877, D(G(z)): 0.1371 Epoch: [6/20], Batch Num: [354/600] Discriminator Loss: 0.9652, Generator Loss: 1.9373 D(x): 0.7081, D(G(z)): 0.1969 Epoch: [6/20], Batch Num: [355/600] Discriminator Loss: 0.8879, Generator Loss: 1.9298 D(x): 0.8194, D(G(z)): 0.3452 Epoch: [6/20], Batch Num: [356/600] Discriminator Loss: 0.7060, Generator Loss: 1.8538 D(x): 0.8375, D(G(z)): 0.3020 Epoch: [6/20], Batch Num: [357/600] Discriminator Loss: 0.5288, Generator Loss: 2.5429 D(x): 0.8801, D(G(z)): 0.2431 Epoch: [6/20], Batch Num: [358/600] Discriminator Loss: 0.6903, Generator Loss: 2.7077 D(x): 0.7804, D(G(z)): 0.1599 Epoch: [6/20], Batch Num: [359/600] Discriminator Loss: 0.6145, Generator Loss: 2.5886 D(x): 0.7752, D(G(z)): 0.1534 Epoch: [6/20], Batch Num: [360/600] Discriminator Loss: 0.5614, Generator Loss: 2.3459 D(x): 0.7844, D(G(z)): 0.1617 Epoch: [6/20], Batch Num: [361/600] Discriminator Loss: 0.5955, Generator Loss: 2.1369 D(x): 0.8313, D(G(z)): 0.2109 Epoch: [6/20], Batch Num: [362/600] Discriminator Loss: 0.5809, Generator Loss: 1.9135 D(x): 0.8230, D(G(z)): 0.2131 Epoch: [6/20], Batch Num: [363/600] Discriminator Loss: 0.5443, Generator Loss: 2.1568 D(x): 0.8631, D(G(z)): 0.2214 Epoch: [6/20], Batch Num: [364/600] Discriminator Loss: 0.6217, Generator Loss: 2.2041 D(x): 0.8655, D(G(z)): 0.2553 Epoch: [6/20], Batch Num: [365/600] Discriminator Loss: 0.5643, Generator Loss: 2.4862 D(x): 0.8402, D(G(z)): 0.2046 Epoch: [6/20], Batch Num: [366/600] Discriminator Loss: 0.6065, Generator Loss: 2.5839 D(x): 0.8143, D(G(z)): 0.1545 Epoch: [6/20], Batch Num: [367/600] Discriminator Loss: 0.5589, Generator Loss: 2.6906 D(x): 0.8089, D(G(z)): 0.1374 Epoch: [6/20], Batch Num: [368/600] Discriminator Loss: 0.5953, Generator Loss: 2.4237 D(x): 0.8040, D(G(z)): 0.1642 Epoch: [6/20], Batch Num: [369/600] Discriminator Loss: 0.5823, Generator Loss: 2.1305 D(x): 0.8061, D(G(z)): 0.1715 Epoch: [6/20], Batch Num: [370/600] Discriminator Loss: 0.6728, Generator Loss: 2.0787 D(x): 0.8658, D(G(z)): 0.2531 Epoch: [6/20], Batch Num: [371/600] Discriminator Loss: 0.5205, Generator Loss: 2.5075 D(x): 0.9007, D(G(z)): 0.2355 Epoch: [6/20], Batch Num: [372/600] Discriminator Loss: 0.4924, Generator Loss: 2.9315 D(x): 0.8350, D(G(z)): 0.1527 Epoch: [6/20], Batch Num: [373/600] Discriminator Loss: 0.4204, Generator Loss: 2.6240 D(x): 0.8620, D(G(z)): 0.1342 Epoch: [6/20], Batch Num: [374/600] Discriminator Loss: 0.5982, Generator Loss: 2.9202 D(x): 0.8259, D(G(z)): 0.1759 Epoch: [6/20], Batch Num: [375/600] Discriminator Loss: 0.5492, Generator Loss: 2.4982 D(x): 0.8329, D(G(z)): 0.1480 Epoch: [6/20], Batch Num: [376/600] Discriminator Loss: 0.4747, Generator Loss: 2.3873 D(x): 0.8273, D(G(z)): 0.1385 Epoch: [6/20], Batch Num: [377/600] Discriminator Loss: 0.5739, Generator Loss: 2.4058 D(x): 0.8625, D(G(z)): 0.2130 Epoch: [6/20], Batch Num: [378/600] Discriminator Loss: 0.5483, Generator Loss: 2.7642 D(x): 0.8741, D(G(z)): 0.2016 Epoch: [6/20], Batch Num: [379/600] Discriminator Loss: 0.5837, Generator Loss: 2.7602 D(x): 0.8420, D(G(z)): 0.1949 Epoch: [6/20], Batch Num: [380/600] Discriminator Loss: 0.7370, Generator Loss: 3.2096 D(x): 0.8016, D(G(z)): 0.2098 Epoch: [6/20], Batch Num: [381/600] Discriminator Loss: 0.5481, Generator Loss: 2.7641 D(x): 0.7905, D(G(z)): 0.1018 Epoch: [6/20], Batch Num: [382/600] Discriminator Loss: 0.6520, Generator Loss: 2.3870 D(x): 0.7888, D(G(z)): 0.1733 Epoch: [6/20], Batch Num: [383/600] Discriminator Loss: 0.4996, Generator Loss: 2.1301 D(x): 0.8268, D(G(z)): 0.1626 Epoch: [6/20], Batch Num: [384/600] Discriminator Loss: 0.8703, Generator Loss: 2.1545 D(x): 0.8062, D(G(z)): 0.2841 Epoch: [6/20], Batch Num: [385/600] Discriminator Loss: 0.8097, Generator Loss: 2.4422 D(x): 0.8331, D(G(z)): 0.2594 Epoch: [6/20], Batch Num: [386/600] Discriminator Loss: 0.6606, Generator Loss: 2.5960 D(x): 0.8286, D(G(z)): 0.2207 Epoch: [6/20], Batch Num: [387/600] Discriminator Loss: 0.8731, Generator Loss: 2.7151 D(x): 0.7127, D(G(z)): 0.1618 Epoch: [6/20], Batch Num: [388/600] Discriminator Loss: 0.8400, Generator Loss: 2.7308 D(x): 0.7583, D(G(z)): 0.1783 Epoch: [6/20], Batch Num: [389/600] Discriminator Loss: 0.9252, Generator Loss: 1.9462 D(x): 0.7236, D(G(z)): 0.2210 Epoch: [6/20], Batch Num: [390/600] Discriminator Loss: 0.8903, Generator Loss: 1.7993 D(x): 0.7621, D(G(z)): 0.2134 Epoch: [6/20], Batch Num: [391/600] Discriminator Loss: 0.7862, Generator Loss: 1.6580 D(x): 0.8110, D(G(z)): 0.2900 Epoch: [6/20], Batch Num: [392/600] Discriminator Loss: 0.8547, Generator Loss: 2.1868 D(x): 0.8070, D(G(z)): 0.2708 Epoch: [6/20], Batch Num: [393/600] Discriminator Loss: 0.7596, Generator Loss: 2.5469 D(x): 0.7980, D(G(z)): 0.2454 Epoch: [6/20], Batch Num: [394/600] Discriminator Loss: 0.7621, Generator Loss: 2.4057 D(x): 0.7567, D(G(z)): 0.2062 Epoch: [6/20], Batch Num: [395/600] Discriminator Loss: 0.7641, Generator Loss: 2.5332 D(x): 0.7409, D(G(z)): 0.1806 Epoch: [6/20], Batch Num: [396/600] Discriminator Loss: 0.7356, Generator Loss: 2.4981 D(x): 0.7531, D(G(z)): 0.1950 Epoch: [6/20], Batch Num: [397/600] Discriminator Loss: 0.7889, Generator Loss: 2.1965 D(x): 0.7618, D(G(z)): 0.1842 Epoch: [6/20], Batch Num: [398/600] Discriminator Loss: 0.6437, Generator Loss: 2.2654 D(x): 0.8252, D(G(z)): 0.2280 Epoch: [6/20], Batch Num: [399/600] Discriminator Loss: 0.7243, Generator Loss: 2.4052 D(x): 0.8459, D(G(z)): 0.2784 Epoch: 6, Batch Num: [400/600]
Epoch: [6/20], Batch Num: [400/600] Discriminator Loss: 0.5369, Generator Loss: 2.5366 D(x): 0.8385, D(G(z)): 0.2005 Epoch: [6/20], Batch Num: [401/600] Discriminator Loss: 0.5365, Generator Loss: 2.5927 D(x): 0.8358, D(G(z)): 0.1726 Epoch: [6/20], Batch Num: [402/600] Discriminator Loss: 0.6313, Generator Loss: 2.6840 D(x): 0.7759, D(G(z)): 0.1658 Epoch: [6/20], Batch Num: [403/600] Discriminator Loss: 0.4887, Generator Loss: 2.7471 D(x): 0.8351, D(G(z)): 0.1693 Epoch: [6/20], Batch Num: [404/600] Discriminator Loss: 0.6202, Generator Loss: 2.3368 D(x): 0.7951, D(G(z)): 0.1576 Epoch: [6/20], Batch Num: [405/600] Discriminator Loss: 0.4534, Generator Loss: 2.4213 D(x): 0.8373, D(G(z)): 0.1559 Epoch: [6/20], Batch Num: [406/600] Discriminator Loss: 0.5761, Generator Loss: 2.1893 D(x): 0.8705, D(G(z)): 0.2304 Epoch: [6/20], Batch Num: [407/600] Discriminator Loss: 0.5007, Generator Loss: 2.3879 D(x): 0.8497, D(G(z)): 0.2011 Epoch: [6/20], Batch Num: [408/600] Discriminator Loss: 0.4882, Generator Loss: 2.4476 D(x): 0.8498, D(G(z)): 0.1810 Epoch: [6/20], Batch Num: [409/600] Discriminator Loss: 0.5361, Generator Loss: 2.5069 D(x): 0.8202, D(G(z)): 0.1716 Epoch: [6/20], Batch Num: [410/600] Discriminator Loss: 0.5126, Generator Loss: 2.4290 D(x): 0.8151, D(G(z)): 0.1604 Epoch: [6/20], Batch Num: [411/600] Discriminator Loss: 0.6671, Generator Loss: 2.3370 D(x): 0.7817, D(G(z)): 0.1415 Epoch: [6/20], Batch Num: [412/600] Discriminator Loss: 0.7166, Generator Loss: 2.0907 D(x): 0.7846, D(G(z)): 0.2010 Epoch: [6/20], Batch Num: [413/600] Discriminator Loss: 0.6210, Generator Loss: 2.1980 D(x): 0.8483, D(G(z)): 0.2555 Epoch: [6/20], Batch Num: [414/600] Discriminator Loss: 0.6884, Generator Loss: 2.1190 D(x): 0.8510, D(G(z)): 0.2785 Epoch: [6/20], Batch Num: [415/600] Discriminator Loss: 0.6755, Generator Loss: 2.4039 D(x): 0.8100, D(G(z)): 0.2235 Epoch: [6/20], Batch Num: [416/600] Discriminator Loss: 0.7861, Generator Loss: 2.4240 D(x): 0.7247, D(G(z)): 0.1597 Epoch: [6/20], Batch Num: [417/600] Discriminator Loss: 0.7122, Generator Loss: 2.1548 D(x): 0.7635, D(G(z)): 0.1702 Epoch: [6/20], Batch Num: [418/600] Discriminator Loss: 0.5506, Generator Loss: 1.8878 D(x): 0.8486, D(G(z)): 0.1949 Epoch: [6/20], Batch Num: [419/600] Discriminator Loss: 0.6282, Generator Loss: 1.9741 D(x): 0.8955, D(G(z)): 0.3014 Epoch: [6/20], Batch Num: [420/600] Discriminator Loss: 0.6427, Generator Loss: 2.4594 D(x): 0.8611, D(G(z)): 0.2603 Epoch: [6/20], Batch Num: [421/600] Discriminator Loss: 0.4414, Generator Loss: 2.5832 D(x): 0.8434, D(G(z)): 0.1577 Epoch: [6/20], Batch Num: [422/600] Discriminator Loss: 0.5230, Generator Loss: 2.7594 D(x): 0.8256, D(G(z)): 0.1394 Epoch: [6/20], Batch Num: [423/600] Discriminator Loss: 0.5778, Generator Loss: 2.8840 D(x): 0.7953, D(G(z)): 0.1165 Epoch: [6/20], Batch Num: [424/600] Discriminator Loss: 0.6120, Generator Loss: 2.3513 D(x): 0.7842, D(G(z)): 0.1631 Epoch: [6/20], Batch Num: [425/600] Discriminator Loss: 0.5293, Generator Loss: 2.1792 D(x): 0.8384, D(G(z)): 0.2071 Epoch: [6/20], Batch Num: [426/600] Discriminator Loss: 0.7119, Generator Loss: 2.1750 D(x): 0.8201, D(G(z)): 0.2549 Epoch: [6/20], Batch Num: [427/600] Discriminator Loss: 0.5607, Generator Loss: 2.2777 D(x): 0.8868, D(G(z)): 0.2149 Epoch: [6/20], Batch Num: [428/600] Discriminator Loss: 0.4859, Generator Loss: 2.3768 D(x): 0.8557, D(G(z)): 0.2037 Epoch: [6/20], Batch Num: [429/600] Discriminator Loss: 0.5883, Generator Loss: 2.7367 D(x): 0.8590, D(G(z)): 0.2072 Epoch: [6/20], Batch Num: [430/600] Discriminator Loss: 0.6059, Generator Loss: 2.6632 D(x): 0.7782, D(G(z)): 0.1387 Epoch: [6/20], Batch Num: [431/600] Discriminator Loss: 0.4986, Generator Loss: 2.4250 D(x): 0.7896, D(G(z)): 0.1256 Epoch: [6/20], Batch Num: [432/600] Discriminator Loss: 0.4804, Generator Loss: 2.2748 D(x): 0.8483, D(G(z)): 0.1858 Epoch: [6/20], Batch Num: [433/600] Discriminator Loss: 0.4460, Generator Loss: 2.3278 D(x): 0.8852, D(G(z)): 0.1930 Epoch: [6/20], Batch Num: [434/600] Discriminator Loss: 0.5610, Generator Loss: 2.3401 D(x): 0.8709, D(G(z)): 0.2633 Epoch: [6/20], Batch Num: [435/600] Discriminator Loss: 0.5747, Generator Loss: 2.3917 D(x): 0.8320, D(G(z)): 0.2124 Epoch: [6/20], Batch Num: [436/600] Discriminator Loss: 0.6262, Generator Loss: 2.5270 D(x): 0.7779, D(G(z)): 0.1961 Epoch: [6/20], Batch Num: [437/600] Discriminator Loss: 0.5269, Generator Loss: 2.3330 D(x): 0.8591, D(G(z)): 0.2039 Epoch: [6/20], Batch Num: [438/600] Discriminator Loss: 0.7222, Generator Loss: 2.3041 D(x): 0.8184, D(G(z)): 0.2622 Epoch: [6/20], Batch Num: [439/600] Discriminator Loss: 0.5657, Generator Loss: 2.3820 D(x): 0.8293, D(G(z)): 0.2053 Epoch: [6/20], Batch Num: [440/600] Discriminator Loss: 0.7806, Generator Loss: 2.0718 D(x): 0.7096, D(G(z)): 0.1815 Epoch: [6/20], Batch Num: [441/600] Discriminator Loss: 0.6495, Generator Loss: 2.0077 D(x): 0.8752, D(G(z)): 0.2873 Epoch: [6/20], Batch Num: [442/600] Discriminator Loss: 0.7797, Generator Loss: 2.0083 D(x): 0.7870, D(G(z)): 0.2770 Epoch: [6/20], Batch Num: [443/600] Discriminator Loss: 0.5929, Generator Loss: 1.7922 D(x): 0.8140, D(G(z)): 0.2167 Epoch: [6/20], Batch Num: [444/600] Discriminator Loss: 0.7844, Generator Loss: 1.8382 D(x): 0.7813, D(G(z)): 0.2413 Epoch: [6/20], Batch Num: [445/600] Discriminator Loss: 0.6190, Generator Loss: 1.8477 D(x): 0.8545, D(G(z)): 0.2691 Epoch: [6/20], Batch Num: [446/600] Discriminator Loss: 0.7652, Generator Loss: 1.7435 D(x): 0.7600, D(G(z)): 0.2279 Epoch: [6/20], Batch Num: [447/600] Discriminator Loss: 0.9086, Generator Loss: 1.7248 D(x): 0.7555, D(G(z)): 0.2836 Epoch: [6/20], Batch Num: [448/600] Discriminator Loss: 0.8735, Generator Loss: 1.9961 D(x): 0.7464, D(G(z)): 0.2749 Epoch: [6/20], Batch Num: [449/600] Discriminator Loss: 0.8585, Generator Loss: 1.7625 D(x): 0.7427, D(G(z)): 0.2339 Epoch: [6/20], Batch Num: [450/600] Discriminator Loss: 0.9319, Generator Loss: 2.0375 D(x): 0.7480, D(G(z)): 0.3125 Epoch: [6/20], Batch Num: [451/600] Discriminator Loss: 0.8191, Generator Loss: 2.0085 D(x): 0.7631, D(G(z)): 0.2670 Epoch: [6/20], Batch Num: [452/600] Discriminator Loss: 0.7877, Generator Loss: 1.9268 D(x): 0.7206, D(G(z)): 0.2328 Epoch: [6/20], Batch Num: [453/600] Discriminator Loss: 0.8512, Generator Loss: 1.6187 D(x): 0.7561, D(G(z)): 0.2846 Epoch: [6/20], Batch Num: [454/600] Discriminator Loss: 0.9290, Generator Loss: 2.0249 D(x): 0.7298, D(G(z)): 0.2944 Epoch: [6/20], Batch Num: [455/600] Discriminator Loss: 0.8152, Generator Loss: 1.6139 D(x): 0.7756, D(G(z)): 0.2677 Epoch: [6/20], Batch Num: [456/600] Discriminator Loss: 0.8104, Generator Loss: 2.0085 D(x): 0.7674, D(G(z)): 0.2695 Epoch: [6/20], Batch Num: [457/600] Discriminator Loss: 0.7097, Generator Loss: 1.9761 D(x): 0.7486, D(G(z)): 0.2115 Epoch: [6/20], Batch Num: [458/600] Discriminator Loss: 0.5992, Generator Loss: 1.6359 D(x): 0.8079, D(G(z)): 0.2176 Epoch: [6/20], Batch Num: [459/600] Discriminator Loss: 0.5989, Generator Loss: 1.6929 D(x): 0.8189, D(G(z)): 0.2425 Epoch: [6/20], Batch Num: [460/600] Discriminator Loss: 0.6052, Generator Loss: 1.9073 D(x): 0.8102, D(G(z)): 0.2290 Epoch: [6/20], Batch Num: [461/600] Discriminator Loss: 0.7588, Generator Loss: 1.9253 D(x): 0.7626, D(G(z)): 0.2588 Epoch: [6/20], Batch Num: [462/600] Discriminator Loss: 0.7813, Generator Loss: 1.7147 D(x): 0.7392, D(G(z)): 0.2360 Epoch: [6/20], Batch Num: [463/600] Discriminator Loss: 0.6430, Generator Loss: 1.6494 D(x): 0.7866, D(G(z)): 0.2366 Epoch: [6/20], Batch Num: [464/600] Discriminator Loss: 0.6666, Generator Loss: 1.7948 D(x): 0.8342, D(G(z)): 0.2899 Epoch: [6/20], Batch Num: [465/600] Discriminator Loss: 0.6584, Generator Loss: 1.8467 D(x): 0.8269, D(G(z)): 0.2799 Epoch: [6/20], Batch Num: [466/600] Discriminator Loss: 0.5756, Generator Loss: 1.9172 D(x): 0.8286, D(G(z)): 0.2232 Epoch: [6/20], Batch Num: [467/600] Discriminator Loss: 0.5631, Generator Loss: 1.9987 D(x): 0.7801, D(G(z)): 0.1855 Epoch: [6/20], Batch Num: [468/600] Discriminator Loss: 0.5809, Generator Loss: 2.0025 D(x): 0.8188, D(G(z)): 0.2005 Epoch: [6/20], Batch Num: [469/600] Discriminator Loss: 0.5719, Generator Loss: 1.9135 D(x): 0.7881, D(G(z)): 0.1839 Epoch: [6/20], Batch Num: [470/600] Discriminator Loss: 0.8375, Generator Loss: 1.7321 D(x): 0.7640, D(G(z)): 0.2646 Epoch: [6/20], Batch Num: [471/600] Discriminator Loss: 0.6134, Generator Loss: 1.7499 D(x): 0.8089, D(G(z)): 0.2336 Epoch: [6/20], Batch Num: [472/600] Discriminator Loss: 0.5228, Generator Loss: 1.6977 D(x): 0.8682, D(G(z)): 0.2373 Epoch: [6/20], Batch Num: [473/600] Discriminator Loss: 0.6010, Generator Loss: 1.8576 D(x): 0.8291, D(G(z)): 0.2296 Epoch: [6/20], Batch Num: [474/600] Discriminator Loss: 0.4735, Generator Loss: 2.0609 D(x): 0.8738, D(G(z)): 0.2219 Epoch: [6/20], Batch Num: [475/600] Discriminator Loss: 0.5579, Generator Loss: 2.3063 D(x): 0.8053, D(G(z)): 0.1791 Epoch: [6/20], Batch Num: [476/600] Discriminator Loss: 0.5213, Generator Loss: 1.9399 D(x): 0.8187, D(G(z)): 0.1763 Epoch: [6/20], Batch Num: [477/600] Discriminator Loss: 0.4763, Generator Loss: 1.9093 D(x): 0.8399, D(G(z)): 0.1853 Epoch: [6/20], Batch Num: [478/600] Discriminator Loss: 0.5774, Generator Loss: 1.9830 D(x): 0.8613, D(G(z)): 0.2448 Epoch: [6/20], Batch Num: [479/600] Discriminator Loss: 0.6431, Generator Loss: 1.9991 D(x): 0.7927, D(G(z)): 0.2205 Epoch: [6/20], Batch Num: [480/600] Discriminator Loss: 0.5571, Generator Loss: 2.0607 D(x): 0.8320, D(G(z)): 0.2059 Epoch: [6/20], Batch Num: [481/600] Discriminator Loss: 0.7164, Generator Loss: 1.9774 D(x): 0.8071, D(G(z)): 0.2523 Epoch: [6/20], Batch Num: [482/600] Discriminator Loss: 0.6724, Generator Loss: 2.2464 D(x): 0.8255, D(G(z)): 0.2566 Epoch: [6/20], Batch Num: [483/600] Discriminator Loss: 0.6092, Generator Loss: 2.1946 D(x): 0.7786, D(G(z)): 0.1730 Epoch: [6/20], Batch Num: [484/600] Discriminator Loss: 0.6432, Generator Loss: 2.0087 D(x): 0.8048, D(G(z)): 0.2040 Epoch: [6/20], Batch Num: [485/600] Discriminator Loss: 0.5277, Generator Loss: 1.9965 D(x): 0.8394, D(G(z)): 0.1979 Epoch: [6/20], Batch Num: [486/600] Discriminator Loss: 0.6512, Generator Loss: 2.0535 D(x): 0.8269, D(G(z)): 0.2534 Epoch: [6/20], Batch Num: [487/600] Discriminator Loss: 0.8431, Generator Loss: 2.0042 D(x): 0.7666, D(G(z)): 0.2474 Epoch: [6/20], Batch Num: [488/600] Discriminator Loss: 0.6454, Generator Loss: 2.2152 D(x): 0.8546, D(G(z)): 0.2585 Epoch: [6/20], Batch Num: [489/600] Discriminator Loss: 0.5466, Generator Loss: 2.5248 D(x): 0.8449, D(G(z)): 0.1921 Epoch: [6/20], Batch Num: [490/600] Discriminator Loss: 0.8651, Generator Loss: 2.5372 D(x): 0.6877, D(G(z)): 0.1490 Epoch: [6/20], Batch Num: [491/600] Discriminator Loss: 0.7654, Generator Loss: 2.1189 D(x): 0.7683, D(G(z)): 0.2088 Epoch: [6/20], Batch Num: [492/600] Discriminator Loss: 0.7309, Generator Loss: 1.7181 D(x): 0.7937, D(G(z)): 0.2095 Epoch: [6/20], Batch Num: [493/600] Discriminator Loss: 0.5923, Generator Loss: 1.6710 D(x): 0.8883, D(G(z)): 0.2931 Epoch: [6/20], Batch Num: [494/600] Discriminator Loss: 0.6361, Generator Loss: 2.1121 D(x): 0.8624, D(G(z)): 0.2677 Epoch: [6/20], Batch Num: [495/600] Discriminator Loss: 0.6271, Generator Loss: 2.4541 D(x): 0.8295, D(G(z)): 0.2120 Epoch: [6/20], Batch Num: [496/600] Discriminator Loss: 0.6680, Generator Loss: 2.5489 D(x): 0.7690, D(G(z)): 0.1695 Epoch: [6/20], Batch Num: [497/600] Discriminator Loss: 0.5701, Generator Loss: 2.3423 D(x): 0.8104, D(G(z)): 0.1677 Epoch: [6/20], Batch Num: [498/600] Discriminator Loss: 0.5607, Generator Loss: 2.1757 D(x): 0.8284, D(G(z)): 0.1723 Epoch: [6/20], Batch Num: [499/600] Discriminator Loss: 0.4965, Generator Loss: 2.2507 D(x): 0.8944, D(G(z)): 0.1961 Epoch: 6, Batch Num: [500/600]
Epoch: [6/20], Batch Num: [500/600] Discriminator Loss: 0.5709, Generator Loss: 2.3887 D(x): 0.8708, D(G(z)): 0.2168 Epoch: [6/20], Batch Num: [501/600] Discriminator Loss: 0.7236, Generator Loss: 2.2527 D(x): 0.7970, D(G(z)): 0.2358 Epoch: [6/20], Batch Num: [502/600] Discriminator Loss: 0.5447, Generator Loss: 2.6525 D(x): 0.8594, D(G(z)): 0.2061 Epoch: [6/20], Batch Num: [503/600] Discriminator Loss: 0.7209, Generator Loss: 2.3679 D(x): 0.7752, D(G(z)): 0.1838 Epoch: [6/20], Batch Num: [504/600] Discriminator Loss: 0.6061, Generator Loss: 2.3250 D(x): 0.8682, D(G(z)): 0.2051 Epoch: [6/20], Batch Num: [505/600] Discriminator Loss: 0.6290, Generator Loss: 2.1245 D(x): 0.8104, D(G(z)): 0.1981 Epoch: [6/20], Batch Num: [506/600] Discriminator Loss: 0.7731, Generator Loss: 2.5855 D(x): 0.8759, D(G(z)): 0.2938 Epoch: [6/20], Batch Num: [507/600] Discriminator Loss: 0.6638, Generator Loss: 2.7294 D(x): 0.8276, D(G(z)): 0.2126 Epoch: [6/20], Batch Num: [508/600] Discriminator Loss: 0.6118, Generator Loss: 2.7791 D(x): 0.8087, D(G(z)): 0.1735 Epoch: [6/20], Batch Num: [509/600] Discriminator Loss: 0.7560, Generator Loss: 2.7422 D(x): 0.7586, D(G(z)): 0.1593 Epoch: [6/20], Batch Num: [510/600] Discriminator Loss: 0.7407, Generator Loss: 2.3124 D(x): 0.8092, D(G(z)): 0.2096 Epoch: [6/20], Batch Num: [511/600] Discriminator Loss: 0.6415, Generator Loss: 2.3526 D(x): 0.8460, D(G(z)): 0.2165 Epoch: [6/20], Batch Num: [512/600] Discriminator Loss: 0.6197, Generator Loss: 2.3636 D(x): 0.8705, D(G(z)): 0.2506 Epoch: [6/20], Batch Num: [513/600] Discriminator Loss: 0.6112, Generator Loss: 2.4244 D(x): 0.8319, D(G(z)): 0.2197 Epoch: [6/20], Batch Num: [514/600] Discriminator Loss: 0.5989, Generator Loss: 2.4156 D(x): 0.8171, D(G(z)): 0.1871 Epoch: [6/20], Batch Num: [515/600] Discriminator Loss: 0.6035, Generator Loss: 2.1007 D(x): 0.7891, D(G(z)): 0.1670 Epoch: [6/20], Batch Num: [516/600] Discriminator Loss: 0.5723, Generator Loss: 2.0278 D(x): 0.8202, D(G(z)): 0.1815 Epoch: [6/20], Batch Num: [517/600] Discriminator Loss: 0.5994, Generator Loss: 1.8622 D(x): 0.8544, D(G(z)): 0.2470 Epoch: [6/20], Batch Num: [518/600] Discriminator Loss: 0.6444, Generator Loss: 2.2458 D(x): 0.8648, D(G(z)): 0.2893 Epoch: [6/20], Batch Num: [519/600] Discriminator Loss: 0.6188, Generator Loss: 2.4697 D(x): 0.8236, D(G(z)): 0.2023 Epoch: [6/20], Batch Num: [520/600] Discriminator Loss: 0.6323, Generator Loss: 2.7358 D(x): 0.7711, D(G(z)): 0.1405 Epoch: [6/20], Batch Num: [521/600] Discriminator Loss: 0.6262, Generator Loss: 2.5263 D(x): 0.7758, D(G(z)): 0.1217 Epoch: [6/20], Batch Num: [522/600] Discriminator Loss: 0.5183, Generator Loss: 2.2449 D(x): 0.8163, D(G(z)): 0.1550 Epoch: [6/20], Batch Num: [523/600] Discriminator Loss: 0.5693, Generator Loss: 1.8466 D(x): 0.8382, D(G(z)): 0.1933 Epoch: [6/20], Batch Num: [524/600] Discriminator Loss: 0.5625, Generator Loss: 1.9894 D(x): 0.8735, D(G(z)): 0.2484 Epoch: [6/20], Batch Num: [525/600] Discriminator Loss: 0.6102, Generator Loss: 2.3860 D(x): 0.8809, D(G(z)): 0.2308 Epoch: [6/20], Batch Num: [526/600] Discriminator Loss: 0.4714, Generator Loss: 2.6770 D(x): 0.8714, D(G(z)): 0.1745 Epoch: [6/20], Batch Num: [527/600] Discriminator Loss: 0.3581, Generator Loss: 3.1962 D(x): 0.8791, D(G(z)): 0.1274 Epoch: [6/20], Batch Num: [528/600] Discriminator Loss: 0.3695, Generator Loss: 3.0261 D(x): 0.8576, D(G(z)): 0.0951 Epoch: [6/20], Batch Num: [529/600] Discriminator Loss: 0.5241, Generator Loss: 2.7083 D(x): 0.7761, D(G(z)): 0.1031 Epoch: [6/20], Batch Num: [530/600] Discriminator Loss: 0.3933, Generator Loss: 2.3763 D(x): 0.8383, D(G(z)): 0.1067 Epoch: [6/20], Batch Num: [531/600] Discriminator Loss: 0.4365, Generator Loss: 1.8916 D(x): 0.8898, D(G(z)): 0.1769 Epoch: [6/20], Batch Num: [532/600] Discriminator Loss: 0.5717, Generator Loss: 1.9412 D(x): 0.9376, D(G(z)): 0.2969 Epoch: [6/20], Batch Num: [533/600] Discriminator Loss: 0.5439, Generator Loss: 2.4802 D(x): 0.9128, D(G(z)): 0.2740 Epoch: [6/20], Batch Num: [534/600] Discriminator Loss: 0.5630, Generator Loss: 3.1052 D(x): 0.8010, D(G(z)): 0.1488 Epoch: [6/20], Batch Num: [535/600] Discriminator Loss: 0.5333, Generator Loss: 2.8017 D(x): 0.7897, D(G(z)): 0.1018 Epoch: [6/20], Batch Num: [536/600] Discriminator Loss: 0.5141, Generator Loss: 2.2693 D(x): 0.7715, D(G(z)): 0.1026 Epoch: [6/20], Batch Num: [537/600] Discriminator Loss: 0.6296, Generator Loss: 1.9121 D(x): 0.8154, D(G(z)): 0.1748 Epoch: [6/20], Batch Num: [538/600] Discriminator Loss: 0.6192, Generator Loss: 1.8670 D(x): 0.8523, D(G(z)): 0.2596 Epoch: [6/20], Batch Num: [539/600] Discriminator Loss: 0.6435, Generator Loss: 2.1314 D(x): 0.8812, D(G(z)): 0.2728 Epoch: [6/20], Batch Num: [540/600] Discriminator Loss: 0.6301, Generator Loss: 2.5530 D(x): 0.8903, D(G(z)): 0.2290 Epoch: [6/20], Batch Num: [541/600] Discriminator Loss: 0.6581, Generator Loss: 2.7768 D(x): 0.7837, D(G(z)): 0.1979 Epoch: [6/20], Batch Num: [542/600] Discriminator Loss: 0.5625, Generator Loss: 2.7994 D(x): 0.7872, D(G(z)): 0.1216 Epoch: [6/20], Batch Num: [543/600] Discriminator Loss: 0.7003, Generator Loss: 2.5841 D(x): 0.7431, D(G(z)): 0.1430 Epoch: [6/20], Batch Num: [544/600] Discriminator Loss: 0.5367, Generator Loss: 2.2774 D(x): 0.7947, D(G(z)): 0.1416 Epoch: [6/20], Batch Num: [545/600] Discriminator Loss: 0.5302, Generator Loss: 2.0280 D(x): 0.8444, D(G(z)): 0.1991 Epoch: [6/20], Batch Num: [546/600] Discriminator Loss: 0.6804, Generator Loss: 1.8893 D(x): 0.8397, D(G(z)): 0.2787 Epoch: [6/20], Batch Num: [547/600] Discriminator Loss: 0.5685, Generator Loss: 2.3452 D(x): 0.9004, D(G(z)): 0.2935 Epoch: [6/20], Batch Num: [548/600] Discriminator Loss: 0.6163, Generator Loss: 2.3449 D(x): 0.7721, D(G(z)): 0.1710 Epoch: [6/20], Batch Num: [549/600] Discriminator Loss: 0.5420, Generator Loss: 2.4416 D(x): 0.8016, D(G(z)): 0.1513 Epoch: [6/20], Batch Num: [550/600] Discriminator Loss: 0.5726, Generator Loss: 2.3750 D(x): 0.7769, D(G(z)): 0.1477 Epoch: [6/20], Batch Num: [551/600] Discriminator Loss: 0.5324, Generator Loss: 2.2363 D(x): 0.8050, D(G(z)): 0.1444 Epoch: [6/20], Batch Num: [552/600] Discriminator Loss: 0.5862, Generator Loss: 2.0466 D(x): 0.8177, D(G(z)): 0.2157 Epoch: [6/20], Batch Num: [553/600] Discriminator Loss: 0.5807, Generator Loss: 1.9369 D(x): 0.8157, D(G(z)): 0.2216 Epoch: [6/20], Batch Num: [554/600] Discriminator Loss: 0.4900, Generator Loss: 2.2313 D(x): 0.8922, D(G(z)): 0.2329 Epoch: [6/20], Batch Num: [555/600] Discriminator Loss: 0.6015, Generator Loss: 2.4081 D(x): 0.7985, D(G(z)): 0.1599 Epoch: [6/20], Batch Num: [556/600] Discriminator Loss: 0.4637, Generator Loss: 2.2904 D(x): 0.8298, D(G(z)): 0.1383 Epoch: [6/20], Batch Num: [557/600] Discriminator Loss: 0.4962, Generator Loss: 2.2513 D(x): 0.8344, D(G(z)): 0.1749 Epoch: [6/20], Batch Num: [558/600] Discriminator Loss: 0.5751, Generator Loss: 2.3429 D(x): 0.8084, D(G(z)): 0.1915 Epoch: [6/20], Batch Num: [559/600] Discriminator Loss: 0.5537, Generator Loss: 1.9891 D(x): 0.8380, D(G(z)): 0.2154 Epoch: [6/20], Batch Num: [560/600] Discriminator Loss: 0.5331, Generator Loss: 2.2587 D(x): 0.8516, D(G(z)): 0.2284 Epoch: [6/20], Batch Num: [561/600] Discriminator Loss: 0.4955, Generator Loss: 2.2446 D(x): 0.8418, D(G(z)): 0.1891 Epoch: [6/20], Batch Num: [562/600] Discriminator Loss: 0.5829, Generator Loss: 2.0814 D(x): 0.8064, D(G(z)): 0.1894 Epoch: [6/20], Batch Num: [563/600] Discriminator Loss: 0.6387, Generator Loss: 2.2639 D(x): 0.8260, D(G(z)): 0.2193 Epoch: [6/20], Batch Num: [564/600] Discriminator Loss: 0.6815, Generator Loss: 2.4501 D(x): 0.7855, D(G(z)): 0.2049 Epoch: [6/20], Batch Num: [565/600] Discriminator Loss: 0.6329, Generator Loss: 2.5948 D(x): 0.8049, D(G(z)): 0.1736 Epoch: [6/20], Batch Num: [566/600] Discriminator Loss: 0.6771, Generator Loss: 2.1831 D(x): 0.7912, D(G(z)): 0.1935 Epoch: [6/20], Batch Num: [567/600] Discriminator Loss: 0.7444, Generator Loss: 2.0499 D(x): 0.7868, D(G(z)): 0.2347 Epoch: [6/20], Batch Num: [568/600] Discriminator Loss: 0.6823, Generator Loss: 2.3691 D(x): 0.8641, D(G(z)): 0.2521 Epoch: [6/20], Batch Num: [569/600] Discriminator Loss: 0.5551, Generator Loss: 2.4284 D(x): 0.8296, D(G(z)): 0.1817 Epoch: [6/20], Batch Num: [570/600] Discriminator Loss: 0.6605, Generator Loss: 2.3475 D(x): 0.7983, D(G(z)): 0.1949 Epoch: [6/20], Batch Num: [571/600] Discriminator Loss: 0.7991, Generator Loss: 2.4593 D(x): 0.7591, D(G(z)): 0.2019 Epoch: [6/20], Batch Num: [572/600] Discriminator Loss: 1.0924, Generator Loss: 2.1356 D(x): 0.6493, D(G(z)): 0.1750 Epoch: [6/20], Batch Num: [573/600] Discriminator Loss: 0.7065, Generator Loss: 1.8391 D(x): 0.8039, D(G(z)): 0.2024 Epoch: [6/20], Batch Num: [574/600] Discriminator Loss: 1.0003, Generator Loss: 2.0497 D(x): 0.7768, D(G(z)): 0.3090 Epoch: [6/20], Batch Num: [575/600] Discriminator Loss: 0.6203, Generator Loss: 2.3975 D(x): 0.8928, D(G(z)): 0.2846 Epoch: [6/20], Batch Num: [576/600] Discriminator Loss: 0.6387, Generator Loss: 2.8517 D(x): 0.7985, D(G(z)): 0.1858 Epoch: [6/20], Batch Num: [577/600] Discriminator Loss: 0.6682, Generator Loss: 3.0400 D(x): 0.7599, D(G(z)): 0.1204 Epoch: [6/20], Batch Num: [578/600] Discriminator Loss: 0.5910, Generator Loss: 2.7236 D(x): 0.7382, D(G(z)): 0.0949 Epoch: [6/20], Batch Num: [579/600] Discriminator Loss: 0.5833, Generator Loss: 2.2451 D(x): 0.7905, D(G(z)): 0.1501 Epoch: [6/20], Batch Num: [580/600] Discriminator Loss: 0.5353, Generator Loss: 1.9095 D(x): 0.7929, D(G(z)): 0.1561 Epoch: [6/20], Batch Num: [581/600] Discriminator Loss: 0.6941, Generator Loss: 1.8798 D(x): 0.8385, D(G(z)): 0.2692 Epoch: [6/20], Batch Num: [582/600] Discriminator Loss: 0.5248, Generator Loss: 2.3462 D(x): 0.9038, D(G(z)): 0.2453 Epoch: [6/20], Batch Num: [583/600] Discriminator Loss: 0.5162, Generator Loss: 3.0041 D(x): 0.8769, D(G(z)): 0.2127 Epoch: [6/20], Batch Num: [584/600] Discriminator Loss: 0.4635, Generator Loss: 3.2785 D(x): 0.8135, D(G(z)): 0.0903 Epoch: [6/20], Batch Num: [585/600] Discriminator Loss: 0.6581, Generator Loss: 3.0838 D(x): 0.7555, D(G(z)): 0.0837 Epoch: [6/20], Batch Num: [586/600] Discriminator Loss: 0.5777, Generator Loss: 2.5435 D(x): 0.7929, D(G(z)): 0.1166 Epoch: [6/20], Batch Num: [587/600] Discriminator Loss: 0.6078, Generator Loss: 2.2855 D(x): 0.8327, D(G(z)): 0.1912 Epoch: [6/20], Batch Num: [588/600] Discriminator Loss: 0.5860, Generator Loss: 2.3968 D(x): 0.8852, D(G(z)): 0.2380 Epoch: [6/20], Batch Num: [589/600] Discriminator Loss: 0.6556, Generator Loss: 2.9845 D(x): 0.8672, D(G(z)): 0.2523 Epoch: [6/20], Batch Num: [590/600] Discriminator Loss: 0.6129, Generator Loss: 3.2134 D(x): 0.8377, D(G(z)): 0.1662 Epoch: [6/20], Batch Num: [591/600] Discriminator Loss: 0.5941, Generator Loss: 3.0478 D(x): 0.7908, D(G(z)): 0.1322 Epoch: [6/20], Batch Num: [592/600] Discriminator Loss: 0.6771, Generator Loss: 2.7859 D(x): 0.7540, D(G(z)): 0.0931 Epoch: [6/20], Batch Num: [593/600] Discriminator Loss: 0.6557, Generator Loss: 2.4251 D(x): 0.8002, D(G(z)): 0.1734 Epoch: [6/20], Batch Num: [594/600] Discriminator Loss: 0.8320, Generator Loss: 1.9977 D(x): 0.8081, D(G(z)): 0.2777 Epoch: [6/20], Batch Num: [595/600] Discriminator Loss: 0.8299, Generator Loss: 2.0921 D(x): 0.8210, D(G(z)): 0.2982 Epoch: [6/20], Batch Num: [596/600] Discriminator Loss: 0.7701, Generator Loss: 2.1226 D(x): 0.8124, D(G(z)): 0.2652 Epoch: [6/20], Batch Num: [597/600] Discriminator Loss: 0.8161, Generator Loss: 2.1061 D(x): 0.7874, D(G(z)): 0.2453 Epoch: [6/20], Batch Num: [598/600] Discriminator Loss: 0.9217, Generator Loss: 2.2645 D(x): 0.6886, D(G(z)): 0.2068 Epoch: [6/20], Batch Num: [599/600] Discriminator Loss: 0.9923, Generator Loss: 1.9197 D(x): 0.7360, D(G(z)): 0.2886 Epoch: 7, Batch Num: [0/600]
Epoch: [7/20], Batch Num: [0/600] Discriminator Loss: 0.9578, Generator Loss: 1.8545 D(x): 0.7644, D(G(z)): 0.2909 Epoch: [7/20], Batch Num: [1/600] Discriminator Loss: 1.0467, Generator Loss: 1.9305 D(x): 0.7278, D(G(z)): 0.3086 Epoch: [7/20], Batch Num: [2/600] Discriminator Loss: 0.8455, Generator Loss: 1.5671 D(x): 0.7372, D(G(z)): 0.2488 Epoch: [7/20], Batch Num: [3/600] Discriminator Loss: 0.8273, Generator Loss: 1.8711 D(x): 0.8024, D(G(z)): 0.3016 Epoch: [7/20], Batch Num: [4/600] Discriminator Loss: 0.8965, Generator Loss: 2.0802 D(x): 0.7627, D(G(z)): 0.2780 Epoch: [7/20], Batch Num: [5/600] Discriminator Loss: 0.8899, Generator Loss: 1.9734 D(x): 0.7119, D(G(z)): 0.2408 Epoch: [7/20], Batch Num: [6/600] Discriminator Loss: 0.8628, Generator Loss: 1.9256 D(x): 0.7045, D(G(z)): 0.2055 Epoch: [7/20], Batch Num: [7/600] Discriminator Loss: 0.7239, Generator Loss: 1.8714 D(x): 0.7921, D(G(z)): 0.2418 Epoch: [7/20], Batch Num: [8/600] Discriminator Loss: 0.7245, Generator Loss: 2.0362 D(x): 0.7848, D(G(z)): 0.2365 Epoch: [7/20], Batch Num: [9/600] Discriminator Loss: 0.6515, Generator Loss: 1.8857 D(x): 0.8132, D(G(z)): 0.2290 Epoch: [7/20], Batch Num: [10/600] Discriminator Loss: 0.6608, Generator Loss: 2.0340 D(x): 0.8143, D(G(z)): 0.2382 Epoch: [7/20], Batch Num: [11/600] Discriminator Loss: 0.5643, Generator Loss: 2.1264 D(x): 0.8438, D(G(z)): 0.2338 Epoch: [7/20], Batch Num: [12/600] Discriminator Loss: 0.5079, Generator Loss: 2.3244 D(x): 0.8835, D(G(z)): 0.2122 Epoch: [7/20], Batch Num: [13/600] Discriminator Loss: 0.6383, Generator Loss: 2.4827 D(x): 0.7982, D(G(z)): 0.2162 Epoch: [7/20], Batch Num: [14/600] Discriminator Loss: 0.4804, Generator Loss: 2.6592 D(x): 0.8194, D(G(z)): 0.1525 Epoch: [7/20], Batch Num: [15/600] Discriminator Loss: 0.5239, Generator Loss: 2.4745 D(x): 0.8037, D(G(z)): 0.1430 Epoch: [7/20], Batch Num: [16/600] Discriminator Loss: 0.4607, Generator Loss: 2.4233 D(x): 0.8237, D(G(z)): 0.1410 Epoch: [7/20], Batch Num: [17/600] Discriminator Loss: 0.4876, Generator Loss: 2.3789 D(x): 0.8452, D(G(z)): 0.1870 Epoch: [7/20], Batch Num: [18/600] Discriminator Loss: 0.5326, Generator Loss: 2.2974 D(x): 0.8578, D(G(z)): 0.1983 Epoch: [7/20], Batch Num: [19/600] Discriminator Loss: 0.3887, Generator Loss: 2.3510 D(x): 0.8687, D(G(z)): 0.1603 Epoch: [7/20], Batch Num: [20/600] Discriminator Loss: 0.3988, Generator Loss: 2.4773 D(x): 0.8837, D(G(z)): 0.1801 Epoch: [7/20], Batch Num: [21/600] Discriminator Loss: 0.3692, Generator Loss: 2.6410 D(x): 0.8941, D(G(z)): 0.1742 Epoch: [7/20], Batch Num: [22/600] Discriminator Loss: 0.3773, Generator Loss: 2.5363 D(x): 0.8792, D(G(z)): 0.1504 Epoch: [7/20], Batch Num: [23/600] Discriminator Loss: 0.4465, Generator Loss: 2.4615 D(x): 0.8373, D(G(z)): 0.1467 Epoch: [7/20], Batch Num: [24/600] Discriminator Loss: 0.3253, Generator Loss: 2.5400 D(x): 0.8939, D(G(z)): 0.1410 Epoch: [7/20], Batch Num: [25/600] Discriminator Loss: 0.4330, Generator Loss: 2.5334 D(x): 0.8425, D(G(z)): 0.1510 Epoch: [7/20], Batch Num: [26/600] Discriminator Loss: 0.3509, Generator Loss: 2.5454 D(x): 0.9014, D(G(z)): 0.1689 Epoch: [7/20], Batch Num: [27/600] Discriminator Loss: 0.4160, Generator Loss: 2.5717 D(x): 0.8693, D(G(z)): 0.1674 Epoch: [7/20], Batch Num: [28/600] Discriminator Loss: 0.4069, Generator Loss: 2.7527 D(x): 0.8720, D(G(z)): 0.1681 Epoch: [7/20], Batch Num: [29/600] Discriminator Loss: 0.4159, Generator Loss: 2.8441 D(x): 0.8680, D(G(z)): 0.1716 Epoch: [7/20], Batch Num: [30/600] Discriminator Loss: 0.4906, Generator Loss: 2.5650 D(x): 0.8296, D(G(z)): 0.1542 Epoch: [7/20], Batch Num: [31/600] Discriminator Loss: 0.5005, Generator Loss: 2.6603 D(x): 0.8584, D(G(z)): 0.1940 Epoch: [7/20], Batch Num: [32/600] Discriminator Loss: 0.4523, Generator Loss: 2.3091 D(x): 0.8600, D(G(z)): 0.1615 Epoch: [7/20], Batch Num: [33/600] Discriminator Loss: 0.6514, Generator Loss: 2.8205 D(x): 0.8099, D(G(z)): 0.2088 Epoch: [7/20], Batch Num: [34/600] Discriminator Loss: 0.6073, Generator Loss: 2.1496 D(x): 0.8030, D(G(z)): 0.1943 Epoch: [7/20], Batch Num: [35/600] Discriminator Loss: 0.7523, Generator Loss: 2.3806 D(x): 0.8006, D(G(z)): 0.2580 Epoch: [7/20], Batch Num: [36/600] Discriminator Loss: 0.9845, Generator Loss: 2.4138 D(x): 0.7257, D(G(z)): 0.2535 Epoch: [7/20], Batch Num: [37/600] Discriminator Loss: 0.9558, Generator Loss: 1.9071 D(x): 0.7086, D(G(z)): 0.2615 Epoch: [7/20], Batch Num: [38/600] Discriminator Loss: 0.9216, Generator Loss: 2.1648 D(x): 0.7829, D(G(z)): 0.2950 Epoch: [7/20], Batch Num: [39/600] Discriminator Loss: 1.0661, Generator Loss: 2.0501 D(x): 0.6750, D(G(z)): 0.2916 Epoch: [7/20], Batch Num: [40/600] Discriminator Loss: 1.0639, Generator Loss: 1.8031 D(x): 0.6743, D(G(z)): 0.2649 Epoch: [7/20], Batch Num: [41/600] Discriminator Loss: 1.2350, Generator Loss: 1.5020 D(x): 0.6563, D(G(z)): 0.2992 Epoch: [7/20], Batch Num: [42/600] Discriminator Loss: 1.1865, Generator Loss: 1.3909 D(x): 0.6950, D(G(z)): 0.3555 Epoch: [7/20], Batch Num: [43/600] Discriminator Loss: 1.2447, Generator Loss: 1.4775 D(x): 0.7091, D(G(z)): 0.3792 Epoch: [7/20], Batch Num: [44/600] Discriminator Loss: 1.1679, Generator Loss: 1.8650 D(x): 0.7121, D(G(z)): 0.3900 Epoch: [7/20], Batch Num: [45/600] Discriminator Loss: 0.9291, Generator Loss: 1.9848 D(x): 0.7168, D(G(z)): 0.2678 Epoch: [7/20], Batch Num: [46/600] Discriminator Loss: 1.1906, Generator Loss: 1.7836 D(x): 0.5872, D(G(z)): 0.2331 Epoch: [7/20], Batch Num: [47/600] Discriminator Loss: 1.0164, Generator Loss: 1.4174 D(x): 0.6607, D(G(z)): 0.2692 Epoch: [7/20], Batch Num: [48/600] Discriminator Loss: 1.0726, Generator Loss: 1.1980 D(x): 0.6862, D(G(z)): 0.3445 Epoch: [7/20], Batch Num: [49/600] Discriminator Loss: 0.9725, Generator Loss: 1.6575 D(x): 0.7949, D(G(z)): 0.4000 Epoch: [7/20], Batch Num: [50/600] Discriminator Loss: 1.0412, Generator Loss: 1.9535 D(x): 0.7037, D(G(z)): 0.3333 Epoch: [7/20], Batch Num: [51/600] Discriminator Loss: 0.9288, Generator Loss: 2.2560 D(x): 0.6998, D(G(z)): 0.2638 Epoch: [7/20], Batch Num: [52/600] Discriminator Loss: 0.9569, Generator Loss: 2.2970 D(x): 0.6209, D(G(z)): 0.1943 Epoch: [7/20], Batch Num: [53/600] Discriminator Loss: 0.8860, Generator Loss: 2.0782 D(x): 0.6804, D(G(z)): 0.2408 Epoch: [7/20], Batch Num: [54/600] Discriminator Loss: 0.7906, Generator Loss: 1.5993 D(x): 0.7127, D(G(z)): 0.2274 Epoch: [7/20], Batch Num: [55/600] Discriminator Loss: 0.8347, Generator Loss: 1.5365 D(x): 0.7604, D(G(z)): 0.2981 Epoch: [7/20], Batch Num: [56/600] Discriminator Loss: 0.7581, Generator Loss: 1.7323 D(x): 0.7631, D(G(z)): 0.2950 Epoch: [7/20], Batch Num: [57/600] Discriminator Loss: 0.7689, Generator Loss: 1.8366 D(x): 0.8160, D(G(z)): 0.3227 Epoch: [7/20], Batch Num: [58/600] Discriminator Loss: 0.6802, Generator Loss: 2.1526 D(x): 0.7646, D(G(z)): 0.2339 Epoch: [7/20], Batch Num: [59/600] Discriminator Loss: 0.6817, Generator Loss: 2.3260 D(x): 0.7202, D(G(z)): 0.1992 Epoch: [7/20], Batch Num: [60/600] Discriminator Loss: 0.7709, Generator Loss: 2.2228 D(x): 0.7119, D(G(z)): 0.2132 Epoch: [7/20], Batch Num: [61/600] Discriminator Loss: 0.7496, Generator Loss: 1.8254 D(x): 0.7069, D(G(z)): 0.1932 Epoch: [7/20], Batch Num: [62/600] Discriminator Loss: 0.7327, Generator Loss: 1.6355 D(x): 0.7688, D(G(z)): 0.2769 Epoch: [7/20], Batch Num: [63/600] Discriminator Loss: 0.6691, Generator Loss: 1.9478 D(x): 0.8319, D(G(z)): 0.3012 Epoch: [7/20], Batch Num: [64/600] Discriminator Loss: 0.5594, Generator Loss: 2.2911 D(x): 0.8835, D(G(z)): 0.2670 Epoch: [7/20], Batch Num: [65/600] Discriminator Loss: 0.5959, Generator Loss: 2.6242 D(x): 0.7907, D(G(z)): 0.2055 Epoch: [7/20], Batch Num: [66/600] Discriminator Loss: 0.6809, Generator Loss: 2.7990 D(x): 0.7345, D(G(z)): 0.1527 Epoch: [7/20], Batch Num: [67/600] Discriminator Loss: 0.6710, Generator Loss: 2.4976 D(x): 0.7054, D(G(z)): 0.1402 Epoch: [7/20], Batch Num: [68/600] Discriminator Loss: 0.4921, Generator Loss: 2.2840 D(x): 0.8127, D(G(z)): 0.1537 Epoch: [7/20], Batch Num: [69/600] Discriminator Loss: 0.6850, Generator Loss: 2.0059 D(x): 0.8442, D(G(z)): 0.2693 Epoch: [7/20], Batch Num: [70/600] Discriminator Loss: 0.7401, Generator Loss: 2.1959 D(x): 0.7772, D(G(z)): 0.2483 Epoch: [7/20], Batch Num: [71/600] Discriminator Loss: 0.7771, Generator Loss: 2.4501 D(x): 0.8062, D(G(z)): 0.2781 Epoch: [7/20], Batch Num: [72/600] Discriminator Loss: 0.6298, Generator Loss: 2.7057 D(x): 0.8229, D(G(z)): 0.2085 Epoch: [7/20], Batch Num: [73/600] Discriminator Loss: 0.6602, Generator Loss: 2.7132 D(x): 0.7475, D(G(z)): 0.1423 Epoch: [7/20], Batch Num: [74/600] Discriminator Loss: 0.5925, Generator Loss: 2.3258 D(x): 0.7619, D(G(z)): 0.1502 Epoch: [7/20], Batch Num: [75/600] Discriminator Loss: 0.8040, Generator Loss: 2.0559 D(x): 0.7585, D(G(z)): 0.2240 Epoch: [7/20], Batch Num: [76/600] Discriminator Loss: 0.8546, Generator Loss: 1.9970 D(x): 0.7689, D(G(z)): 0.2885 Epoch: [7/20], Batch Num: [77/600] Discriminator Loss: 0.7423, Generator Loss: 2.2056 D(x): 0.8183, D(G(z)): 0.2892 Epoch: [7/20], Batch Num: [78/600] Discriminator Loss: 0.8390, Generator Loss: 2.6329 D(x): 0.7609, D(G(z)): 0.2360 Epoch: [7/20], Batch Num: [79/600] Discriminator Loss: 0.9768, Generator Loss: 2.4894 D(x): 0.6574, D(G(z)): 0.2093 Epoch: [7/20], Batch Num: [80/600] Discriminator Loss: 0.8446, Generator Loss: 2.1222 D(x): 0.7046, D(G(z)): 0.1944 Epoch: [7/20], Batch Num: [81/600] Discriminator Loss: 0.9936, Generator Loss: 2.0049 D(x): 0.6942, D(G(z)): 0.2492 Epoch: [7/20], Batch Num: [82/600] Discriminator Loss: 0.9321, Generator Loss: 1.8303 D(x): 0.7562, D(G(z)): 0.3033 Epoch: [7/20], Batch Num: [83/600] Discriminator Loss: 0.9902, Generator Loss: 2.3926 D(x): 0.8125, D(G(z)): 0.3382 Epoch: [7/20], Batch Num: [84/600] Discriminator Loss: 1.0023, Generator Loss: 2.6911 D(x): 0.6987, D(G(z)): 0.2481 Epoch: [7/20], Batch Num: [85/600] Discriminator Loss: 1.0611, Generator Loss: 2.4773 D(x): 0.5978, D(G(z)): 0.1532 Epoch: [7/20], Batch Num: [86/600] Discriminator Loss: 0.9812, Generator Loss: 1.8895 D(x): 0.6637, D(G(z)): 0.2009 Epoch: [7/20], Batch Num: [87/600] Discriminator Loss: 1.1234, Generator Loss: 1.9238 D(x): 0.7268, D(G(z)): 0.3233 Epoch: [7/20], Batch Num: [88/600] Discriminator Loss: 1.0346, Generator Loss: 2.1638 D(x): 0.7781, D(G(z)): 0.3270 Epoch: [7/20], Batch Num: [89/600] Discriminator Loss: 1.1839, Generator Loss: 2.1525 D(x): 0.6114, D(G(z)): 0.2233 Epoch: [7/20], Batch Num: [90/600] Discriminator Loss: 1.0811, Generator Loss: 1.8516 D(x): 0.7233, D(G(z)): 0.2898 Epoch: [7/20], Batch Num: [91/600] Discriminator Loss: 1.0766, Generator Loss: 1.9884 D(x): 0.6977, D(G(z)): 0.2712 Epoch: [7/20], Batch Num: [92/600] Discriminator Loss: 1.0610, Generator Loss: 1.9879 D(x): 0.6940, D(G(z)): 0.2979 Epoch: [7/20], Batch Num: [93/600] Discriminator Loss: 0.9386, Generator Loss: 1.8383 D(x): 0.7053, D(G(z)): 0.2889 Epoch: [7/20], Batch Num: [94/600] Discriminator Loss: 0.9743, Generator Loss: 1.8514 D(x): 0.6921, D(G(z)): 0.2864 Epoch: [7/20], Batch Num: [95/600] Discriminator Loss: 0.9371, Generator Loss: 1.7474 D(x): 0.6944, D(G(z)): 0.2533 Epoch: [7/20], Batch Num: [96/600] Discriminator Loss: 1.1755, Generator Loss: 1.6086 D(x): 0.6336, D(G(z)): 0.2789 Epoch: [7/20], Batch Num: [97/600] Discriminator Loss: 0.9225, Generator Loss: 1.4686 D(x): 0.7248, D(G(z)): 0.3090 Epoch: [7/20], Batch Num: [98/600] Discriminator Loss: 0.9669, Generator Loss: 1.7720 D(x): 0.7146, D(G(z)): 0.3056 Epoch: [7/20], Batch Num: [99/600] Discriminator Loss: 0.8739, Generator Loss: 1.8526 D(x): 0.6994, D(G(z)): 0.2692 Epoch: 7, Batch Num: [100/600]
Epoch: [7/20], Batch Num: [100/600] Discriminator Loss: 0.8948, Generator Loss: 1.8776 D(x): 0.6954, D(G(z)): 0.2619 Epoch: [7/20], Batch Num: [101/600] Discriminator Loss: 0.8212, Generator Loss: 1.9626 D(x): 0.7250, D(G(z)): 0.2511 Epoch: [7/20], Batch Num: [102/600] Discriminator Loss: 0.8056, Generator Loss: 1.8506 D(x): 0.7082, D(G(z)): 0.2376 Epoch: [7/20], Batch Num: [103/600] Discriminator Loss: 0.8694, Generator Loss: 1.7871 D(x): 0.6953, D(G(z)): 0.2598 Epoch: [7/20], Batch Num: [104/600] Discriminator Loss: 0.7546, Generator Loss: 1.9408 D(x): 0.7445, D(G(z)): 0.2518 Epoch: [7/20], Batch Num: [105/600] Discriminator Loss: 0.7515, Generator Loss: 1.8091 D(x): 0.7132, D(G(z)): 0.2243 Epoch: [7/20], Batch Num: [106/600] Discriminator Loss: 0.6085, Generator Loss: 1.9111 D(x): 0.7769, D(G(z)): 0.2156 Epoch: [7/20], Batch Num: [107/600] Discriminator Loss: 0.7066, Generator Loss: 2.0273 D(x): 0.7749, D(G(z)): 0.2438 Epoch: [7/20], Batch Num: [108/600] Discriminator Loss: 0.6239, Generator Loss: 2.0705 D(x): 0.7744, D(G(z)): 0.2098 Epoch: [7/20], Batch Num: [109/600] Discriminator Loss: 0.6138, Generator Loss: 2.3545 D(x): 0.8009, D(G(z)): 0.2265 Epoch: [7/20], Batch Num: [110/600] Discriminator Loss: 0.7380, Generator Loss: 2.3298 D(x): 0.7618, D(G(z)): 0.2366 Epoch: [7/20], Batch Num: [111/600] Discriminator Loss: 0.6788, Generator Loss: 2.4566 D(x): 0.7377, D(G(z)): 0.1694 Epoch: [7/20], Batch Num: [112/600] Discriminator Loss: 0.5517, Generator Loss: 2.1840 D(x): 0.7842, D(G(z)): 0.1472 Epoch: [7/20], Batch Num: [113/600] Discriminator Loss: 0.5932, Generator Loss: 2.3808 D(x): 0.8202, D(G(z)): 0.2106 Epoch: [7/20], Batch Num: [114/600] Discriminator Loss: 0.7701, Generator Loss: 2.2084 D(x): 0.7858, D(G(z)): 0.2464 Epoch: [7/20], Batch Num: [115/600] Discriminator Loss: 0.6176, Generator Loss: 2.4396 D(x): 0.8253, D(G(z)): 0.2338 Epoch: [7/20], Batch Num: [116/600] Discriminator Loss: 0.6383, Generator Loss: 2.7896 D(x): 0.8189, D(G(z)): 0.2192 Epoch: [7/20], Batch Num: [117/600] Discriminator Loss: 0.5011, Generator Loss: 3.0880 D(x): 0.7937, D(G(z)): 0.1300 Epoch: [7/20], Batch Num: [118/600] Discriminator Loss: 0.4903, Generator Loss: 2.3842 D(x): 0.7865, D(G(z)): 0.1089 Epoch: [7/20], Batch Num: [119/600] Discriminator Loss: 0.5622, Generator Loss: 2.1550 D(x): 0.7886, D(G(z)): 0.1682 Epoch: [7/20], Batch Num: [120/600] Discriminator Loss: 0.5579, Generator Loss: 2.0026 D(x): 0.8316, D(G(z)): 0.2109 Epoch: [7/20], Batch Num: [121/600] Discriminator Loss: 0.6676, Generator Loss: 2.3908 D(x): 0.8847, D(G(z)): 0.2968 Epoch: [7/20], Batch Num: [122/600] Discriminator Loss: 0.7095, Generator Loss: 2.6427 D(x): 0.8112, D(G(z)): 0.2343 Epoch: [7/20], Batch Num: [123/600] Discriminator Loss: 0.6747, Generator Loss: 2.5027 D(x): 0.7606, D(G(z)): 0.1532 Epoch: [7/20], Batch Num: [124/600] Discriminator Loss: 0.8184, Generator Loss: 2.3934 D(x): 0.7144, D(G(z)): 0.1715 Epoch: [7/20], Batch Num: [125/600] Discriminator Loss: 0.8272, Generator Loss: 1.9281 D(x): 0.7456, D(G(z)): 0.2196 Epoch: [7/20], Batch Num: [126/600] Discriminator Loss: 0.7464, Generator Loss: 2.2039 D(x): 0.7822, D(G(z)): 0.2216 Epoch: [7/20], Batch Num: [127/600] Discriminator Loss: 0.8197, Generator Loss: 2.0117 D(x): 0.8096, D(G(z)): 0.2802 Epoch: [7/20], Batch Num: [128/600] Discriminator Loss: 1.0218, Generator Loss: 1.9882 D(x): 0.7307, D(G(z)): 0.3004 Epoch: [7/20], Batch Num: [129/600] Discriminator Loss: 0.8682, Generator Loss: 1.9362 D(x): 0.7188, D(G(z)): 0.2397 Epoch: [7/20], Batch Num: [130/600] Discriminator Loss: 0.7547, Generator Loss: 1.9889 D(x): 0.7887, D(G(z)): 0.2633 Epoch: [7/20], Batch Num: [131/600] Discriminator Loss: 0.8647, Generator Loss: 1.8917 D(x): 0.7487, D(G(z)): 0.2704 Epoch: [7/20], Batch Num: [132/600] Discriminator Loss: 0.8969, Generator Loss: 1.9086 D(x): 0.7214, D(G(z)): 0.2400 Epoch: [7/20], Batch Num: [133/600] Discriminator Loss: 0.9964, Generator Loss: 1.9543 D(x): 0.6982, D(G(z)): 0.2743 Epoch: [7/20], Batch Num: [134/600] Discriminator Loss: 0.8803, Generator Loss: 1.7647 D(x): 0.7386, D(G(z)): 0.2892 Epoch: [7/20], Batch Num: [135/600] Discriminator Loss: 1.1239, Generator Loss: 1.6949 D(x): 0.6733, D(G(z)): 0.2923 Epoch: [7/20], Batch Num: [136/600] Discriminator Loss: 0.9138, Generator Loss: 1.9333 D(x): 0.7406, D(G(z)): 0.3290 Epoch: [7/20], Batch Num: [137/600] Discriminator Loss: 0.8943, Generator Loss: 1.7086 D(x): 0.7167, D(G(z)): 0.2809 Epoch: [7/20], Batch Num: [138/600] Discriminator Loss: 0.8059, Generator Loss: 1.7155 D(x): 0.7597, D(G(z)): 0.2762 Epoch: [7/20], Batch Num: [139/600] Discriminator Loss: 0.8084, Generator Loss: 1.7691 D(x): 0.7306, D(G(z)): 0.2837 Epoch: [7/20], Batch Num: [140/600] Discriminator Loss: 0.9497, Generator Loss: 1.7200 D(x): 0.6530, D(G(z)): 0.2553 Epoch: [7/20], Batch Num: [141/600] Discriminator Loss: 0.9182, Generator Loss: 1.5441 D(x): 0.7433, D(G(z)): 0.3353 Epoch: [7/20], Batch Num: [142/600] Discriminator Loss: 0.8591, Generator Loss: 1.4146 D(x): 0.7509, D(G(z)): 0.3036 Epoch: [7/20], Batch Num: [143/600] Discriminator Loss: 0.8548, Generator Loss: 1.6831 D(x): 0.7900, D(G(z)): 0.3531 Epoch: [7/20], Batch Num: [144/600] Discriminator Loss: 0.7349, Generator Loss: 1.9881 D(x): 0.7425, D(G(z)): 0.2600 Epoch: [7/20], Batch Num: [145/600] Discriminator Loss: 0.7616, Generator Loss: 1.9415 D(x): 0.7292, D(G(z)): 0.2531 Epoch: [7/20], Batch Num: [146/600] Discriminator Loss: 0.7423, Generator Loss: 2.0576 D(x): 0.7218, D(G(z)): 0.2275 Epoch: [7/20], Batch Num: [147/600] Discriminator Loss: 0.8267, Generator Loss: 1.8641 D(x): 0.7031, D(G(z)): 0.2464 Epoch: [7/20], Batch Num: [148/600] Discriminator Loss: 0.8444, Generator Loss: 1.5324 D(x): 0.7135, D(G(z)): 0.2732 Epoch: [7/20], Batch Num: [149/600] Discriminator Loss: 0.6795, Generator Loss: 1.6187 D(x): 0.8431, D(G(z)): 0.3108 Epoch: [7/20], Batch Num: [150/600] Discriminator Loss: 0.5466, Generator Loss: 2.0161 D(x): 0.8660, D(G(z)): 0.2680 Epoch: [7/20], Batch Num: [151/600] Discriminator Loss: 0.6416, Generator Loss: 2.3026 D(x): 0.8178, D(G(z)): 0.2525 Epoch: [7/20], Batch Num: [152/600] Discriminator Loss: 0.7226, Generator Loss: 2.3652 D(x): 0.7070, D(G(z)): 0.1869 Epoch: [7/20], Batch Num: [153/600] Discriminator Loss: 0.6598, Generator Loss: 2.1951 D(x): 0.7658, D(G(z)): 0.2233 Epoch: [7/20], Batch Num: [154/600] Discriminator Loss: 0.6622, Generator Loss: 2.0022 D(x): 0.7681, D(G(z)): 0.2067 Epoch: [7/20], Batch Num: [155/600] Discriminator Loss: 0.6669, Generator Loss: 1.7626 D(x): 0.8087, D(G(z)): 0.2464 Epoch: [7/20], Batch Num: [156/600] Discriminator Loss: 0.5529, Generator Loss: 2.0272 D(x): 0.8535, D(G(z)): 0.2362 Epoch: [7/20], Batch Num: [157/600] Discriminator Loss: 0.6731, Generator Loss: 2.6500 D(x): 0.8507, D(G(z)): 0.2953 Epoch: [7/20], Batch Num: [158/600] Discriminator Loss: 0.5578, Generator Loss: 2.6291 D(x): 0.8035, D(G(z)): 0.1766 Epoch: [7/20], Batch Num: [159/600] Discriminator Loss: 0.6712, Generator Loss: 2.5767 D(x): 0.7226, D(G(z)): 0.1289 Epoch: [7/20], Batch Num: [160/600] Discriminator Loss: 0.5411, Generator Loss: 2.1347 D(x): 0.7778, D(G(z)): 0.1668 Epoch: [7/20], Batch Num: [161/600] Discriminator Loss: 0.6663, Generator Loss: 1.7901 D(x): 0.7738, D(G(z)): 0.1963 Epoch: [7/20], Batch Num: [162/600] Discriminator Loss: 0.7095, Generator Loss: 2.0151 D(x): 0.9008, D(G(z)): 0.3524 Epoch: [7/20], Batch Num: [163/600] Discriminator Loss: 0.7560, Generator Loss: 2.6614 D(x): 0.8533, D(G(z)): 0.3037 Epoch: [7/20], Batch Num: [164/600] Discriminator Loss: 0.7402, Generator Loss: 2.7068 D(x): 0.7832, D(G(z)): 0.2171 Epoch: [7/20], Batch Num: [165/600] Discriminator Loss: 0.7716, Generator Loss: 3.0791 D(x): 0.6978, D(G(z)): 0.1519 Epoch: [7/20], Batch Num: [166/600] Discriminator Loss: 0.8525, Generator Loss: 2.4911 D(x): 0.6707, D(G(z)): 0.1491 Epoch: [7/20], Batch Num: [167/600] Discriminator Loss: 0.6992, Generator Loss: 1.8176 D(x): 0.8241, D(G(z)): 0.2568 Epoch: [7/20], Batch Num: [168/600] Discriminator Loss: 0.9076, Generator Loss: 1.6954 D(x): 0.7581, D(G(z)): 0.2781 Epoch: [7/20], Batch Num: [169/600] Discriminator Loss: 0.9166, Generator Loss: 2.1504 D(x): 0.8501, D(G(z)): 0.3972 Epoch: [7/20], Batch Num: [170/600] Discriminator Loss: 0.8047, Generator Loss: 2.3762 D(x): 0.8072, D(G(z)): 0.2874 Epoch: [7/20], Batch Num: [171/600] Discriminator Loss: 0.7783, Generator Loss: 2.5576 D(x): 0.7052, D(G(z)): 0.1725 Epoch: [7/20], Batch Num: [172/600] Discriminator Loss: 0.9686, Generator Loss: 2.2888 D(x): 0.6864, D(G(z)): 0.2240 Epoch: [7/20], Batch Num: [173/600] Discriminator Loss: 0.8871, Generator Loss: 1.7088 D(x): 0.6824, D(G(z)): 0.2128 Epoch: [7/20], Batch Num: [174/600] Discriminator Loss: 0.7010, Generator Loss: 1.6296 D(x): 0.7904, D(G(z)): 0.2649 Epoch: [7/20], Batch Num: [175/600] Discriminator Loss: 0.8050, Generator Loss: 1.7831 D(x): 0.8545, D(G(z)): 0.3512 Epoch: [7/20], Batch Num: [176/600] Discriminator Loss: 0.7956, Generator Loss: 1.9727 D(x): 0.8013, D(G(z)): 0.3136 Epoch: [7/20], Batch Num: [177/600] Discriminator Loss: 0.7031, Generator Loss: 2.3850 D(x): 0.7685, D(G(z)): 0.2333 Epoch: [7/20], Batch Num: [178/600] Discriminator Loss: 0.7510, Generator Loss: 2.3320 D(x): 0.7391, D(G(z)): 0.1842 Epoch: [7/20], Batch Num: [179/600] Discriminator Loss: 0.6965, Generator Loss: 2.2375 D(x): 0.7207, D(G(z)): 0.1785 Epoch: [7/20], Batch Num: [180/600] Discriminator Loss: 0.7852, Generator Loss: 1.7333 D(x): 0.7258, D(G(z)): 0.2178 Epoch: [7/20], Batch Num: [181/600] Discriminator Loss: 0.7422, Generator Loss: 1.7117 D(x): 0.7825, D(G(z)): 0.2546 Epoch: [7/20], Batch Num: [182/600] Discriminator Loss: 0.8113, Generator Loss: 1.8660 D(x): 0.8333, D(G(z)): 0.3414 Epoch: [7/20], Batch Num: [183/600] Discriminator Loss: 0.6460, Generator Loss: 2.0589 D(x): 0.8425, D(G(z)): 0.2684 Epoch: [7/20], Batch Num: [184/600] Discriminator Loss: 0.7710, Generator Loss: 2.1617 D(x): 0.7372, D(G(z)): 0.2271 Epoch: [7/20], Batch Num: [185/600] Discriminator Loss: 0.5888, Generator Loss: 2.4318 D(x): 0.8073, D(G(z)): 0.2025 Epoch: [7/20], Batch Num: [186/600] Discriminator Loss: 0.7695, Generator Loss: 2.2043 D(x): 0.7236, D(G(z)): 0.2065 Epoch: [7/20], Batch Num: [187/600] Discriminator Loss: 0.5802, Generator Loss: 2.3629 D(x): 0.8038, D(G(z)): 0.1996 Epoch: [7/20], Batch Num: [188/600] Discriminator Loss: 0.5051, Generator Loss: 2.3321 D(x): 0.8721, D(G(z)): 0.2435 Epoch: [7/20], Batch Num: [189/600] Discriminator Loss: 0.5748, Generator Loss: 2.6811 D(x): 0.8095, D(G(z)): 0.1898 Epoch: [7/20], Batch Num: [190/600] Discriminator Loss: 0.5472, Generator Loss: 2.5067 D(x): 0.8231, D(G(z)): 0.1785 Epoch: [7/20], Batch Num: [191/600] Discriminator Loss: 0.6636, Generator Loss: 2.6499 D(x): 0.7700, D(G(z)): 0.1857 Epoch: [7/20], Batch Num: [192/600] Discriminator Loss: 0.5415, Generator Loss: 2.3544 D(x): 0.8255, D(G(z)): 0.2023 Epoch: [7/20], Batch Num: [193/600] Discriminator Loss: 0.5134, Generator Loss: 2.3317 D(x): 0.8213, D(G(z)): 0.1749 Epoch: [7/20], Batch Num: [194/600] Discriminator Loss: 0.5490, Generator Loss: 2.2043 D(x): 0.8298, D(G(z)): 0.1893 Epoch: [7/20], Batch Num: [195/600] Discriminator Loss: 0.5845, Generator Loss: 2.2264 D(x): 0.8577, D(G(z)): 0.2478 Epoch: [7/20], Batch Num: [196/600] Discriminator Loss: 0.6054, Generator Loss: 2.2043 D(x): 0.8468, D(G(z)): 0.2547 Epoch: [7/20], Batch Num: [197/600] Discriminator Loss: 0.7298, Generator Loss: 2.4845 D(x): 0.7639, D(G(z)): 0.2207 Epoch: [7/20], Batch Num: [198/600] Discriminator Loss: 0.7688, Generator Loss: 2.6649 D(x): 0.7391, D(G(z)): 0.1987 Epoch: [7/20], Batch Num: [199/600] Discriminator Loss: 0.5823, Generator Loss: 2.4379 D(x): 0.8019, D(G(z)): 0.1580 Epoch: 7, Batch Num: [200/600]
Epoch: [7/20], Batch Num: [200/600] Discriminator Loss: 0.9648, Generator Loss: 2.5637 D(x): 0.6844, D(G(z)): 0.2033 Epoch: [7/20], Batch Num: [201/600] Discriminator Loss: 0.6821, Generator Loss: 2.2620 D(x): 0.8052, D(G(z)): 0.2291 Epoch: [7/20], Batch Num: [202/600] Discriminator Loss: 0.6862, Generator Loss: 2.2710 D(x): 0.8037, D(G(z)): 0.2521 Epoch: [7/20], Batch Num: [203/600] Discriminator Loss: 0.6636, Generator Loss: 2.1031 D(x): 0.7869, D(G(z)): 0.2202 Epoch: [7/20], Batch Num: [204/600] Discriminator Loss: 0.7231, Generator Loss: 2.2872 D(x): 0.7959, D(G(z)): 0.2639 Epoch: [7/20], Batch Num: [205/600] Discriminator Loss: 0.7352, Generator Loss: 2.1744 D(x): 0.7720, D(G(z)): 0.2219 Epoch: [7/20], Batch Num: [206/600] Discriminator Loss: 0.7274, Generator Loss: 2.2171 D(x): 0.7449, D(G(z)): 0.1720 Epoch: [7/20], Batch Num: [207/600] Discriminator Loss: 0.7010, Generator Loss: 2.0255 D(x): 0.7890, D(G(z)): 0.2209 Epoch: [7/20], Batch Num: [208/600] Discriminator Loss: 1.0072, Generator Loss: 2.1314 D(x): 0.7270, D(G(z)): 0.3040 Epoch: [7/20], Batch Num: [209/600] Discriminator Loss: 0.9054, Generator Loss: 2.0762 D(x): 0.7381, D(G(z)): 0.2755 Epoch: [7/20], Batch Num: [210/600] Discriminator Loss: 0.7643, Generator Loss: 2.0378 D(x): 0.7734, D(G(z)): 0.2130 Epoch: [7/20], Batch Num: [211/600] Discriminator Loss: 0.9459, Generator Loss: 2.0040 D(x): 0.7067, D(G(z)): 0.2747 Epoch: [7/20], Batch Num: [212/600] Discriminator Loss: 0.7244, Generator Loss: 1.7714 D(x): 0.7495, D(G(z)): 0.2291 Epoch: [7/20], Batch Num: [213/600] Discriminator Loss: 0.9300, Generator Loss: 2.0022 D(x): 0.7755, D(G(z)): 0.3265 Epoch: [7/20], Batch Num: [214/600] Discriminator Loss: 0.7814, Generator Loss: 2.1969 D(x): 0.7701, D(G(z)): 0.2516 Epoch: [7/20], Batch Num: [215/600] Discriminator Loss: 0.7110, Generator Loss: 2.1096 D(x): 0.7830, D(G(z)): 0.2336 Epoch: [7/20], Batch Num: [216/600] Discriminator Loss: 0.6265, Generator Loss: 1.9507 D(x): 0.7872, D(G(z)): 0.1877 Epoch: [7/20], Batch Num: [217/600] Discriminator Loss: 0.6004, Generator Loss: 1.9637 D(x): 0.8004, D(G(z)): 0.2198 Epoch: [7/20], Batch Num: [218/600] Discriminator Loss: 0.7194, Generator Loss: 1.6276 D(x): 0.7418, D(G(z)): 0.2112 Epoch: [7/20], Batch Num: [219/600] Discriminator Loss: 0.7153, Generator Loss: 1.6790 D(x): 0.8179, D(G(z)): 0.2786 Epoch: [7/20], Batch Num: [220/600] Discriminator Loss: 0.6473, Generator Loss: 1.8826 D(x): 0.8050, D(G(z)): 0.2568 Epoch: [7/20], Batch Num: [221/600] Discriminator Loss: 0.5803, Generator Loss: 2.0701 D(x): 0.8239, D(G(z)): 0.2366 Epoch: [7/20], Batch Num: [222/600] Discriminator Loss: 0.6274, Generator Loss: 1.8851 D(x): 0.7806, D(G(z)): 0.2105 Epoch: [7/20], Batch Num: [223/600] Discriminator Loss: 0.5492, Generator Loss: 2.0543 D(x): 0.8378, D(G(z)): 0.2273 Epoch: [7/20], Batch Num: [224/600] Discriminator Loss: 0.6240, Generator Loss: 2.3409 D(x): 0.8237, D(G(z)): 0.2359 Epoch: [7/20], Batch Num: [225/600] Discriminator Loss: 0.4771, Generator Loss: 1.9321 D(x): 0.8276, D(G(z)): 0.1785 Epoch: [7/20], Batch Num: [226/600] Discriminator Loss: 0.5570, Generator Loss: 2.2167 D(x): 0.8274, D(G(z)): 0.1884 Epoch: [7/20], Batch Num: [227/600] Discriminator Loss: 0.5482, Generator Loss: 2.2204 D(x): 0.8509, D(G(z)): 0.2285 Epoch: [7/20], Batch Num: [228/600] Discriminator Loss: 0.5845, Generator Loss: 1.9597 D(x): 0.8137, D(G(z)): 0.2026 Epoch: [7/20], Batch Num: [229/600] Discriminator Loss: 0.5805, Generator Loss: 2.0321 D(x): 0.8014, D(G(z)): 0.2138 Epoch: [7/20], Batch Num: [230/600] Discriminator Loss: 0.4791, Generator Loss: 2.2440 D(x): 0.8746, D(G(z)): 0.2264 Epoch: [7/20], Batch Num: [231/600] Discriminator Loss: 0.5491, Generator Loss: 2.2914 D(x): 0.8595, D(G(z)): 0.2384 Epoch: [7/20], Batch Num: [232/600] Discriminator Loss: 0.5044, Generator Loss: 2.3344 D(x): 0.8129, D(G(z)): 0.1588 Epoch: [7/20], Batch Num: [233/600] Discriminator Loss: 0.5285, Generator Loss: 2.5497 D(x): 0.8357, D(G(z)): 0.2054 Epoch: [7/20], Batch Num: [234/600] Discriminator Loss: 0.3965, Generator Loss: 2.4682 D(x): 0.8518, D(G(z)): 0.1503 Epoch: [7/20], Batch Num: [235/600] Discriminator Loss: 0.4726, Generator Loss: 2.1567 D(x): 0.8369, D(G(z)): 0.1629 Epoch: [7/20], Batch Num: [236/600] Discriminator Loss: 0.5523, Generator Loss: 1.9930 D(x): 0.8669, D(G(z)): 0.2429 Epoch: [7/20], Batch Num: [237/600] Discriminator Loss: 0.4970, Generator Loss: 2.2842 D(x): 0.8782, D(G(z)): 0.2273 Epoch: [7/20], Batch Num: [238/600] Discriminator Loss: 0.5414, Generator Loss: 2.5158 D(x): 0.8464, D(G(z)): 0.1921 Epoch: [7/20], Batch Num: [239/600] Discriminator Loss: 0.5949, Generator Loss: 2.6975 D(x): 0.8099, D(G(z)): 0.1771 Epoch: [7/20], Batch Num: [240/600] Discriminator Loss: 0.6572, Generator Loss: 2.3368 D(x): 0.7624, D(G(z)): 0.1370 Epoch: [7/20], Batch Num: [241/600] Discriminator Loss: 0.5657, Generator Loss: 2.1182 D(x): 0.7743, D(G(z)): 0.1527 Epoch: [7/20], Batch Num: [242/600] Discriminator Loss: 0.6623, Generator Loss: 1.7892 D(x): 0.8353, D(G(z)): 0.2779 Epoch: [7/20], Batch Num: [243/600] Discriminator Loss: 0.5859, Generator Loss: 2.2042 D(x): 0.8935, D(G(z)): 0.2977 Epoch: [7/20], Batch Num: [244/600] Discriminator Loss: 0.5227, Generator Loss: 2.5108 D(x): 0.8329, D(G(z)): 0.1820 Epoch: [7/20], Batch Num: [245/600] Discriminator Loss: 0.4451, Generator Loss: 2.8354 D(x): 0.8436, D(G(z)): 0.1547 Epoch: [7/20], Batch Num: [246/600] Discriminator Loss: 0.7628, Generator Loss: 2.5557 D(x): 0.7080, D(G(z)): 0.1263 Epoch: [7/20], Batch Num: [247/600] Discriminator Loss: 0.6775, Generator Loss: 2.3553 D(x): 0.8030, D(G(z)): 0.1948 Epoch: [7/20], Batch Num: [248/600] Discriminator Loss: 0.7184, Generator Loss: 1.8916 D(x): 0.7810, D(G(z)): 0.2400 Epoch: [7/20], Batch Num: [249/600] Discriminator Loss: 0.6640, Generator Loss: 1.9990 D(x): 0.8434, D(G(z)): 0.2795 Epoch: [7/20], Batch Num: [250/600] Discriminator Loss: 0.7800, Generator Loss: 2.4533 D(x): 0.7891, D(G(z)): 0.2812 Epoch: [7/20], Batch Num: [251/600] Discriminator Loss: 0.6701, Generator Loss: 2.4669 D(x): 0.8054, D(G(z)): 0.1956 Epoch: [7/20], Batch Num: [252/600] Discriminator Loss: 0.6151, Generator Loss: 2.5543 D(x): 0.7943, D(G(z)): 0.1846 Epoch: [7/20], Batch Num: [253/600] Discriminator Loss: 0.7653, Generator Loss: 2.2197 D(x): 0.7332, D(G(z)): 0.1615 Epoch: [7/20], Batch Num: [254/600] Discriminator Loss: 0.6891, Generator Loss: 1.8759 D(x): 0.8151, D(G(z)): 0.2468 Epoch: [7/20], Batch Num: [255/600] Discriminator Loss: 0.7141, Generator Loss: 2.0226 D(x): 0.8481, D(G(z)): 0.2669 Epoch: [7/20], Batch Num: [256/600] Discriminator Loss: 0.6838, Generator Loss: 2.2445 D(x): 0.8121, D(G(z)): 0.2220 Epoch: [7/20], Batch Num: [257/600] Discriminator Loss: 0.5014, Generator Loss: 2.3129 D(x): 0.8340, D(G(z)): 0.1749 Epoch: [7/20], Batch Num: [258/600] Discriminator Loss: 0.7001, Generator Loss: 2.3493 D(x): 0.7690, D(G(z)): 0.1770 Epoch: [7/20], Batch Num: [259/600] Discriminator Loss: 0.6126, Generator Loss: 2.2859 D(x): 0.7895, D(G(z)): 0.1931 Epoch: [7/20], Batch Num: [260/600] Discriminator Loss: 0.6798, Generator Loss: 2.2915 D(x): 0.7957, D(G(z)): 0.2399 Epoch: [7/20], Batch Num: [261/600] Discriminator Loss: 0.7549, Generator Loss: 2.2566 D(x): 0.7686, D(G(z)): 0.2246 Epoch: [7/20], Batch Num: [262/600] Discriminator Loss: 0.6641, Generator Loss: 2.1787 D(x): 0.8035, D(G(z)): 0.2111 Epoch: [7/20], Batch Num: [263/600] Discriminator Loss: 0.6250, Generator Loss: 2.3684 D(x): 0.8150, D(G(z)): 0.1936 Epoch: [7/20], Batch Num: [264/600] Discriminator Loss: 0.4859, Generator Loss: 2.5206 D(x): 0.8220, D(G(z)): 0.1407 Epoch: [7/20], Batch Num: [265/600] Discriminator Loss: 0.6445, Generator Loss: 2.4096 D(x): 0.7950, D(G(z)): 0.2019 Epoch: [7/20], Batch Num: [266/600] Discriminator Loss: 0.6269, Generator Loss: 2.5499 D(x): 0.8283, D(G(z)): 0.2197 Epoch: [7/20], Batch Num: [267/600] Discriminator Loss: 0.6315, Generator Loss: 2.4024 D(x): 0.7628, D(G(z)): 0.1514 Epoch: [7/20], Batch Num: [268/600] Discriminator Loss: 0.7387, Generator Loss: 2.4867 D(x): 0.7984, D(G(z)): 0.2179 Epoch: [7/20], Batch Num: [269/600] Discriminator Loss: 0.7373, Generator Loss: 2.7052 D(x): 0.8322, D(G(z)): 0.2476 Epoch: [7/20], Batch Num: [270/600] Discriminator Loss: 0.4610, Generator Loss: 2.7501 D(x): 0.8401, D(G(z)): 0.1525 Epoch: [7/20], Batch Num: [271/600] Discriminator Loss: 0.7046, Generator Loss: 2.5503 D(x): 0.7268, D(G(z)): 0.1307 Epoch: [7/20], Batch Num: [272/600] Discriminator Loss: 0.6632, Generator Loss: 2.3337 D(x): 0.8055, D(G(z)): 0.1933 Epoch: [7/20], Batch Num: [273/600] Discriminator Loss: 0.7375, Generator Loss: 2.1254 D(x): 0.7838, D(G(z)): 0.1993 Epoch: [7/20], Batch Num: [274/600] Discriminator Loss: 0.7336, Generator Loss: 2.0100 D(x): 0.8222, D(G(z)): 0.2534 Epoch: [7/20], Batch Num: [275/600] Discriminator Loss: 0.6971, Generator Loss: 2.5422 D(x): 0.8076, D(G(z)): 0.2474 Epoch: [7/20], Batch Num: [276/600] Discriminator Loss: 0.5723, Generator Loss: 2.7185 D(x): 0.8117, D(G(z)): 0.1880 Epoch: [7/20], Batch Num: [277/600] Discriminator Loss: 0.7756, Generator Loss: 2.8078 D(x): 0.7506, D(G(z)): 0.1546 Epoch: [7/20], Batch Num: [278/600] Discriminator Loss: 0.7995, Generator Loss: 2.6544 D(x): 0.7321, D(G(z)): 0.1448 Epoch: [7/20], Batch Num: [279/600] Discriminator Loss: 0.6800, Generator Loss: 2.1237 D(x): 0.7889, D(G(z)): 0.1823 Epoch: [7/20], Batch Num: [280/600] Discriminator Loss: 0.6836, Generator Loss: 2.2266 D(x): 0.8083, D(G(z)): 0.2316 Epoch: [7/20], Batch Num: [281/600] Discriminator Loss: 0.8850, Generator Loss: 2.0715 D(x): 0.7537, D(G(z)): 0.2346 Epoch: [7/20], Batch Num: [282/600] Discriminator Loss: 0.7560, Generator Loss: 2.3786 D(x): 0.7867, D(G(z)): 0.2323 Epoch: [7/20], Batch Num: [283/600] Discriminator Loss: 0.7475, Generator Loss: 2.2803 D(x): 0.7612, D(G(z)): 0.1973 Epoch: [7/20], Batch Num: [284/600] Discriminator Loss: 0.6944, Generator Loss: 2.1941 D(x): 0.7817, D(G(z)): 0.2027 Epoch: [7/20], Batch Num: [285/600] Discriminator Loss: 0.6112, Generator Loss: 2.2253 D(x): 0.8280, D(G(z)): 0.1710 Epoch: [7/20], Batch Num: [286/600] Discriminator Loss: 0.6048, Generator Loss: 2.2812 D(x): 0.8215, D(G(z)): 0.1881 Epoch: [7/20], Batch Num: [287/600] Discriminator Loss: 0.5756, Generator Loss: 2.3411 D(x): 0.8401, D(G(z)): 0.1876 Epoch: [7/20], Batch Num: [288/600] Discriminator Loss: 0.5595, Generator Loss: 2.5659 D(x): 0.8230, D(G(z)): 0.1753 Epoch: [7/20], Batch Num: [289/600] Discriminator Loss: 0.4421, Generator Loss: 2.6335 D(x): 0.8808, D(G(z)): 0.1937 Epoch: [7/20], Batch Num: [290/600] Discriminator Loss: 0.4342, Generator Loss: 2.4926 D(x): 0.8750, D(G(z)): 0.1694 Epoch: [7/20], Batch Num: [291/600] Discriminator Loss: 0.4795, Generator Loss: 2.7568 D(x): 0.8424, D(G(z)): 0.1569 Epoch: [7/20], Batch Num: [292/600] Discriminator Loss: 0.7111, Generator Loss: 2.6027 D(x): 0.7690, D(G(z)): 0.1880 Epoch: [7/20], Batch Num: [293/600] Discriminator Loss: 0.4942, Generator Loss: 2.4448 D(x): 0.8495, D(G(z)): 0.1818 Epoch: [7/20], Batch Num: [294/600] Discriminator Loss: 0.4401, Generator Loss: 2.6980 D(x): 0.8899, D(G(z)): 0.2049 Epoch: [7/20], Batch Num: [295/600] Discriminator Loss: 0.4387, Generator Loss: 2.6226 D(x): 0.8460, D(G(z)): 0.1480 Epoch: [7/20], Batch Num: [296/600] Discriminator Loss: 0.4367, Generator Loss: 2.5396 D(x): 0.8380, D(G(z)): 0.1452 Epoch: [7/20], Batch Num: [297/600] Discriminator Loss: 0.4396, Generator Loss: 2.1336 D(x): 0.8492, D(G(z)): 0.1535 Epoch: [7/20], Batch Num: [298/600] Discriminator Loss: 0.5541, Generator Loss: 2.3844 D(x): 0.8838, D(G(z)): 0.2302 Epoch: [7/20], Batch Num: [299/600] Discriminator Loss: 0.4515, Generator Loss: 2.5554 D(x): 0.8821, D(G(z)): 0.1997 Epoch: 7, Batch Num: [300/600]
Epoch: [7/20], Batch Num: [300/600] Discriminator Loss: 0.5816, Generator Loss: 2.7158 D(x): 0.8025, D(G(z)): 0.1614 Epoch: [7/20], Batch Num: [301/600] Discriminator Loss: 0.5123, Generator Loss: 2.6850 D(x): 0.8304, D(G(z)): 0.1462 Epoch: [7/20], Batch Num: [302/600] Discriminator Loss: 0.5319, Generator Loss: 2.5198 D(x): 0.8144, D(G(z)): 0.1663 Epoch: [7/20], Batch Num: [303/600] Discriminator Loss: 0.4805, Generator Loss: 2.4427 D(x): 0.8477, D(G(z)): 0.1792 Epoch: [7/20], Batch Num: [304/600] Discriminator Loss: 0.5766, Generator Loss: 2.6160 D(x): 0.8738, D(G(z)): 0.2235 Epoch: [7/20], Batch Num: [305/600] Discriminator Loss: 0.6461, Generator Loss: 2.4194 D(x): 0.8429, D(G(z)): 0.2152 Epoch: [7/20], Batch Num: [306/600] Discriminator Loss: 0.6348, Generator Loss: 2.5866 D(x): 0.8085, D(G(z)): 0.1999 Epoch: [7/20], Batch Num: [307/600] Discriminator Loss: 0.5100, Generator Loss: 2.5739 D(x): 0.8360, D(G(z)): 0.1614 Epoch: [7/20], Batch Num: [308/600] Discriminator Loss: 0.7467, Generator Loss: 2.3390 D(x): 0.7351, D(G(z)): 0.1642 Epoch: [7/20], Batch Num: [309/600] Discriminator Loss: 0.9086, Generator Loss: 1.9417 D(x): 0.7496, D(G(z)): 0.2529 Epoch: [7/20], Batch Num: [310/600] Discriminator Loss: 0.9071, Generator Loss: 1.8090 D(x): 0.8057, D(G(z)): 0.3389 Epoch: [7/20], Batch Num: [311/600] Discriminator Loss: 1.0170, Generator Loss: 2.2635 D(x): 0.7948, D(G(z)): 0.3813 Epoch: [7/20], Batch Num: [312/600] Discriminator Loss: 0.8805, Generator Loss: 2.3752 D(x): 0.7427, D(G(z)): 0.2736 Epoch: [7/20], Batch Num: [313/600] Discriminator Loss: 0.8270, Generator Loss: 2.4329 D(x): 0.6992, D(G(z)): 0.1724 Epoch: [7/20], Batch Num: [314/600] Discriminator Loss: 0.8535, Generator Loss: 2.3322 D(x): 0.6798, D(G(z)): 0.1700 Epoch: [7/20], Batch Num: [315/600] Discriminator Loss: 0.7336, Generator Loss: 2.2354 D(x): 0.7785, D(G(z)): 0.2327 Epoch: [7/20], Batch Num: [316/600] Discriminator Loss: 0.7975, Generator Loss: 2.1087 D(x): 0.7742, D(G(z)): 0.2833 Epoch: [7/20], Batch Num: [317/600] Discriminator Loss: 1.0021, Generator Loss: 1.8586 D(x): 0.7244, D(G(z)): 0.2897 Epoch: [7/20], Batch Num: [318/600] Discriminator Loss: 0.8396, Generator Loss: 1.8230 D(x): 0.8322, D(G(z)): 0.3278 Epoch: [7/20], Batch Num: [319/600] Discriminator Loss: 0.7530, Generator Loss: 2.0041 D(x): 0.7530, D(G(z)): 0.2314 Epoch: [7/20], Batch Num: [320/600] Discriminator Loss: 0.8451, Generator Loss: 2.0310 D(x): 0.7344, D(G(z)): 0.2719 Epoch: [7/20], Batch Num: [321/600] Discriminator Loss: 0.8562, Generator Loss: 1.9305 D(x): 0.7278, D(G(z)): 0.2515 Epoch: [7/20], Batch Num: [322/600] Discriminator Loss: 1.0599, Generator Loss: 1.6225 D(x): 0.6804, D(G(z)): 0.2894 Epoch: [7/20], Batch Num: [323/600] Discriminator Loss: 0.8468, Generator Loss: 1.7280 D(x): 0.7864, D(G(z)): 0.3285 Epoch: [7/20], Batch Num: [324/600] Discriminator Loss: 0.8076, Generator Loss: 1.6077 D(x): 0.7558, D(G(z)): 0.2876 Epoch: [7/20], Batch Num: [325/600] Discriminator Loss: 0.7959, Generator Loss: 1.7338 D(x): 0.7753, D(G(z)): 0.3039 Epoch: [7/20], Batch Num: [326/600] Discriminator Loss: 0.8494, Generator Loss: 1.7852 D(x): 0.7680, D(G(z)): 0.3184 Epoch: [7/20], Batch Num: [327/600] Discriminator Loss: 0.8443, Generator Loss: 1.8561 D(x): 0.6569, D(G(z)): 0.2151 Epoch: [7/20], Batch Num: [328/600] Discriminator Loss: 0.8810, Generator Loss: 1.7746 D(x): 0.7181, D(G(z)): 0.2994 Epoch: [7/20], Batch Num: [329/600] Discriminator Loss: 0.8247, Generator Loss: 1.6489 D(x): 0.7327, D(G(z)): 0.2669 Epoch: [7/20], Batch Num: [330/600] Discriminator Loss: 0.6719, Generator Loss: 1.7173 D(x): 0.7777, D(G(z)): 0.2643 Epoch: [7/20], Batch Num: [331/600] Discriminator Loss: 0.6364, Generator Loss: 1.6805 D(x): 0.7981, D(G(z)): 0.2635 Epoch: [7/20], Batch Num: [332/600] Discriminator Loss: 0.7233, Generator Loss: 1.7255 D(x): 0.7850, D(G(z)): 0.2898 Epoch: [7/20], Batch Num: [333/600] Discriminator Loss: 0.9262, Generator Loss: 1.6367 D(x): 0.7240, D(G(z)): 0.2971 Epoch: [7/20], Batch Num: [334/600] Discriminator Loss: 0.5918, Generator Loss: 1.6756 D(x): 0.7997, D(G(z)): 0.2444 Epoch: [7/20], Batch Num: [335/600] Discriminator Loss: 0.6950, Generator Loss: 1.7765 D(x): 0.7869, D(G(z)): 0.2650 Epoch: [7/20], Batch Num: [336/600] Discriminator Loss: 0.6690, Generator Loss: 1.8262 D(x): 0.8057, D(G(z)): 0.2732 Epoch: [7/20], Batch Num: [337/600] Discriminator Loss: 0.5800, Generator Loss: 1.9439 D(x): 0.8328, D(G(z)): 0.2366 Epoch: [7/20], Batch Num: [338/600] Discriminator Loss: 0.5664, Generator Loss: 2.0690 D(x): 0.8197, D(G(z)): 0.2322 Epoch: [7/20], Batch Num: [339/600] Discriminator Loss: 0.5141, Generator Loss: 2.2550 D(x): 0.8106, D(G(z)): 0.1905 Epoch: [7/20], Batch Num: [340/600] Discriminator Loss: 0.5810, Generator Loss: 2.0572 D(x): 0.7895, D(G(z)): 0.1831 Epoch: [7/20], Batch Num: [341/600] Discriminator Loss: 0.5606, Generator Loss: 2.0127 D(x): 0.8125, D(G(z)): 0.2115 Epoch: [7/20], Batch Num: [342/600] Discriminator Loss: 0.4665, Generator Loss: 2.1081 D(x): 0.8533, D(G(z)): 0.2036 Epoch: [7/20], Batch Num: [343/600] Discriminator Loss: 0.5373, Generator Loss: 2.0330 D(x): 0.8380, D(G(z)): 0.2157 Epoch: [7/20], Batch Num: [344/600] Discriminator Loss: 0.4850, Generator Loss: 2.0567 D(x): 0.8758, D(G(z)): 0.2238 Epoch: [7/20], Batch Num: [345/600] Discriminator Loss: 0.5560, Generator Loss: 2.2747 D(x): 0.8442, D(G(z)): 0.2402 Epoch: [7/20], Batch Num: [346/600] Discriminator Loss: 0.4600, Generator Loss: 2.2987 D(x): 0.8469, D(G(z)): 0.1833 Epoch: [7/20], Batch Num: [347/600] Discriminator Loss: 0.5017, Generator Loss: 2.2397 D(x): 0.8109, D(G(z)): 0.1684 Epoch: [7/20], Batch Num: [348/600] Discriminator Loss: 0.4650, Generator Loss: 2.4681 D(x): 0.8677, D(G(z)): 0.1984 Epoch: [7/20], Batch Num: [349/600] Discriminator Loss: 0.4049, Generator Loss: 2.2627 D(x): 0.8595, D(G(z)): 0.1575 Epoch: [7/20], Batch Num: [350/600] Discriminator Loss: 0.3943, Generator Loss: 2.4483 D(x): 0.9026, D(G(z)): 0.1823 Epoch: [7/20], Batch Num: [351/600] Discriminator Loss: 0.6035, Generator Loss: 2.3595 D(x): 0.7870, D(G(z)): 0.1661 Epoch: [7/20], Batch Num: [352/600] Discriminator Loss: 0.6126, Generator Loss: 2.4030 D(x): 0.8140, D(G(z)): 0.1908 Epoch: [7/20], Batch Num: [353/600] Discriminator Loss: 0.5410, Generator Loss: 1.9757 D(x): 0.8612, D(G(z)): 0.1927 Epoch: [7/20], Batch Num: [354/600] Discriminator Loss: 0.7160, Generator Loss: 2.2904 D(x): 0.8302, D(G(z)): 0.2747 Epoch: [7/20], Batch Num: [355/600] Discriminator Loss: 0.5843, Generator Loss: 2.5192 D(x): 0.8767, D(G(z)): 0.2288 Epoch: [7/20], Batch Num: [356/600] Discriminator Loss: 0.6014, Generator Loss: 2.6038 D(x): 0.7953, D(G(z)): 0.1701 Epoch: [7/20], Batch Num: [357/600] Discriminator Loss: 0.5041, Generator Loss: 2.4959 D(x): 0.8267, D(G(z)): 0.1602 Epoch: [7/20], Batch Num: [358/600] Discriminator Loss: 0.6225, Generator Loss: 2.2316 D(x): 0.7755, D(G(z)): 0.1485 Epoch: [7/20], Batch Num: [359/600] Discriminator Loss: 0.7805, Generator Loss: 1.9998 D(x): 0.8074, D(G(z)): 0.2551 Epoch: [7/20], Batch Num: [360/600] Discriminator Loss: 0.6485, Generator Loss: 2.1030 D(x): 0.8377, D(G(z)): 0.2437 Epoch: [7/20], Batch Num: [361/600] Discriminator Loss: 0.4888, Generator Loss: 2.0664 D(x): 0.8761, D(G(z)): 0.2071 Epoch: [7/20], Batch Num: [362/600] Discriminator Loss: 0.6454, Generator Loss: 2.3514 D(x): 0.8086, D(G(z)): 0.2207 Epoch: [7/20], Batch Num: [363/600] Discriminator Loss: 0.6535, Generator Loss: 2.1166 D(x): 0.7698, D(G(z)): 0.1713 Epoch: [7/20], Batch Num: [364/600] Discriminator Loss: 0.6289, Generator Loss: 2.3110 D(x): 0.8302, D(G(z)): 0.2164 Epoch: [7/20], Batch Num: [365/600] Discriminator Loss: 0.6221, Generator Loss: 2.5984 D(x): 0.8043, D(G(z)): 0.2155 Epoch: [7/20], Batch Num: [366/600] Discriminator Loss: 0.7173, Generator Loss: 2.7574 D(x): 0.8161, D(G(z)): 0.2043 Epoch: [7/20], Batch Num: [367/600] Discriminator Loss: 0.6313, Generator Loss: 2.1880 D(x): 0.7759, D(G(z)): 0.1786 Epoch: [7/20], Batch Num: [368/600] Discriminator Loss: 0.6052, Generator Loss: 2.4935 D(x): 0.8294, D(G(z)): 0.2175 Epoch: [7/20], Batch Num: [369/600] Discriminator Loss: 0.8664, Generator Loss: 2.2564 D(x): 0.7274, D(G(z)): 0.1845 Epoch: [7/20], Batch Num: [370/600] Discriminator Loss: 0.6904, Generator Loss: 2.3360 D(x): 0.8175, D(G(z)): 0.2295 Epoch: [7/20], Batch Num: [371/600] Discriminator Loss: 0.7155, Generator Loss: 2.5652 D(x): 0.8272, D(G(z)): 0.2834 Epoch: [7/20], Batch Num: [372/600] Discriminator Loss: 0.6362, Generator Loss: 3.0497 D(x): 0.7963, D(G(z)): 0.1769 Epoch: [7/20], Batch Num: [373/600] Discriminator Loss: 0.7932, Generator Loss: 2.3330 D(x): 0.7036, D(G(z)): 0.1414 Epoch: [7/20], Batch Num: [374/600] Discriminator Loss: 0.6971, Generator Loss: 2.3489 D(x): 0.7808, D(G(z)): 0.1972 Epoch: [7/20], Batch Num: [375/600] Discriminator Loss: 0.6644, Generator Loss: 2.2692 D(x): 0.8145, D(G(z)): 0.2226 Epoch: [7/20], Batch Num: [376/600] Discriminator Loss: 0.5931, Generator Loss: 2.1111 D(x): 0.8350, D(G(z)): 0.2108 Epoch: [7/20], Batch Num: [377/600] Discriminator Loss: 0.5237, Generator Loss: 2.3243 D(x): 0.8274, D(G(z)): 0.1936 Epoch: [7/20], Batch Num: [378/600] Discriminator Loss: 0.5642, Generator Loss: 2.2448 D(x): 0.8500, D(G(z)): 0.2225 Epoch: [7/20], Batch Num: [379/600] Discriminator Loss: 0.6488, Generator Loss: 2.2449 D(x): 0.7779, D(G(z)): 0.1857 Epoch: [7/20], Batch Num: [380/600] Discriminator Loss: 0.5746, Generator Loss: 2.2610 D(x): 0.8196, D(G(z)): 0.1984 Epoch: [7/20], Batch Num: [381/600] Discriminator Loss: 0.6077, Generator Loss: 2.5671 D(x): 0.8277, D(G(z)): 0.1928 Epoch: [7/20], Batch Num: [382/600] Discriminator Loss: 0.5447, Generator Loss: 2.3314 D(x): 0.8206, D(G(z)): 0.1722 Epoch: [7/20], Batch Num: [383/600] Discriminator Loss: 0.6462, Generator Loss: 2.2709 D(x): 0.7976, D(G(z)): 0.1966 Epoch: [7/20], Batch Num: [384/600] Discriminator Loss: 0.5731, Generator Loss: 2.1860 D(x): 0.8259, D(G(z)): 0.2020 Epoch: [7/20], Batch Num: [385/600] Discriminator Loss: 0.5849, Generator Loss: 2.0190 D(x): 0.8310, D(G(z)): 0.2190 Epoch: [7/20], Batch Num: [386/600] Discriminator Loss: 0.5292, Generator Loss: 2.2153 D(x): 0.8521, D(G(z)): 0.2169 Epoch: [7/20], Batch Num: [387/600] Discriminator Loss: 0.6283, Generator Loss: 2.1404 D(x): 0.8083, D(G(z)): 0.1937 Epoch: [7/20], Batch Num: [388/600] Discriminator Loss: 0.6108, Generator Loss: 2.1456 D(x): 0.7772, D(G(z)): 0.1801 Epoch: [7/20], Batch Num: [389/600] Discriminator Loss: 0.6040, Generator Loss: 2.0118 D(x): 0.7984, D(G(z)): 0.1896 Epoch: [7/20], Batch Num: [390/600] Discriminator Loss: 0.6577, Generator Loss: 2.0831 D(x): 0.8332, D(G(z)): 0.2530 Epoch: [7/20], Batch Num: [391/600] Discriminator Loss: 0.5857, Generator Loss: 2.5263 D(x): 0.8443, D(G(z)): 0.2264 Epoch: [7/20], Batch Num: [392/600] Discriminator Loss: 0.7649, Generator Loss: 2.3399 D(x): 0.7540, D(G(z)): 0.1831 Epoch: [7/20], Batch Num: [393/600] Discriminator Loss: 0.6796, Generator Loss: 2.2620 D(x): 0.7841, D(G(z)): 0.1772 Epoch: [7/20], Batch Num: [394/600] Discriminator Loss: 0.7292, Generator Loss: 2.0520 D(x): 0.8137, D(G(z)): 0.2393 Epoch: [7/20], Batch Num: [395/600] Discriminator Loss: 0.7916, Generator Loss: 2.1385 D(x): 0.7661, D(G(z)): 0.2067 Epoch: [7/20], Batch Num: [396/600] Discriminator Loss: 0.8395, Generator Loss: 2.3106 D(x): 0.7949, D(G(z)): 0.2752 Epoch: [7/20], Batch Num: [397/600] Discriminator Loss: 0.7527, Generator Loss: 2.0785 D(x): 0.7457, D(G(z)): 0.1849 Epoch: [7/20], Batch Num: [398/600] Discriminator Loss: 0.7563, Generator Loss: 2.0461 D(x): 0.8021, D(G(z)): 0.2494 Epoch: [7/20], Batch Num: [399/600] Discriminator Loss: 0.6896, Generator Loss: 1.8945 D(x): 0.7920, D(G(z)): 0.2185 Epoch: 7, Batch Num: [400/600]
Epoch: [7/20], Batch Num: [400/600] Discriminator Loss: 0.7886, Generator Loss: 1.9931 D(x): 0.7748, D(G(z)): 0.2416 Epoch: [7/20], Batch Num: [401/600] Discriminator Loss: 0.7043, Generator Loss: 2.1719 D(x): 0.8019, D(G(z)): 0.2235 Epoch: [7/20], Batch Num: [402/600] Discriminator Loss: 0.6218, Generator Loss: 2.2296 D(x): 0.8054, D(G(z)): 0.1807 Epoch: [7/20], Batch Num: [403/600] Discriminator Loss: 0.7272, Generator Loss: 1.8603 D(x): 0.7432, D(G(z)): 0.1873 Epoch: [7/20], Batch Num: [404/600] Discriminator Loss: 0.6930, Generator Loss: 1.5755 D(x): 0.8017, D(G(z)): 0.2486 Epoch: [7/20], Batch Num: [405/600] Discriminator Loss: 0.6765, Generator Loss: 1.8750 D(x): 0.8502, D(G(z)): 0.2683 Epoch: [7/20], Batch Num: [406/600] Discriminator Loss: 0.7139, Generator Loss: 2.4891 D(x): 0.8555, D(G(z)): 0.2835 Epoch: [7/20], Batch Num: [407/600] Discriminator Loss: 0.6929, Generator Loss: 2.3805 D(x): 0.7580, D(G(z)): 0.1411 Epoch: [7/20], Batch Num: [408/600] Discriminator Loss: 0.5603, Generator Loss: 2.4716 D(x): 0.8001, D(G(z)): 0.1501 Epoch: [7/20], Batch Num: [409/600] Discriminator Loss: 0.7175, Generator Loss: 2.1858 D(x): 0.7621, D(G(z)): 0.1844 Epoch: [7/20], Batch Num: [410/600] Discriminator Loss: 0.7805, Generator Loss: 1.6510 D(x): 0.7480, D(G(z)): 0.1974 Epoch: [7/20], Batch Num: [411/600] Discriminator Loss: 0.6286, Generator Loss: 1.5644 D(x): 0.9094, D(G(z)): 0.2880 Epoch: [7/20], Batch Num: [412/600] Discriminator Loss: 0.6425, Generator Loss: 2.0264 D(x): 0.8726, D(G(z)): 0.2858 Epoch: [7/20], Batch Num: [413/600] Discriminator Loss: 0.5451, Generator Loss: 2.2203 D(x): 0.8324, D(G(z)): 0.1874 Epoch: [7/20], Batch Num: [414/600] Discriminator Loss: 0.4886, Generator Loss: 2.3377 D(x): 0.8380, D(G(z)): 0.1735 Epoch: [7/20], Batch Num: [415/600] Discriminator Loss: 0.4354, Generator Loss: 2.2428 D(x): 0.8432, D(G(z)): 0.1407 Epoch: [7/20], Batch Num: [416/600] Discriminator Loss: 0.3546, Generator Loss: 2.3276 D(x): 0.8788, D(G(z)): 0.1338 Epoch: [7/20], Batch Num: [417/600] Discriminator Loss: 0.4046, Generator Loss: 2.1440 D(x): 0.8475, D(G(z)): 0.1403 Epoch: [7/20], Batch Num: [418/600] Discriminator Loss: 0.3655, Generator Loss: 2.1351 D(x): 0.9083, D(G(z)): 0.1828 Epoch: [7/20], Batch Num: [419/600] Discriminator Loss: 0.3653, Generator Loss: 2.2314 D(x): 0.9123, D(G(z)): 0.1807 Epoch: [7/20], Batch Num: [420/600] Discriminator Loss: 0.3016, Generator Loss: 2.6641 D(x): 0.9472, D(G(z)): 0.1791 Epoch: [7/20], Batch Num: [421/600] Discriminator Loss: 0.2996, Generator Loss: 2.7777 D(x): 0.8990, D(G(z)): 0.1234 Epoch: [7/20], Batch Num: [422/600] Discriminator Loss: 0.3191, Generator Loss: 2.9256 D(x): 0.8609, D(G(z)): 0.0894 Epoch: [7/20], Batch Num: [423/600] Discriminator Loss: 0.4683, Generator Loss: 2.6722 D(x): 0.8252, D(G(z)): 0.1144 Epoch: [7/20], Batch Num: [424/600] Discriminator Loss: 0.3148, Generator Loss: 2.2752 D(x): 0.8949, D(G(z)): 0.1304 Epoch: [7/20], Batch Num: [425/600] Discriminator Loss: 0.3595, Generator Loss: 2.2257 D(x): 0.9200, D(G(z)): 0.1892 Epoch: [7/20], Batch Num: [426/600] Discriminator Loss: 0.4198, Generator Loss: 2.2209 D(x): 0.8729, D(G(z)): 0.1718 Epoch: [7/20], Batch Num: [427/600] Discriminator Loss: 0.3853, Generator Loss: 2.5193 D(x): 0.9360, D(G(z)): 0.2009 Epoch: [7/20], Batch Num: [428/600] Discriminator Loss: 0.4291, Generator Loss: 2.9758 D(x): 0.8748, D(G(z)): 0.1524 Epoch: [7/20], Batch Num: [429/600] Discriminator Loss: 0.4177, Generator Loss: 2.9028 D(x): 0.8601, D(G(z)): 0.1295 Epoch: [7/20], Batch Num: [430/600] Discriminator Loss: 0.3840, Generator Loss: 2.5146 D(x): 0.8637, D(G(z)): 0.0983 Epoch: [7/20], Batch Num: [431/600] Discriminator Loss: 0.4132, Generator Loss: 2.5315 D(x): 0.8856, D(G(z)): 0.1445 Epoch: [7/20], Batch Num: [432/600] Discriminator Loss: 0.3729, Generator Loss: 2.3520 D(x): 0.8855, D(G(z)): 0.1558 Epoch: [7/20], Batch Num: [433/600] Discriminator Loss: 0.5024, Generator Loss: 2.1852 D(x): 0.8779, D(G(z)): 0.1792 Epoch: [7/20], Batch Num: [434/600] Discriminator Loss: 0.5367, Generator Loss: 2.3703 D(x): 0.8653, D(G(z)): 0.2099 Epoch: [7/20], Batch Num: [435/600] Discriminator Loss: 0.5793, Generator Loss: 2.6946 D(x): 0.8595, D(G(z)): 0.1974 Epoch: [7/20], Batch Num: [436/600] Discriminator Loss: 0.5999, Generator Loss: 2.6660 D(x): 0.8431, D(G(z)): 0.1808 Epoch: [7/20], Batch Num: [437/600] Discriminator Loss: 0.8093, Generator Loss: 2.4597 D(x): 0.7585, D(G(z)): 0.1583 Epoch: [7/20], Batch Num: [438/600] Discriminator Loss: 0.8381, Generator Loss: 2.0250 D(x): 0.7627, D(G(z)): 0.1958 Epoch: [7/20], Batch Num: [439/600] Discriminator Loss: 0.6779, Generator Loss: 1.8887 D(x): 0.8274, D(G(z)): 0.2419 Epoch: [7/20], Batch Num: [440/600] Discriminator Loss: 0.8532, Generator Loss: 1.9193 D(x): 0.8032, D(G(z)): 0.2755 Epoch: [7/20], Batch Num: [441/600] Discriminator Loss: 0.6254, Generator Loss: 1.8590 D(x): 0.8221, D(G(z)): 0.2053 Epoch: [7/20], Batch Num: [442/600] Discriminator Loss: 0.6896, Generator Loss: 2.3379 D(x): 0.8179, D(G(z)): 0.2542 Epoch: [7/20], Batch Num: [443/600] Discriminator Loss: 0.7877, Generator Loss: 1.9741 D(x): 0.7141, D(G(z)): 0.1550 Epoch: [7/20], Batch Num: [444/600] Discriminator Loss: 0.8807, Generator Loss: 1.9729 D(x): 0.7761, D(G(z)): 0.2504 Epoch: [7/20], Batch Num: [445/600] Discriminator Loss: 0.8456, Generator Loss: 2.0755 D(x): 0.7642, D(G(z)): 0.2617 Epoch: [7/20], Batch Num: [446/600] Discriminator Loss: 0.6110, Generator Loss: 2.0373 D(x): 0.8359, D(G(z)): 0.2365 Epoch: [7/20], Batch Num: [447/600] Discriminator Loss: 0.8096, Generator Loss: 1.9385 D(x): 0.7437, D(G(z)): 0.2239 Epoch: [7/20], Batch Num: [448/600] Discriminator Loss: 0.9944, Generator Loss: 1.8799 D(x): 0.7028, D(G(z)): 0.1985 Epoch: [7/20], Batch Num: [449/600] Discriminator Loss: 0.7575, Generator Loss: 1.6704 D(x): 0.7621, D(G(z)): 0.2120 Epoch: [7/20], Batch Num: [450/600] Discriminator Loss: 0.7157, Generator Loss: 1.6526 D(x): 0.8167, D(G(z)): 0.2787 Epoch: [7/20], Batch Num: [451/600] Discriminator Loss: 0.7520, Generator Loss: 1.7141 D(x): 0.7773, D(G(z)): 0.2539 Epoch: [7/20], Batch Num: [452/600] Discriminator Loss: 0.7751, Generator Loss: 1.8202 D(x): 0.7817, D(G(z)): 0.2560 Epoch: [7/20], Batch Num: [453/600] Discriminator Loss: 0.7004, Generator Loss: 2.0794 D(x): 0.7982, D(G(z)): 0.2405 Epoch: [7/20], Batch Num: [454/600] Discriminator Loss: 0.5929, Generator Loss: 2.1787 D(x): 0.7840, D(G(z)): 0.1912 Epoch: [7/20], Batch Num: [455/600] Discriminator Loss: 0.6347, Generator Loss: 2.1166 D(x): 0.7717, D(G(z)): 0.1722 Epoch: [7/20], Batch Num: [456/600] Discriminator Loss: 0.5756, Generator Loss: 1.9484 D(x): 0.7936, D(G(z)): 0.1832 Epoch: [7/20], Batch Num: [457/600] Discriminator Loss: 0.6441, Generator Loss: 1.9970 D(x): 0.7873, D(G(z)): 0.1955 Epoch: [7/20], Batch Num: [458/600] Discriminator Loss: 0.5116, Generator Loss: 1.7681 D(x): 0.8690, D(G(z)): 0.2266 Epoch: [7/20], Batch Num: [459/600] Discriminator Loss: 0.5994, Generator Loss: 2.0631 D(x): 0.8491, D(G(z)): 0.2423 Epoch: [7/20], Batch Num: [460/600] Discriminator Loss: 0.4651, Generator Loss: 2.4515 D(x): 0.8547, D(G(z)): 0.1780 Epoch: [7/20], Batch Num: [461/600] Discriminator Loss: 0.4861, Generator Loss: 2.5712 D(x): 0.8205, D(G(z)): 0.1363 Epoch: [7/20], Batch Num: [462/600] Discriminator Loss: 0.5161, Generator Loss: 2.7362 D(x): 0.8301, D(G(z)): 0.1648 Epoch: [7/20], Batch Num: [463/600] Discriminator Loss: 0.4762, Generator Loss: 2.5524 D(x): 0.8271, D(G(z)): 0.1350 Epoch: [7/20], Batch Num: [464/600] Discriminator Loss: 0.4562, Generator Loss: 2.2881 D(x): 0.8449, D(G(z)): 0.1384 Epoch: [7/20], Batch Num: [465/600] Discriminator Loss: 0.4132, Generator Loss: 2.4702 D(x): 0.8797, D(G(z)): 0.1619 Epoch: [7/20], Batch Num: [466/600] Discriminator Loss: 0.5036, Generator Loss: 2.4681 D(x): 0.8455, D(G(z)): 0.1745 Epoch: [7/20], Batch Num: [467/600] Discriminator Loss: 0.2912, Generator Loss: 2.4777 D(x): 0.9141, D(G(z)): 0.1496 Epoch: [7/20], Batch Num: [468/600] Discriminator Loss: 0.4332, Generator Loss: 2.6801 D(x): 0.8894, D(G(z)): 0.1700 Epoch: [7/20], Batch Num: [469/600] Discriminator Loss: 0.4771, Generator Loss: 2.8236 D(x): 0.8388, D(G(z)): 0.1100 Epoch: [7/20], Batch Num: [470/600] Discriminator Loss: 0.5741, Generator Loss: 2.6378 D(x): 0.8043, D(G(z)): 0.1233 Epoch: [7/20], Batch Num: [471/600] Discriminator Loss: 0.3799, Generator Loss: 2.4216 D(x): 0.8749, D(G(z)): 0.1379 Epoch: [7/20], Batch Num: [472/600] Discriminator Loss: 0.5064, Generator Loss: 2.5436 D(x): 0.8859, D(G(z)): 0.1911 Epoch: [7/20], Batch Num: [473/600] Discriminator Loss: 0.5884, Generator Loss: 2.9143 D(x): 0.8514, D(G(z)): 0.2086 Epoch: [7/20], Batch Num: [474/600] Discriminator Loss: 0.4670, Generator Loss: 3.0555 D(x): 0.8569, D(G(z)): 0.1309 Epoch: [7/20], Batch Num: [475/600] Discriminator Loss: 0.4443, Generator Loss: 3.0161 D(x): 0.8355, D(G(z)): 0.0967 Epoch: [7/20], Batch Num: [476/600] Discriminator Loss: 0.7531, Generator Loss: 2.6180 D(x): 0.7572, D(G(z)): 0.1590 Epoch: [7/20], Batch Num: [477/600] Discriminator Loss: 0.6956, Generator Loss: 2.4342 D(x): 0.7894, D(G(z)): 0.1858 Epoch: [7/20], Batch Num: [478/600] Discriminator Loss: 0.5907, Generator Loss: 2.3659 D(x): 0.8588, D(G(z)): 0.2370 Epoch: [7/20], Batch Num: [479/600] Discriminator Loss: 0.6383, Generator Loss: 2.7212 D(x): 0.8622, D(G(z)): 0.2249 Epoch: [7/20], Batch Num: [480/600] Discriminator Loss: 0.6055, Generator Loss: 2.7042 D(x): 0.7969, D(G(z)): 0.1451 Epoch: [7/20], Batch Num: [481/600] Discriminator Loss: 0.7611, Generator Loss: 2.6542 D(x): 0.7497, D(G(z)): 0.1623 Epoch: [7/20], Batch Num: [482/600] Discriminator Loss: 0.4547, Generator Loss: 2.7635 D(x): 0.8722, D(G(z)): 0.1584 Epoch: [7/20], Batch Num: [483/600] Discriminator Loss: 0.5803, Generator Loss: 2.2766 D(x): 0.7803, D(G(z)): 0.1371 Epoch: [7/20], Batch Num: [484/600] Discriminator Loss: 0.6340, Generator Loss: 2.1344 D(x): 0.8422, D(G(z)): 0.2219 Epoch: [7/20], Batch Num: [485/600] Discriminator Loss: 0.5733, Generator Loss: 2.3075 D(x): 0.8669, D(G(z)): 0.2368 Epoch: [7/20], Batch Num: [486/600] Discriminator Loss: 0.6440, Generator Loss: 2.3441 D(x): 0.7974, D(G(z)): 0.1677 Epoch: [7/20], Batch Num: [487/600] Discriminator Loss: 0.6352, Generator Loss: 2.4574 D(x): 0.7972, D(G(z)): 0.1984 Epoch: [7/20], Batch Num: [488/600] Discriminator Loss: 0.6472, Generator Loss: 2.4181 D(x): 0.8056, D(G(z)): 0.1937 Epoch: [7/20], Batch Num: [489/600] Discriminator Loss: 0.5617, Generator Loss: 2.5790 D(x): 0.8258, D(G(z)): 0.2042 Epoch: [7/20], Batch Num: [490/600] Discriminator Loss: 0.8320, Generator Loss: 2.3365 D(x): 0.7458, D(G(z)): 0.2178 Epoch: [7/20], Batch Num: [491/600] Discriminator Loss: 0.7858, Generator Loss: 2.2193 D(x): 0.7746, D(G(z)): 0.2086 Epoch: [7/20], Batch Num: [492/600] Discriminator Loss: 0.8519, Generator Loss: 2.1179 D(x): 0.7739, D(G(z)): 0.2178 Epoch: [7/20], Batch Num: [493/600] Discriminator Loss: 0.5689, Generator Loss: 2.1396 D(x): 0.8171, D(G(z)): 0.1852 Epoch: [7/20], Batch Num: [494/600] Discriminator Loss: 0.5461, Generator Loss: 2.3555 D(x): 0.8446, D(G(z)): 0.1910 Epoch: [7/20], Batch Num: [495/600] Discriminator Loss: 0.5606, Generator Loss: 2.0496 D(x): 0.8055, D(G(z)): 0.1557 Epoch: [7/20], Batch Num: [496/600] Discriminator Loss: 0.7338, Generator Loss: 2.2166 D(x): 0.8119, D(G(z)): 0.2269 Epoch: [7/20], Batch Num: [497/600] Discriminator Loss: 0.4496, Generator Loss: 2.1902 D(x): 0.8778, D(G(z)): 0.2036 Epoch: [7/20], Batch Num: [498/600] Discriminator Loss: 0.7152, Generator Loss: 2.4814 D(x): 0.8084, D(G(z)): 0.2302 Epoch: [7/20], Batch Num: [499/600] Discriminator Loss: 0.6237, Generator Loss: 2.6394 D(x): 0.8006, D(G(z)): 0.1801 Epoch: 7, Batch Num: [500/600]
Epoch: [7/20], Batch Num: [500/600] Discriminator Loss: 0.5476, Generator Loss: 2.7486 D(x): 0.8230, D(G(z)): 0.1610 Epoch: [7/20], Batch Num: [501/600] Discriminator Loss: 0.4432, Generator Loss: 2.5046 D(x): 0.8339, D(G(z)): 0.1410 Epoch: [7/20], Batch Num: [502/600] Discriminator Loss: 0.4870, Generator Loss: 2.4072 D(x): 0.8170, D(G(z)): 0.1114 Epoch: [7/20], Batch Num: [503/600] Discriminator Loss: 0.5071, Generator Loss: 2.1933 D(x): 0.8458, D(G(z)): 0.1755 Epoch: [7/20], Batch Num: [504/600] Discriminator Loss: 0.4265, Generator Loss: 2.3726 D(x): 0.9037, D(G(z)): 0.1862 Epoch: [7/20], Batch Num: [505/600] Discriminator Loss: 0.5349, Generator Loss: 2.6410 D(x): 0.8463, D(G(z)): 0.1999 Epoch: [7/20], Batch Num: [506/600] Discriminator Loss: 0.4580, Generator Loss: 2.6615 D(x): 0.8572, D(G(z)): 0.1454 Epoch: [7/20], Batch Num: [507/600] Discriminator Loss: 0.3922, Generator Loss: 3.1388 D(x): 0.8995, D(G(z)): 0.1639 Epoch: [7/20], Batch Num: [508/600] Discriminator Loss: 0.2804, Generator Loss: 3.2507 D(x): 0.9472, D(G(z)): 0.1218 Epoch: [7/20], Batch Num: [509/600] Discriminator Loss: 0.3611, Generator Loss: 3.3413 D(x): 0.8592, D(G(z)): 0.0876 Epoch: [7/20], Batch Num: [510/600] Discriminator Loss: 0.4443, Generator Loss: 3.4087 D(x): 0.8232, D(G(z)): 0.0691 Epoch: [7/20], Batch Num: [511/600] Discriminator Loss: 0.3065, Generator Loss: 2.9668 D(x): 0.8847, D(G(z)): 0.0902 Epoch: [7/20], Batch Num: [512/600] Discriminator Loss: 0.3254, Generator Loss: 2.8213 D(x): 0.9129, D(G(z)): 0.1300 Epoch: [7/20], Batch Num: [513/600] Discriminator Loss: 0.5228, Generator Loss: 2.6915 D(x): 0.8566, D(G(z)): 0.1855 Epoch: [7/20], Batch Num: [514/600] Discriminator Loss: 0.2969, Generator Loss: 2.8122 D(x): 0.9420, D(G(z)): 0.1778 Epoch: [7/20], Batch Num: [515/600] Discriminator Loss: 0.4457, Generator Loss: 3.3950 D(x): 0.8629, D(G(z)): 0.1396 Epoch: [7/20], Batch Num: [516/600] Discriminator Loss: 0.3967, Generator Loss: 3.3630 D(x): 0.8598, D(G(z)): 0.1067 Epoch: [7/20], Batch Num: [517/600] Discriminator Loss: 0.3554, Generator Loss: 3.0969 D(x): 0.8699, D(G(z)): 0.0836 Epoch: [7/20], Batch Num: [518/600] Discriminator Loss: 0.3838, Generator Loss: 3.0088 D(x): 0.8474, D(G(z)): 0.0991 Epoch: [7/20], Batch Num: [519/600] Discriminator Loss: 0.2944, Generator Loss: 3.0156 D(x): 0.9308, D(G(z)): 0.1319 Epoch: [7/20], Batch Num: [520/600] Discriminator Loss: 0.3745, Generator Loss: 3.0132 D(x): 0.9233, D(G(z)): 0.1620 Epoch: [7/20], Batch Num: [521/600] Discriminator Loss: 0.3679, Generator Loss: 3.2131 D(x): 0.8912, D(G(z)): 0.1355 Epoch: [7/20], Batch Num: [522/600] Discriminator Loss: 0.4966, Generator Loss: 3.1301 D(x): 0.8484, D(G(z)): 0.1383 Epoch: [7/20], Batch Num: [523/600] Discriminator Loss: 0.3826, Generator Loss: 3.1863 D(x): 0.8897, D(G(z)): 0.1151 Epoch: [7/20], Batch Num: [524/600] Discriminator Loss: 0.3660, Generator Loss: 3.1861 D(x): 0.8785, D(G(z)): 0.1134 Epoch: [7/20], Batch Num: [525/600] Discriminator Loss: 0.5720, Generator Loss: 2.9439 D(x): 0.8320, D(G(z)): 0.1523 Epoch: [7/20], Batch Num: [526/600] Discriminator Loss: 0.4382, Generator Loss: 2.5577 D(x): 0.8717, D(G(z)): 0.1435 Epoch: [7/20], Batch Num: [527/600] Discriminator Loss: 0.6309, Generator Loss: 2.5628 D(x): 0.8472, D(G(z)): 0.1919 Epoch: [7/20], Batch Num: [528/600] Discriminator Loss: 0.5529, Generator Loss: 2.5721 D(x): 0.8334, D(G(z)): 0.1459 Epoch: [7/20], Batch Num: [529/600] Discriminator Loss: 0.5317, Generator Loss: 2.8420 D(x): 0.8513, D(G(z)): 0.1834 Epoch: [7/20], Batch Num: [530/600] Discriminator Loss: 0.6304, Generator Loss: 2.5157 D(x): 0.8141, D(G(z)): 0.1493 Epoch: [7/20], Batch Num: [531/600] Discriminator Loss: 0.5821, Generator Loss: 2.3124 D(x): 0.8465, D(G(z)): 0.1651 Epoch: [7/20], Batch Num: [532/600] Discriminator Loss: 0.6640, Generator Loss: 2.0543 D(x): 0.8258, D(G(z)): 0.2039 Epoch: [7/20], Batch Num: [533/600] Discriminator Loss: 0.6549, Generator Loss: 2.2423 D(x): 0.8044, D(G(z)): 0.1970 Epoch: [7/20], Batch Num: [534/600] Discriminator Loss: 0.6337, Generator Loss: 2.2161 D(x): 0.8217, D(G(z)): 0.2195 Epoch: [7/20], Batch Num: [535/600] Discriminator Loss: 0.6863, Generator Loss: 2.1478 D(x): 0.7934, D(G(z)): 0.2130 Epoch: [7/20], Batch Num: [536/600] Discriminator Loss: 0.5745, Generator Loss: 2.2010 D(x): 0.8495, D(G(z)): 0.2112 Epoch: [7/20], Batch Num: [537/600] Discriminator Loss: 0.7689, Generator Loss: 2.0139 D(x): 0.7969, D(G(z)): 0.2278 Epoch: [7/20], Batch Num: [538/600] Discriminator Loss: 0.8715, Generator Loss: 1.9594 D(x): 0.7529, D(G(z)): 0.2114 Epoch: [7/20], Batch Num: [539/600] Discriminator Loss: 0.7511, Generator Loss: 2.0639 D(x): 0.8428, D(G(z)): 0.2883 Epoch: [7/20], Batch Num: [540/600] Discriminator Loss: 0.6046, Generator Loss: 2.2626 D(x): 0.8127, D(G(z)): 0.1826 Epoch: [7/20], Batch Num: [541/600] Discriminator Loss: 0.8315, Generator Loss: 1.8581 D(x): 0.7253, D(G(z)): 0.2007 Epoch: [7/20], Batch Num: [542/600] Discriminator Loss: 0.8915, Generator Loss: 1.8152 D(x): 0.7625, D(G(z)): 0.2529 Epoch: [7/20], Batch Num: [543/600] Discriminator Loss: 0.9273, Generator Loss: 1.7360 D(x): 0.7132, D(G(z)): 0.2733 Epoch: [7/20], Batch Num: [544/600] Discriminator Loss: 0.6762, Generator Loss: 2.0334 D(x): 0.8367, D(G(z)): 0.2583 Epoch: [7/20], Batch Num: [545/600] Discriminator Loss: 0.7254, Generator Loss: 2.0084 D(x): 0.7682, D(G(z)): 0.2053 Epoch: [7/20], Batch Num: [546/600] Discriminator Loss: 0.7037, Generator Loss: 2.0380 D(x): 0.8135, D(G(z)): 0.2214 Epoch: [7/20], Batch Num: [547/600] Discriminator Loss: 0.5634, Generator Loss: 2.1683 D(x): 0.8241, D(G(z)): 0.1859 Epoch: [7/20], Batch Num: [548/600] Discriminator Loss: 0.5445, Generator Loss: 1.9816 D(x): 0.8359, D(G(z)): 0.1933 Epoch: [7/20], Batch Num: [549/600] Discriminator Loss: 0.6563, Generator Loss: 2.1510 D(x): 0.7828, D(G(z)): 0.1873 Epoch: [7/20], Batch Num: [550/600] Discriminator Loss: 0.5334, Generator Loss: 2.0415 D(x): 0.8387, D(G(z)): 0.2021 Epoch: [7/20], Batch Num: [551/600] Discriminator Loss: 0.4232, Generator Loss: 2.0190 D(x): 0.8727, D(G(z)): 0.1682 Epoch: [7/20], Batch Num: [552/600] Discriminator Loss: 0.3903, Generator Loss: 2.3735 D(x): 0.9234, D(G(z)): 0.2110 Epoch: [7/20], Batch Num: [553/600] Discriminator Loss: 0.3910, Generator Loss: 2.6281 D(x): 0.8740, D(G(z)): 0.1603 Epoch: [7/20], Batch Num: [554/600] Discriminator Loss: 0.3718, Generator Loss: 2.9273 D(x): 0.8776, D(G(z)): 0.1478 Epoch: [7/20], Batch Num: [555/600] Discriminator Loss: 0.4025, Generator Loss: 2.9820 D(x): 0.8346, D(G(z)): 0.1105 Epoch: [7/20], Batch Num: [556/600] Discriminator Loss: 0.3293, Generator Loss: 3.0065 D(x): 0.8707, D(G(z)): 0.1048 Epoch: [7/20], Batch Num: [557/600] Discriminator Loss: 0.4091, Generator Loss: 2.6951 D(x): 0.8560, D(G(z)): 0.1065 Epoch: [7/20], Batch Num: [558/600] Discriminator Loss: 0.3228, Generator Loss: 2.5287 D(x): 0.8981, D(G(z)): 0.1344 Epoch: [7/20], Batch Num: [559/600] Discriminator Loss: 0.3467, Generator Loss: 2.5878 D(x): 0.9341, D(G(z)): 0.1873 Epoch: [7/20], Batch Num: [560/600] Discriminator Loss: 0.4067, Generator Loss: 3.0124 D(x): 0.8965, D(G(z)): 0.1672 Epoch: [7/20], Batch Num: [561/600] Discriminator Loss: 0.3583, Generator Loss: 3.1687 D(x): 0.8806, D(G(z)): 0.1076 Epoch: [7/20], Batch Num: [562/600] Discriminator Loss: 0.4258, Generator Loss: 3.2354 D(x): 0.8656, D(G(z)): 0.1219 Epoch: [7/20], Batch Num: [563/600] Discriminator Loss: 0.3126, Generator Loss: 3.3962 D(x): 0.8969, D(G(z)): 0.1056 Epoch: [7/20], Batch Num: [564/600] Discriminator Loss: 0.3523, Generator Loss: 3.1445 D(x): 0.8854, D(G(z)): 0.1023 Epoch: [7/20], Batch Num: [565/600] Discriminator Loss: 0.4070, Generator Loss: 2.7812 D(x): 0.8712, D(G(z)): 0.1109 Epoch: [7/20], Batch Num: [566/600] Discriminator Loss: 0.4754, Generator Loss: 2.4555 D(x): 0.8477, D(G(z)): 0.1432 Epoch: [7/20], Batch Num: [567/600] Discriminator Loss: 0.3674, Generator Loss: 2.4627 D(x): 0.9050, D(G(z)): 0.1580 Epoch: [7/20], Batch Num: [568/600] Discriminator Loss: 0.5290, Generator Loss: 2.5282 D(x): 0.9051, D(G(z)): 0.2080 Epoch: [7/20], Batch Num: [569/600] Discriminator Loss: 0.4311, Generator Loss: 3.0045 D(x): 0.8742, D(G(z)): 0.1609 Epoch: [7/20], Batch Num: [570/600] Discriminator Loss: 0.6172, Generator Loss: 3.1011 D(x): 0.8011, D(G(z)): 0.1325 Epoch: [7/20], Batch Num: [571/600] Discriminator Loss: 0.5631, Generator Loss: 2.8265 D(x): 0.8341, D(G(z)): 0.1520 Epoch: [7/20], Batch Num: [572/600] Discriminator Loss: 0.9635, Generator Loss: 2.7848 D(x): 0.7271, D(G(z)): 0.1673 Epoch: [7/20], Batch Num: [573/600] Discriminator Loss: 0.7057, Generator Loss: 2.5025 D(x): 0.7844, D(G(z)): 0.1610 Epoch: [7/20], Batch Num: [574/600] Discriminator Loss: 0.6747, Generator Loss: 2.0882 D(x): 0.8147, D(G(z)): 0.2234 Epoch: [7/20], Batch Num: [575/600] Discriminator Loss: 0.9015, Generator Loss: 2.6877 D(x): 0.8124, D(G(z)): 0.2679 Epoch: [7/20], Batch Num: [576/600] Discriminator Loss: 0.9052, Generator Loss: 2.7148 D(x): 0.7469, D(G(z)): 0.2222 Epoch: [7/20], Batch Num: [577/600] Discriminator Loss: 0.9119, Generator Loss: 2.7597 D(x): 0.7145, D(G(z)): 0.1621 Epoch: [7/20], Batch Num: [578/600] Discriminator Loss: 0.5862, Generator Loss: 2.4198 D(x): 0.8069, D(G(z)): 0.1819 Epoch: [7/20], Batch Num: [579/600] Discriminator Loss: 0.7022, Generator Loss: 1.8482 D(x): 0.7517, D(G(z)): 0.1624 Epoch: [7/20], Batch Num: [580/600] Discriminator Loss: 0.9099, Generator Loss: 2.0284 D(x): 0.7968, D(G(z)): 0.2902 Epoch: [7/20], Batch Num: [581/600] Discriminator Loss: 0.9841, Generator Loss: 2.5299 D(x): 0.8079, D(G(z)): 0.3314 Epoch: [7/20], Batch Num: [582/600] Discriminator Loss: 0.7045, Generator Loss: 2.9062 D(x): 0.7644, D(G(z)): 0.1578 Epoch: [7/20], Batch Num: [583/600] Discriminator Loss: 0.8294, Generator Loss: 2.4442 D(x): 0.7078, D(G(z)): 0.1379 Epoch: [7/20], Batch Num: [584/600] Discriminator Loss: 0.7455, Generator Loss: 2.3521 D(x): 0.7780, D(G(z)): 0.1882 Epoch: [7/20], Batch Num: [585/600] Discriminator Loss: 0.6808, Generator Loss: 1.7552 D(x): 0.8061, D(G(z)): 0.2005 Epoch: [7/20], Batch Num: [586/600] Discriminator Loss: 0.7299, Generator Loss: 1.6544 D(x): 0.8337, D(G(z)): 0.2581 Epoch: [7/20], Batch Num: [587/600] Discriminator Loss: 0.8146, Generator Loss: 2.0673 D(x): 0.8476, D(G(z)): 0.3130 Epoch: [7/20], Batch Num: [588/600] Discriminator Loss: 0.6561, Generator Loss: 2.4099 D(x): 0.8070, D(G(z)): 0.2296 Epoch: [7/20], Batch Num: [589/600] Discriminator Loss: 0.7574, Generator Loss: 2.3939 D(x): 0.7416, D(G(z)): 0.1773 Epoch: [7/20], Batch Num: [590/600] Discriminator Loss: 0.7879, Generator Loss: 2.2359 D(x): 0.7285, D(G(z)): 0.1590 Epoch: [7/20], Batch Num: [591/600] Discriminator Loss: 0.6574, Generator Loss: 1.8022 D(x): 0.7618, D(G(z)): 0.1625 Epoch: [7/20], Batch Num: [592/600] Discriminator Loss: 0.5294, Generator Loss: 1.7006 D(x): 0.8726, D(G(z)): 0.2378 Epoch: [7/20], Batch Num: [593/600] Discriminator Loss: 0.5476, Generator Loss: 2.0525 D(x): 0.8592, D(G(z)): 0.2238 Epoch: [7/20], Batch Num: [594/600] Discriminator Loss: 0.5758, Generator Loss: 2.4535 D(x): 0.9067, D(G(z)): 0.2773 Epoch: [7/20], Batch Num: [595/600] Discriminator Loss: 0.4350, Generator Loss: 2.9036 D(x): 0.8853, D(G(z)): 0.1707 Epoch: [7/20], Batch Num: [596/600] Discriminator Loss: 0.6245, Generator Loss: 3.0219 D(x): 0.7622, D(G(z)): 0.0961 Epoch: [7/20], Batch Num: [597/600] Discriminator Loss: 0.8755, Generator Loss: 2.5573 D(x): 0.6380, D(G(z)): 0.1016 Epoch: [7/20], Batch Num: [598/600] Discriminator Loss: 0.4994, Generator Loss: 2.0606 D(x): 0.8286, D(G(z)): 0.1407 Epoch: [7/20], Batch Num: [599/600] Discriminator Loss: 0.5687, Generator Loss: 2.1362 D(x): 0.8763, D(G(z)): 0.2526 Epoch: 8, Batch Num: [0/600]
Epoch: [8/20], Batch Num: [0/600] Discriminator Loss: 0.4696, Generator Loss: 2.2924 D(x): 0.9241, D(G(z)): 0.2434 Epoch: [8/20], Batch Num: [1/600] Discriminator Loss: 0.3580, Generator Loss: 2.9697 D(x): 0.9406, D(G(z)): 0.2028 Epoch: [8/20], Batch Num: [2/600] Discriminator Loss: 0.2631, Generator Loss: 3.4941 D(x): 0.9183, D(G(z)): 0.1112 Epoch: [8/20], Batch Num: [3/600] Discriminator Loss: 0.4611, Generator Loss: 3.4713 D(x): 0.7983, D(G(z)): 0.0743 Epoch: [8/20], Batch Num: [4/600] Discriminator Loss: 0.4387, Generator Loss: 3.2454 D(x): 0.8262, D(G(z)): 0.0695 Epoch: [8/20], Batch Num: [5/600] Discriminator Loss: 0.3220, Generator Loss: 2.9944 D(x): 0.8708, D(G(z)): 0.0777 Epoch: [8/20], Batch Num: [6/600] Discriminator Loss: 0.3654, Generator Loss: 2.6114 D(x): 0.8709, D(G(z)): 0.1197 Epoch: [8/20], Batch Num: [7/600] Discriminator Loss: 0.2791, Generator Loss: 2.4613 D(x): 0.9461, D(G(z)): 0.1658 Epoch: [8/20], Batch Num: [8/600] Discriminator Loss: 0.4520, Generator Loss: 2.4616 D(x): 0.9152, D(G(z)): 0.1971 Epoch: [8/20], Batch Num: [9/600] Discriminator Loss: 0.3321, Generator Loss: 2.6660 D(x): 0.9207, D(G(z)): 0.1420 Epoch: [8/20], Batch Num: [10/600] Discriminator Loss: 0.3970, Generator Loss: 2.7952 D(x): 0.8704, D(G(z)): 0.1456 Epoch: [8/20], Batch Num: [11/600] Discriminator Loss: 0.4519, Generator Loss: 3.2355 D(x): 0.9155, D(G(z)): 0.1636 Epoch: [8/20], Batch Num: [12/600] Discriminator Loss: 0.4913, Generator Loss: 3.0845 D(x): 0.8337, D(G(z)): 0.1414 Epoch: [8/20], Batch Num: [13/600] Discriminator Loss: 0.4620, Generator Loss: 2.8440 D(x): 0.8496, D(G(z)): 0.1205 Epoch: [8/20], Batch Num: [14/600] Discriminator Loss: 0.5328, Generator Loss: 2.7044 D(x): 0.8269, D(G(z)): 0.1345 Epoch: [8/20], Batch Num: [15/600] Discriminator Loss: 0.5466, Generator Loss: 2.5507 D(x): 0.8510, D(G(z)): 0.1838 Epoch: [8/20], Batch Num: [16/600] Discriminator Loss: 0.7171, Generator Loss: 2.4036 D(x): 0.8367, D(G(z)): 0.2329 Epoch: [8/20], Batch Num: [17/600] Discriminator Loss: 0.5357, Generator Loss: 2.5073 D(x): 0.8564, D(G(z)): 0.1871 Epoch: [8/20], Batch Num: [18/600] Discriminator Loss: 0.6522, Generator Loss: 2.7827 D(x): 0.8242, D(G(z)): 0.1782 Epoch: [8/20], Batch Num: [19/600] Discriminator Loss: 0.6717, Generator Loss: 2.8866 D(x): 0.7928, D(G(z)): 0.1674 Epoch: [8/20], Batch Num: [20/600] Discriminator Loss: 0.7435, Generator Loss: 2.7948 D(x): 0.8057, D(G(z)): 0.1690 Epoch: [8/20], Batch Num: [21/600] Discriminator Loss: 0.6681, Generator Loss: 2.3867 D(x): 0.7596, D(G(z)): 0.1596 Epoch: [8/20], Batch Num: [22/600] Discriminator Loss: 0.7431, Generator Loss: 2.1706 D(x): 0.8486, D(G(z)): 0.2159 Epoch: [8/20], Batch Num: [23/600] Discriminator Loss: 0.9230, Generator Loss: 2.1780 D(x): 0.8035, D(G(z)): 0.2682 Epoch: [8/20], Batch Num: [24/600] Discriminator Loss: 0.7704, Generator Loss: 2.5280 D(x): 0.8113, D(G(z)): 0.2333 Epoch: [8/20], Batch Num: [25/600] Discriminator Loss: 0.5330, Generator Loss: 2.3539 D(x): 0.8195, D(G(z)): 0.1655 Epoch: [8/20], Batch Num: [26/600] Discriminator Loss: 0.8660, Generator Loss: 2.5555 D(x): 0.7702, D(G(z)): 0.2651 Epoch: [8/20], Batch Num: [27/600] Discriminator Loss: 0.9489, Generator Loss: 2.2158 D(x): 0.6678, D(G(z)): 0.1727 Epoch: [8/20], Batch Num: [28/600] Discriminator Loss: 0.7673, Generator Loss: 2.0533 D(x): 0.7615, D(G(z)): 0.2212 Epoch: [8/20], Batch Num: [29/600] Discriminator Loss: 0.7306, Generator Loss: 1.6990 D(x): 0.7619, D(G(z)): 0.2084 Epoch: [8/20], Batch Num: [30/600] Discriminator Loss: 0.8160, Generator Loss: 1.5836 D(x): 0.8219, D(G(z)): 0.3338 Epoch: [8/20], Batch Num: [31/600] Discriminator Loss: 0.8762, Generator Loss: 2.0803 D(x): 0.8583, D(G(z)): 0.3397 Epoch: [8/20], Batch Num: [32/600] Discriminator Loss: 0.8197, Generator Loss: 2.2292 D(x): 0.8044, D(G(z)): 0.2830 Epoch: [8/20], Batch Num: [33/600] Discriminator Loss: 0.7978, Generator Loss: 2.4779 D(x): 0.7017, D(G(z)): 0.1876 Epoch: [8/20], Batch Num: [34/600] Discriminator Loss: 1.0598, Generator Loss: 2.1143 D(x): 0.5779, D(G(z)): 0.1855 Epoch: [8/20], Batch Num: [35/600] Discriminator Loss: 0.8519, Generator Loss: 1.8610 D(x): 0.7278, D(G(z)): 0.2352 Epoch: [8/20], Batch Num: [36/600] Discriminator Loss: 1.1601, Generator Loss: 1.5124 D(x): 0.7209, D(G(z)): 0.3566 Epoch: [8/20], Batch Num: [37/600] Discriminator Loss: 1.1707, Generator Loss: 1.5636 D(x): 0.6911, D(G(z)): 0.3451 Epoch: [8/20], Batch Num: [38/600] Discriminator Loss: 0.8741, Generator Loss: 1.7360 D(x): 0.7981, D(G(z)): 0.3505 Epoch: [8/20], Batch Num: [39/600] Discriminator Loss: 1.1177, Generator Loss: 2.1766 D(x): 0.6839, D(G(z)): 0.3281 Epoch: [8/20], Batch Num: [40/600] Discriminator Loss: 0.8846, Generator Loss: 2.4844 D(x): 0.6698, D(G(z)): 0.2045 Epoch: [8/20], Batch Num: [41/600] Discriminator Loss: 0.8410, Generator Loss: 2.2446 D(x): 0.6668, D(G(z)): 0.1782 Epoch: [8/20], Batch Num: [42/600] Discriminator Loss: 0.9540, Generator Loss: 2.0766 D(x): 0.6257, D(G(z)): 0.1674 Epoch: [8/20], Batch Num: [43/600] Discriminator Loss: 0.6719, Generator Loss: 1.5961 D(x): 0.7898, D(G(z)): 0.2205 Epoch: [8/20], Batch Num: [44/600] Discriminator Loss: 0.8626, Generator Loss: 1.7774 D(x): 0.7844, D(G(z)): 0.3075 Epoch: [8/20], Batch Num: [45/600] Discriminator Loss: 0.7651, Generator Loss: 1.9034 D(x): 0.8043, D(G(z)): 0.2850 Epoch: [8/20], Batch Num: [46/600] Discriminator Loss: 0.6900, Generator Loss: 2.0434 D(x): 0.7966, D(G(z)): 0.2494 Epoch: [8/20], Batch Num: [47/600] Discriminator Loss: 0.6575, Generator Loss: 2.3585 D(x): 0.8257, D(G(z)): 0.2469 Epoch: [8/20], Batch Num: [48/600] Discriminator Loss: 0.5606, Generator Loss: 2.4917 D(x): 0.8026, D(G(z)): 0.1891 Epoch: [8/20], Batch Num: [49/600] Discriminator Loss: 0.6466, Generator Loss: 2.6129 D(x): 0.7377, D(G(z)): 0.1701 Epoch: [8/20], Batch Num: [50/600] Discriminator Loss: 0.5003, Generator Loss: 2.4765 D(x): 0.7958, D(G(z)): 0.1426 Epoch: [8/20], Batch Num: [51/600] Discriminator Loss: 0.5462, Generator Loss: 2.4036 D(x): 0.8078, D(G(z)): 0.1742 Epoch: [8/20], Batch Num: [52/600] Discriminator Loss: 0.4347, Generator Loss: 2.4531 D(x): 0.8463, D(G(z)): 0.1705 Epoch: [8/20], Batch Num: [53/600] Discriminator Loss: 0.3438, Generator Loss: 2.1986 D(x): 0.9312, D(G(z)): 0.1967 Epoch: [8/20], Batch Num: [54/600] Discriminator Loss: 0.4507, Generator Loss: 2.7138 D(x): 0.8738, D(G(z)): 0.1805 Epoch: [8/20], Batch Num: [55/600] Discriminator Loss: 0.4297, Generator Loss: 2.4765 D(x): 0.8500, D(G(z)): 0.1543 Epoch: [8/20], Batch Num: [56/600] Discriminator Loss: 0.3684, Generator Loss: 2.8826 D(x): 0.8771, D(G(z)): 0.1489 Epoch: [8/20], Batch Num: [57/600] Discriminator Loss: 0.4263, Generator Loss: 2.5560 D(x): 0.8388, D(G(z)): 0.1442 Epoch: [8/20], Batch Num: [58/600] Discriminator Loss: 0.4548, Generator Loss: 2.5449 D(x): 0.8557, D(G(z)): 0.1585 Epoch: [8/20], Batch Num: [59/600] Discriminator Loss: 0.4902, Generator Loss: 2.5228 D(x): 0.8028, D(G(z)): 0.1611 Epoch: [8/20], Batch Num: [60/600] Discriminator Loss: 0.4498, Generator Loss: 2.5070 D(x): 0.8761, D(G(z)): 0.1838 Epoch: [8/20], Batch Num: [61/600] Discriminator Loss: 0.5308, Generator Loss: 2.1533 D(x): 0.8260, D(G(z)): 0.1816 Epoch: [8/20], Batch Num: [62/600] Discriminator Loss: 0.5193, Generator Loss: 2.5315 D(x): 0.9069, D(G(z)): 0.2400 Epoch: [8/20], Batch Num: [63/600] Discriminator Loss: 0.6855, Generator Loss: 2.7175 D(x): 0.8063, D(G(z)): 0.2178 Epoch: [8/20], Batch Num: [64/600] Discriminator Loss: 0.5531, Generator Loss: 3.0910 D(x): 0.7893, D(G(z)): 0.1185 Epoch: [8/20], Batch Num: [65/600] Discriminator Loss: 0.6133, Generator Loss: 2.8378 D(x): 0.7944, D(G(z)): 0.1291 Epoch: [8/20], Batch Num: [66/600] Discriminator Loss: 0.5857, Generator Loss: 2.3956 D(x): 0.8086, D(G(z)): 0.1528 Epoch: [8/20], Batch Num: [67/600] Discriminator Loss: 0.5274, Generator Loss: 2.2041 D(x): 0.8438, D(G(z)): 0.1773 Epoch: [8/20], Batch Num: [68/600] Discriminator Loss: 0.6181, Generator Loss: 2.2631 D(x): 0.8436, D(G(z)): 0.2213 Epoch: [8/20], Batch Num: [69/600] Discriminator Loss: 0.7307, Generator Loss: 2.3430 D(x): 0.8325, D(G(z)): 0.2395 Epoch: [8/20], Batch Num: [70/600] Discriminator Loss: 0.7162, Generator Loss: 2.5977 D(x): 0.7722, D(G(z)): 0.1873 Epoch: [8/20], Batch Num: [71/600] Discriminator Loss: 0.7405, Generator Loss: 2.4319 D(x): 0.7439, D(G(z)): 0.1572 Epoch: [8/20], Batch Num: [72/600] Discriminator Loss: 0.7295, Generator Loss: 2.6223 D(x): 0.8284, D(G(z)): 0.2429 Epoch: [8/20], Batch Num: [73/600] Discriminator Loss: 0.8923, Generator Loss: 2.3670 D(x): 0.7418, D(G(z)): 0.1897 Epoch: [8/20], Batch Num: [74/600] Discriminator Loss: 0.8120, Generator Loss: 2.1006 D(x): 0.7315, D(G(z)): 0.2142 Epoch: [8/20], Batch Num: [75/600] Discriminator Loss: 1.0118, Generator Loss: 2.1083 D(x): 0.7217, D(G(z)): 0.2294 Epoch: [8/20], Batch Num: [76/600] Discriminator Loss: 1.1224, Generator Loss: 2.3118 D(x): 0.7061, D(G(z)): 0.3175 Epoch: [8/20], Batch Num: [77/600] Discriminator Loss: 0.9594, Generator Loss: 2.3549 D(x): 0.7593, D(G(z)): 0.2526 Epoch: [8/20], Batch Num: [78/600] Discriminator Loss: 1.0853, Generator Loss: 2.4051 D(x): 0.6546, D(G(z)): 0.2078 Epoch: [8/20], Batch Num: [79/600] Discriminator Loss: 0.8431, Generator Loss: 1.9749 D(x): 0.7232, D(G(z)): 0.2106 Epoch: [8/20], Batch Num: [80/600] Discriminator Loss: 1.0401, Generator Loss: 1.8453 D(x): 0.6955, D(G(z)): 0.2308 Epoch: [8/20], Batch Num: [81/600] Discriminator Loss: 0.8458, Generator Loss: 1.8928 D(x): 0.7704, D(G(z)): 0.2898 Epoch: [8/20], Batch Num: [82/600] Discriminator Loss: 0.8221, Generator Loss: 1.8687 D(x): 0.7571, D(G(z)): 0.2660 Epoch: [8/20], Batch Num: [83/600] Discriminator Loss: 0.9820, Generator Loss: 1.9146 D(x): 0.7338, D(G(z)): 0.2766 Epoch: [8/20], Batch Num: [84/600] Discriminator Loss: 0.9418, Generator Loss: 1.9161 D(x): 0.7162, D(G(z)): 0.2508 Epoch: [8/20], Batch Num: [85/600] Discriminator Loss: 0.7949, Generator Loss: 2.0010 D(x): 0.7680, D(G(z)): 0.2425 Epoch: [8/20], Batch Num: [86/600] Discriminator Loss: 0.6035, Generator Loss: 1.9750 D(x): 0.7958, D(G(z)): 0.2187 Epoch: [8/20], Batch Num: [87/600] Discriminator Loss: 0.7095, Generator Loss: 1.9610 D(x): 0.7512, D(G(z)): 0.2254 Epoch: [8/20], Batch Num: [88/600] Discriminator Loss: 0.7919, Generator Loss: 1.7049 D(x): 0.7374, D(G(z)): 0.2100 Epoch: [8/20], Batch Num: [89/600] Discriminator Loss: 0.7893, Generator Loss: 1.6495 D(x): 0.7700, D(G(z)): 0.2419 Epoch: [8/20], Batch Num: [90/600] Discriminator Loss: 0.6724, Generator Loss: 1.6804 D(x): 0.8022, D(G(z)): 0.2691 Epoch: [8/20], Batch Num: [91/600] Discriminator Loss: 0.6868, Generator Loss: 1.6422 D(x): 0.8637, D(G(z)): 0.3019 Epoch: [8/20], Batch Num: [92/600] Discriminator Loss: 0.6154, Generator Loss: 2.1962 D(x): 0.8429, D(G(z)): 0.2650 Epoch: [8/20], Batch Num: [93/600] Discriminator Loss: 0.6651, Generator Loss: 2.2435 D(x): 0.7732, D(G(z)): 0.2002 Epoch: [8/20], Batch Num: [94/600] Discriminator Loss: 0.6783, Generator Loss: 2.3080 D(x): 0.7491, D(G(z)): 0.1652 Epoch: [8/20], Batch Num: [95/600] Discriminator Loss: 0.7919, Generator Loss: 2.3613 D(x): 0.6882, D(G(z)): 0.1639 Epoch: [8/20], Batch Num: [96/600] Discriminator Loss: 0.5913, Generator Loss: 1.9297 D(x): 0.7713, D(G(z)): 0.1670 Epoch: [8/20], Batch Num: [97/600] Discriminator Loss: 0.6157, Generator Loss: 1.6879 D(x): 0.7836, D(G(z)): 0.2096 Epoch: [8/20], Batch Num: [98/600] Discriminator Loss: 0.7451, Generator Loss: 1.6788 D(x): 0.8441, D(G(z)): 0.3130 Epoch: [8/20], Batch Num: [99/600] Discriminator Loss: 0.4951, Generator Loss: 1.8140 D(x): 0.9092, D(G(z)): 0.2761 Epoch: 8, Batch Num: [100/600]
Epoch: [8/20], Batch Num: [100/600] Discriminator Loss: 0.6004, Generator Loss: 2.2460 D(x): 0.8390, D(G(z)): 0.2503 Epoch: [8/20], Batch Num: [101/600] Discriminator Loss: 0.4739, Generator Loss: 2.5551 D(x): 0.8300, D(G(z)): 0.1577 Epoch: [8/20], Batch Num: [102/600] Discriminator Loss: 0.6228, Generator Loss: 2.6552 D(x): 0.7423, D(G(z)): 0.1547 Epoch: [8/20], Batch Num: [103/600] Discriminator Loss: 0.6244, Generator Loss: 2.4837 D(x): 0.7120, D(G(z)): 0.1147 Epoch: [8/20], Batch Num: [104/600] Discriminator Loss: 0.5478, Generator Loss: 1.9803 D(x): 0.8067, D(G(z)): 0.1665 Epoch: [8/20], Batch Num: [105/600] Discriminator Loss: 0.6103, Generator Loss: 1.8618 D(x): 0.8448, D(G(z)): 0.2672 Epoch: [8/20], Batch Num: [106/600] Discriminator Loss: 0.4638, Generator Loss: 2.0006 D(x): 0.8603, D(G(z)): 0.2116 Epoch: [8/20], Batch Num: [107/600] Discriminator Loss: 0.6364, Generator Loss: 2.1534 D(x): 0.8516, D(G(z)): 0.2768 Epoch: [8/20], Batch Num: [108/600] Discriminator Loss: 0.6191, Generator Loss: 2.4911 D(x): 0.8303, D(G(z)): 0.2332 Epoch: [8/20], Batch Num: [109/600] Discriminator Loss: 0.6412, Generator Loss: 2.4600 D(x): 0.8070, D(G(z)): 0.2208 Epoch: [8/20], Batch Num: [110/600] Discriminator Loss: 0.5063, Generator Loss: 2.5309 D(x): 0.7937, D(G(z)): 0.1339 Epoch: [8/20], Batch Num: [111/600] Discriminator Loss: 0.5855, Generator Loss: 2.6625 D(x): 0.7630, D(G(z)): 0.1535 Epoch: [8/20], Batch Num: [112/600] Discriminator Loss: 0.5676, Generator Loss: 2.3664 D(x): 0.7839, D(G(z)): 0.1735 Epoch: [8/20], Batch Num: [113/600] Discriminator Loss: 0.6374, Generator Loss: 2.3637 D(x): 0.7948, D(G(z)): 0.2028 Epoch: [8/20], Batch Num: [114/600] Discriminator Loss: 0.6079, Generator Loss: 2.0051 D(x): 0.8479, D(G(z)): 0.2369 Epoch: [8/20], Batch Num: [115/600] Discriminator Loss: 0.6886, Generator Loss: 2.4017 D(x): 0.8535, D(G(z)): 0.2848 Epoch: [8/20], Batch Num: [116/600] Discriminator Loss: 0.5860, Generator Loss: 2.6479 D(x): 0.8585, D(G(z)): 0.2112 Epoch: [8/20], Batch Num: [117/600] Discriminator Loss: 0.6558, Generator Loss: 2.7088 D(x): 0.7918, D(G(z)): 0.1861 Epoch: [8/20], Batch Num: [118/600] Discriminator Loss: 0.8438, Generator Loss: 2.7625 D(x): 0.7426, D(G(z)): 0.1930 Epoch: [8/20], Batch Num: [119/600] Discriminator Loss: 0.8821, Generator Loss: 2.5981 D(x): 0.6996, D(G(z)): 0.1794 Epoch: [8/20], Batch Num: [120/600] Discriminator Loss: 1.0075, Generator Loss: 2.3242 D(x): 0.7018, D(G(z)): 0.2264 Epoch: [8/20], Batch Num: [121/600] Discriminator Loss: 1.0405, Generator Loss: 1.8974 D(x): 0.6898, D(G(z)): 0.2316 Epoch: [8/20], Batch Num: [122/600] Discriminator Loss: 0.9599, Generator Loss: 1.9112 D(x): 0.7345, D(G(z)): 0.2796 Epoch: [8/20], Batch Num: [123/600] Discriminator Loss: 1.0262, Generator Loss: 1.8120 D(x): 0.7478, D(G(z)): 0.3121 Epoch: [8/20], Batch Num: [124/600] Discriminator Loss: 1.1738, Generator Loss: 1.9659 D(x): 0.6764, D(G(z)): 0.3347 Epoch: [8/20], Batch Num: [125/600] Discriminator Loss: 1.0292, Generator Loss: 2.0262 D(x): 0.7120, D(G(z)): 0.2680 Epoch: [8/20], Batch Num: [126/600] Discriminator Loss: 1.1966, Generator Loss: 2.0845 D(x): 0.6512, D(G(z)): 0.2780 Epoch: [8/20], Batch Num: [127/600] Discriminator Loss: 1.0131, Generator Loss: 2.1100 D(x): 0.6509, D(G(z)): 0.2105 Epoch: [8/20], Batch Num: [128/600] Discriminator Loss: 1.0961, Generator Loss: 1.9019 D(x): 0.6511, D(G(z)): 0.2784 Epoch: [8/20], Batch Num: [129/600] Discriminator Loss: 1.2802, Generator Loss: 1.5791 D(x): 0.5888, D(G(z)): 0.2558 Epoch: [8/20], Batch Num: [130/600] Discriminator Loss: 1.1570, Generator Loss: 1.5531 D(x): 0.6729, D(G(z)): 0.3054 Epoch: [8/20], Batch Num: [131/600] Discriminator Loss: 1.0298, Generator Loss: 1.6000 D(x): 0.7199, D(G(z)): 0.3337 Epoch: [8/20], Batch Num: [132/600] Discriminator Loss: 0.8912, Generator Loss: 1.7861 D(x): 0.7603, D(G(z)): 0.3196 Epoch: [8/20], Batch Num: [133/600] Discriminator Loss: 0.8069, Generator Loss: 1.9827 D(x): 0.7557, D(G(z)): 0.2772 Epoch: [8/20], Batch Num: [134/600] Discriminator Loss: 0.6206, Generator Loss: 2.0630 D(x): 0.8085, D(G(z)): 0.2484 Epoch: [8/20], Batch Num: [135/600] Discriminator Loss: 0.8101, Generator Loss: 2.4656 D(x): 0.7157, D(G(z)): 0.2230 Epoch: [8/20], Batch Num: [136/600] Discriminator Loss: 0.6726, Generator Loss: 2.4929 D(x): 0.7575, D(G(z)): 0.1854 Epoch: [8/20], Batch Num: [137/600] Discriminator Loss: 0.5001, Generator Loss: 2.4281 D(x): 0.8126, D(G(z)): 0.1650 Epoch: [8/20], Batch Num: [138/600] Discriminator Loss: 0.5572, Generator Loss: 2.6860 D(x): 0.8044, D(G(z)): 0.1884 Epoch: [8/20], Batch Num: [139/600] Discriminator Loss: 0.5457, Generator Loss: 2.6778 D(x): 0.7946, D(G(z)): 0.1696 Epoch: [8/20], Batch Num: [140/600] Discriminator Loss: 0.5106, Generator Loss: 2.4592 D(x): 0.7788, D(G(z)): 0.1336 Epoch: [8/20], Batch Num: [141/600] Discriminator Loss: 0.3662, Generator Loss: 2.6958 D(x): 0.8798, D(G(z)): 0.1646 Epoch: [8/20], Batch Num: [142/600] Discriminator Loss: 0.4317, Generator Loss: 3.0483 D(x): 0.9316, D(G(z)): 0.2126 Epoch: [8/20], Batch Num: [143/600] Discriminator Loss: 0.3855, Generator Loss: 3.0982 D(x): 0.9121, D(G(z)): 0.1612 Epoch: [8/20], Batch Num: [144/600] Discriminator Loss: 0.4430, Generator Loss: 3.5666 D(x): 0.8586, D(G(z)): 0.1701 Epoch: [8/20], Batch Num: [145/600] Discriminator Loss: 0.4053, Generator Loss: 3.4939 D(x): 0.8357, D(G(z)): 0.1192 Epoch: [8/20], Batch Num: [146/600] Discriminator Loss: 0.4711, Generator Loss: 3.2979 D(x): 0.8204, D(G(z)): 0.0982 Epoch: [8/20], Batch Num: [147/600] Discriminator Loss: 0.6116, Generator Loss: 2.8044 D(x): 0.7468, D(G(z)): 0.0969 Epoch: [8/20], Batch Num: [148/600] Discriminator Loss: 0.5862, Generator Loss: 2.4060 D(x): 0.8479, D(G(z)): 0.1999 Epoch: [8/20], Batch Num: [149/600] Discriminator Loss: 0.5059, Generator Loss: 2.7872 D(x): 0.9072, D(G(z)): 0.2382 Epoch: [8/20], Batch Num: [150/600] Discriminator Loss: 0.4751, Generator Loss: 3.1488 D(x): 0.8651, D(G(z)): 0.1835 Epoch: [8/20], Batch Num: [151/600] Discriminator Loss: 0.5319, Generator Loss: 2.8169 D(x): 0.7696, D(G(z)): 0.1261 Epoch: [8/20], Batch Num: [152/600] Discriminator Loss: 0.6780, Generator Loss: 2.6074 D(x): 0.7823, D(G(z)): 0.1828 Epoch: [8/20], Batch Num: [153/600] Discriminator Loss: 0.7709, Generator Loss: 2.2693 D(x): 0.7975, D(G(z)): 0.2601 Epoch: [8/20], Batch Num: [154/600] Discriminator Loss: 0.9092, Generator Loss: 2.0727 D(x): 0.7458, D(G(z)): 0.2315 Epoch: [8/20], Batch Num: [155/600] Discriminator Loss: 1.0764, Generator Loss: 2.2650 D(x): 0.7571, D(G(z)): 0.3453 Epoch: [8/20], Batch Num: [156/600] Discriminator Loss: 1.0096, Generator Loss: 2.5564 D(x): 0.7560, D(G(z)): 0.3064 Epoch: [8/20], Batch Num: [157/600] Discriminator Loss: 1.0166, Generator Loss: 2.5968 D(x): 0.6842, D(G(z)): 0.2292 Epoch: [8/20], Batch Num: [158/600] Discriminator Loss: 1.2594, Generator Loss: 2.0687 D(x): 0.6124, D(G(z)): 0.2298 Epoch: [8/20], Batch Num: [159/600] Discriminator Loss: 1.0745, Generator Loss: 1.6390 D(x): 0.7055, D(G(z)): 0.2903 Epoch: [8/20], Batch Num: [160/600] Discriminator Loss: 0.9263, Generator Loss: 1.6491 D(x): 0.7762, D(G(z)): 0.2973 Epoch: [8/20], Batch Num: [161/600] Discriminator Loss: 1.0464, Generator Loss: 1.6807 D(x): 0.8157, D(G(z)): 0.3733 Epoch: [8/20], Batch Num: [162/600] Discriminator Loss: 1.0243, Generator Loss: 2.0909 D(x): 0.7417, D(G(z)): 0.2991 Epoch: [8/20], Batch Num: [163/600] Discriminator Loss: 0.9795, Generator Loss: 2.2119 D(x): 0.7147, D(G(z)): 0.2583 Epoch: [8/20], Batch Num: [164/600] Discriminator Loss: 1.2009, Generator Loss: 2.2247 D(x): 0.6313, D(G(z)): 0.2441 Epoch: [8/20], Batch Num: [165/600] Discriminator Loss: 1.0082, Generator Loss: 1.8827 D(x): 0.6511, D(G(z)): 0.1958 Epoch: [8/20], Batch Num: [166/600] Discriminator Loss: 1.0127, Generator Loss: 1.5906 D(x): 0.7373, D(G(z)): 0.2893 Epoch: [8/20], Batch Num: [167/600] Discriminator Loss: 0.7874, Generator Loss: 1.5836 D(x): 0.8492, D(G(z)): 0.3261 Epoch: [8/20], Batch Num: [168/600] Discriminator Loss: 0.6272, Generator Loss: 1.9733 D(x): 0.8805, D(G(z)): 0.2880 Epoch: [8/20], Batch Num: [169/600] Discriminator Loss: 0.6777, Generator Loss: 2.0262 D(x): 0.8325, D(G(z)): 0.2660 Epoch: [8/20], Batch Num: [170/600] Discriminator Loss: 0.5497, Generator Loss: 2.3246 D(x): 0.8509, D(G(z)): 0.2060 Epoch: [8/20], Batch Num: [171/600] Discriminator Loss: 0.6845, Generator Loss: 2.1256 D(x): 0.7703, D(G(z)): 0.1939 Epoch: [8/20], Batch Num: [172/600] Discriminator Loss: 0.5506, Generator Loss: 2.2496 D(x): 0.8139, D(G(z)): 0.1706 Epoch: [8/20], Batch Num: [173/600] Discriminator Loss: 0.6348, Generator Loss: 2.0601 D(x): 0.8084, D(G(z)): 0.2284 Epoch: [8/20], Batch Num: [174/600] Discriminator Loss: 0.4790, Generator Loss: 1.9058 D(x): 0.8483, D(G(z)): 0.1881 Epoch: [8/20], Batch Num: [175/600] Discriminator Loss: 0.5994, Generator Loss: 1.9216 D(x): 0.8599, D(G(z)): 0.2474 Epoch: [8/20], Batch Num: [176/600] Discriminator Loss: 0.6489, Generator Loss: 1.8828 D(x): 0.8707, D(G(z)): 0.2591 Epoch: [8/20], Batch Num: [177/600] Discriminator Loss: 0.5444, Generator Loss: 1.9311 D(x): 0.8294, D(G(z)): 0.1917 Epoch: [8/20], Batch Num: [178/600] Discriminator Loss: 0.5258, Generator Loss: 2.2806 D(x): 0.9170, D(G(z)): 0.2487 Epoch: [8/20], Batch Num: [179/600] Discriminator Loss: 0.3601, Generator Loss: 2.5054 D(x): 0.8991, D(G(z)): 0.1707 Epoch: [8/20], Batch Num: [180/600] Discriminator Loss: 0.5436, Generator Loss: 2.3553 D(x): 0.8618, D(G(z)): 0.1952 Epoch: [8/20], Batch Num: [181/600] Discriminator Loss: 0.4769, Generator Loss: 2.6634 D(x): 0.8498, D(G(z)): 0.1639 Epoch: [8/20], Batch Num: [182/600] Discriminator Loss: 0.5007, Generator Loss: 2.4121 D(x): 0.8211, D(G(z)): 0.1464 Epoch: [8/20], Batch Num: [183/600] Discriminator Loss: 0.4824, Generator Loss: 2.4052 D(x): 0.8146, D(G(z)): 0.1307 Epoch: [8/20], Batch Num: [184/600] Discriminator Loss: 0.6407, Generator Loss: 2.3502 D(x): 0.8134, D(G(z)): 0.1742 Epoch: [8/20], Batch Num: [185/600] Discriminator Loss: 0.5241, Generator Loss: 2.2308 D(x): 0.8579, D(G(z)): 0.1899 Epoch: [8/20], Batch Num: [186/600] Discriminator Loss: 0.6079, Generator Loss: 2.0585 D(x): 0.8857, D(G(z)): 0.2400 Epoch: [8/20], Batch Num: [187/600] Discriminator Loss: 0.5648, Generator Loss: 1.9349 D(x): 0.8443, D(G(z)): 0.2000 Epoch: [8/20], Batch Num: [188/600] Discriminator Loss: 0.5346, Generator Loss: 2.2624 D(x): 0.8938, D(G(z)): 0.2418 Epoch: [8/20], Batch Num: [189/600] Discriminator Loss: 0.5073, Generator Loss: 2.2407 D(x): 0.8421, D(G(z)): 0.1639 Epoch: [8/20], Batch Num: [190/600] Discriminator Loss: 0.4391, Generator Loss: 2.4245 D(x): 0.8566, D(G(z)): 0.1564 Epoch: [8/20], Batch Num: [191/600] Discriminator Loss: 0.5005, Generator Loss: 2.4718 D(x): 0.9000, D(G(z)): 0.2209 Epoch: [8/20], Batch Num: [192/600] Discriminator Loss: 0.5639, Generator Loss: 2.5096 D(x): 0.8215, D(G(z)): 0.1713 Epoch: [8/20], Batch Num: [193/600] Discriminator Loss: 0.6152, Generator Loss: 2.6122 D(x): 0.7942, D(G(z)): 0.1659 Epoch: [8/20], Batch Num: [194/600] Discriminator Loss: 0.4792, Generator Loss: 2.3945 D(x): 0.8561, D(G(z)): 0.1705 Epoch: [8/20], Batch Num: [195/600] Discriminator Loss: 0.5615, Generator Loss: 2.1820 D(x): 0.8136, D(G(z)): 0.1799 Epoch: [8/20], Batch Num: [196/600] Discriminator Loss: 0.6256, Generator Loss: 2.3205 D(x): 0.8847, D(G(z)): 0.2518 Epoch: [8/20], Batch Num: [197/600] Discriminator Loss: 0.5377, Generator Loss: 2.3117 D(x): 0.8385, D(G(z)): 0.1736 Epoch: [8/20], Batch Num: [198/600] Discriminator Loss: 0.5585, Generator Loss: 2.0086 D(x): 0.8490, D(G(z)): 0.1714 Epoch: [8/20], Batch Num: [199/600] Discriminator Loss: 0.6053, Generator Loss: 2.3640 D(x): 0.8134, D(G(z)): 0.1783 Epoch: 8, Batch Num: [200/600]
Epoch: [8/20], Batch Num: [200/600] Discriminator Loss: 0.6476, Generator Loss: 2.3366 D(x): 0.8307, D(G(z)): 0.2293 Epoch: [8/20], Batch Num: [201/600] Discriminator Loss: 0.6103, Generator Loss: 2.3870 D(x): 0.8578, D(G(z)): 0.2146 Epoch: [8/20], Batch Num: [202/600] Discriminator Loss: 0.7153, Generator Loss: 2.3310 D(x): 0.7563, D(G(z)): 0.1787 Epoch: [8/20], Batch Num: [203/600] Discriminator Loss: 0.4653, Generator Loss: 2.2862 D(x): 0.8281, D(G(z)): 0.1415 Epoch: [8/20], Batch Num: [204/600] Discriminator Loss: 0.6198, Generator Loss: 2.4709 D(x): 0.8726, D(G(z)): 0.2470 Epoch: [8/20], Batch Num: [205/600] Discriminator Loss: 0.6675, Generator Loss: 2.2741 D(x): 0.8215, D(G(z)): 0.2212 Epoch: [8/20], Batch Num: [206/600] Discriminator Loss: 0.6045, Generator Loss: 2.2115 D(x): 0.8406, D(G(z)): 0.2093 Epoch: [8/20], Batch Num: [207/600] Discriminator Loss: 0.6763, Generator Loss: 1.9652 D(x): 0.8140, D(G(z)): 0.2303 Epoch: [8/20], Batch Num: [208/600] Discriminator Loss: 0.7782, Generator Loss: 1.9929 D(x): 0.7941, D(G(z)): 0.2442 Epoch: [8/20], Batch Num: [209/600] Discriminator Loss: 0.7115, Generator Loss: 2.1125 D(x): 0.7838, D(G(z)): 0.2261 Epoch: [8/20], Batch Num: [210/600] Discriminator Loss: 0.6322, Generator Loss: 2.0246 D(x): 0.8014, D(G(z)): 0.2064 Epoch: [8/20], Batch Num: [211/600] Discriminator Loss: 0.6760, Generator Loss: 1.9026 D(x): 0.8435, D(G(z)): 0.2686 Epoch: [8/20], Batch Num: [212/600] Discriminator Loss: 0.6137, Generator Loss: 1.8212 D(x): 0.8367, D(G(z)): 0.2514 Epoch: [8/20], Batch Num: [213/600] Discriminator Loss: 0.6152, Generator Loss: 1.9397 D(x): 0.8310, D(G(z)): 0.2417 Epoch: [8/20], Batch Num: [214/600] Discriminator Loss: 0.6694, Generator Loss: 2.1707 D(x): 0.8281, D(G(z)): 0.2570 Epoch: [8/20], Batch Num: [215/600] Discriminator Loss: 0.6389, Generator Loss: 2.1563 D(x): 0.8113, D(G(z)): 0.2058 Epoch: [8/20], Batch Num: [216/600] Discriminator Loss: 0.6661, Generator Loss: 2.1604 D(x): 0.7864, D(G(z)): 0.2089 Epoch: [8/20], Batch Num: [217/600] Discriminator Loss: 0.7883, Generator Loss: 1.8270 D(x): 0.7585, D(G(z)): 0.2538 Epoch: [8/20], Batch Num: [218/600] Discriminator Loss: 0.6136, Generator Loss: 1.8500 D(x): 0.7965, D(G(z)): 0.2010 Epoch: [8/20], Batch Num: [219/600] Discriminator Loss: 0.6417, Generator Loss: 1.9682 D(x): 0.8422, D(G(z)): 0.2523 Epoch: [8/20], Batch Num: [220/600] Discriminator Loss: 0.6843, Generator Loss: 1.9992 D(x): 0.8578, D(G(z)): 0.2938 Epoch: [8/20], Batch Num: [221/600] Discriminator Loss: 0.5167, Generator Loss: 2.0515 D(x): 0.8610, D(G(z)): 0.2004 Epoch: [8/20], Batch Num: [222/600] Discriminator Loss: 0.6314, Generator Loss: 1.9444 D(x): 0.7493, D(G(z)): 0.1747 Epoch: [8/20], Batch Num: [223/600] Discriminator Loss: 0.7288, Generator Loss: 1.8751 D(x): 0.7398, D(G(z)): 0.1939 Epoch: [8/20], Batch Num: [224/600] Discriminator Loss: 0.7226, Generator Loss: 1.8473 D(x): 0.7501, D(G(z)): 0.2185 Epoch: [8/20], Batch Num: [225/600] Discriminator Loss: 0.7042, Generator Loss: 1.7656 D(x): 0.8233, D(G(z)): 0.2885 Epoch: [8/20], Batch Num: [226/600] Discriminator Loss: 0.5742, Generator Loss: 1.8998 D(x): 0.8364, D(G(z)): 0.2562 Epoch: [8/20], Batch Num: [227/600] Discriminator Loss: 0.7685, Generator Loss: 1.9048 D(x): 0.7714, D(G(z)): 0.2406 Epoch: [8/20], Batch Num: [228/600] Discriminator Loss: 0.6581, Generator Loss: 1.9704 D(x): 0.8127, D(G(z)): 0.2486 Epoch: [8/20], Batch Num: [229/600] Discriminator Loss: 0.5847, Generator Loss: 2.1802 D(x): 0.8080, D(G(z)): 0.2232 Epoch: [8/20], Batch Num: [230/600] Discriminator Loss: 0.5681, Generator Loss: 2.0459 D(x): 0.7776, D(G(z)): 0.1774 Epoch: [8/20], Batch Num: [231/600] Discriminator Loss: 0.6600, Generator Loss: 1.9323 D(x): 0.7716, D(G(z)): 0.1988 Epoch: [8/20], Batch Num: [232/600] Discriminator Loss: 0.6003, Generator Loss: 1.6740 D(x): 0.7929, D(G(z)): 0.2006 Epoch: [8/20], Batch Num: [233/600] Discriminator Loss: 0.6092, Generator Loss: 1.5944 D(x): 0.8465, D(G(z)): 0.2882 Epoch: [8/20], Batch Num: [234/600] Discriminator Loss: 0.6480, Generator Loss: 1.8626 D(x): 0.8680, D(G(z)): 0.2950 Epoch: [8/20], Batch Num: [235/600] Discriminator Loss: 0.5917, Generator Loss: 1.8986 D(x): 0.8137, D(G(z)): 0.2189 Epoch: [8/20], Batch Num: [236/600] Discriminator Loss: 0.6613, Generator Loss: 2.2844 D(x): 0.8141, D(G(z)): 0.2308 Epoch: [8/20], Batch Num: [237/600] Discriminator Loss: 0.5272, Generator Loss: 2.3023 D(x): 0.8194, D(G(z)): 0.1862 Epoch: [8/20], Batch Num: [238/600] Discriminator Loss: 0.6749, Generator Loss: 2.3793 D(x): 0.7387, D(G(z)): 0.1699 Epoch: [8/20], Batch Num: [239/600] Discriminator Loss: 0.7405, Generator Loss: 2.0715 D(x): 0.7291, D(G(z)): 0.1789 Epoch: [8/20], Batch Num: [240/600] Discriminator Loss: 0.6382, Generator Loss: 1.7935 D(x): 0.8073, D(G(z)): 0.2148 Epoch: [8/20], Batch Num: [241/600] Discriminator Loss: 0.4963, Generator Loss: 1.7774 D(x): 0.8767, D(G(z)): 0.2255 Epoch: [8/20], Batch Num: [242/600] Discriminator Loss: 0.7404, Generator Loss: 1.8945 D(x): 0.7933, D(G(z)): 0.2526 Epoch: [8/20], Batch Num: [243/600] Discriminator Loss: 0.6441, Generator Loss: 2.2274 D(x): 0.8410, D(G(z)): 0.2688 Epoch: [8/20], Batch Num: [244/600] Discriminator Loss: 0.5720, Generator Loss: 2.3152 D(x): 0.7975, D(G(z)): 0.1687 Epoch: [8/20], Batch Num: [245/600] Discriminator Loss: 0.5126, Generator Loss: 2.4624 D(x): 0.8496, D(G(z)): 0.1846 Epoch: [8/20], Batch Num: [246/600] Discriminator Loss: 0.5558, Generator Loss: 2.4027 D(x): 0.7714, D(G(z)): 0.1415 Epoch: [8/20], Batch Num: [247/600] Discriminator Loss: 0.5836, Generator Loss: 2.0898 D(x): 0.7697, D(G(z)): 0.1462 Epoch: [8/20], Batch Num: [248/600] Discriminator Loss: 0.5020, Generator Loss: 2.1176 D(x): 0.8396, D(G(z)): 0.1961 Epoch: [8/20], Batch Num: [249/600] Discriminator Loss: 0.6314, Generator Loss: 1.9084 D(x): 0.8515, D(G(z)): 0.2522 Epoch: [8/20], Batch Num: [250/600] Discriminator Loss: 0.5117, Generator Loss: 2.3716 D(x): 0.8841, D(G(z)): 0.2291 Epoch: [8/20], Batch Num: [251/600] Discriminator Loss: 0.4874, Generator Loss: 2.2617 D(x): 0.8289, D(G(z)): 0.1779 Epoch: [8/20], Batch Num: [252/600] Discriminator Loss: 0.4401, Generator Loss: 2.5500 D(x): 0.8294, D(G(z)): 0.1388 Epoch: [8/20], Batch Num: [253/600] Discriminator Loss: 0.6334, Generator Loss: 2.4190 D(x): 0.7503, D(G(z)): 0.1334 Epoch: [8/20], Batch Num: [254/600] Discriminator Loss: 0.4182, Generator Loss: 2.0662 D(x): 0.8484, D(G(z)): 0.1273 Epoch: [8/20], Batch Num: [255/600] Discriminator Loss: 0.5149, Generator Loss: 2.1316 D(x): 0.8798, D(G(z)): 0.2439 Epoch: [8/20], Batch Num: [256/600] Discriminator Loss: 0.5364, Generator Loss: 2.3842 D(x): 0.8309, D(G(z)): 0.2101 Epoch: [8/20], Batch Num: [257/600] Discriminator Loss: 0.5721, Generator Loss: 2.4250 D(x): 0.8130, D(G(z)): 0.1933 Epoch: [8/20], Batch Num: [258/600] Discriminator Loss: 0.5493, Generator Loss: 2.5042 D(x): 0.8208, D(G(z)): 0.1736 Epoch: [8/20], Batch Num: [259/600] Discriminator Loss: 0.4975, Generator Loss: 2.7419 D(x): 0.8462, D(G(z)): 0.1509 Epoch: [8/20], Batch Num: [260/600] Discriminator Loss: 0.5259, Generator Loss: 2.7647 D(x): 0.8059, D(G(z)): 0.1420 Epoch: [8/20], Batch Num: [261/600] Discriminator Loss: 0.5642, Generator Loss: 2.5057 D(x): 0.7862, D(G(z)): 0.1451 Epoch: [8/20], Batch Num: [262/600] Discriminator Loss: 0.6084, Generator Loss: 2.2796 D(x): 0.8326, D(G(z)): 0.1805 Epoch: [8/20], Batch Num: [263/600] Discriminator Loss: 0.4390, Generator Loss: 2.7068 D(x): 0.9344, D(G(z)): 0.2189 Epoch: [8/20], Batch Num: [264/600] Discriminator Loss: 0.4846, Generator Loss: 2.5394 D(x): 0.8231, D(G(z)): 0.1415 Epoch: [8/20], Batch Num: [265/600] Discriminator Loss: 0.4656, Generator Loss: 2.8030 D(x): 0.8256, D(G(z)): 0.1338 Epoch: [8/20], Batch Num: [266/600] Discriminator Loss: 0.6390, Generator Loss: 2.5610 D(x): 0.7961, D(G(z)): 0.1473 Epoch: [8/20], Batch Num: [267/600] Discriminator Loss: 0.4734, Generator Loss: 2.5840 D(x): 0.8436, D(G(z)): 0.1716 Epoch: [8/20], Batch Num: [268/600] Discriminator Loss: 0.5017, Generator Loss: 2.0468 D(x): 0.8575, D(G(z)): 0.1680 Epoch: [8/20], Batch Num: [269/600] Discriminator Loss: 0.6016, Generator Loss: 2.3224 D(x): 0.8762, D(G(z)): 0.2291 Epoch: [8/20], Batch Num: [270/600] Discriminator Loss: 0.5294, Generator Loss: 2.7102 D(x): 0.8456, D(G(z)): 0.1791 Epoch: [8/20], Batch Num: [271/600] Discriminator Loss: 0.5753, Generator Loss: 2.9064 D(x): 0.8223, D(G(z)): 0.1642 Epoch: [8/20], Batch Num: [272/600] Discriminator Loss: 0.4985, Generator Loss: 2.6990 D(x): 0.7996, D(G(z)): 0.1205 Epoch: [8/20], Batch Num: [273/600] Discriminator Loss: 0.4959, Generator Loss: 2.5563 D(x): 0.8498, D(G(z)): 0.1592 Epoch: [8/20], Batch Num: [274/600] Discriminator Loss: 0.6380, Generator Loss: 2.2146 D(x): 0.7759, D(G(z)): 0.1505 Epoch: [8/20], Batch Num: [275/600] Discriminator Loss: 0.5516, Generator Loss: 2.2730 D(x): 0.8475, D(G(z)): 0.2121 Epoch: [8/20], Batch Num: [276/600] Discriminator Loss: 0.6604, Generator Loss: 2.4308 D(x): 0.8195, D(G(z)): 0.2324 Epoch: [8/20], Batch Num: [277/600] Discriminator Loss: 0.5057, Generator Loss: 2.4783 D(x): 0.8728, D(G(z)): 0.2088 Epoch: [8/20], Batch Num: [278/600] Discriminator Loss: 0.4776, Generator Loss: 2.9060 D(x): 0.8157, D(G(z)): 0.1177 Epoch: [8/20], Batch Num: [279/600] Discriminator Loss: 0.5666, Generator Loss: 2.7476 D(x): 0.8216, D(G(z)): 0.1420 Epoch: [8/20], Batch Num: [280/600] Discriminator Loss: 0.4272, Generator Loss: 2.6145 D(x): 0.8564, D(G(z)): 0.1392 Epoch: [8/20], Batch Num: [281/600] Discriminator Loss: 0.6357, Generator Loss: 2.6833 D(x): 0.7898, D(G(z)): 0.1612 Epoch: [8/20], Batch Num: [282/600] Discriminator Loss: 0.5660, Generator Loss: 1.9324 D(x): 0.8220, D(G(z)): 0.1817 Epoch: [8/20], Batch Num: [283/600] Discriminator Loss: 0.5946, Generator Loss: 2.3241 D(x): 0.8645, D(G(z)): 0.2312 Epoch: [8/20], Batch Num: [284/600] Discriminator Loss: 0.6081, Generator Loss: 2.9470 D(x): 0.8285, D(G(z)): 0.2172 Epoch: [8/20], Batch Num: [285/600] Discriminator Loss: 0.5628, Generator Loss: 2.9764 D(x): 0.8021, D(G(z)): 0.1472 Epoch: [8/20], Batch Num: [286/600] Discriminator Loss: 0.4691, Generator Loss: 2.6565 D(x): 0.8275, D(G(z)): 0.1155 Epoch: [8/20], Batch Num: [287/600] Discriminator Loss: 0.5573, Generator Loss: 2.3843 D(x): 0.7921, D(G(z)): 0.1351 Epoch: [8/20], Batch Num: [288/600] Discriminator Loss: 0.4438, Generator Loss: 1.8582 D(x): 0.8661, D(G(z)): 0.1590 Epoch: [8/20], Batch Num: [289/600] Discriminator Loss: 0.5224, Generator Loss: 2.3251 D(x): 0.9330, D(G(z)): 0.2818 Epoch: [8/20], Batch Num: [290/600] Discriminator Loss: 0.4249, Generator Loss: 2.9599 D(x): 0.8978, D(G(z)): 0.1965 Epoch: [8/20], Batch Num: [291/600] Discriminator Loss: 0.3522, Generator Loss: 3.2854 D(x): 0.8435, D(G(z)): 0.0843 Epoch: [8/20], Batch Num: [292/600] Discriminator Loss: 0.4399, Generator Loss: 3.0880 D(x): 0.8206, D(G(z)): 0.0755 Epoch: [8/20], Batch Num: [293/600] Discriminator Loss: 0.4757, Generator Loss: 2.6479 D(x): 0.8096, D(G(z)): 0.0744 Epoch: [8/20], Batch Num: [294/600] Discriminator Loss: 0.3609, Generator Loss: 2.2034 D(x): 0.8796, D(G(z)): 0.1300 Epoch: [8/20], Batch Num: [295/600] Discriminator Loss: 0.4991, Generator Loss: 1.9154 D(x): 0.9098, D(G(z)): 0.2340 Epoch: [8/20], Batch Num: [296/600] Discriminator Loss: 0.4235, Generator Loss: 2.5439 D(x): 0.9220, D(G(z)): 0.2101 Epoch: [8/20], Batch Num: [297/600] Discriminator Loss: 0.5459, Generator Loss: 2.8732 D(x): 0.8552, D(G(z)): 0.1783 Epoch: [8/20], Batch Num: [298/600] Discriminator Loss: 0.2430, Generator Loss: 2.9276 D(x): 0.9280, D(G(z)): 0.1198 Epoch: [8/20], Batch Num: [299/600] Discriminator Loss: 0.4034, Generator Loss: 3.1191 D(x): 0.8570, D(G(z)): 0.1051 Epoch: 8, Batch Num: [300/600]
Epoch: [8/20], Batch Num: [300/600] Discriminator Loss: 0.3968, Generator Loss: 3.2484 D(x): 0.8827, D(G(z)): 0.1254 Epoch: [8/20], Batch Num: [301/600] Discriminator Loss: 0.3464, Generator Loss: 2.9154 D(x): 0.8814, D(G(z)): 0.0987 Epoch: [8/20], Batch Num: [302/600] Discriminator Loss: 0.4272, Generator Loss: 2.5844 D(x): 0.8600, D(G(z)): 0.1184 Epoch: [8/20], Batch Num: [303/600] Discriminator Loss: 0.3325, Generator Loss: 2.5573 D(x): 0.9361, D(G(z)): 0.1659 Epoch: [8/20], Batch Num: [304/600] Discriminator Loss: 0.3448, Generator Loss: 3.0346 D(x): 0.9587, D(G(z)): 0.1773 Epoch: [8/20], Batch Num: [305/600] Discriminator Loss: 0.5435, Generator Loss: 3.2643 D(x): 0.8500, D(G(z)): 0.1777 Epoch: [8/20], Batch Num: [306/600] Discriminator Loss: 0.4212, Generator Loss: 3.5674 D(x): 0.8589, D(G(z)): 0.0962 Epoch: [8/20], Batch Num: [307/600] Discriminator Loss: 0.6371, Generator Loss: 3.3388 D(x): 0.7568, D(G(z)): 0.0965 Epoch: [8/20], Batch Num: [308/600] Discriminator Loss: 0.4135, Generator Loss: 2.6346 D(x): 0.8533, D(G(z)): 0.0833 Epoch: [8/20], Batch Num: [309/600] Discriminator Loss: 0.5054, Generator Loss: 2.3997 D(x): 0.8704, D(G(z)): 0.1882 Epoch: [8/20], Batch Num: [310/600] Discriminator Loss: 0.5355, Generator Loss: 2.7387 D(x): 0.9128, D(G(z)): 0.2314 Epoch: [8/20], Batch Num: [311/600] Discriminator Loss: 0.4963, Generator Loss: 2.9750 D(x): 0.8907, D(G(z)): 0.1962 Epoch: [8/20], Batch Num: [312/600] Discriminator Loss: 0.4679, Generator Loss: 3.4995 D(x): 0.8299, D(G(z)): 0.1227 Epoch: [8/20], Batch Num: [313/600] Discriminator Loss: 0.5257, Generator Loss: 3.3298 D(x): 0.7814, D(G(z)): 0.0856 Epoch: [8/20], Batch Num: [314/600] Discriminator Loss: 0.4350, Generator Loss: 2.7634 D(x): 0.8179, D(G(z)): 0.1003 Epoch: [8/20], Batch Num: [315/600] Discriminator Loss: 0.6549, Generator Loss: 2.3079 D(x): 0.8131, D(G(z)): 0.1790 Epoch: [8/20], Batch Num: [316/600] Discriminator Loss: 0.4743, Generator Loss: 2.0857 D(x): 0.9054, D(G(z)): 0.2293 Epoch: [8/20], Batch Num: [317/600] Discriminator Loss: 0.7754, Generator Loss: 2.7966 D(x): 0.8719, D(G(z)): 0.2867 Epoch: [8/20], Batch Num: [318/600] Discriminator Loss: 0.5688, Generator Loss: 3.0656 D(x): 0.8780, D(G(z)): 0.2111 Epoch: [8/20], Batch Num: [319/600] Discriminator Loss: 0.5153, Generator Loss: 3.2336 D(x): 0.8164, D(G(z)): 0.1231 Epoch: [8/20], Batch Num: [320/600] Discriminator Loss: 0.6619, Generator Loss: 3.1928 D(x): 0.7648, D(G(z)): 0.1223 Epoch: [8/20], Batch Num: [321/600] Discriminator Loss: 0.4939, Generator Loss: 2.7467 D(x): 0.8052, D(G(z)): 0.1077 Epoch: [8/20], Batch Num: [322/600] Discriminator Loss: 0.6449, Generator Loss: 2.2291 D(x): 0.8005, D(G(z)): 0.1620 Epoch: [8/20], Batch Num: [323/600] Discriminator Loss: 0.5839, Generator Loss: 2.1249 D(x): 0.8621, D(G(z)): 0.2099 Epoch: [8/20], Batch Num: [324/600] Discriminator Loss: 0.7659, Generator Loss: 2.1027 D(x): 0.8097, D(G(z)): 0.2285 Epoch: [8/20], Batch Num: [325/600] Discriminator Loss: 0.7221, Generator Loss: 2.2780 D(x): 0.8200, D(G(z)): 0.2287 Epoch: [8/20], Batch Num: [326/600] Discriminator Loss: 0.7065, Generator Loss: 2.6107 D(x): 0.8274, D(G(z)): 0.2347 Epoch: [8/20], Batch Num: [327/600] Discriminator Loss: 0.5290, Generator Loss: 3.0093 D(x): 0.8201, D(G(z)): 0.1567 Epoch: [8/20], Batch Num: [328/600] Discriminator Loss: 0.7205, Generator Loss: 2.5758 D(x): 0.7071, D(G(z)): 0.1060 Epoch: [8/20], Batch Num: [329/600] Discriminator Loss: 0.7406, Generator Loss: 2.5260 D(x): 0.7450, D(G(z)): 0.1506 Epoch: [8/20], Batch Num: [330/600] Discriminator Loss: 0.7864, Generator Loss: 2.1260 D(x): 0.7540, D(G(z)): 0.2304 Epoch: [8/20], Batch Num: [331/600] Discriminator Loss: 0.9006, Generator Loss: 1.9771 D(x): 0.8174, D(G(z)): 0.2841 Epoch: [8/20], Batch Num: [332/600] Discriminator Loss: 0.8460, Generator Loss: 2.0150 D(x): 0.7722, D(G(z)): 0.2767 Epoch: [8/20], Batch Num: [333/600] Discriminator Loss: 0.7327, Generator Loss: 2.5242 D(x): 0.7958, D(G(z)): 0.2192 Epoch: [8/20], Batch Num: [334/600] Discriminator Loss: 0.7589, Generator Loss: 2.6223 D(x): 0.7219, D(G(z)): 0.1529 Epoch: [8/20], Batch Num: [335/600] Discriminator Loss: 0.5854, Generator Loss: 2.5783 D(x): 0.7869, D(G(z)): 0.1457 Epoch: [8/20], Batch Num: [336/600] Discriminator Loss: 0.7293, Generator Loss: 2.0174 D(x): 0.7402, D(G(z)): 0.1594 Epoch: [8/20], Batch Num: [337/600] Discriminator Loss: 0.8309, Generator Loss: 1.9295 D(x): 0.7737, D(G(z)): 0.2628 Epoch: [8/20], Batch Num: [338/600] Discriminator Loss: 0.9014, Generator Loss: 2.1463 D(x): 0.7832, D(G(z)): 0.3141 Epoch: [8/20], Batch Num: [339/600] Discriminator Loss: 0.7357, Generator Loss: 2.6763 D(x): 0.8149, D(G(z)): 0.2146 Epoch: [8/20], Batch Num: [340/600] Discriminator Loss: 0.5475, Generator Loss: 2.6756 D(x): 0.8292, D(G(z)): 0.1718 Epoch: [8/20], Batch Num: [341/600] Discriminator Loss: 0.8117, Generator Loss: 2.7385 D(x): 0.7013, D(G(z)): 0.1331 Epoch: [8/20], Batch Num: [342/600] Discriminator Loss: 0.6697, Generator Loss: 2.3433 D(x): 0.7476, D(G(z)): 0.1580 Epoch: [8/20], Batch Num: [343/600] Discriminator Loss: 0.5878, Generator Loss: 2.1290 D(x): 0.8113, D(G(z)): 0.1952 Epoch: [8/20], Batch Num: [344/600] Discriminator Loss: 0.6317, Generator Loss: 2.0640 D(x): 0.7970, D(G(z)): 0.2006 Epoch: [8/20], Batch Num: [345/600] Discriminator Loss: 0.4541, Generator Loss: 2.2284 D(x): 0.9051, D(G(z)): 0.2304 Epoch: [8/20], Batch Num: [346/600] Discriminator Loss: 0.7057, Generator Loss: 2.3326 D(x): 0.8079, D(G(z)): 0.2408 Epoch: [8/20], Batch Num: [347/600] Discriminator Loss: 0.5903, Generator Loss: 2.4721 D(x): 0.7932, D(G(z)): 0.1578 Epoch: [8/20], Batch Num: [348/600] Discriminator Loss: 0.6466, Generator Loss: 2.4857 D(x): 0.7651, D(G(z)): 0.1467 Epoch: [8/20], Batch Num: [349/600] Discriminator Loss: 0.5286, Generator Loss: 2.5147 D(x): 0.8181, D(G(z)): 0.1419 Epoch: [8/20], Batch Num: [350/600] Discriminator Loss: 0.5704, Generator Loss: 2.2361 D(x): 0.7943, D(G(z)): 0.1338 Epoch: [8/20], Batch Num: [351/600] Discriminator Loss: 0.5275, Generator Loss: 2.1036 D(x): 0.8587, D(G(z)): 0.2137 Epoch: [8/20], Batch Num: [352/600] Discriminator Loss: 0.4502, Generator Loss: 2.1880 D(x): 0.8773, D(G(z)): 0.1698 Epoch: [8/20], Batch Num: [353/600] Discriminator Loss: 0.5842, Generator Loss: 2.2455 D(x): 0.8619, D(G(z)): 0.2179 Epoch: [8/20], Batch Num: [354/600] Discriminator Loss: 0.5449, Generator Loss: 2.7755 D(x): 0.8548, D(G(z)): 0.1865 Epoch: [8/20], Batch Num: [355/600] Discriminator Loss: 0.6397, Generator Loss: 2.7128 D(x): 0.7844, D(G(z)): 0.1325 Epoch: [8/20], Batch Num: [356/600] Discriminator Loss: 0.5150, Generator Loss: 2.3239 D(x): 0.7846, D(G(z)): 0.1022 Epoch: [8/20], Batch Num: [357/600] Discriminator Loss: 0.4710, Generator Loss: 2.3800 D(x): 0.8809, D(G(z)): 0.2028 Epoch: [8/20], Batch Num: [358/600] Discriminator Loss: 0.4524, Generator Loss: 2.2851 D(x): 0.8815, D(G(z)): 0.1838 Epoch: [8/20], Batch Num: [359/600] Discriminator Loss: 0.4799, Generator Loss: 2.5408 D(x): 0.8835, D(G(z)): 0.2083 Epoch: [8/20], Batch Num: [360/600] Discriminator Loss: 0.4660, Generator Loss: 2.8311 D(x): 0.8630, D(G(z)): 0.1808 Epoch: [8/20], Batch Num: [361/600] Discriminator Loss: 0.4615, Generator Loss: 2.7564 D(x): 0.8534, D(G(z)): 0.1486 Epoch: [8/20], Batch Num: [362/600] Discriminator Loss: 0.4432, Generator Loss: 2.8341 D(x): 0.8500, D(G(z)): 0.1438 Epoch: [8/20], Batch Num: [363/600] Discriminator Loss: 0.7413, Generator Loss: 2.3004 D(x): 0.7476, D(G(z)): 0.1271 Epoch: [8/20], Batch Num: [364/600] Discriminator Loss: 0.5920, Generator Loss: 2.0906 D(x): 0.8200, D(G(z)): 0.1858 Epoch: [8/20], Batch Num: [365/600] Discriminator Loss: 0.5998, Generator Loss: 1.8989 D(x): 0.8694, D(G(z)): 0.2526 Epoch: [8/20], Batch Num: [366/600] Discriminator Loss: 0.6900, Generator Loss: 2.2797 D(x): 0.8931, D(G(z)): 0.2899 Epoch: [8/20], Batch Num: [367/600] Discriminator Loss: 0.6866, Generator Loss: 2.5865 D(x): 0.8180, D(G(z)): 0.2482 Epoch: [8/20], Batch Num: [368/600] Discriminator Loss: 0.5861, Generator Loss: 2.4119 D(x): 0.7839, D(G(z)): 0.1441 Epoch: [8/20], Batch Num: [369/600] Discriminator Loss: 0.5341, Generator Loss: 2.1715 D(x): 0.8343, D(G(z)): 0.1711 Epoch: [8/20], Batch Num: [370/600] Discriminator Loss: 0.6509, Generator Loss: 2.1099 D(x): 0.7865, D(G(z)): 0.1647 Epoch: [8/20], Batch Num: [371/600] Discriminator Loss: 0.5596, Generator Loss: 2.0311 D(x): 0.8352, D(G(z)): 0.1901 Epoch: [8/20], Batch Num: [372/600] Discriminator Loss: 0.5893, Generator Loss: 1.7701 D(x): 0.8423, D(G(z)): 0.2168 Epoch: [8/20], Batch Num: [373/600] Discriminator Loss: 0.6962, Generator Loss: 2.0151 D(x): 0.8834, D(G(z)): 0.2975 Epoch: [8/20], Batch Num: [374/600] Discriminator Loss: 0.6226, Generator Loss: 2.4516 D(x): 0.8185, D(G(z)): 0.2028 Epoch: [8/20], Batch Num: [375/600] Discriminator Loss: 0.5420, Generator Loss: 2.5877 D(x): 0.7929, D(G(z)): 0.1396 Epoch: [8/20], Batch Num: [376/600] Discriminator Loss: 0.4548, Generator Loss: 2.5326 D(x): 0.8389, D(G(z)): 0.1546 Epoch: [8/20], Batch Num: [377/600] Discriminator Loss: 0.4964, Generator Loss: 2.4943 D(x): 0.8350, D(G(z)): 0.1513 Epoch: [8/20], Batch Num: [378/600] Discriminator Loss: 0.4811, Generator Loss: 2.1130 D(x): 0.8561, D(G(z)): 0.1495 Epoch: [8/20], Batch Num: [379/600] Discriminator Loss: 0.5327, Generator Loss: 2.2930 D(x): 0.8551, D(G(z)): 0.1924 Epoch: [8/20], Batch Num: [380/600] Discriminator Loss: 0.5708, Generator Loss: 2.1112 D(x): 0.8722, D(G(z)): 0.2394 Epoch: [8/20], Batch Num: [381/600] Discriminator Loss: 0.4595, Generator Loss: 2.1983 D(x): 0.8817, D(G(z)): 0.2003 Epoch: [8/20], Batch Num: [382/600] Discriminator Loss: 0.5066, Generator Loss: 2.2111 D(x): 0.8926, D(G(z)): 0.2089 Epoch: [8/20], Batch Num: [383/600] Discriminator Loss: 0.5566, Generator Loss: 2.4348 D(x): 0.8277, D(G(z)): 0.1621 Epoch: [8/20], Batch Num: [384/600] Discriminator Loss: 0.4179, Generator Loss: 2.5590 D(x): 0.8703, D(G(z)): 0.1626 Epoch: [8/20], Batch Num: [385/600] Discriminator Loss: 0.5795, Generator Loss: 2.3855 D(x): 0.8484, D(G(z)): 0.1801 Epoch: [8/20], Batch Num: [386/600] Discriminator Loss: 0.4666, Generator Loss: 2.5346 D(x): 0.8722, D(G(z)): 0.1820 Epoch: [8/20], Batch Num: [387/600] Discriminator Loss: 0.4510, Generator Loss: 2.3510 D(x): 0.8551, D(G(z)): 0.1628 Epoch: [8/20], Batch Num: [388/600] Discriminator Loss: 0.4364, Generator Loss: 2.3520 D(x): 0.8639, D(G(z)): 0.1685 Epoch: [8/20], Batch Num: [389/600] Discriminator Loss: 0.3867, Generator Loss: 2.8670 D(x): 0.9266, D(G(z)): 0.1971 Epoch: [8/20], Batch Num: [390/600] Discriminator Loss: 0.4201, Generator Loss: 2.8763 D(x): 0.8889, D(G(z)): 0.1847 Epoch: [8/20], Batch Num: [391/600] Discriminator Loss: 0.4385, Generator Loss: 2.9557 D(x): 0.8455, D(G(z)): 0.1247 Epoch: [8/20], Batch Num: [392/600] Discriminator Loss: 0.4163, Generator Loss: 2.7986 D(x): 0.8409, D(G(z)): 0.1068 Epoch: [8/20], Batch Num: [393/600] Discriminator Loss: 0.5154, Generator Loss: 2.3824 D(x): 0.8053, D(G(z)): 0.1015 Epoch: [8/20], Batch Num: [394/600] Discriminator Loss: 0.4051, Generator Loss: 2.4963 D(x): 0.9103, D(G(z)): 0.1916 Epoch: [8/20], Batch Num: [395/600] Discriminator Loss: 0.4184, Generator Loss: 2.4565 D(x): 0.9064, D(G(z)): 0.1951 Epoch: [8/20], Batch Num: [396/600] Discriminator Loss: 0.3861, Generator Loss: 2.4199 D(x): 0.9000, D(G(z)): 0.1658 Epoch: [8/20], Batch Num: [397/600] Discriminator Loss: 0.4139, Generator Loss: 2.5924 D(x): 0.9009, D(G(z)): 0.1845 Epoch: [8/20], Batch Num: [398/600] Discriminator Loss: 0.3446, Generator Loss: 2.9063 D(x): 0.8781, D(G(z)): 0.1228 Epoch: [8/20], Batch Num: [399/600] Discriminator Loss: 0.4937, Generator Loss: 2.3786 D(x): 0.8289, D(G(z)): 0.1279 Epoch: 8, Batch Num: [400/600]
Epoch: [8/20], Batch Num: [400/600] Discriminator Loss: 0.5716, Generator Loss: 2.4519 D(x): 0.8183, D(G(z)): 0.1465 Epoch: [8/20], Batch Num: [401/600] Discriminator Loss: 0.4507, Generator Loss: 2.1543 D(x): 0.8998, D(G(z)): 0.1801 Epoch: [8/20], Batch Num: [402/600] Discriminator Loss: 0.4920, Generator Loss: 2.4077 D(x): 0.8700, D(G(z)): 0.1827 Epoch: [8/20], Batch Num: [403/600] Discriminator Loss: 0.4590, Generator Loss: 2.2292 D(x): 0.8690, D(G(z)): 0.1881 Epoch: [8/20], Batch Num: [404/600] Discriminator Loss: 0.5706, Generator Loss: 2.5750 D(x): 0.8872, D(G(z)): 0.2227 Epoch: [8/20], Batch Num: [405/600] Discriminator Loss: 0.5144, Generator Loss: 2.6282 D(x): 0.8088, D(G(z)): 0.1281 Epoch: [8/20], Batch Num: [406/600] Discriminator Loss: 0.6311, Generator Loss: 2.4248 D(x): 0.7780, D(G(z)): 0.1530 Epoch: [8/20], Batch Num: [407/600] Discriminator Loss: 0.6526, Generator Loss: 2.4633 D(x): 0.8014, D(G(z)): 0.1797 Epoch: [8/20], Batch Num: [408/600] Discriminator Loss: 0.7823, Generator Loss: 1.8937 D(x): 0.7815, D(G(z)): 0.2103 Epoch: [8/20], Batch Num: [409/600] Discriminator Loss: 0.7900, Generator Loss: 1.7393 D(x): 0.8199, D(G(z)): 0.2841 Epoch: [8/20], Batch Num: [410/600] Discriminator Loss: 0.6507, Generator Loss: 2.2448 D(x): 0.8887, D(G(z)): 0.2757 Epoch: [8/20], Batch Num: [411/600] Discriminator Loss: 0.5838, Generator Loss: 2.6591 D(x): 0.8469, D(G(z)): 0.2113 Epoch: [8/20], Batch Num: [412/600] Discriminator Loss: 0.6427, Generator Loss: 2.8382 D(x): 0.7855, D(G(z)): 0.1806 Epoch: [8/20], Batch Num: [413/600] Discriminator Loss: 0.6957, Generator Loss: 2.6267 D(x): 0.7495, D(G(z)): 0.1183 Epoch: [8/20], Batch Num: [414/600] Discriminator Loss: 0.8060, Generator Loss: 2.3606 D(x): 0.7065, D(G(z)): 0.1390 Epoch: [8/20], Batch Num: [415/600] Discriminator Loss: 0.6374, Generator Loss: 2.2367 D(x): 0.8252, D(G(z)): 0.2115 Epoch: [8/20], Batch Num: [416/600] Discriminator Loss: 0.7323, Generator Loss: 2.1539 D(x): 0.8251, D(G(z)): 0.2444 Epoch: [8/20], Batch Num: [417/600] Discriminator Loss: 0.6747, Generator Loss: 2.1561 D(x): 0.8762, D(G(z)): 0.2740 Epoch: [8/20], Batch Num: [418/600] Discriminator Loss: 0.7125, Generator Loss: 2.3449 D(x): 0.8044, D(G(z)): 0.2387 Epoch: [8/20], Batch Num: [419/600] Discriminator Loss: 0.7758, Generator Loss: 2.6162 D(x): 0.7544, D(G(z)): 0.2018 Epoch: [8/20], Batch Num: [420/600] Discriminator Loss: 0.8416, Generator Loss: 2.5356 D(x): 0.7208, D(G(z)): 0.1886 Epoch: [8/20], Batch Num: [421/600] Discriminator Loss: 0.8656, Generator Loss: 2.2576 D(x): 0.7174, D(G(z)): 0.1616 Epoch: [8/20], Batch Num: [422/600] Discriminator Loss: 0.7320, Generator Loss: 2.0854 D(x): 0.7471, D(G(z)): 0.1672 Epoch: [8/20], Batch Num: [423/600] Discriminator Loss: 0.7839, Generator Loss: 1.7040 D(x): 0.8144, D(G(z)): 0.2814 Epoch: [8/20], Batch Num: [424/600] Discriminator Loss: 0.7962, Generator Loss: 1.8791 D(x): 0.8591, D(G(z)): 0.3192 Epoch: [8/20], Batch Num: [425/600] Discriminator Loss: 0.9057, Generator Loss: 2.6731 D(x): 0.7924, D(G(z)): 0.2844 Epoch: [8/20], Batch Num: [426/600] Discriminator Loss: 0.7040, Generator Loss: 2.4929 D(x): 0.7401, D(G(z)): 0.1514 Epoch: [8/20], Batch Num: [427/600] Discriminator Loss: 0.6520, Generator Loss: 2.5578 D(x): 0.7505, D(G(z)): 0.1348 Epoch: [8/20], Batch Num: [428/600] Discriminator Loss: 0.7473, Generator Loss: 2.2343 D(x): 0.7304, D(G(z)): 0.1678 Epoch: [8/20], Batch Num: [429/600] Discriminator Loss: 0.6370, Generator Loss: 2.0009 D(x): 0.8111, D(G(z)): 0.2149 Epoch: [8/20], Batch Num: [430/600] Discriminator Loss: 0.6253, Generator Loss: 1.7297 D(x): 0.8305, D(G(z)): 0.2343 Epoch: [8/20], Batch Num: [431/600] Discriminator Loss: 0.6881, Generator Loss: 2.1954 D(x): 0.8465, D(G(z)): 0.2613 Epoch: [8/20], Batch Num: [432/600] Discriminator Loss: 0.6710, Generator Loss: 2.4232 D(x): 0.8106, D(G(z)): 0.2393 Epoch: [8/20], Batch Num: [433/600] Discriminator Loss: 0.6632, Generator Loss: 2.9677 D(x): 0.7772, D(G(z)): 0.1522 Epoch: [8/20], Batch Num: [434/600] Discriminator Loss: 0.6364, Generator Loss: 2.5253 D(x): 0.7348, D(G(z)): 0.1342 Epoch: [8/20], Batch Num: [435/600] Discriminator Loss: 0.7949, Generator Loss: 2.1366 D(x): 0.7353, D(G(z)): 0.1925 Epoch: [8/20], Batch Num: [436/600] Discriminator Loss: 0.6722, Generator Loss: 1.9335 D(x): 0.7944, D(G(z)): 0.2153 Epoch: [8/20], Batch Num: [437/600] Discriminator Loss: 0.7563, Generator Loss: 2.0672 D(x): 0.8417, D(G(z)): 0.2783 Epoch: [8/20], Batch Num: [438/600] Discriminator Loss: 0.7256, Generator Loss: 2.2767 D(x): 0.8700, D(G(z)): 0.2829 Epoch: [8/20], Batch Num: [439/600] Discriminator Loss: 0.7057, Generator Loss: 2.7211 D(x): 0.7786, D(G(z)): 0.1937 Epoch: [8/20], Batch Num: [440/600] Discriminator Loss: 0.6956, Generator Loss: 2.6186 D(x): 0.7390, D(G(z)): 0.1534 Epoch: [8/20], Batch Num: [441/600] Discriminator Loss: 0.8815, Generator Loss: 2.3935 D(x): 0.7012, D(G(z)): 0.2038 Epoch: [8/20], Batch Num: [442/600] Discriminator Loss: 0.8714, Generator Loss: 1.8372 D(x): 0.7254, D(G(z)): 0.2078 Epoch: [8/20], Batch Num: [443/600] Discriminator Loss: 0.9346, Generator Loss: 1.8675 D(x): 0.7581, D(G(z)): 0.2818 Epoch: [8/20], Batch Num: [444/600] Discriminator Loss: 0.8247, Generator Loss: 2.2988 D(x): 0.7960, D(G(z)): 0.2838 Epoch: [8/20], Batch Num: [445/600] Discriminator Loss: 0.8191, Generator Loss: 2.0271 D(x): 0.7582, D(G(z)): 0.2801 Epoch: [8/20], Batch Num: [446/600] Discriminator Loss: 0.8508, Generator Loss: 2.4361 D(x): 0.7750, D(G(z)): 0.2701 Epoch: [8/20], Batch Num: [447/600] Discriminator Loss: 0.7663, Generator Loss: 2.5292 D(x): 0.7084, D(G(z)): 0.1693 Epoch: [8/20], Batch Num: [448/600] Discriminator Loss: 0.7154, Generator Loss: 2.2722 D(x): 0.7113, D(G(z)): 0.1577 Epoch: [8/20], Batch Num: [449/600] Discriminator Loss: 0.8330, Generator Loss: 1.7286 D(x): 0.7087, D(G(z)): 0.2158 Epoch: [8/20], Batch Num: [450/600] Discriminator Loss: 1.0198, Generator Loss: 1.6171 D(x): 0.7127, D(G(z)): 0.3000 Epoch: [8/20], Batch Num: [451/600] Discriminator Loss: 0.8905, Generator Loss: 1.8662 D(x): 0.8193, D(G(z)): 0.3464 Epoch: [8/20], Batch Num: [452/600] Discriminator Loss: 0.8709, Generator Loss: 1.8571 D(x): 0.7109, D(G(z)): 0.2409 Epoch: [8/20], Batch Num: [453/600] Discriminator Loss: 0.9144, Generator Loss: 1.8745 D(x): 0.6588, D(G(z)): 0.2194 Epoch: [8/20], Batch Num: [454/600] Discriminator Loss: 0.8251, Generator Loss: 1.8963 D(x): 0.7160, D(G(z)): 0.2325 Epoch: [8/20], Batch Num: [455/600] Discriminator Loss: 0.7483, Generator Loss: 1.6327 D(x): 0.7335, D(G(z)): 0.2305 Epoch: [8/20], Batch Num: [456/600] Discriminator Loss: 0.7992, Generator Loss: 1.8391 D(x): 0.7729, D(G(z)): 0.2822 Epoch: [8/20], Batch Num: [457/600] Discriminator Loss: 0.6775, Generator Loss: 1.8636 D(x): 0.7746, D(G(z)): 0.2336 Epoch: [8/20], Batch Num: [458/600] Discriminator Loss: 0.8022, Generator Loss: 2.0842 D(x): 0.7312, D(G(z)): 0.2415 Epoch: [8/20], Batch Num: [459/600] Discriminator Loss: 0.8667, Generator Loss: 1.7111 D(x): 0.6849, D(G(z)): 0.2109 Epoch: [8/20], Batch Num: [460/600] Discriminator Loss: 0.6911, Generator Loss: 1.7753 D(x): 0.7626, D(G(z)): 0.2256 Epoch: [8/20], Batch Num: [461/600] Discriminator Loss: 0.7731, Generator Loss: 2.0190 D(x): 0.8215, D(G(z)): 0.2972 Epoch: [8/20], Batch Num: [462/600] Discriminator Loss: 0.6071, Generator Loss: 2.2338 D(x): 0.8256, D(G(z)): 0.2496 Epoch: [8/20], Batch Num: [463/600] Discriminator Loss: 0.6236, Generator Loss: 2.1835 D(x): 0.7462, D(G(z)): 0.1730 Epoch: [8/20], Batch Num: [464/600] Discriminator Loss: 0.6034, Generator Loss: 2.0359 D(x): 0.7885, D(G(z)): 0.2031 Epoch: [8/20], Batch Num: [465/600] Discriminator Loss: 0.6463, Generator Loss: 2.2587 D(x): 0.8038, D(G(z)): 0.2289 Epoch: [8/20], Batch Num: [466/600] Discriminator Loss: 0.5917, Generator Loss: 2.1186 D(x): 0.7869, D(G(z)): 0.1966 Epoch: [8/20], Batch Num: [467/600] Discriminator Loss: 0.6837, Generator Loss: 2.1673 D(x): 0.8071, D(G(z)): 0.2287 Epoch: [8/20], Batch Num: [468/600] Discriminator Loss: 0.5521, Generator Loss: 2.2199 D(x): 0.7926, D(G(z)): 0.1831 Epoch: [8/20], Batch Num: [469/600] Discriminator Loss: 0.7060, Generator Loss: 2.0095 D(x): 0.7367, D(G(z)): 0.1987 Epoch: [8/20], Batch Num: [470/600] Discriminator Loss: 0.6202, Generator Loss: 2.0325 D(x): 0.8545, D(G(z)): 0.2363 Epoch: [8/20], Batch Num: [471/600] Discriminator Loss: 0.8013, Generator Loss: 2.1291 D(x): 0.7274, D(G(z)): 0.2439 Epoch: [8/20], Batch Num: [472/600] Discriminator Loss: 0.5985, Generator Loss: 2.4692 D(x): 0.8194, D(G(z)): 0.1998 Epoch: [8/20], Batch Num: [473/600] Discriminator Loss: 0.7422, Generator Loss: 2.0704 D(x): 0.7249, D(G(z)): 0.1923 Epoch: [8/20], Batch Num: [474/600] Discriminator Loss: 0.8090, Generator Loss: 1.8278 D(x): 0.7193, D(G(z)): 0.2135 Epoch: [8/20], Batch Num: [475/600] Discriminator Loss: 0.9294, Generator Loss: 1.8216 D(x): 0.7410, D(G(z)): 0.2885 Epoch: [8/20], Batch Num: [476/600] Discriminator Loss: 0.8883, Generator Loss: 2.0004 D(x): 0.8113, D(G(z)): 0.3305 Epoch: [8/20], Batch Num: [477/600] Discriminator Loss: 0.7113, Generator Loss: 2.4680 D(x): 0.8011, D(G(z)): 0.2547 Epoch: [8/20], Batch Num: [478/600] Discriminator Loss: 0.8252, Generator Loss: 2.3109 D(x): 0.6824, D(G(z)): 0.1686 Epoch: [8/20], Batch Num: [479/600] Discriminator Loss: 0.9510, Generator Loss: 2.2913 D(x): 0.6550, D(G(z)): 0.1938 Epoch: [8/20], Batch Num: [480/600] Discriminator Loss: 0.9704, Generator Loss: 1.8425 D(x): 0.6538, D(G(z)): 0.2011 Epoch: [8/20], Batch Num: [481/600] Discriminator Loss: 1.1412, Generator Loss: 1.6291 D(x): 0.7331, D(G(z)): 0.3838 Epoch: [8/20], Batch Num: [482/600] Discriminator Loss: 0.9613, Generator Loss: 1.7258 D(x): 0.7651, D(G(z)): 0.3199 Epoch: [8/20], Batch Num: [483/600] Discriminator Loss: 1.1859, Generator Loss: 2.0569 D(x): 0.6524, D(G(z)): 0.3257 Epoch: [8/20], Batch Num: [484/600] Discriminator Loss: 1.2142, Generator Loss: 1.6992 D(x): 0.5899, D(G(z)): 0.2596 Epoch: [8/20], Batch Num: [485/600] Discriminator Loss: 1.0414, Generator Loss: 1.8521 D(x): 0.6711, D(G(z)): 0.2802 Epoch: [8/20], Batch Num: [486/600] Discriminator Loss: 0.9580, Generator Loss: 1.5359 D(x): 0.6503, D(G(z)): 0.2421 Epoch: [8/20], Batch Num: [487/600] Discriminator Loss: 1.0231, Generator Loss: 1.8305 D(x): 0.7230, D(G(z)): 0.3652 Epoch: [8/20], Batch Num: [488/600] Discriminator Loss: 0.8841, Generator Loss: 1.7697 D(x): 0.6907, D(G(z)): 0.2622 Epoch: [8/20], Batch Num: [489/600] Discriminator Loss: 0.8658, Generator Loss: 1.5969 D(x): 0.7037, D(G(z)): 0.2686 Epoch: [8/20], Batch Num: [490/600] Discriminator Loss: 0.9839, Generator Loss: 1.6591 D(x): 0.6360, D(G(z)): 0.2798 Epoch: [8/20], Batch Num: [491/600] Discriminator Loss: 0.9331, Generator Loss: 1.6256 D(x): 0.6897, D(G(z)): 0.2860 Epoch: [8/20], Batch Num: [492/600] Discriminator Loss: 0.8483, Generator Loss: 1.8036 D(x): 0.7375, D(G(z)): 0.3037 Epoch: [8/20], Batch Num: [493/600] Discriminator Loss: 0.7588, Generator Loss: 1.8879 D(x): 0.7000, D(G(z)): 0.2098 Epoch: [8/20], Batch Num: [494/600] Discriminator Loss: 0.7252, Generator Loss: 1.8722 D(x): 0.7068, D(G(z)): 0.2107 Epoch: [8/20], Batch Num: [495/600] Discriminator Loss: 0.8058, Generator Loss: 1.7209 D(x): 0.7171, D(G(z)): 0.2345 Epoch: [8/20], Batch Num: [496/600] Discriminator Loss: 0.7247, Generator Loss: 1.8014 D(x): 0.7644, D(G(z)): 0.2314 Epoch: [8/20], Batch Num: [497/600] Discriminator Loss: 0.6999, Generator Loss: 2.0815 D(x): 0.7963, D(G(z)): 0.2698 Epoch: [8/20], Batch Num: [498/600] Discriminator Loss: 0.6091, Generator Loss: 2.1877 D(x): 0.7865, D(G(z)): 0.2206 Epoch: [8/20], Batch Num: [499/600] Discriminator Loss: 0.5445, Generator Loss: 2.1677 D(x): 0.7816, D(G(z)): 0.1785 Epoch: 8, Batch Num: [500/600]
Epoch: [8/20], Batch Num: [500/600] Discriminator Loss: 0.5277, Generator Loss: 2.1441 D(x): 0.8357, D(G(z)): 0.2115 Epoch: [8/20], Batch Num: [501/600] Discriminator Loss: 0.5324, Generator Loss: 2.1596 D(x): 0.7985, D(G(z)): 0.1782 Epoch: [8/20], Batch Num: [502/600] Discriminator Loss: 0.6919, Generator Loss: 2.1842 D(x): 0.7922, D(G(z)): 0.2450 Epoch: [8/20], Batch Num: [503/600] Discriminator Loss: 0.7000, Generator Loss: 2.3486 D(x): 0.8067, D(G(z)): 0.2510 Epoch: [8/20], Batch Num: [504/600] Discriminator Loss: 0.5641, Generator Loss: 2.4328 D(x): 0.8101, D(G(z)): 0.2107 Epoch: [8/20], Batch Num: [505/600] Discriminator Loss: 0.6254, Generator Loss: 2.3637 D(x): 0.7567, D(G(z)): 0.1805 Epoch: [8/20], Batch Num: [506/600] Discriminator Loss: 0.6058, Generator Loss: 2.2919 D(x): 0.8311, D(G(z)): 0.2150 Epoch: [8/20], Batch Num: [507/600] Discriminator Loss: 0.7447, Generator Loss: 2.3025 D(x): 0.8154, D(G(z)): 0.2603 Epoch: [8/20], Batch Num: [508/600] Discriminator Loss: 0.6151, Generator Loss: 2.5043 D(x): 0.8227, D(G(z)): 0.2033 Epoch: [8/20], Batch Num: [509/600] Discriminator Loss: 0.6331, Generator Loss: 2.2864 D(x): 0.7628, D(G(z)): 0.1656 Epoch: [8/20], Batch Num: [510/600] Discriminator Loss: 0.7964, Generator Loss: 2.1184 D(x): 0.7823, D(G(z)): 0.2471 Epoch: [8/20], Batch Num: [511/600] Discriminator Loss: 0.7463, Generator Loss: 2.1312 D(x): 0.7660, D(G(z)): 0.2360 Epoch: [8/20], Batch Num: [512/600] Discriminator Loss: 0.8683, Generator Loss: 2.1003 D(x): 0.7648, D(G(z)): 0.2779 Epoch: [8/20], Batch Num: [513/600] Discriminator Loss: 0.9457, Generator Loss: 2.2351 D(x): 0.7249, D(G(z)): 0.2674 Epoch: [8/20], Batch Num: [514/600] Discriminator Loss: 0.8665, Generator Loss: 1.7397 D(x): 0.7003, D(G(z)): 0.2138 Epoch: [8/20], Batch Num: [515/600] Discriminator Loss: 1.1302, Generator Loss: 1.6203 D(x): 0.7369, D(G(z)): 0.3558 Epoch: [8/20], Batch Num: [516/600] Discriminator Loss: 0.9817, Generator Loss: 1.8715 D(x): 0.7526, D(G(z)): 0.3555 Epoch: [8/20], Batch Num: [517/600] Discriminator Loss: 0.9136, Generator Loss: 1.6552 D(x): 0.7329, D(G(z)): 0.2924 Epoch: [8/20], Batch Num: [518/600] Discriminator Loss: 0.9317, Generator Loss: 1.9363 D(x): 0.7425, D(G(z)): 0.3074 Epoch: [8/20], Batch Num: [519/600] Discriminator Loss: 0.8285, Generator Loss: 1.7271 D(x): 0.7409, D(G(z)): 0.2785 Epoch: [8/20], Batch Num: [520/600] Discriminator Loss: 0.9392, Generator Loss: 1.7336 D(x): 0.7051, D(G(z)): 0.2764 Epoch: [8/20], Batch Num: [521/600] Discriminator Loss: 0.7555, Generator Loss: 1.7717 D(x): 0.7788, D(G(z)): 0.2750 Epoch: [8/20], Batch Num: [522/600] Discriminator Loss: 1.0430, Generator Loss: 1.8900 D(x): 0.7190, D(G(z)): 0.3409 Epoch: [8/20], Batch Num: [523/600] Discriminator Loss: 0.8151, Generator Loss: 2.0148 D(x): 0.7374, D(G(z)): 0.2553 Epoch: [8/20], Batch Num: [524/600] Discriminator Loss: 0.9187, Generator Loss: 1.7904 D(x): 0.6687, D(G(z)): 0.2118 Epoch: [8/20], Batch Num: [525/600] Discriminator Loss: 0.5425, Generator Loss: 1.7145 D(x): 0.8171, D(G(z)): 0.2231 Epoch: [8/20], Batch Num: [526/600] Discriminator Loss: 0.7566, Generator Loss: 1.9769 D(x): 0.7731, D(G(z)): 0.2725 Epoch: [8/20], Batch Num: [527/600] Discriminator Loss: 0.6362, Generator Loss: 2.0626 D(x): 0.8066, D(G(z)): 0.2597 Epoch: [8/20], Batch Num: [528/600] Discriminator Loss: 0.6423, Generator Loss: 1.9986 D(x): 0.7816, D(G(z)): 0.2194 Epoch: [8/20], Batch Num: [529/600] Discriminator Loss: 0.5310, Generator Loss: 2.2283 D(x): 0.8399, D(G(z)): 0.2370 Epoch: [8/20], Batch Num: [530/600] Discriminator Loss: 0.4994, Generator Loss: 2.3920 D(x): 0.8156, D(G(z)): 0.1888 Epoch: [8/20], Batch Num: [531/600] Discriminator Loss: 0.5707, Generator Loss: 2.5425 D(x): 0.7946, D(G(z)): 0.1938 Epoch: [8/20], Batch Num: [532/600] Discriminator Loss: 0.5085, Generator Loss: 2.5360 D(x): 0.8595, D(G(z)): 0.2166 Epoch: [8/20], Batch Num: [533/600] Discriminator Loss: 0.4679, Generator Loss: 2.6801 D(x): 0.8109, D(G(z)): 0.1514 Epoch: [8/20], Batch Num: [534/600] Discriminator Loss: 0.3843, Generator Loss: 2.5204 D(x): 0.8581, D(G(z)): 0.1613 Epoch: [8/20], Batch Num: [535/600] Discriminator Loss: 0.5058, Generator Loss: 2.6454 D(x): 0.8168, D(G(z)): 0.1653 Epoch: [8/20], Batch Num: [536/600] Discriminator Loss: 0.4386, Generator Loss: 2.3479 D(x): 0.8682, D(G(z)): 0.1831 Epoch: [8/20], Batch Num: [537/600] Discriminator Loss: 0.4231, Generator Loss: 2.6403 D(x): 0.8771, D(G(z)): 0.1857 Epoch: [8/20], Batch Num: [538/600] Discriminator Loss: 0.3745, Generator Loss: 2.5985 D(x): 0.9071, D(G(z)): 0.1875 Epoch: [8/20], Batch Num: [539/600] Discriminator Loss: 0.4432, Generator Loss: 2.8350 D(x): 0.8760, D(G(z)): 0.1872 Epoch: [8/20], Batch Num: [540/600] Discriminator Loss: 0.5368, Generator Loss: 2.6694 D(x): 0.8022, D(G(z)): 0.1404 Epoch: [8/20], Batch Num: [541/600] Discriminator Loss: 0.3829, Generator Loss: 2.5809 D(x): 0.8525, D(G(z)): 0.1310 Epoch: [8/20], Batch Num: [542/600] Discriminator Loss: 0.5328, Generator Loss: 2.0566 D(x): 0.8088, D(G(z)): 0.1441 Epoch: [8/20], Batch Num: [543/600] Discriminator Loss: 0.4805, Generator Loss: 2.0298 D(x): 0.9018, D(G(z)): 0.2173 Epoch: [8/20], Batch Num: [544/600] Discriminator Loss: 0.6330, Generator Loss: 2.4176 D(x): 0.8850, D(G(z)): 0.2816 Epoch: [8/20], Batch Num: [545/600] Discriminator Loss: 0.6049, Generator Loss: 2.9139 D(x): 0.8590, D(G(z)): 0.2378 Epoch: [8/20], Batch Num: [546/600] Discriminator Loss: 0.6721, Generator Loss: 2.9362 D(x): 0.7687, D(G(z)): 0.1568 Epoch: [8/20], Batch Num: [547/600] Discriminator Loss: 0.6761, Generator Loss: 2.8676 D(x): 0.7498, D(G(z)): 0.1619 Epoch: [8/20], Batch Num: [548/600] Discriminator Loss: 0.7478, Generator Loss: 2.2170 D(x): 0.7294, D(G(z)): 0.1994 Epoch: [8/20], Batch Num: [549/600] Discriminator Loss: 0.8117, Generator Loss: 1.9676 D(x): 0.8110, D(G(z)): 0.2671 Epoch: [8/20], Batch Num: [550/600] Discriminator Loss: 1.0321, Generator Loss: 1.8592 D(x): 0.7204, D(G(z)): 0.3082 Epoch: [8/20], Batch Num: [551/600] Discriminator Loss: 1.0176, Generator Loss: 2.1234 D(x): 0.7996, D(G(z)): 0.3436 Epoch: [8/20], Batch Num: [552/600] Discriminator Loss: 0.7870, Generator Loss: 2.3576 D(x): 0.7503, D(G(z)): 0.2624 Epoch: [8/20], Batch Num: [553/600] Discriminator Loss: 0.9980, Generator Loss: 2.1886 D(x): 0.6749, D(G(z)): 0.2437 Epoch: [8/20], Batch Num: [554/600] Discriminator Loss: 0.8546, Generator Loss: 1.9934 D(x): 0.7005, D(G(z)): 0.2187 Epoch: [8/20], Batch Num: [555/600] Discriminator Loss: 1.2650, Generator Loss: 1.2854 D(x): 0.5805, D(G(z)): 0.2644 Epoch: [8/20], Batch Num: [556/600] Discriminator Loss: 1.0581, Generator Loss: 1.4660 D(x): 0.7697, D(G(z)): 0.3975 Epoch: [8/20], Batch Num: [557/600] Discriminator Loss: 1.0207, Generator Loss: 1.8513 D(x): 0.7601, D(G(z)): 0.3923 Epoch: [8/20], Batch Num: [558/600] Discriminator Loss: 0.9594, Generator Loss: 2.1751 D(x): 0.6984, D(G(z)): 0.2470 Epoch: [8/20], Batch Num: [559/600] Discriminator Loss: 0.8574, Generator Loss: 2.4178 D(x): 0.7222, D(G(z)): 0.2421 Epoch: [8/20], Batch Num: [560/600] Discriminator Loss: 0.9038, Generator Loss: 2.0926 D(x): 0.6190, D(G(z)): 0.1770 Epoch: [8/20], Batch Num: [561/600] Discriminator Loss: 0.8115, Generator Loss: 1.7937 D(x): 0.6763, D(G(z)): 0.1992 Epoch: [8/20], Batch Num: [562/600] Discriminator Loss: 0.7986, Generator Loss: 1.6017 D(x): 0.7919, D(G(z)): 0.3017 Epoch: [8/20], Batch Num: [563/600] Discriminator Loss: 0.8932, Generator Loss: 1.8183 D(x): 0.8172, D(G(z)): 0.3661 Epoch: [8/20], Batch Num: [564/600] Discriminator Loss: 0.7630, Generator Loss: 2.1743 D(x): 0.7747, D(G(z)): 0.2860 Epoch: [8/20], Batch Num: [565/600] Discriminator Loss: 0.8360, Generator Loss: 2.3237 D(x): 0.6410, D(G(z)): 0.1923 Epoch: [8/20], Batch Num: [566/600] Discriminator Loss: 0.7568, Generator Loss: 2.1789 D(x): 0.7491, D(G(z)): 0.2381 Epoch: [8/20], Batch Num: [567/600] Discriminator Loss: 0.7820, Generator Loss: 1.9013 D(x): 0.6621, D(G(z)): 0.1792 Epoch: [8/20], Batch Num: [568/600] Discriminator Loss: 0.6955, Generator Loss: 1.5999 D(x): 0.7759, D(G(z)): 0.2455 Epoch: [8/20], Batch Num: [569/600] Discriminator Loss: 0.7888, Generator Loss: 1.6348 D(x): 0.7896, D(G(z)): 0.3007 Epoch: [8/20], Batch Num: [570/600] Discriminator Loss: 0.6821, Generator Loss: 1.9280 D(x): 0.8568, D(G(z)): 0.3179 Epoch: [8/20], Batch Num: [571/600] Discriminator Loss: 0.7079, Generator Loss: 2.2064 D(x): 0.7600, D(G(z)): 0.2368 Epoch: [8/20], Batch Num: [572/600] Discriminator Loss: 0.7550, Generator Loss: 2.3231 D(x): 0.7260, D(G(z)): 0.2092 Epoch: [8/20], Batch Num: [573/600] Discriminator Loss: 0.7748, Generator Loss: 2.1874 D(x): 0.6914, D(G(z)): 0.1780 Epoch: [8/20], Batch Num: [574/600] Discriminator Loss: 0.9153, Generator Loss: 1.7128 D(x): 0.6784, D(G(z)): 0.2331 Epoch: [8/20], Batch Num: [575/600] Discriminator Loss: 0.8922, Generator Loss: 1.6391 D(x): 0.7694, D(G(z)): 0.3254 Epoch: [8/20], Batch Num: [576/600] Discriminator Loss: 0.8735, Generator Loss: 1.7540 D(x): 0.7797, D(G(z)): 0.3428 Epoch: [8/20], Batch Num: [577/600] Discriminator Loss: 1.0627, Generator Loss: 2.2900 D(x): 0.7204, D(G(z)): 0.3172 Epoch: [8/20], Batch Num: [578/600] Discriminator Loss: 0.8924, Generator Loss: 2.3587 D(x): 0.6947, D(G(z)): 0.2323 Epoch: [8/20], Batch Num: [579/600] Discriminator Loss: 1.0666, Generator Loss: 2.1750 D(x): 0.6435, D(G(z)): 0.2253 Epoch: [8/20], Batch Num: [580/600] Discriminator Loss: 0.8860, Generator Loss: 1.6876 D(x): 0.6831, D(G(z)): 0.2255 Epoch: [8/20], Batch Num: [581/600] Discriminator Loss: 0.8439, Generator Loss: 1.3055 D(x): 0.6990, D(G(z)): 0.2544 Epoch: [8/20], Batch Num: [582/600] Discriminator Loss: 1.1033, Generator Loss: 1.4866 D(x): 0.7780, D(G(z)): 0.4251 Epoch: [8/20], Batch Num: [583/600] Discriminator Loss: 0.8818, Generator Loss: 1.5844 D(x): 0.7913, D(G(z)): 0.3730 Epoch: [8/20], Batch Num: [584/600] Discriminator Loss: 0.9569, Generator Loss: 2.1935 D(x): 0.7162, D(G(z)): 0.2964 Epoch: [8/20], Batch Num: [585/600] Discriminator Loss: 1.1274, Generator Loss: 2.0128 D(x): 0.5666, D(G(z)): 0.1979 Epoch: [8/20], Batch Num: [586/600] Discriminator Loss: 0.9189, Generator Loss: 1.9312 D(x): 0.6745, D(G(z)): 0.2271 Epoch: [8/20], Batch Num: [587/600] Discriminator Loss: 0.9352, Generator Loss: 1.8324 D(x): 0.6940, D(G(z)): 0.2594 Epoch: [8/20], Batch Num: [588/600] Discriminator Loss: 1.0089, Generator Loss: 1.5314 D(x): 0.6758, D(G(z)): 0.2789 Epoch: [8/20], Batch Num: [589/600] Discriminator Loss: 0.8288, Generator Loss: 1.7315 D(x): 0.7924, D(G(z)): 0.3309 Epoch: [8/20], Batch Num: [590/600] Discriminator Loss: 0.8024, Generator Loss: 1.7685 D(x): 0.7896, D(G(z)): 0.3023 Epoch: [8/20], Batch Num: [591/600] Discriminator Loss: 0.8279, Generator Loss: 2.2975 D(x): 0.7420, D(G(z)): 0.2717 Epoch: [8/20], Batch Num: [592/600] Discriminator Loss: 0.7863, Generator Loss: 2.3785 D(x): 0.7141, D(G(z)): 0.2091 Epoch: [8/20], Batch Num: [593/600] Discriminator Loss: 0.7177, Generator Loss: 2.2473 D(x): 0.7198, D(G(z)): 0.1857 Epoch: [8/20], Batch Num: [594/600] Discriminator Loss: 0.7298, Generator Loss: 1.9873 D(x): 0.6953, D(G(z)): 0.1745 Epoch: [8/20], Batch Num: [595/600] Discriminator Loss: 0.7028, Generator Loss: 1.7477 D(x): 0.7905, D(G(z)): 0.2448 Epoch: [8/20], Batch Num: [596/600] Discriminator Loss: 0.7074, Generator Loss: 1.7869 D(x): 0.8105, D(G(z)): 0.2844 Epoch: [8/20], Batch Num: [597/600] Discriminator Loss: 0.7852, Generator Loss: 2.1949 D(x): 0.8222, D(G(z)): 0.3157 Epoch: [8/20], Batch Num: [598/600] Discriminator Loss: 0.6173, Generator Loss: 2.5464 D(x): 0.8255, D(G(z)): 0.2490 Epoch: [8/20], Batch Num: [599/600] Discriminator Loss: 0.6360, Generator Loss: 2.7326 D(x): 0.7742, D(G(z)): 0.1978 Epoch: 9, Batch Num: [0/600]
Epoch: [9/20], Batch Num: [0/600] Discriminator Loss: 0.7457, Generator Loss: 2.4994 D(x): 0.6869, D(G(z)): 0.1316 Epoch: [9/20], Batch Num: [1/600] Discriminator Loss: 0.5122, Generator Loss: 2.3394 D(x): 0.7949, D(G(z)): 0.1520 Epoch: [9/20], Batch Num: [2/600] Discriminator Loss: 0.6908, Generator Loss: 1.7992 D(x): 0.7301, D(G(z)): 0.1704 Epoch: [9/20], Batch Num: [3/600] Discriminator Loss: 0.5421, Generator Loss: 1.8640 D(x): 0.8660, D(G(z)): 0.2468 Epoch: [9/20], Batch Num: [4/600] Discriminator Loss: 0.6093, Generator Loss: 1.7480 D(x): 0.8662, D(G(z)): 0.2714 Epoch: [9/20], Batch Num: [5/600] Discriminator Loss: 0.7208, Generator Loss: 2.0557 D(x): 0.8312, D(G(z)): 0.3020 Epoch: [9/20], Batch Num: [6/600] Discriminator Loss: 0.7709, Generator Loss: 2.4356 D(x): 0.7948, D(G(z)): 0.2377 Epoch: [9/20], Batch Num: [7/600] Discriminator Loss: 0.8196, Generator Loss: 2.4463 D(x): 0.7391, D(G(z)): 0.1767 Epoch: [9/20], Batch Num: [8/600] Discriminator Loss: 0.5497, Generator Loss: 2.1606 D(x): 0.7897, D(G(z)): 0.1520 Epoch: [9/20], Batch Num: [9/600] Discriminator Loss: 0.8035, Generator Loss: 1.9992 D(x): 0.7344, D(G(z)): 0.1955 Epoch: [9/20], Batch Num: [10/600] Discriminator Loss: 0.8253, Generator Loss: 1.9878 D(x): 0.8189, D(G(z)): 0.3044 Epoch: [9/20], Batch Num: [11/600] Discriminator Loss: 0.6701, Generator Loss: 1.9512 D(x): 0.7892, D(G(z)): 0.2367 Epoch: [9/20], Batch Num: [12/600] Discriminator Loss: 0.6952, Generator Loss: 1.8653 D(x): 0.8220, D(G(z)): 0.2645 Epoch: [9/20], Batch Num: [13/600] Discriminator Loss: 0.7639, Generator Loss: 2.0862 D(x): 0.7789, D(G(z)): 0.2579 Epoch: [9/20], Batch Num: [14/600] Discriminator Loss: 0.6517, Generator Loss: 2.1180 D(x): 0.7981, D(G(z)): 0.2109 Epoch: [9/20], Batch Num: [15/600] Discriminator Loss: 0.6544, Generator Loss: 2.1341 D(x): 0.7490, D(G(z)): 0.1738 Epoch: [9/20], Batch Num: [16/600] Discriminator Loss: 0.7216, Generator Loss: 2.1353 D(x): 0.7981, D(G(z)): 0.2326 Epoch: [9/20], Batch Num: [17/600] Discriminator Loss: 0.8631, Generator Loss: 2.1886 D(x): 0.7580, D(G(z)): 0.2688 Epoch: [9/20], Batch Num: [18/600] Discriminator Loss: 0.6993, Generator Loss: 2.1009 D(x): 0.8023, D(G(z)): 0.2541 Epoch: [9/20], Batch Num: [19/600] Discriminator Loss: 0.7741, Generator Loss: 2.5353 D(x): 0.7695, D(G(z)): 0.2676 Epoch: [9/20], Batch Num: [20/600] Discriminator Loss: 0.5485, Generator Loss: 2.3444 D(x): 0.8021, D(G(z)): 0.1739 Epoch: [9/20], Batch Num: [21/600] Discriminator Loss: 0.6552, Generator Loss: 2.2848 D(x): 0.7759, D(G(z)): 0.1916 Epoch: [9/20], Batch Num: [22/600] Discriminator Loss: 0.6092, Generator Loss: 2.5298 D(x): 0.7924, D(G(z)): 0.2074 Epoch: [9/20], Batch Num: [23/600] Discriminator Loss: 0.5314, Generator Loss: 2.2149 D(x): 0.8095, D(G(z)): 0.1663 Epoch: [9/20], Batch Num: [24/600] Discriminator Loss: 0.5899, Generator Loss: 2.1224 D(x): 0.8067, D(G(z)): 0.2007 Epoch: [9/20], Batch Num: [25/600] Discriminator Loss: 0.3971, Generator Loss: 2.2020 D(x): 0.8893, D(G(z)): 0.1861 Epoch: [9/20], Batch Num: [26/600] Discriminator Loss: 0.5879, Generator Loss: 2.1753 D(x): 0.8326, D(G(z)): 0.2231 Epoch: [9/20], Batch Num: [27/600] Discriminator Loss: 0.5499, Generator Loss: 2.4683 D(x): 0.8512, D(G(z)): 0.2303 Epoch: [9/20], Batch Num: [28/600] Discriminator Loss: 0.5273, Generator Loss: 2.7333 D(x): 0.8241, D(G(z)): 0.1701 Epoch: [9/20], Batch Num: [29/600] Discriminator Loss: 0.6024, Generator Loss: 2.8764 D(x): 0.7673, D(G(z)): 0.1423 Epoch: [9/20], Batch Num: [30/600] Discriminator Loss: 0.5721, Generator Loss: 2.5477 D(x): 0.7611, D(G(z)): 0.1343 Epoch: [9/20], Batch Num: [31/600] Discriminator Loss: 0.4940, Generator Loss: 2.2061 D(x): 0.8300, D(G(z)): 0.1832 Epoch: [9/20], Batch Num: [32/600] Discriminator Loss: 0.5662, Generator Loss: 2.5954 D(x): 0.8584, D(G(z)): 0.2401 Epoch: [9/20], Batch Num: [33/600] Discriminator Loss: 0.6324, Generator Loss: 2.1773 D(x): 0.8261, D(G(z)): 0.2195 Epoch: [9/20], Batch Num: [34/600] Discriminator Loss: 0.6205, Generator Loss: 2.6676 D(x): 0.8664, D(G(z)): 0.2526 Epoch: [9/20], Batch Num: [35/600] Discriminator Loss: 0.6209, Generator Loss: 2.7653 D(x): 0.7923, D(G(z)): 0.1854 Epoch: [9/20], Batch Num: [36/600] Discriminator Loss: 0.5736, Generator Loss: 2.9202 D(x): 0.8220, D(G(z)): 0.1846 Epoch: [9/20], Batch Num: [37/600] Discriminator Loss: 0.5637, Generator Loss: 2.6221 D(x): 0.7680, D(G(z)): 0.1246 Epoch: [9/20], Batch Num: [38/600] Discriminator Loss: 0.8227, Generator Loss: 2.4035 D(x): 0.7217, D(G(z)): 0.1904 Epoch: [9/20], Batch Num: [39/600] Discriminator Loss: 0.6169, Generator Loss: 1.7312 D(x): 0.7995, D(G(z)): 0.1946 Epoch: [9/20], Batch Num: [40/600] Discriminator Loss: 0.8906, Generator Loss: 2.0089 D(x): 0.8570, D(G(z)): 0.3691 Epoch: [9/20], Batch Num: [41/600] Discriminator Loss: 0.7032, Generator Loss: 2.3846 D(x): 0.8016, D(G(z)): 0.2583 Epoch: [9/20], Batch Num: [42/600] Discriminator Loss: 0.7952, Generator Loss: 2.6728 D(x): 0.7346, D(G(z)): 0.2123 Epoch: [9/20], Batch Num: [43/600] Discriminator Loss: 0.8890, Generator Loss: 2.4380 D(x): 0.6846, D(G(z)): 0.1930 Epoch: [9/20], Batch Num: [44/600] Discriminator Loss: 0.9079, Generator Loss: 1.8317 D(x): 0.6839, D(G(z)): 0.2095 Epoch: [9/20], Batch Num: [45/600] Discriminator Loss: 0.9184, Generator Loss: 1.8509 D(x): 0.8177, D(G(z)): 0.3737 Epoch: [9/20], Batch Num: [46/600] Discriminator Loss: 0.8455, Generator Loss: 2.1395 D(x): 0.7795, D(G(z)): 0.3229 Epoch: [9/20], Batch Num: [47/600] Discriminator Loss: 0.7290, Generator Loss: 2.4158 D(x): 0.7610, D(G(z)): 0.2368 Epoch: [9/20], Batch Num: [48/600] Discriminator Loss: 0.8524, Generator Loss: 2.6279 D(x): 0.6732, D(G(z)): 0.1738 Epoch: [9/20], Batch Num: [49/600] Discriminator Loss: 0.8337, Generator Loss: 2.1688 D(x): 0.6783, D(G(z)): 0.1717 Epoch: [9/20], Batch Num: [50/600] Discriminator Loss: 0.7524, Generator Loss: 1.9321 D(x): 0.7635, D(G(z)): 0.2347 Epoch: [9/20], Batch Num: [51/600] Discriminator Loss: 0.6430, Generator Loss: 1.7399 D(x): 0.7987, D(G(z)): 0.2438 Epoch: [9/20], Batch Num: [52/600] Discriminator Loss: 0.5480, Generator Loss: 2.1274 D(x): 0.8521, D(G(z)): 0.2410 Epoch: [9/20], Batch Num: [53/600] Discriminator Loss: 0.6115, Generator Loss: 2.4938 D(x): 0.8732, D(G(z)): 0.2837 Epoch: [9/20], Batch Num: [54/600] Discriminator Loss: 0.5922, Generator Loss: 2.9040 D(x): 0.7938, D(G(z)): 0.1960 Epoch: [9/20], Batch Num: [55/600] Discriminator Loss: 0.6176, Generator Loss: 3.2046 D(x): 0.7291, D(G(z)): 0.1301 Epoch: [9/20], Batch Num: [56/600] Discriminator Loss: 0.5472, Generator Loss: 2.5530 D(x): 0.7573, D(G(z)): 0.1078 Epoch: [9/20], Batch Num: [57/600] Discriminator Loss: 0.4097, Generator Loss: 2.3074 D(x): 0.8589, D(G(z)): 0.1613 Epoch: [9/20], Batch Num: [58/600] Discriminator Loss: 0.5330, Generator Loss: 2.1846 D(x): 0.8591, D(G(z)): 0.2209 Epoch: [9/20], Batch Num: [59/600] Discriminator Loss: 0.4367, Generator Loss: 2.8132 D(x): 0.8941, D(G(z)): 0.2112 Epoch: [9/20], Batch Num: [60/600] Discriminator Loss: 0.4823, Generator Loss: 2.9550 D(x): 0.8517, D(G(z)): 0.1751 Epoch: [9/20], Batch Num: [61/600] Discriminator Loss: 0.4773, Generator Loss: 3.0581 D(x): 0.8482, D(G(z)): 0.1635 Epoch: [9/20], Batch Num: [62/600] Discriminator Loss: 0.5233, Generator Loss: 2.9435 D(x): 0.8268, D(G(z)): 0.1502 Epoch: [9/20], Batch Num: [63/600] Discriminator Loss: 0.4772, Generator Loss: 3.0800 D(x): 0.8503, D(G(z)): 0.1437 Epoch: [9/20], Batch Num: [64/600] Discriminator Loss: 0.4536, Generator Loss: 2.8747 D(x): 0.8341, D(G(z)): 0.1119 Epoch: [9/20], Batch Num: [65/600] Discriminator Loss: 0.4918, Generator Loss: 2.8910 D(x): 0.8652, D(G(z)): 0.1915 Epoch: [9/20], Batch Num: [66/600] Discriminator Loss: 0.5590, Generator Loss: 2.8592 D(x): 0.9034, D(G(z)): 0.2309 Epoch: [9/20], Batch Num: [67/600] Discriminator Loss: 0.7148, Generator Loss: 2.7716 D(x): 0.7717, D(G(z)): 0.1804 Epoch: [9/20], Batch Num: [68/600] Discriminator Loss: 0.5703, Generator Loss: 3.2045 D(x): 0.8287, D(G(z)): 0.1605 Epoch: [9/20], Batch Num: [69/600] Discriminator Loss: 0.6395, Generator Loss: 3.0324 D(x): 0.8287, D(G(z)): 0.2035 Epoch: [9/20], Batch Num: [70/600] Discriminator Loss: 0.5890, Generator Loss: 2.7690 D(x): 0.7974, D(G(z)): 0.1582 Epoch: [9/20], Batch Num: [71/600] Discriminator Loss: 0.6015, Generator Loss: 2.6154 D(x): 0.8435, D(G(z)): 0.2334 Epoch: [9/20], Batch Num: [72/600] Discriminator Loss: 0.6442, Generator Loss: 2.8303 D(x): 0.8250, D(G(z)): 0.1924 Epoch: [9/20], Batch Num: [73/600] Discriminator Loss: 0.5516, Generator Loss: 2.5250 D(x): 0.8369, D(G(z)): 0.1874 Epoch: [9/20], Batch Num: [74/600] Discriminator Loss: 0.8892, Generator Loss: 2.7304 D(x): 0.7669, D(G(z)): 0.2332 Epoch: [9/20], Batch Num: [75/600] Discriminator Loss: 0.7745, Generator Loss: 2.3800 D(x): 0.7932, D(G(z)): 0.2188 Epoch: [9/20], Batch Num: [76/600] Discriminator Loss: 0.7192, Generator Loss: 2.1393 D(x): 0.7740, D(G(z)): 0.1878 Epoch: [9/20], Batch Num: [77/600] Discriminator Loss: 0.8640, Generator Loss: 2.2517 D(x): 0.7950, D(G(z)): 0.2875 Epoch: [9/20], Batch Num: [78/600] Discriminator Loss: 0.7493, Generator Loss: 2.3137 D(x): 0.8200, D(G(z)): 0.2583 Epoch: [9/20], Batch Num: [79/600] Discriminator Loss: 0.9754, Generator Loss: 2.5815 D(x): 0.7383, D(G(z)): 0.2591 Epoch: [9/20], Batch Num: [80/600] Discriminator Loss: 1.0209, Generator Loss: 2.5507 D(x): 0.7074, D(G(z)): 0.2133 Epoch: [9/20], Batch Num: [81/600] Discriminator Loss: 0.9090, Generator Loss: 2.3090 D(x): 0.7149, D(G(z)): 0.2161 Epoch: [9/20], Batch Num: [82/600] Discriminator Loss: 0.8644, Generator Loss: 2.0171 D(x): 0.7844, D(G(z)): 0.2517 Epoch: [9/20], Batch Num: [83/600] Discriminator Loss: 0.8541, Generator Loss: 2.1911 D(x): 0.8418, D(G(z)): 0.3271 Epoch: [9/20], Batch Num: [84/600] Discriminator Loss: 0.6357, Generator Loss: 2.6898 D(x): 0.8787, D(G(z)): 0.2478 Epoch: [9/20], Batch Num: [85/600] Discriminator Loss: 0.9854, Generator Loss: 2.6741 D(x): 0.6799, D(G(z)): 0.1354 Epoch: [9/20], Batch Num: [86/600] Discriminator Loss: 1.0680, Generator Loss: 2.4188 D(x): 0.6654, D(G(z)): 0.1872 Epoch: [9/20], Batch Num: [87/600] Discriminator Loss: 0.9953, Generator Loss: 1.9581 D(x): 0.7564, D(G(z)): 0.2727 Epoch: [9/20], Batch Num: [88/600] Discriminator Loss: 0.8466, Generator Loss: 1.8766 D(x): 0.7526, D(G(z)): 0.2590 Epoch: [9/20], Batch Num: [89/600] Discriminator Loss: 0.7601, Generator Loss: 1.7817 D(x): 0.8408, D(G(z)): 0.2964 Epoch: [9/20], Batch Num: [90/600] Discriminator Loss: 0.7053, Generator Loss: 2.0896 D(x): 0.8344, D(G(z)): 0.3077 Epoch: [9/20], Batch Num: [91/600] Discriminator Loss: 0.7517, Generator Loss: 2.3294 D(x): 0.7854, D(G(z)): 0.2455 Epoch: [9/20], Batch Num: [92/600] Discriminator Loss: 0.6354, Generator Loss: 2.6272 D(x): 0.7942, D(G(z)): 0.2044 Epoch: [9/20], Batch Num: [93/600] Discriminator Loss: 0.7375, Generator Loss: 2.4584 D(x): 0.6959, D(G(z)): 0.1417 Epoch: [9/20], Batch Num: [94/600] Discriminator Loss: 0.8764, Generator Loss: 2.2082 D(x): 0.7057, D(G(z)): 0.2054 Epoch: [9/20], Batch Num: [95/600] Discriminator Loss: 0.6886, Generator Loss: 1.8175 D(x): 0.7780, D(G(z)): 0.2120 Epoch: [9/20], Batch Num: [96/600] Discriminator Loss: 0.6846, Generator Loss: 1.6379 D(x): 0.8627, D(G(z)): 0.3154 Epoch: [9/20], Batch Num: [97/600] Discriminator Loss: 0.8525, Generator Loss: 1.7966 D(x): 0.8249, D(G(z)): 0.3665 Epoch: [9/20], Batch Num: [98/600] Discriminator Loss: 0.8326, Generator Loss: 2.3248 D(x): 0.7828, D(G(z)): 0.2963 Epoch: [9/20], Batch Num: [99/600] Discriminator Loss: 0.5867, Generator Loss: 2.5446 D(x): 0.8409, D(G(z)): 0.2223 Epoch: 9, Batch Num: [100/600]
Epoch: [9/20], Batch Num: [100/600] Discriminator Loss: 0.8567, Generator Loss: 2.6325 D(x): 0.6632, D(G(z)): 0.1547 Epoch: [9/20], Batch Num: [101/600] Discriminator Loss: 0.8411, Generator Loss: 2.2258 D(x): 0.6580, D(G(z)): 0.1531 Epoch: [9/20], Batch Num: [102/600] Discriminator Loss: 0.7059, Generator Loss: 1.8072 D(x): 0.7622, D(G(z)): 0.2188 Epoch: [9/20], Batch Num: [103/600] Discriminator Loss: 0.7571, Generator Loss: 1.7987 D(x): 0.8242, D(G(z)): 0.2901 Epoch: [9/20], Batch Num: [104/600] Discriminator Loss: 0.7385, Generator Loss: 1.8645 D(x): 0.8498, D(G(z)): 0.3241 Epoch: [9/20], Batch Num: [105/600] Discriminator Loss: 1.0068, Generator Loss: 1.8582 D(x): 0.7573, D(G(z)): 0.3467 Epoch: [9/20], Batch Num: [106/600] Discriminator Loss: 0.9218, Generator Loss: 1.9684 D(x): 0.7790, D(G(z)): 0.3189 Epoch: [9/20], Batch Num: [107/600] Discriminator Loss: 0.8632, Generator Loss: 1.9405 D(x): 0.7136, D(G(z)): 0.2517 Epoch: [9/20], Batch Num: [108/600] Discriminator Loss: 0.9374, Generator Loss: 1.9546 D(x): 0.6828, D(G(z)): 0.2623 Epoch: [9/20], Batch Num: [109/600] Discriminator Loss: 0.7858, Generator Loss: 1.7967 D(x): 0.7165, D(G(z)): 0.2281 Epoch: [9/20], Batch Num: [110/600] Discriminator Loss: 0.9642, Generator Loss: 1.4904 D(x): 0.6870, D(G(z)): 0.2385 Epoch: [9/20], Batch Num: [111/600] Discriminator Loss: 0.9261, Generator Loss: 1.4334 D(x): 0.7595, D(G(z)): 0.3383 Epoch: [9/20], Batch Num: [112/600] Discriminator Loss: 0.8600, Generator Loss: 1.4958 D(x): 0.7840, D(G(z)): 0.3290 Epoch: [9/20], Batch Num: [113/600] Discriminator Loss: 0.8470, Generator Loss: 1.7493 D(x): 0.8145, D(G(z)): 0.3710 Epoch: [9/20], Batch Num: [114/600] Discriminator Loss: 0.6750, Generator Loss: 2.1209 D(x): 0.8239, D(G(z)): 0.2850 Epoch: [9/20], Batch Num: [115/600] Discriminator Loss: 0.8658, Generator Loss: 2.0592 D(x): 0.7030, D(G(z)): 0.2567 Epoch: [9/20], Batch Num: [116/600] Discriminator Loss: 0.7964, Generator Loss: 2.1594 D(x): 0.6932, D(G(z)): 0.2131 Epoch: [9/20], Batch Num: [117/600] Discriminator Loss: 0.7805, Generator Loss: 2.0402 D(x): 0.7166, D(G(z)): 0.2257 Epoch: [9/20], Batch Num: [118/600] Discriminator Loss: 0.6438, Generator Loss: 1.8797 D(x): 0.7659, D(G(z)): 0.2030 Epoch: [9/20], Batch Num: [119/600] Discriminator Loss: 0.8130, Generator Loss: 1.8707 D(x): 0.7688, D(G(z)): 0.2920 Epoch: [9/20], Batch Num: [120/600] Discriminator Loss: 0.6396, Generator Loss: 1.7703 D(x): 0.8101, D(G(z)): 0.2677 Epoch: [9/20], Batch Num: [121/600] Discriminator Loss: 0.6219, Generator Loss: 2.2290 D(x): 0.8511, D(G(z)): 0.3002 Epoch: [9/20], Batch Num: [122/600] Discriminator Loss: 0.8022, Generator Loss: 2.2522 D(x): 0.7894, D(G(z)): 0.3061 Epoch: [9/20], Batch Num: [123/600] Discriminator Loss: 0.7831, Generator Loss: 2.4517 D(x): 0.7435, D(G(z)): 0.2435 Epoch: [9/20], Batch Num: [124/600] Discriminator Loss: 0.7073, Generator Loss: 2.1728 D(x): 0.7344, D(G(z)): 0.1937 Epoch: [9/20], Batch Num: [125/600] Discriminator Loss: 0.8610, Generator Loss: 1.9998 D(x): 0.7058, D(G(z)): 0.2575 Epoch: [9/20], Batch Num: [126/600] Discriminator Loss: 0.7791, Generator Loss: 2.0866 D(x): 0.7387, D(G(z)): 0.2441 Epoch: [9/20], Batch Num: [127/600] Discriminator Loss: 0.7058, Generator Loss: 2.0972 D(x): 0.7839, D(G(z)): 0.2626 Epoch: [9/20], Batch Num: [128/600] Discriminator Loss: 0.7135, Generator Loss: 1.8086 D(x): 0.7679, D(G(z)): 0.2483 Epoch: [9/20], Batch Num: [129/600] Discriminator Loss: 0.6392, Generator Loss: 2.0776 D(x): 0.8078, D(G(z)): 0.2573 Epoch: [9/20], Batch Num: [130/600] Discriminator Loss: 0.7370, Generator Loss: 1.9383 D(x): 0.7599, D(G(z)): 0.2335 Epoch: [9/20], Batch Num: [131/600] Discriminator Loss: 0.7916, Generator Loss: 1.8135 D(x): 0.7378, D(G(z)): 0.2713 Epoch: [9/20], Batch Num: [132/600] Discriminator Loss: 0.7399, Generator Loss: 1.6978 D(x): 0.7231, D(G(z)): 0.2324 Epoch: [9/20], Batch Num: [133/600] Discriminator Loss: 0.8054, Generator Loss: 1.8538 D(x): 0.8056, D(G(z)): 0.3114 Epoch: [9/20], Batch Num: [134/600] Discriminator Loss: 0.9536, Generator Loss: 2.0100 D(x): 0.7252, D(G(z)): 0.3257 Epoch: [9/20], Batch Num: [135/600] Discriminator Loss: 0.8167, Generator Loss: 2.0577 D(x): 0.7073, D(G(z)): 0.2420 Epoch: [9/20], Batch Num: [136/600] Discriminator Loss: 0.8217, Generator Loss: 1.6660 D(x): 0.6880, D(G(z)): 0.2310 Epoch: [9/20], Batch Num: [137/600] Discriminator Loss: 0.8395, Generator Loss: 1.6558 D(x): 0.7690, D(G(z)): 0.3076 Epoch: [9/20], Batch Num: [138/600] Discriminator Loss: 1.1171, Generator Loss: 1.4901 D(x): 0.6748, D(G(z)): 0.3425 Epoch: [9/20], Batch Num: [139/600] Discriminator Loss: 1.1244, Generator Loss: 1.6294 D(x): 0.7566, D(G(z)): 0.4205 Epoch: [9/20], Batch Num: [140/600] Discriminator Loss: 1.2500, Generator Loss: 1.9287 D(x): 0.6362, D(G(z)): 0.3340 Epoch: [9/20], Batch Num: [141/600] Discriminator Loss: 1.1950, Generator Loss: 1.7974 D(x): 0.6202, D(G(z)): 0.3212 Epoch: [9/20], Batch Num: [142/600] Discriminator Loss: 1.1598, Generator Loss: 1.5944 D(x): 0.5863, D(G(z)): 0.2315 Epoch: [9/20], Batch Num: [143/600] Discriminator Loss: 1.2466, Generator Loss: 1.2936 D(x): 0.5719, D(G(z)): 0.3084 Epoch: [9/20], Batch Num: [144/600] Discriminator Loss: 1.3165, Generator Loss: 1.1838 D(x): 0.6608, D(G(z)): 0.4479 Epoch: [9/20], Batch Num: [145/600] Discriminator Loss: 1.1252, Generator Loss: 1.2032 D(x): 0.7085, D(G(z)): 0.4071 Epoch: [9/20], Batch Num: [146/600] Discriminator Loss: 1.2366, Generator Loss: 1.4301 D(x): 0.6833, D(G(z)): 0.4421 Epoch: [9/20], Batch Num: [147/600] Discriminator Loss: 1.1540, Generator Loss: 1.4803 D(x): 0.6299, D(G(z)): 0.3512 Epoch: [9/20], Batch Num: [148/600] Discriminator Loss: 1.2295, Generator Loss: 1.5356 D(x): 0.5738, D(G(z)): 0.3198 Epoch: [9/20], Batch Num: [149/600] Discriminator Loss: 1.2111, Generator Loss: 1.2627 D(x): 0.5950, D(G(z)): 0.3466 Epoch: [9/20], Batch Num: [150/600] Discriminator Loss: 0.9755, Generator Loss: 1.3569 D(x): 0.6494, D(G(z)): 0.3139 Epoch: [9/20], Batch Num: [151/600] Discriminator Loss: 1.1127, Generator Loss: 1.1882 D(x): 0.6255, D(G(z)): 0.3608 Epoch: [9/20], Batch Num: [152/600] Discriminator Loss: 0.9582, Generator Loss: 1.2491 D(x): 0.7355, D(G(z)): 0.4044 Epoch: [9/20], Batch Num: [153/600] Discriminator Loss: 1.0191, Generator Loss: 1.2378 D(x): 0.7237, D(G(z)): 0.4183 Epoch: [9/20], Batch Num: [154/600] Discriminator Loss: 0.8835, Generator Loss: 1.4141 D(x): 0.7490, D(G(z)): 0.3721 Epoch: [9/20], Batch Num: [155/600] Discriminator Loss: 0.8947, Generator Loss: 1.4737 D(x): 0.7043, D(G(z)): 0.3315 Epoch: [9/20], Batch Num: [156/600] Discriminator Loss: 0.8133, Generator Loss: 1.4433 D(x): 0.6948, D(G(z)): 0.2844 Epoch: [9/20], Batch Num: [157/600] Discriminator Loss: 0.8815, Generator Loss: 1.5807 D(x): 0.6477, D(G(z)): 0.2709 Epoch: [9/20], Batch Num: [158/600] Discriminator Loss: 0.8191, Generator Loss: 1.5458 D(x): 0.6794, D(G(z)): 0.2638 Epoch: [9/20], Batch Num: [159/600] Discriminator Loss: 0.7486, Generator Loss: 1.4858 D(x): 0.7273, D(G(z)): 0.2764 Epoch: [9/20], Batch Num: [160/600] Discriminator Loss: 0.7288, Generator Loss: 1.4538 D(x): 0.7751, D(G(z)): 0.3176 Epoch: [9/20], Batch Num: [161/600] Discriminator Loss: 0.6701, Generator Loss: 1.5753 D(x): 0.7935, D(G(z)): 0.2930 Epoch: [9/20], Batch Num: [162/600] Discriminator Loss: 0.6372, Generator Loss: 1.6296 D(x): 0.8403, D(G(z)): 0.3130 Epoch: [9/20], Batch Num: [163/600] Discriminator Loss: 0.6265, Generator Loss: 1.8294 D(x): 0.8243, D(G(z)): 0.2917 Epoch: [9/20], Batch Num: [164/600] Discriminator Loss: 0.5573, Generator Loss: 2.0629 D(x): 0.8105, D(G(z)): 0.2431 Epoch: [9/20], Batch Num: [165/600] Discriminator Loss: 0.5298, Generator Loss: 2.0627 D(x): 0.7858, D(G(z)): 0.1748 Epoch: [9/20], Batch Num: [166/600] Discriminator Loss: 0.4880, Generator Loss: 2.3039 D(x): 0.8223, D(G(z)): 0.1825 Epoch: [9/20], Batch Num: [167/600] Discriminator Loss: 0.4357, Generator Loss: 2.3271 D(x): 0.8325, D(G(z)): 0.1660 Epoch: [9/20], Batch Num: [168/600] Discriminator Loss: 0.4253, Generator Loss: 2.4393 D(x): 0.8285, D(G(z)): 0.1469 Epoch: [9/20], Batch Num: [169/600] Discriminator Loss: 0.3905, Generator Loss: 2.6139 D(x): 0.8767, D(G(z)): 0.1884 Epoch: [9/20], Batch Num: [170/600] Discriminator Loss: 0.4294, Generator Loss: 2.4544 D(x): 0.8625, D(G(z)): 0.1865 Epoch: [9/20], Batch Num: [171/600] Discriminator Loss: 0.3740, Generator Loss: 2.3869 D(x): 0.9022, D(G(z)): 0.1726 Epoch: [9/20], Batch Num: [172/600] Discriminator Loss: 0.3909, Generator Loss: 2.6900 D(x): 0.8585, D(G(z)): 0.1437 Epoch: [9/20], Batch Num: [173/600] Discriminator Loss: 0.3758, Generator Loss: 3.2095 D(x): 0.8993, D(G(z)): 0.1807 Epoch: [9/20], Batch Num: [174/600] Discriminator Loss: 0.3688, Generator Loss: 3.3291 D(x): 0.8467, D(G(z)): 0.1178 Epoch: [9/20], Batch Num: [175/600] Discriminator Loss: 0.4167, Generator Loss: 3.0231 D(x): 0.8528, D(G(z)): 0.1473 Epoch: [9/20], Batch Num: [176/600] Discriminator Loss: 0.4538, Generator Loss: 2.9301 D(x): 0.8091, D(G(z)): 0.0921 Epoch: [9/20], Batch Num: [177/600] Discriminator Loss: 0.4019, Generator Loss: 2.5035 D(x): 0.8485, D(G(z)): 0.1322 Epoch: [9/20], Batch Num: [178/600] Discriminator Loss: 0.5252, Generator Loss: 1.9622 D(x): 0.8353, D(G(z)): 0.1832 Epoch: [9/20], Batch Num: [179/600] Discriminator Loss: 0.6768, Generator Loss: 2.4153 D(x): 0.8627, D(G(z)): 0.2557 Epoch: [9/20], Batch Num: [180/600] Discriminator Loss: 0.6015, Generator Loss: 3.0525 D(x): 0.8379, D(G(z)): 0.2119 Epoch: [9/20], Batch Num: [181/600] Discriminator Loss: 0.5422, Generator Loss: 3.1573 D(x): 0.7998, D(G(z)): 0.1201 Epoch: [9/20], Batch Num: [182/600] Discriminator Loss: 0.7803, Generator Loss: 2.8563 D(x): 0.7023, D(G(z)): 0.1239 Epoch: [9/20], Batch Num: [183/600] Discriminator Loss: 0.7646, Generator Loss: 2.3838 D(x): 0.7832, D(G(z)): 0.2119 Epoch: [9/20], Batch Num: [184/600] Discriminator Loss: 0.5556, Generator Loss: 2.3290 D(x): 0.8656, D(G(z)): 0.2037 Epoch: [9/20], Batch Num: [185/600] Discriminator Loss: 0.7511, Generator Loss: 2.3922 D(x): 0.8018, D(G(z)): 0.2354 Epoch: [9/20], Batch Num: [186/600] Discriminator Loss: 0.8869, Generator Loss: 2.5271 D(x): 0.7437, D(G(z)): 0.2533 Epoch: [9/20], Batch Num: [187/600] Discriminator Loss: 0.8918, Generator Loss: 2.2268 D(x): 0.7271, D(G(z)): 0.2038 Epoch: [9/20], Batch Num: [188/600] Discriminator Loss: 0.9135, Generator Loss: 1.9362 D(x): 0.7200, D(G(z)): 0.2071 Epoch: [9/20], Batch Num: [189/600] Discriminator Loss: 1.0204, Generator Loss: 1.8477 D(x): 0.7295, D(G(z)): 0.2653 Epoch: [9/20], Batch Num: [190/600] Discriminator Loss: 0.9349, Generator Loss: 1.7229 D(x): 0.7741, D(G(z)): 0.2775 Epoch: [9/20], Batch Num: [191/600] Discriminator Loss: 1.1360, Generator Loss: 2.0929 D(x): 0.7405, D(G(z)): 0.3194 Epoch: [9/20], Batch Num: [192/600] Discriminator Loss: 1.2762, Generator Loss: 2.4189 D(x): 0.6686, D(G(z)): 0.2521 Epoch: [9/20], Batch Num: [193/600] Discriminator Loss: 1.0817, Generator Loss: 2.1596 D(x): 0.6428, D(G(z)): 0.2015 Epoch: [9/20], Batch Num: [194/600] Discriminator Loss: 0.8857, Generator Loss: 1.7589 D(x): 0.7371, D(G(z)): 0.2229 Epoch: [9/20], Batch Num: [195/600] Discriminator Loss: 1.2221, Generator Loss: 1.6409 D(x): 0.6559, D(G(z)): 0.2833 Epoch: [9/20], Batch Num: [196/600] Discriminator Loss: 1.2198, Generator Loss: 1.5982 D(x): 0.6823, D(G(z)): 0.3215 Epoch: [9/20], Batch Num: [197/600] Discriminator Loss: 1.1193, Generator Loss: 1.7500 D(x): 0.7614, D(G(z)): 0.3628 Epoch: [9/20], Batch Num: [198/600] Discriminator Loss: 1.1441, Generator Loss: 1.9270 D(x): 0.6623, D(G(z)): 0.2717 Epoch: [9/20], Batch Num: [199/600] Discriminator Loss: 1.0371, Generator Loss: 1.8036 D(x): 0.6625, D(G(z)): 0.2705 Epoch: 9, Batch Num: [200/600]
Epoch: [9/20], Batch Num: [200/600] Discriminator Loss: 1.1654, Generator Loss: 1.6942 D(x): 0.6112, D(G(z)): 0.2225 Epoch: [9/20], Batch Num: [201/600] Discriminator Loss: 0.9916, Generator Loss: 1.6587 D(x): 0.6794, D(G(z)): 0.2308 Epoch: [9/20], Batch Num: [202/600] Discriminator Loss: 1.0239, Generator Loss: 1.4560 D(x): 0.7144, D(G(z)): 0.3158 Epoch: [9/20], Batch Num: [203/600] Discriminator Loss: 0.9605, Generator Loss: 1.2713 D(x): 0.7253, D(G(z)): 0.2984 Epoch: [9/20], Batch Num: [204/600] Discriminator Loss: 0.8710, Generator Loss: 1.4606 D(x): 0.7538, D(G(z)): 0.3116 Epoch: [9/20], Batch Num: [205/600] Discriminator Loss: 0.8713, Generator Loss: 1.5672 D(x): 0.7775, D(G(z)): 0.3154 Epoch: [9/20], Batch Num: [206/600] Discriminator Loss: 0.7363, Generator Loss: 1.7656 D(x): 0.7478, D(G(z)): 0.2513 Epoch: [9/20], Batch Num: [207/600] Discriminator Loss: 0.7118, Generator Loss: 1.8382 D(x): 0.7768, D(G(z)): 0.2695 Epoch: [9/20], Batch Num: [208/600] Discriminator Loss: 0.7655, Generator Loss: 1.8208 D(x): 0.6839, D(G(z)): 0.1868 Epoch: [9/20], Batch Num: [209/600] Discriminator Loss: 0.6075, Generator Loss: 1.7717 D(x): 0.7449, D(G(z)): 0.2045 Epoch: [9/20], Batch Num: [210/600] Discriminator Loss: 0.6877, Generator Loss: 1.7303 D(x): 0.7360, D(G(z)): 0.2071 Epoch: [9/20], Batch Num: [211/600] Discriminator Loss: 0.7950, Generator Loss: 1.5685 D(x): 0.7610, D(G(z)): 0.2601 Epoch: [9/20], Batch Num: [212/600] Discriminator Loss: 0.6324, Generator Loss: 1.4790 D(x): 0.8139, D(G(z)): 0.2597 Epoch: [9/20], Batch Num: [213/600] Discriminator Loss: 0.6161, Generator Loss: 1.6860 D(x): 0.8367, D(G(z)): 0.2748 Epoch: [9/20], Batch Num: [214/600] Discriminator Loss: 0.5718, Generator Loss: 1.8788 D(x): 0.8163, D(G(z)): 0.2310 Epoch: [9/20], Batch Num: [215/600] Discriminator Loss: 0.5702, Generator Loss: 1.9815 D(x): 0.8273, D(G(z)): 0.2190 Epoch: [9/20], Batch Num: [216/600] Discriminator Loss: 0.5018, Generator Loss: 2.1067 D(x): 0.8049, D(G(z)): 0.1781 Epoch: [9/20], Batch Num: [217/600] Discriminator Loss: 0.4540, Generator Loss: 2.2079 D(x): 0.8394, D(G(z)): 0.1577 Epoch: [9/20], Batch Num: [218/600] Discriminator Loss: 0.6090, Generator Loss: 2.2056 D(x): 0.7730, D(G(z)): 0.1695 Epoch: [9/20], Batch Num: [219/600] Discriminator Loss: 0.6357, Generator Loss: 2.0061 D(x): 0.7336, D(G(z)): 0.1616 Epoch: [9/20], Batch Num: [220/600] Discriminator Loss: 0.5470, Generator Loss: 1.9821 D(x): 0.8225, D(G(z)): 0.1676 Epoch: [9/20], Batch Num: [221/600] Discriminator Loss: 0.5759, Generator Loss: 1.8807 D(x): 0.8353, D(G(z)): 0.2089 Epoch: [9/20], Batch Num: [222/600] Discriminator Loss: 0.5700, Generator Loss: 1.8653 D(x): 0.8537, D(G(z)): 0.2312 Epoch: [9/20], Batch Num: [223/600] Discriminator Loss: 0.5180, Generator Loss: 1.8906 D(x): 0.8972, D(G(z)): 0.2328 Epoch: [9/20], Batch Num: [224/600] Discriminator Loss: 0.5486, Generator Loss: 2.1146 D(x): 0.8559, D(G(z)): 0.2340 Epoch: [9/20], Batch Num: [225/600] Discriminator Loss: 0.6316, Generator Loss: 2.3590 D(x): 0.8305, D(G(z)): 0.2184 Epoch: [9/20], Batch Num: [226/600] Discriminator Loss: 0.3965, Generator Loss: 2.5149 D(x): 0.8570, D(G(z)): 0.1596 Epoch: [9/20], Batch Num: [227/600] Discriminator Loss: 0.6127, Generator Loss: 2.8062 D(x): 0.7806, D(G(z)): 0.1605 Epoch: [9/20], Batch Num: [228/600] Discriminator Loss: 0.6015, Generator Loss: 2.4211 D(x): 0.7850, D(G(z)): 0.1603 Epoch: [9/20], Batch Num: [229/600] Discriminator Loss: 0.7153, Generator Loss: 2.1359 D(x): 0.7445, D(G(z)): 0.1616 Epoch: [9/20], Batch Num: [230/600] Discriminator Loss: 0.5376, Generator Loss: 1.9575 D(x): 0.8400, D(G(z)): 0.2041 Epoch: [9/20], Batch Num: [231/600] Discriminator Loss: 0.5890, Generator Loss: 1.7446 D(x): 0.8673, D(G(z)): 0.2386 Epoch: [9/20], Batch Num: [232/600] Discriminator Loss: 0.5849, Generator Loss: 1.9911 D(x): 0.8801, D(G(z)): 0.2487 Epoch: [9/20], Batch Num: [233/600] Discriminator Loss: 0.6585, Generator Loss: 2.2044 D(x): 0.8591, D(G(z)): 0.2991 Epoch: [9/20], Batch Num: [234/600] Discriminator Loss: 0.8545, Generator Loss: 2.3011 D(x): 0.7204, D(G(z)): 0.2197 Epoch: [9/20], Batch Num: [235/600] Discriminator Loss: 0.7159, Generator Loss: 2.2589 D(x): 0.7551, D(G(z)): 0.1799 Epoch: [9/20], Batch Num: [236/600] Discriminator Loss: 0.5825, Generator Loss: 2.0178 D(x): 0.7916, D(G(z)): 0.1864 Epoch: [9/20], Batch Num: [237/600] Discriminator Loss: 0.7069, Generator Loss: 1.9453 D(x): 0.7713, D(G(z)): 0.2134 Epoch: [9/20], Batch Num: [238/600] Discriminator Loss: 0.5730, Generator Loss: 1.6839 D(x): 0.8401, D(G(z)): 0.2288 Epoch: [9/20], Batch Num: [239/600] Discriminator Loss: 0.7476, Generator Loss: 1.5889 D(x): 0.8063, D(G(z)): 0.2826 Epoch: [9/20], Batch Num: [240/600] Discriminator Loss: 0.8654, Generator Loss: 1.8289 D(x): 0.7851, D(G(z)): 0.3185 Epoch: [9/20], Batch Num: [241/600] Discriminator Loss: 0.8648, Generator Loss: 2.0992 D(x): 0.7449, D(G(z)): 0.2708 Epoch: [9/20], Batch Num: [242/600] Discriminator Loss: 0.7877, Generator Loss: 2.1929 D(x): 0.7267, D(G(z)): 0.2337 Epoch: [9/20], Batch Num: [243/600] Discriminator Loss: 0.8780, Generator Loss: 1.9010 D(x): 0.7284, D(G(z)): 0.2402 Epoch: [9/20], Batch Num: [244/600] Discriminator Loss: 0.7848, Generator Loss: 1.5492 D(x): 0.7781, D(G(z)): 0.2701 Epoch: [9/20], Batch Num: [245/600] Discriminator Loss: 0.7906, Generator Loss: 1.8867 D(x): 0.8214, D(G(z)): 0.2990 Epoch: [9/20], Batch Num: [246/600] Discriminator Loss: 0.7107, Generator Loss: 1.8678 D(x): 0.8244, D(G(z)): 0.3010 Epoch: [9/20], Batch Num: [247/600] Discriminator Loss: 0.6980, Generator Loss: 1.9443 D(x): 0.8095, D(G(z)): 0.2564 Epoch: [9/20], Batch Num: [248/600] Discriminator Loss: 0.6733, Generator Loss: 2.0443 D(x): 0.7931, D(G(z)): 0.2331 Epoch: [9/20], Batch Num: [249/600] Discriminator Loss: 0.6513, Generator Loss: 1.8858 D(x): 0.7627, D(G(z)): 0.2117 Epoch: [9/20], Batch Num: [250/600] Discriminator Loss: 0.6786, Generator Loss: 1.7859 D(x): 0.7771, D(G(z)): 0.2174 Epoch: [9/20], Batch Num: [251/600] Discriminator Loss: 0.6573, Generator Loss: 1.8067 D(x): 0.8384, D(G(z)): 0.2628 Epoch: [9/20], Batch Num: [252/600] Discriminator Loss: 0.6940, Generator Loss: 1.9502 D(x): 0.8598, D(G(z)): 0.2957 Epoch: [9/20], Batch Num: [253/600] Discriminator Loss: 0.6704, Generator Loss: 1.9926 D(x): 0.8264, D(G(z)): 0.2694 Epoch: [9/20], Batch Num: [254/600] Discriminator Loss: 0.5666, Generator Loss: 2.2061 D(x): 0.8093, D(G(z)): 0.2107 Epoch: [9/20], Batch Num: [255/600] Discriminator Loss: 0.6345, Generator Loss: 2.1461 D(x): 0.7627, D(G(z)): 0.1973 Epoch: [9/20], Batch Num: [256/600] Discriminator Loss: 0.5736, Generator Loss: 2.1563 D(x): 0.8142, D(G(z)): 0.2093 Epoch: [9/20], Batch Num: [257/600] Discriminator Loss: 0.6310, Generator Loss: 1.9834 D(x): 0.8139, D(G(z)): 0.2370 Epoch: [9/20], Batch Num: [258/600] Discriminator Loss: 0.4748, Generator Loss: 1.7085 D(x): 0.8955, D(G(z)): 0.2427 Epoch: [9/20], Batch Num: [259/600] Discriminator Loss: 0.5560, Generator Loss: 1.9889 D(x): 0.8562, D(G(z)): 0.2385 Epoch: [9/20], Batch Num: [260/600] Discriminator Loss: 0.5079, Generator Loss: 1.9679 D(x): 0.8662, D(G(z)): 0.2096 Epoch: [9/20], Batch Num: [261/600] Discriminator Loss: 0.5537, Generator Loss: 2.1546 D(x): 0.8406, D(G(z)): 0.2220 Epoch: [9/20], Batch Num: [262/600] Discriminator Loss: 0.4825, Generator Loss: 2.2947 D(x): 0.8510, D(G(z)): 0.2000 Epoch: [9/20], Batch Num: [263/600] Discriminator Loss: 0.4949, Generator Loss: 2.1803 D(x): 0.8107, D(G(z)): 0.1729 Epoch: [9/20], Batch Num: [264/600] Discriminator Loss: 0.4429, Generator Loss: 1.8201 D(x): 0.8341, D(G(z)): 0.1739 Epoch: [9/20], Batch Num: [265/600] Discriminator Loss: 0.5035, Generator Loss: 2.0325 D(x): 0.8769, D(G(z)): 0.2480 Epoch: [9/20], Batch Num: [266/600] Discriminator Loss: 0.7099, Generator Loss: 2.2428 D(x): 0.8398, D(G(z)): 0.2771 Epoch: [9/20], Batch Num: [267/600] Discriminator Loss: 0.5344, Generator Loss: 2.4442 D(x): 0.8649, D(G(z)): 0.2393 Epoch: [9/20], Batch Num: [268/600] Discriminator Loss: 0.5603, Generator Loss: 2.1643 D(x): 0.8089, D(G(z)): 0.1801 Epoch: [9/20], Batch Num: [269/600] Discriminator Loss: 0.5706, Generator Loss: 2.0748 D(x): 0.7978, D(G(z)): 0.1616 Epoch: [9/20], Batch Num: [270/600] Discriminator Loss: 0.6130, Generator Loss: 2.1034 D(x): 0.8108, D(G(z)): 0.2080 Epoch: [9/20], Batch Num: [271/600] Discriminator Loss: 0.6572, Generator Loss: 1.9294 D(x): 0.8761, D(G(z)): 0.2995 Epoch: [9/20], Batch Num: [272/600] Discriminator Loss: 0.5912, Generator Loss: 2.5066 D(x): 0.8555, D(G(z)): 0.2341 Epoch: [9/20], Batch Num: [273/600] Discriminator Loss: 0.5818, Generator Loss: 2.4973 D(x): 0.7854, D(G(z)): 0.1789 Epoch: [9/20], Batch Num: [274/600] Discriminator Loss: 0.5672, Generator Loss: 2.2574 D(x): 0.8000, D(G(z)): 0.1595 Epoch: [9/20], Batch Num: [275/600] Discriminator Loss: 0.5490, Generator Loss: 2.0689 D(x): 0.8257, D(G(z)): 0.1932 Epoch: [9/20], Batch Num: [276/600] Discriminator Loss: 0.4951, Generator Loss: 2.0780 D(x): 0.8218, D(G(z)): 0.1585 Epoch: [9/20], Batch Num: [277/600] Discriminator Loss: 0.4900, Generator Loss: 1.9315 D(x): 0.8687, D(G(z)): 0.2310 Epoch: [9/20], Batch Num: [278/600] Discriminator Loss: 0.5251, Generator Loss: 2.2277 D(x): 0.9267, D(G(z)): 0.2768 Epoch: [9/20], Batch Num: [279/600] Discriminator Loss: 0.5760, Generator Loss: 2.5289 D(x): 0.8037, D(G(z)): 0.1992 Epoch: [9/20], Batch Num: [280/600] Discriminator Loss: 0.5252, Generator Loss: 2.7114 D(x): 0.8204, D(G(z)): 0.1681 Epoch: [9/20], Batch Num: [281/600] Discriminator Loss: 0.6920, Generator Loss: 2.5919 D(x): 0.7470, D(G(z)): 0.1544 Epoch: [9/20], Batch Num: [282/600] Discriminator Loss: 0.5042, Generator Loss: 2.3063 D(x): 0.8616, D(G(z)): 0.1723 Epoch: [9/20], Batch Num: [283/600] Discriminator Loss: 0.6120, Generator Loss: 2.1239 D(x): 0.7948, D(G(z)): 0.1942 Epoch: [9/20], Batch Num: [284/600] Discriminator Loss: 0.7157, Generator Loss: 1.8768 D(x): 0.8528, D(G(z)): 0.2916 Epoch: [9/20], Batch Num: [285/600] Discriminator Loss: 0.7588, Generator Loss: 2.3912 D(x): 0.8559, D(G(z)): 0.2936 Epoch: [9/20], Batch Num: [286/600] Discriminator Loss: 0.6047, Generator Loss: 2.5653 D(x): 0.8325, D(G(z)): 0.1810 Epoch: [9/20], Batch Num: [287/600] Discriminator Loss: 0.7832, Generator Loss: 2.9311 D(x): 0.7315, D(G(z)): 0.1621 Epoch: [9/20], Batch Num: [288/600] Discriminator Loss: 0.6464, Generator Loss: 2.3762 D(x): 0.7622, D(G(z)): 0.1401 Epoch: [9/20], Batch Num: [289/600] Discriminator Loss: 0.6407, Generator Loss: 1.8516 D(x): 0.7885, D(G(z)): 0.1955 Epoch: [9/20], Batch Num: [290/600] Discriminator Loss: 0.8619, Generator Loss: 1.7898 D(x): 0.7926, D(G(z)): 0.2697 Epoch: [9/20], Batch Num: [291/600] Discriminator Loss: 0.6938, Generator Loss: 2.2018 D(x): 0.8412, D(G(z)): 0.2978 Epoch: [9/20], Batch Num: [292/600] Discriminator Loss: 0.8320, Generator Loss: 2.2898 D(x): 0.7473, D(G(z)): 0.2334 Epoch: [9/20], Batch Num: [293/600] Discriminator Loss: 0.9779, Generator Loss: 2.0008 D(x): 0.6708, D(G(z)): 0.2039 Epoch: [9/20], Batch Num: [294/600] Discriminator Loss: 0.8093, Generator Loss: 2.0371 D(x): 0.7381, D(G(z)): 0.2168 Epoch: [9/20], Batch Num: [295/600] Discriminator Loss: 0.9445, Generator Loss: 1.8438 D(x): 0.7327, D(G(z)): 0.2731 Epoch: [9/20], Batch Num: [296/600] Discriminator Loss: 0.8635, Generator Loss: 1.8220 D(x): 0.7859, D(G(z)): 0.2777 Epoch: [9/20], Batch Num: [297/600] Discriminator Loss: 0.9504, Generator Loss: 2.1694 D(x): 0.7570, D(G(z)): 0.2906 Epoch: [9/20], Batch Num: [298/600] Discriminator Loss: 1.0185, Generator Loss: 2.4762 D(x): 0.6749, D(G(z)): 0.2269 Epoch: [9/20], Batch Num: [299/600] Discriminator Loss: 0.9051, Generator Loss: 2.2855 D(x): 0.6991, D(G(z)): 0.2077 Epoch: 9, Batch Num: [300/600]
Epoch: [9/20], Batch Num: [300/600] Discriminator Loss: 0.9829, Generator Loss: 2.0918 D(x): 0.6945, D(G(z)): 0.2124 Epoch: [9/20], Batch Num: [301/600] Discriminator Loss: 0.7103, Generator Loss: 1.7242 D(x): 0.7842, D(G(z)): 0.2328 Epoch: [9/20], Batch Num: [302/600] Discriminator Loss: 0.8695, Generator Loss: 1.8751 D(x): 0.8014, D(G(z)): 0.3059 Epoch: [9/20], Batch Num: [303/600] Discriminator Loss: 0.7735, Generator Loss: 1.8575 D(x): 0.7804, D(G(z)): 0.2620 Epoch: [9/20], Batch Num: [304/600] Discriminator Loss: 0.7027, Generator Loss: 2.1146 D(x): 0.7892, D(G(z)): 0.2231 Epoch: [9/20], Batch Num: [305/600] Discriminator Loss: 0.8414, Generator Loss: 2.2532 D(x): 0.7301, D(G(z)): 0.2254 Epoch: [9/20], Batch Num: [306/600] Discriminator Loss: 0.7697, Generator Loss: 2.0757 D(x): 0.7343, D(G(z)): 0.2087 Epoch: [9/20], Batch Num: [307/600] Discriminator Loss: 0.7911, Generator Loss: 1.8964 D(x): 0.7435, D(G(z)): 0.1911 Epoch: [9/20], Batch Num: [308/600] Discriminator Loss: 0.6439, Generator Loss: 1.9378 D(x): 0.8395, D(G(z)): 0.2834 Epoch: [9/20], Batch Num: [309/600] Discriminator Loss: 0.6489, Generator Loss: 2.0178 D(x): 0.8269, D(G(z)): 0.2597 Epoch: [9/20], Batch Num: [310/600] Discriminator Loss: 0.6289, Generator Loss: 2.1264 D(x): 0.7816, D(G(z)): 0.1999 Epoch: [9/20], Batch Num: [311/600] Discriminator Loss: 0.7032, Generator Loss: 2.1245 D(x): 0.7676, D(G(z)): 0.2014 Epoch: [9/20], Batch Num: [312/600] Discriminator Loss: 0.6707, Generator Loss: 1.9980 D(x): 0.7654, D(G(z)): 0.2102 Epoch: [9/20], Batch Num: [313/600] Discriminator Loss: 0.5737, Generator Loss: 1.9253 D(x): 0.8118, D(G(z)): 0.1890 Epoch: [9/20], Batch Num: [314/600] Discriminator Loss: 0.7243, Generator Loss: 1.9254 D(x): 0.8172, D(G(z)): 0.2731 Epoch: [9/20], Batch Num: [315/600] Discriminator Loss: 0.6317, Generator Loss: 2.2200 D(x): 0.8051, D(G(z)): 0.2250 Epoch: [9/20], Batch Num: [316/600] Discriminator Loss: 0.5459, Generator Loss: 2.4686 D(x): 0.8438, D(G(z)): 0.2052 Epoch: [9/20], Batch Num: [317/600] Discriminator Loss: 0.4700, Generator Loss: 2.3798 D(x): 0.8486, D(G(z)): 0.1779 Epoch: [9/20], Batch Num: [318/600] Discriminator Loss: 0.5385, Generator Loss: 2.6489 D(x): 0.8231, D(G(z)): 0.1592 Epoch: [9/20], Batch Num: [319/600] Discriminator Loss: 0.5554, Generator Loss: 2.5301 D(x): 0.7773, D(G(z)): 0.1387 Epoch: [9/20], Batch Num: [320/600] Discriminator Loss: 0.5399, Generator Loss: 2.4105 D(x): 0.8095, D(G(z)): 0.1705 Epoch: [9/20], Batch Num: [321/600] Discriminator Loss: 0.5670, Generator Loss: 1.9903 D(x): 0.8180, D(G(z)): 0.1685 Epoch: [9/20], Batch Num: [322/600] Discriminator Loss: 0.5878, Generator Loss: 2.1692 D(x): 0.8663, D(G(z)): 0.2430 Epoch: [9/20], Batch Num: [323/600] Discriminator Loss: 0.6283, Generator Loss: 2.5410 D(x): 0.8641, D(G(z)): 0.2383 Epoch: [9/20], Batch Num: [324/600] Discriminator Loss: 0.6917, Generator Loss: 2.3978 D(x): 0.7490, D(G(z)): 0.1611 Epoch: [9/20], Batch Num: [325/600] Discriminator Loss: 0.5894, Generator Loss: 2.3094 D(x): 0.8393, D(G(z)): 0.2124 Epoch: [9/20], Batch Num: [326/600] Discriminator Loss: 0.5427, Generator Loss: 2.3217 D(x): 0.8397, D(G(z)): 0.1866 Epoch: [9/20], Batch Num: [327/600] Discriminator Loss: 0.6947, Generator Loss: 2.4831 D(x): 0.7899, D(G(z)): 0.1896 Epoch: [9/20], Batch Num: [328/600] Discriminator Loss: 0.6797, Generator Loss: 2.2963 D(x): 0.7672, D(G(z)): 0.1656 Epoch: [9/20], Batch Num: [329/600] Discriminator Loss: 0.6520, Generator Loss: 2.3388 D(x): 0.8447, D(G(z)): 0.2365 Epoch: [9/20], Batch Num: [330/600] Discriminator Loss: 0.6917, Generator Loss: 1.9408 D(x): 0.7736, D(G(z)): 0.1996 Epoch: [9/20], Batch Num: [331/600] Discriminator Loss: 0.5248, Generator Loss: 2.1286 D(x): 0.8514, D(G(z)): 0.2187 Epoch: [9/20], Batch Num: [332/600] Discriminator Loss: 0.5473, Generator Loss: 2.0456 D(x): 0.8561, D(G(z)): 0.2253 Epoch: [9/20], Batch Num: [333/600] Discriminator Loss: 0.5229, Generator Loss: 2.2019 D(x): 0.8721, D(G(z)): 0.1974 Epoch: [9/20], Batch Num: [334/600] Discriminator Loss: 0.6571, Generator Loss: 2.4277 D(x): 0.8228, D(G(z)): 0.2364 Epoch: [9/20], Batch Num: [335/600] Discriminator Loss: 0.7665, Generator Loss: 2.6333 D(x): 0.7514, D(G(z)): 0.1923 Epoch: [9/20], Batch Num: [336/600] Discriminator Loss: 0.4761, Generator Loss: 2.3012 D(x): 0.8317, D(G(z)): 0.1644 Epoch: [9/20], Batch Num: [337/600] Discriminator Loss: 0.6867, Generator Loss: 2.0987 D(x): 0.7547, D(G(z)): 0.1896 Epoch: [9/20], Batch Num: [338/600] Discriminator Loss: 0.6487, Generator Loss: 1.7461 D(x): 0.7960, D(G(z)): 0.2269 Epoch: [9/20], Batch Num: [339/600] Discriminator Loss: 0.6646, Generator Loss: 1.9969 D(x): 0.8371, D(G(z)): 0.2702 Epoch: [9/20], Batch Num: [340/600] Discriminator Loss: 0.8141, Generator Loss: 2.1127 D(x): 0.7838, D(G(z)): 0.2633 Epoch: [9/20], Batch Num: [341/600] Discriminator Loss: 0.8304, Generator Loss: 2.1258 D(x): 0.7239, D(G(z)): 0.2406 Epoch: [9/20], Batch Num: [342/600] Discriminator Loss: 0.9031, Generator Loss: 2.1141 D(x): 0.7366, D(G(z)): 0.2315 Epoch: [9/20], Batch Num: [343/600] Discriminator Loss: 0.7699, Generator Loss: 1.9567 D(x): 0.7398, D(G(z)): 0.1885 Epoch: [9/20], Batch Num: [344/600] Discriminator Loss: 0.8239, Generator Loss: 1.7409 D(x): 0.7421, D(G(z)): 0.2324 Epoch: [9/20], Batch Num: [345/600] Discriminator Loss: 0.8516, Generator Loss: 1.6132 D(x): 0.7683, D(G(z)): 0.2939 Epoch: [9/20], Batch Num: [346/600] Discriminator Loss: 0.9012, Generator Loss: 1.6720 D(x): 0.7969, D(G(z)): 0.3345 Epoch: [9/20], Batch Num: [347/600] Discriminator Loss: 1.0393, Generator Loss: 1.9161 D(x): 0.6908, D(G(z)): 0.2940 Epoch: [9/20], Batch Num: [348/600] Discriminator Loss: 0.9172, Generator Loss: 1.9377 D(x): 0.7259, D(G(z)): 0.2741 Epoch: [9/20], Batch Num: [349/600] Discriminator Loss: 0.8736, Generator Loss: 1.9260 D(x): 0.7150, D(G(z)): 0.2463 Epoch: [9/20], Batch Num: [350/600] Discriminator Loss: 0.7771, Generator Loss: 2.0149 D(x): 0.7406, D(G(z)): 0.2265 Epoch: [9/20], Batch Num: [351/600] Discriminator Loss: 0.7588, Generator Loss: 1.5481 D(x): 0.7588, D(G(z)): 0.2476 Epoch: [9/20], Batch Num: [352/600] Discriminator Loss: 0.8058, Generator Loss: 1.7519 D(x): 0.8119, D(G(z)): 0.3342 Epoch: [9/20], Batch Num: [353/600] Discriminator Loss: 0.7928, Generator Loss: 1.7073 D(x): 0.7531, D(G(z)): 0.2644 Epoch: [9/20], Batch Num: [354/600] Discriminator Loss: 0.7772, Generator Loss: 1.8423 D(x): 0.7876, D(G(z)): 0.2809 Epoch: [9/20], Batch Num: [355/600] Discriminator Loss: 0.7106, Generator Loss: 1.7242 D(x): 0.7526, D(G(z)): 0.2332 Epoch: [9/20], Batch Num: [356/600] Discriminator Loss: 0.7482, Generator Loss: 1.8506 D(x): 0.7723, D(G(z)): 0.2839 Epoch: [9/20], Batch Num: [357/600] Discriminator Loss: 0.8263, Generator Loss: 1.9631 D(x): 0.7551, D(G(z)): 0.2495 Epoch: [9/20], Batch Num: [358/600] Discriminator Loss: 0.7248, Generator Loss: 1.7502 D(x): 0.7509, D(G(z)): 0.2205 Epoch: [9/20], Batch Num: [359/600] Discriminator Loss: 0.8136, Generator Loss: 1.8643 D(x): 0.7524, D(G(z)): 0.2655 Epoch: [9/20], Batch Num: [360/600] Discriminator Loss: 0.8330, Generator Loss: 1.7808 D(x): 0.7708, D(G(z)): 0.2908 Epoch: [9/20], Batch Num: [361/600] Discriminator Loss: 0.5997, Generator Loss: 1.9246 D(x): 0.8657, D(G(z)): 0.2776 Epoch: [9/20], Batch Num: [362/600] Discriminator Loss: 0.5805, Generator Loss: 2.1733 D(x): 0.8015, D(G(z)): 0.2086 Epoch: [9/20], Batch Num: [363/600] Discriminator Loss: 0.6283, Generator Loss: 2.3105 D(x): 0.7655, D(G(z)): 0.2034 Epoch: [9/20], Batch Num: [364/600] Discriminator Loss: 0.6009, Generator Loss: 2.0853 D(x): 0.7894, D(G(z)): 0.1961 Epoch: [9/20], Batch Num: [365/600] Discriminator Loss: 0.6068, Generator Loss: 1.9292 D(x): 0.7979, D(G(z)): 0.2071 Epoch: [9/20], Batch Num: [366/600] Discriminator Loss: 0.4880, Generator Loss: 1.8707 D(x): 0.8348, D(G(z)): 0.2041 Epoch: [9/20], Batch Num: [367/600] Discriminator Loss: 0.6422, Generator Loss: 1.7085 D(x): 0.8285, D(G(z)): 0.2582 Epoch: [9/20], Batch Num: [368/600] Discriminator Loss: 0.5829, Generator Loss: 1.7655 D(x): 0.8394, D(G(z)): 0.2473 Epoch: [9/20], Batch Num: [369/600] Discriminator Loss: 0.5471, Generator Loss: 2.0100 D(x): 0.8992, D(G(z)): 0.2754 Epoch: [9/20], Batch Num: [370/600] Discriminator Loss: 0.4282, Generator Loss: 2.2398 D(x): 0.8573, D(G(z)): 0.1779 Epoch: [9/20], Batch Num: [371/600] Discriminator Loss: 0.4711, Generator Loss: 2.3410 D(x): 0.8192, D(G(z)): 0.1633 Epoch: [9/20], Batch Num: [372/600] Discriminator Loss: 0.5649, Generator Loss: 2.0045 D(x): 0.7757, D(G(z)): 0.1536 Epoch: [9/20], Batch Num: [373/600] Discriminator Loss: 0.4839, Generator Loss: 2.1143 D(x): 0.8651, D(G(z)): 0.2034 Epoch: [9/20], Batch Num: [374/600] Discriminator Loss: 0.4668, Generator Loss: 2.1563 D(x): 0.8304, D(G(z)): 0.1695 Epoch: [9/20], Batch Num: [375/600] Discriminator Loss: 0.5960, Generator Loss: 2.0321 D(x): 0.8515, D(G(z)): 0.2394 Epoch: [9/20], Batch Num: [376/600] Discriminator Loss: 0.5543, Generator Loss: 2.2005 D(x): 0.8711, D(G(z)): 0.2466 Epoch: [9/20], Batch Num: [377/600] Discriminator Loss: 0.5951, Generator Loss: 2.1763 D(x): 0.8098, D(G(z)): 0.1965 Epoch: [9/20], Batch Num: [378/600] Discriminator Loss: 0.4750, Generator Loss: 2.4068 D(x): 0.8610, D(G(z)): 0.1822 Epoch: [9/20], Batch Num: [379/600] Discriminator Loss: 0.5123, Generator Loss: 2.2337 D(x): 0.8004, D(G(z)): 0.1456 Epoch: [9/20], Batch Num: [380/600] Discriminator Loss: 0.4786, Generator Loss: 2.2462 D(x): 0.8319, D(G(z)): 0.1605 Epoch: [9/20], Batch Num: [381/600] Discriminator Loss: 0.6694, Generator Loss: 2.0522 D(x): 0.7893, D(G(z)): 0.2018 Epoch: [9/20], Batch Num: [382/600] Discriminator Loss: 0.7029, Generator Loss: 2.0976 D(x): 0.8252, D(G(z)): 0.2692 Epoch: [9/20], Batch Num: [383/600] Discriminator Loss: 0.5820, Generator Loss: 2.0234 D(x): 0.8665, D(G(z)): 0.2497 Epoch: [9/20], Batch Num: [384/600] Discriminator Loss: 0.7337, Generator Loss: 2.4661 D(x): 0.8031, D(G(z)): 0.2317 Epoch: [9/20], Batch Num: [385/600] Discriminator Loss: 0.7511, Generator Loss: 2.2464 D(x): 0.7678, D(G(z)): 0.1877 Epoch: [9/20], Batch Num: [386/600] Discriminator Loss: 0.6308, Generator Loss: 2.3477 D(x): 0.7703, D(G(z)): 0.1612 Epoch: [9/20], Batch Num: [387/600] Discriminator Loss: 0.6772, Generator Loss: 1.8480 D(x): 0.7654, D(G(z)): 0.1690 Epoch: [9/20], Batch Num: [388/600] Discriminator Loss: 0.6814, Generator Loss: 1.5970 D(x): 0.8026, D(G(z)): 0.2408 Epoch: [9/20], Batch Num: [389/600] Discriminator Loss: 1.0306, Generator Loss: 1.8592 D(x): 0.8110, D(G(z)): 0.3687 Epoch: [9/20], Batch Num: [390/600] Discriminator Loss: 0.8990, Generator Loss: 2.3477 D(x): 0.7777, D(G(z)): 0.2898 Epoch: [9/20], Batch Num: [391/600] Discriminator Loss: 0.9166, Generator Loss: 2.5198 D(x): 0.6699, D(G(z)): 0.2036 Epoch: [9/20], Batch Num: [392/600] Discriminator Loss: 0.9673, Generator Loss: 2.1560 D(x): 0.6310, D(G(z)): 0.1748 Epoch: [9/20], Batch Num: [393/600] Discriminator Loss: 0.8309, Generator Loss: 1.7638 D(x): 0.6982, D(G(z)): 0.1977 Epoch: [9/20], Batch Num: [394/600] Discriminator Loss: 0.9412, Generator Loss: 1.5660 D(x): 0.7823, D(G(z)): 0.3295 Epoch: [9/20], Batch Num: [395/600] Discriminator Loss: 0.9859, Generator Loss: 1.7146 D(x): 0.7713, D(G(z)): 0.3569 Epoch: [9/20], Batch Num: [396/600] Discriminator Loss: 1.0079, Generator Loss: 1.7103 D(x): 0.7011, D(G(z)): 0.2861 Epoch: [9/20], Batch Num: [397/600] Discriminator Loss: 0.6539, Generator Loss: 2.0022 D(x): 0.7711, D(G(z)): 0.2173 Epoch: [9/20], Batch Num: [398/600] Discriminator Loss: 0.9407, Generator Loss: 2.3429 D(x): 0.7216, D(G(z)): 0.2695 Epoch: [9/20], Batch Num: [399/600] Discriminator Loss: 1.0416, Generator Loss: 2.1407 D(x): 0.6370, D(G(z)): 0.1874 Epoch: 9, Batch Num: [400/600]
Epoch: [9/20], Batch Num: [400/600] Discriminator Loss: 0.9254, Generator Loss: 2.1685 D(x): 0.6710, D(G(z)): 0.1941 Epoch: [9/20], Batch Num: [401/600] Discriminator Loss: 0.8983, Generator Loss: 1.9301 D(x): 0.7581, D(G(z)): 0.3080 Epoch: [9/20], Batch Num: [402/600] Discriminator Loss: 0.8776, Generator Loss: 1.7252 D(x): 0.7897, D(G(z)): 0.3094 Epoch: [9/20], Batch Num: [403/600] Discriminator Loss: 0.8986, Generator Loss: 1.8216 D(x): 0.7207, D(G(z)): 0.2903 Epoch: [9/20], Batch Num: [404/600] Discriminator Loss: 0.6998, Generator Loss: 1.9304 D(x): 0.7718, D(G(z)): 0.2284 Epoch: [9/20], Batch Num: [405/600] Discriminator Loss: 0.7954, Generator Loss: 2.1712 D(x): 0.7316, D(G(z)): 0.2463 Epoch: [9/20], Batch Num: [406/600] Discriminator Loss: 0.8461, Generator Loss: 2.2207 D(x): 0.7315, D(G(z)): 0.2357 Epoch: [9/20], Batch Num: [407/600] Discriminator Loss: 0.7413, Generator Loss: 2.1253 D(x): 0.7409, D(G(z)): 0.2145 Epoch: [9/20], Batch Num: [408/600] Discriminator Loss: 0.6761, Generator Loss: 2.0632 D(x): 0.8118, D(G(z)): 0.2628 Epoch: [9/20], Batch Num: [409/600] Discriminator Loss: 0.5708, Generator Loss: 2.3066 D(x): 0.8329, D(G(z)): 0.2266 Epoch: [9/20], Batch Num: [410/600] Discriminator Loss: 0.5715, Generator Loss: 2.3058 D(x): 0.8093, D(G(z)): 0.2069 Epoch: [9/20], Batch Num: [411/600] Discriminator Loss: 0.7341, Generator Loss: 2.3309 D(x): 0.7660, D(G(z)): 0.2225 Epoch: [9/20], Batch Num: [412/600] Discriminator Loss: 0.5871, Generator Loss: 2.7726 D(x): 0.8051, D(G(z)): 0.1851 Epoch: [9/20], Batch Num: [413/600] Discriminator Loss: 0.5085, Generator Loss: 2.3204 D(x): 0.8084, D(G(z)): 0.1512 Epoch: [9/20], Batch Num: [414/600] Discriminator Loss: 0.4989, Generator Loss: 2.2407 D(x): 0.7978, D(G(z)): 0.1487 Epoch: [9/20], Batch Num: [415/600] Discriminator Loss: 0.4692, Generator Loss: 2.1820 D(x): 0.8641, D(G(z)): 0.1909 Epoch: [9/20], Batch Num: [416/600] Discriminator Loss: 0.5990, Generator Loss: 2.5031 D(x): 0.8663, D(G(z)): 0.2399 Epoch: [9/20], Batch Num: [417/600] Discriminator Loss: 0.5300, Generator Loss: 2.4948 D(x): 0.8163, D(G(z)): 0.1713 Epoch: [9/20], Batch Num: [418/600] Discriminator Loss: 0.6286, Generator Loss: 2.5958 D(x): 0.8184, D(G(z)): 0.2082 Epoch: [9/20], Batch Num: [419/600] Discriminator Loss: 0.5557, Generator Loss: 2.7514 D(x): 0.8257, D(G(z)): 0.1911 Epoch: [9/20], Batch Num: [420/600] Discriminator Loss: 0.6844, Generator Loss: 2.5358 D(x): 0.7538, D(G(z)): 0.1747 Epoch: [9/20], Batch Num: [421/600] Discriminator Loss: 0.5311, Generator Loss: 2.3231 D(x): 0.7720, D(G(z)): 0.1306 Epoch: [9/20], Batch Num: [422/600] Discriminator Loss: 0.6470, Generator Loss: 2.0932 D(x): 0.8021, D(G(z)): 0.2016 Epoch: [9/20], Batch Num: [423/600] Discriminator Loss: 0.6051, Generator Loss: 2.0181 D(x): 0.8482, D(G(z)): 0.2210 Epoch: [9/20], Batch Num: [424/600] Discriminator Loss: 0.5895, Generator Loss: 2.2687 D(x): 0.8682, D(G(z)): 0.2452 Epoch: [9/20], Batch Num: [425/600] Discriminator Loss: 0.6044, Generator Loss: 2.3131 D(x): 0.8466, D(G(z)): 0.2234 Epoch: [9/20], Batch Num: [426/600] Discriminator Loss: 0.6493, Generator Loss: 2.5976 D(x): 0.8299, D(G(z)): 0.2341 Epoch: [9/20], Batch Num: [427/600] Discriminator Loss: 0.6875, Generator Loss: 2.7252 D(x): 0.7474, D(G(z)): 0.1388 Epoch: [9/20], Batch Num: [428/600] Discriminator Loss: 0.6667, Generator Loss: 2.3784 D(x): 0.7740, D(G(z)): 0.1489 Epoch: [9/20], Batch Num: [429/600] Discriminator Loss: 0.5526, Generator Loss: 2.2410 D(x): 0.8165, D(G(z)): 0.1812 Epoch: [9/20], Batch Num: [430/600] Discriminator Loss: 0.4875, Generator Loss: 1.9839 D(x): 0.8624, D(G(z)): 0.1846 Epoch: [9/20], Batch Num: [431/600] Discriminator Loss: 0.6509, Generator Loss: 1.7624 D(x): 0.7911, D(G(z)): 0.2023 Epoch: [9/20], Batch Num: [432/600] Discriminator Loss: 0.6082, Generator Loss: 2.1002 D(x): 0.8467, D(G(z)): 0.2505 Epoch: [9/20], Batch Num: [433/600] Discriminator Loss: 0.4507, Generator Loss: 2.4444 D(x): 0.8958, D(G(z)): 0.2251 Epoch: [9/20], Batch Num: [434/600] Discriminator Loss: 0.6047, Generator Loss: 2.6510 D(x): 0.8105, D(G(z)): 0.1998 Epoch: [9/20], Batch Num: [435/600] Discriminator Loss: 0.6069, Generator Loss: 2.5360 D(x): 0.7671, D(G(z)): 0.1559 Epoch: [9/20], Batch Num: [436/600] Discriminator Loss: 0.5837, Generator Loss: 2.3328 D(x): 0.7852, D(G(z)): 0.1606 Epoch: [9/20], Batch Num: [437/600] Discriminator Loss: 0.5164, Generator Loss: 2.0578 D(x): 0.8089, D(G(z)): 0.1655 Epoch: [9/20], Batch Num: [438/600] Discriminator Loss: 0.6119, Generator Loss: 1.9993 D(x): 0.8206, D(G(z)): 0.2234 Epoch: [9/20], Batch Num: [439/600] Discriminator Loss: 0.4490, Generator Loss: 1.9938 D(x): 0.8947, D(G(z)): 0.2277 Epoch: [9/20], Batch Num: [440/600] Discriminator Loss: 0.6856, Generator Loss: 2.2599 D(x): 0.8063, D(G(z)): 0.2521 Epoch: [9/20], Batch Num: [441/600] Discriminator Loss: 0.4690, Generator Loss: 2.4089 D(x): 0.8753, D(G(z)): 0.1992 Epoch: [9/20], Batch Num: [442/600] Discriminator Loss: 0.6548, Generator Loss: 2.4090 D(x): 0.7982, D(G(z)): 0.1787 Epoch: [9/20], Batch Num: [443/600] Discriminator Loss: 0.5667, Generator Loss: 2.6699 D(x): 0.7783, D(G(z)): 0.1227 Epoch: [9/20], Batch Num: [444/600] Discriminator Loss: 0.6490, Generator Loss: 2.3938 D(x): 0.7769, D(G(z)): 0.1640 Epoch: [9/20], Batch Num: [445/600] Discriminator Loss: 0.6304, Generator Loss: 2.1471 D(x): 0.8056, D(G(z)): 0.1991 Epoch: [9/20], Batch Num: [446/600] Discriminator Loss: 0.7439, Generator Loss: 1.9448 D(x): 0.7632, D(G(z)): 0.2038 Epoch: [9/20], Batch Num: [447/600] Discriminator Loss: 0.6215, Generator Loss: 1.7367 D(x): 0.8352, D(G(z)): 0.2283 Epoch: [9/20], Batch Num: [448/600] Discriminator Loss: 0.6905, Generator Loss: 2.2162 D(x): 0.8651, D(G(z)): 0.2829 Epoch: [9/20], Batch Num: [449/600] Discriminator Loss: 0.5422, Generator Loss: 2.4023 D(x): 0.8366, D(G(z)): 0.1826 Epoch: [9/20], Batch Num: [450/600] Discriminator Loss: 0.8483, Generator Loss: 2.5034 D(x): 0.7191, D(G(z)): 0.1800 Epoch: [9/20], Batch Num: [451/600] Discriminator Loss: 0.8733, Generator Loss: 2.1359 D(x): 0.6989, D(G(z)): 0.1939 Epoch: [9/20], Batch Num: [452/600] Discriminator Loss: 0.7280, Generator Loss: 2.0470 D(x): 0.7849, D(G(z)): 0.2107 Epoch: [9/20], Batch Num: [453/600] Discriminator Loss: 0.7769, Generator Loss: 1.6946 D(x): 0.7655, D(G(z)): 0.2179 Epoch: [9/20], Batch Num: [454/600] Discriminator Loss: 0.9083, Generator Loss: 1.8096 D(x): 0.8243, D(G(z)): 0.3390 Epoch: [9/20], Batch Num: [455/600] Discriminator Loss: 0.6608, Generator Loss: 2.0818 D(x): 0.8403, D(G(z)): 0.2539 Epoch: [9/20], Batch Num: [456/600] Discriminator Loss: 0.7176, Generator Loss: 2.4194 D(x): 0.7554, D(G(z)): 0.2024 Epoch: [9/20], Batch Num: [457/600] Discriminator Loss: 0.9314, Generator Loss: 2.4761 D(x): 0.6862, D(G(z)): 0.1974 Epoch: [9/20], Batch Num: [458/600] Discriminator Loss: 0.7120, Generator Loss: 1.9968 D(x): 0.7384, D(G(z)): 0.1752 Epoch: [9/20], Batch Num: [459/600] Discriminator Loss: 0.7216, Generator Loss: 1.8474 D(x): 0.7509, D(G(z)): 0.2087 Epoch: [9/20], Batch Num: [460/600] Discriminator Loss: 0.6446, Generator Loss: 1.7998 D(x): 0.8108, D(G(z)): 0.2151 Epoch: [9/20], Batch Num: [461/600] Discriminator Loss: 0.8337, Generator Loss: 1.9093 D(x): 0.8379, D(G(z)): 0.3103 Epoch: [9/20], Batch Num: [462/600] Discriminator Loss: 0.7131, Generator Loss: 1.9068 D(x): 0.7950, D(G(z)): 0.2528 Epoch: [9/20], Batch Num: [463/600] Discriminator Loss: 0.7393, Generator Loss: 2.1111 D(x): 0.7385, D(G(z)): 0.1917 Epoch: [9/20], Batch Num: [464/600] Discriminator Loss: 0.6376, Generator Loss: 2.0475 D(x): 0.7806, D(G(z)): 0.2064 Epoch: [9/20], Batch Num: [465/600] Discriminator Loss: 0.6787, Generator Loss: 2.0106 D(x): 0.7598, D(G(z)): 0.1940 Epoch: [9/20], Batch Num: [466/600] Discriminator Loss: 0.7318, Generator Loss: 1.8436 D(x): 0.7739, D(G(z)): 0.2247 Epoch: [9/20], Batch Num: [467/600] Discriminator Loss: 0.6467, Generator Loss: 1.8071 D(x): 0.7912, D(G(z)): 0.2049 Epoch: [9/20], Batch Num: [468/600] Discriminator Loss: 0.8041, Generator Loss: 1.7974 D(x): 0.7566, D(G(z)): 0.2294 Epoch: [9/20], Batch Num: [469/600] Discriminator Loss: 0.6405, Generator Loss: 1.7731 D(x): 0.8481, D(G(z)): 0.2735 Epoch: [9/20], Batch Num: [470/600] Discriminator Loss: 0.8234, Generator Loss: 1.9582 D(x): 0.7713, D(G(z)): 0.2726 Epoch: [9/20], Batch Num: [471/600] Discriminator Loss: 0.7226, Generator Loss: 2.0116 D(x): 0.7712, D(G(z)): 0.2372 Epoch: [9/20], Batch Num: [472/600] Discriminator Loss: 0.5597, Generator Loss: 2.0913 D(x): 0.7917, D(G(z)): 0.1840 Epoch: [9/20], Batch Num: [473/600] Discriminator Loss: 0.5526, Generator Loss: 1.9625 D(x): 0.8130, D(G(z)): 0.1970 Epoch: [9/20], Batch Num: [474/600] Discriminator Loss: 0.8533, Generator Loss: 1.9646 D(x): 0.7997, D(G(z)): 0.2627 Epoch: [9/20], Batch Num: [475/600] Discriminator Loss: 0.7193, Generator Loss: 2.2511 D(x): 0.7598, D(G(z)): 0.2217 Epoch: [9/20], Batch Num: [476/600] Discriminator Loss: 0.7004, Generator Loss: 1.8144 D(x): 0.7382, D(G(z)): 0.1652 Epoch: [9/20], Batch Num: [477/600] Discriminator Loss: 0.6818, Generator Loss: 1.7699 D(x): 0.7939, D(G(z)): 0.2287 Epoch: [9/20], Batch Num: [478/600] Discriminator Loss: 0.6312, Generator Loss: 1.8142 D(x): 0.8433, D(G(z)): 0.2457 Epoch: [9/20], Batch Num: [479/600] Discriminator Loss: 0.6740, Generator Loss: 1.9972 D(x): 0.8374, D(G(z)): 0.2713 Epoch: [9/20], Batch Num: [480/600] Discriminator Loss: 0.7037, Generator Loss: 1.9063 D(x): 0.7344, D(G(z)): 0.1871 Epoch: [9/20], Batch Num: [481/600] Discriminator Loss: 0.6839, Generator Loss: 2.1332 D(x): 0.8063, D(G(z)): 0.2214 Epoch: [9/20], Batch Num: [482/600] Discriminator Loss: 0.7987, Generator Loss: 2.1474 D(x): 0.7710, D(G(z)): 0.2561 Epoch: [9/20], Batch Num: [483/600] Discriminator Loss: 0.6827, Generator Loss: 1.9060 D(x): 0.7781, D(G(z)): 0.2211 Epoch: [9/20], Batch Num: [484/600] Discriminator Loss: 0.7668, Generator Loss: 2.0566 D(x): 0.7573, D(G(z)): 0.2295 Epoch: [9/20], Batch Num: [485/600] Discriminator Loss: 0.7177, Generator Loss: 1.8183 D(x): 0.7564, D(G(z)): 0.2053 Epoch: [9/20], Batch Num: [486/600] Discriminator Loss: 0.8688, Generator Loss: 1.7199 D(x): 0.7190, D(G(z)): 0.2312 Epoch: [9/20], Batch Num: [487/600] Discriminator Loss: 0.6904, Generator Loss: 1.5059 D(x): 0.7797, D(G(z)): 0.2345 Epoch: [9/20], Batch Num: [488/600] Discriminator Loss: 0.8291, Generator Loss: 1.7337 D(x): 0.8180, D(G(z)): 0.3312 Epoch: [9/20], Batch Num: [489/600] Discriminator Loss: 0.9049, Generator Loss: 1.8272 D(x): 0.7859, D(G(z)): 0.3235 Epoch: [9/20], Batch Num: [490/600] Discriminator Loss: 0.8797, Generator Loss: 2.0817 D(x): 0.7195, D(G(z)): 0.2560 Epoch: [9/20], Batch Num: [491/600] Discriminator Loss: 0.7935, Generator Loss: 2.0279 D(x): 0.6941, D(G(z)): 0.1858 Epoch: [9/20], Batch Num: [492/600] Discriminator Loss: 0.7930, Generator Loss: 1.8572 D(x): 0.6880, D(G(z)): 0.1776 Epoch: [9/20], Batch Num: [493/600] Discriminator Loss: 0.8048, Generator Loss: 1.4050 D(x): 0.6998, D(G(z)): 0.2212 Epoch: [9/20], Batch Num: [494/600] Discriminator Loss: 0.8045, Generator Loss: 1.4949 D(x): 0.8016, D(G(z)): 0.3055 Epoch: [9/20], Batch Num: [495/600] Discriminator Loss: 0.7006, Generator Loss: 1.5289 D(x): 0.8279, D(G(z)): 0.2798 Epoch: [9/20], Batch Num: [496/600] Discriminator Loss: 0.7285, Generator Loss: 1.9407 D(x): 0.8513, D(G(z)): 0.3383 Epoch: [9/20], Batch Num: [497/600] Discriminator Loss: 0.6457, Generator Loss: 2.0719 D(x): 0.8252, D(G(z)): 0.2461 Epoch: [9/20], Batch Num: [498/600] Discriminator Loss: 0.6897, Generator Loss: 2.4682 D(x): 0.7603, D(G(z)): 0.2208 Epoch: [9/20], Batch Num: [499/600] Discriminator Loss: 0.7061, Generator Loss: 2.4810 D(x): 0.7160, D(G(z)): 0.1608 Epoch: 9, Batch Num: [500/600]
Epoch: [9/20], Batch Num: [500/600] Discriminator Loss: 0.7266, Generator Loss: 2.2281 D(x): 0.6988, D(G(z)): 0.1612 Epoch: [9/20], Batch Num: [501/600] Discriminator Loss: 0.7302, Generator Loss: 1.8157 D(x): 0.7039, D(G(z)): 0.1945 Epoch: [9/20], Batch Num: [502/600] Discriminator Loss: 0.5799, Generator Loss: 1.7299 D(x): 0.8449, D(G(z)): 0.2393 Epoch: [9/20], Batch Num: [503/600] Discriminator Loss: 0.5350, Generator Loss: 1.6512 D(x): 0.8833, D(G(z)): 0.2619 Epoch: [9/20], Batch Num: [504/600] Discriminator Loss: 0.5988, Generator Loss: 1.9017 D(x): 0.8673, D(G(z)): 0.2919 Epoch: [9/20], Batch Num: [505/600] Discriminator Loss: 0.6199, Generator Loss: 2.2964 D(x): 0.8427, D(G(z)): 0.2588 Epoch: [9/20], Batch Num: [506/600] Discriminator Loss: 0.6831, Generator Loss: 2.5040 D(x): 0.7313, D(G(z)): 0.1719 Epoch: [9/20], Batch Num: [507/600] Discriminator Loss: 0.5963, Generator Loss: 2.4026 D(x): 0.7267, D(G(z)): 0.1228 Epoch: [9/20], Batch Num: [508/600] Discriminator Loss: 0.6710, Generator Loss: 2.5044 D(x): 0.7687, D(G(z)): 0.1852 Epoch: [9/20], Batch Num: [509/600] Discriminator Loss: 0.6401, Generator Loss: 1.9100 D(x): 0.7824, D(G(z)): 0.1706 Epoch: [9/20], Batch Num: [510/600] Discriminator Loss: 0.7092, Generator Loss: 1.6115 D(x): 0.8132, D(G(z)): 0.2632 Epoch: [9/20], Batch Num: [511/600] Discriminator Loss: 0.6370, Generator Loss: 1.6247 D(x): 0.8590, D(G(z)): 0.2898 Epoch: [9/20], Batch Num: [512/600] Discriminator Loss: 0.6979, Generator Loss: 2.0543 D(x): 0.8671, D(G(z)): 0.3191 Epoch: [9/20], Batch Num: [513/600] Discriminator Loss: 0.6295, Generator Loss: 2.5210 D(x): 0.8223, D(G(z)): 0.2361 Epoch: [9/20], Batch Num: [514/600] Discriminator Loss: 0.7382, Generator Loss: 2.6460 D(x): 0.6813, D(G(z)): 0.1364 Epoch: [9/20], Batch Num: [515/600] Discriminator Loss: 0.6698, Generator Loss: 2.2445 D(x): 0.7230, D(G(z)): 0.1502 Epoch: [9/20], Batch Num: [516/600] Discriminator Loss: 0.6748, Generator Loss: 2.1097 D(x): 0.7338, D(G(z)): 0.1839 Epoch: [9/20], Batch Num: [517/600] Discriminator Loss: 0.6331, Generator Loss: 1.5664 D(x): 0.8407, D(G(z)): 0.2677 Epoch: [9/20], Batch Num: [518/600] Discriminator Loss: 0.8666, Generator Loss: 1.8596 D(x): 0.8165, D(G(z)): 0.3420 Epoch: [9/20], Batch Num: [519/600] Discriminator Loss: 0.6728, Generator Loss: 1.9848 D(x): 0.8301, D(G(z)): 0.2577 Epoch: [9/20], Batch Num: [520/600] Discriminator Loss: 0.7294, Generator Loss: 2.3626 D(x): 0.7555, D(G(z)): 0.2395 Epoch: [9/20], Batch Num: [521/600] Discriminator Loss: 0.8313, Generator Loss: 2.1776 D(x): 0.6561, D(G(z)): 0.1516 Epoch: [9/20], Batch Num: [522/600] Discriminator Loss: 0.7919, Generator Loss: 1.8714 D(x): 0.7320, D(G(z)): 0.2228 Epoch: [9/20], Batch Num: [523/600] Discriminator Loss: 0.7784, Generator Loss: 1.8110 D(x): 0.7855, D(G(z)): 0.2931 Epoch: [9/20], Batch Num: [524/600] Discriminator Loss: 0.7895, Generator Loss: 1.7048 D(x): 0.7651, D(G(z)): 0.2539 Epoch: [9/20], Batch Num: [525/600] Discriminator Loss: 0.8109, Generator Loss: 1.7591 D(x): 0.7926, D(G(z)): 0.3274 Epoch: [9/20], Batch Num: [526/600] Discriminator Loss: 0.8367, Generator Loss: 2.1750 D(x): 0.7692, D(G(z)): 0.2951 Epoch: [9/20], Batch Num: [527/600] Discriminator Loss: 1.0820, Generator Loss: 2.0362 D(x): 0.6695, D(G(z)): 0.2768 Epoch: [9/20], Batch Num: [528/600] Discriminator Loss: 0.7704, Generator Loss: 1.8791 D(x): 0.7300, D(G(z)): 0.1979 Epoch: [9/20], Batch Num: [529/600] Discriminator Loss: 0.8269, Generator Loss: 1.6038 D(x): 0.7083, D(G(z)): 0.2489 Epoch: [9/20], Batch Num: [530/600] Discriminator Loss: 0.8893, Generator Loss: 1.6563 D(x): 0.7695, D(G(z)): 0.3095 Epoch: [9/20], Batch Num: [531/600] Discriminator Loss: 0.7426, Generator Loss: 1.7434 D(x): 0.8115, D(G(z)): 0.2824 Epoch: [9/20], Batch Num: [532/600] Discriminator Loss: 0.7881, Generator Loss: 1.7891 D(x): 0.7762, D(G(z)): 0.2834 Epoch: [9/20], Batch Num: [533/600] Discriminator Loss: 0.8805, Generator Loss: 2.0533 D(x): 0.7326, D(G(z)): 0.2740 Epoch: [9/20], Batch Num: [534/600] Discriminator Loss: 0.9248, Generator Loss: 1.8611 D(x): 0.6949, D(G(z)): 0.2284 Epoch: [9/20], Batch Num: [535/600] Discriminator Loss: 1.0073, Generator Loss: 2.0330 D(x): 0.6764, D(G(z)): 0.2604 Epoch: [9/20], Batch Num: [536/600] Discriminator Loss: 0.7498, Generator Loss: 1.7680 D(x): 0.7624, D(G(z)): 0.2337 Epoch: [9/20], Batch Num: [537/600] Discriminator Loss: 0.9115, Generator Loss: 1.8863 D(x): 0.7060, D(G(z)): 0.2715 Epoch: [9/20], Batch Num: [538/600] Discriminator Loss: 0.8677, Generator Loss: 1.7833 D(x): 0.7720, D(G(z)): 0.3011 Epoch: [9/20], Batch Num: [539/600] Discriminator Loss: 0.7406, Generator Loss: 1.7901 D(x): 0.7687, D(G(z)): 0.2533 Epoch: [9/20], Batch Num: [540/600] Discriminator Loss: 0.7491, Generator Loss: 1.9390 D(x): 0.7770, D(G(z)): 0.2779 Epoch: [9/20], Batch Num: [541/600] Discriminator Loss: 0.6867, Generator Loss: 2.0832 D(x): 0.7736, D(G(z)): 0.2395 Epoch: [9/20], Batch Num: [542/600] Discriminator Loss: 0.8406, Generator Loss: 1.9666 D(x): 0.7053, D(G(z)): 0.2434 Epoch: [9/20], Batch Num: [543/600] Discriminator Loss: 0.6811, Generator Loss: 1.6905 D(x): 0.7736, D(G(z)): 0.2263 Epoch: [9/20], Batch Num: [544/600] Discriminator Loss: 0.7877, Generator Loss: 1.5997 D(x): 0.7555, D(G(z)): 0.2605 Epoch: [9/20], Batch Num: [545/600] Discriminator Loss: 0.6219, Generator Loss: 1.5421 D(x): 0.8035, D(G(z)): 0.2579 Epoch: [9/20], Batch Num: [546/600] Discriminator Loss: 0.7190, Generator Loss: 1.5488 D(x): 0.8380, D(G(z)): 0.3208 Epoch: [9/20], Batch Num: [547/600] Discriminator Loss: 0.6754, Generator Loss: 1.8506 D(x): 0.8080, D(G(z)): 0.2672 Epoch: [9/20], Batch Num: [548/600] Discriminator Loss: 0.6794, Generator Loss: 1.6924 D(x): 0.7649, D(G(z)): 0.2389 Epoch: [9/20], Batch Num: [549/600] Discriminator Loss: 0.7609, Generator Loss: 1.7305 D(x): 0.7519, D(G(z)): 0.2533 Epoch: [9/20], Batch Num: [550/600] Discriminator Loss: 0.6840, Generator Loss: 1.7579 D(x): 0.7650, D(G(z)): 0.2475 Epoch: [9/20], Batch Num: [551/600] Discriminator Loss: 0.6972, Generator Loss: 1.7798 D(x): 0.7404, D(G(z)): 0.2123 Epoch: [9/20], Batch Num: [552/600] Discriminator Loss: 0.7416, Generator Loss: 1.7568 D(x): 0.8151, D(G(z)): 0.3018 Epoch: [9/20], Batch Num: [553/600] Discriminator Loss: 0.7040, Generator Loss: 1.7616 D(x): 0.8313, D(G(z)): 0.2553 Epoch: [9/20], Batch Num: [554/600] Discriminator Loss: 0.6762, Generator Loss: 1.9685 D(x): 0.7920, D(G(z)): 0.2244 Epoch: [9/20], Batch Num: [555/600] Discriminator Loss: 0.6474, Generator Loss: 2.0477 D(x): 0.7794, D(G(z)): 0.2191 Epoch: [9/20], Batch Num: [556/600] Discriminator Loss: 0.7414, Generator Loss: 2.2614 D(x): 0.7608, D(G(z)): 0.2328 Epoch: [9/20], Batch Num: [557/600] Discriminator Loss: 0.7222, Generator Loss: 1.8674 D(x): 0.7777, D(G(z)): 0.2281 Epoch: [9/20], Batch Num: [558/600] Discriminator Loss: 0.7254, Generator Loss: 1.7904 D(x): 0.7586, D(G(z)): 0.2312 Epoch: [9/20], Batch Num: [559/600] Discriminator Loss: 0.5348, Generator Loss: 1.6986 D(x): 0.8316, D(G(z)): 0.2313 Epoch: [9/20], Batch Num: [560/600] Discriminator Loss: 0.6416, Generator Loss: 1.5822 D(x): 0.8269, D(G(z)): 0.2589 Epoch: [9/20], Batch Num: [561/600] Discriminator Loss: 0.7141, Generator Loss: 1.6813 D(x): 0.8339, D(G(z)): 0.3190 Epoch: [9/20], Batch Num: [562/600] Discriminator Loss: 0.6650, Generator Loss: 1.9418 D(x): 0.8015, D(G(z)): 0.2319 Epoch: [9/20], Batch Num: [563/600] Discriminator Loss: 0.6659, Generator Loss: 1.9161 D(x): 0.7922, D(G(z)): 0.2133 Epoch: [9/20], Batch Num: [564/600] Discriminator Loss: 0.4692, Generator Loss: 2.0934 D(x): 0.8644, D(G(z)): 0.1996 Epoch: [9/20], Batch Num: [565/600] Discriminator Loss: 0.5823, Generator Loss: 2.0518 D(x): 0.7936, D(G(z)): 0.1824 Epoch: [9/20], Batch Num: [566/600] Discriminator Loss: 0.5706, Generator Loss: 2.2126 D(x): 0.8136, D(G(z)): 0.1616 Epoch: [9/20], Batch Num: [567/600] Discriminator Loss: 0.5863, Generator Loss: 2.0207 D(x): 0.8110, D(G(z)): 0.1984 Epoch: [9/20], Batch Num: [568/600] Discriminator Loss: 0.5882, Generator Loss: 1.7584 D(x): 0.8201, D(G(z)): 0.2015 Epoch: [9/20], Batch Num: [569/600] Discriminator Loss: 0.4856, Generator Loss: 2.1289 D(x): 0.8773, D(G(z)): 0.2140 Epoch: [9/20], Batch Num: [570/600] Discriminator Loss: 0.4282, Generator Loss: 1.8827 D(x): 0.8977, D(G(z)): 0.2054 Epoch: [9/20], Batch Num: [571/600] Discriminator Loss: 0.5297, Generator Loss: 1.9073 D(x): 0.8742, D(G(z)): 0.2396 Epoch: [9/20], Batch Num: [572/600] Discriminator Loss: 0.5225, Generator Loss: 2.4286 D(x): 0.8461, D(G(z)): 0.1992 Epoch: [9/20], Batch Num: [573/600] Discriminator Loss: 0.5633, Generator Loss: 2.5203 D(x): 0.8221, D(G(z)): 0.1962 Epoch: [9/20], Batch Num: [574/600] Discriminator Loss: 0.5691, Generator Loss: 2.5015 D(x): 0.8084, D(G(z)): 0.1702 Epoch: [9/20], Batch Num: [575/600] Discriminator Loss: 0.4683, Generator Loss: 2.7087 D(x): 0.8513, D(G(z)): 0.1584 Epoch: [9/20], Batch Num: [576/600] Discriminator Loss: 0.6247, Generator Loss: 2.2212 D(x): 0.7945, D(G(z)): 0.1816 Epoch: [9/20], Batch Num: [577/600] Discriminator Loss: 0.5438, Generator Loss: 1.9826 D(x): 0.8155, D(G(z)): 0.1739 Epoch: [9/20], Batch Num: [578/600] Discriminator Loss: 0.5764, Generator Loss: 2.0095 D(x): 0.8573, D(G(z)): 0.2507 Epoch: [9/20], Batch Num: [579/600] Discriminator Loss: 0.4955, Generator Loss: 2.3761 D(x): 0.8639, D(G(z)): 0.2135 Epoch: [9/20], Batch Num: [580/600] Discriminator Loss: 0.5268, Generator Loss: 2.2537 D(x): 0.8434, D(G(z)): 0.2107 Epoch: [9/20], Batch Num: [581/600] Discriminator Loss: 0.3668, Generator Loss: 2.4698 D(x): 0.9048, D(G(z)): 0.1740 Epoch: [9/20], Batch Num: [582/600] Discriminator Loss: 0.5263, Generator Loss: 2.5292 D(x): 0.7912, D(G(z)): 0.1273 Epoch: [9/20], Batch Num: [583/600] Discriminator Loss: 0.6259, Generator Loss: 2.3821 D(x): 0.7684, D(G(z)): 0.1525 Epoch: [9/20], Batch Num: [584/600] Discriminator Loss: 0.5081, Generator Loss: 2.0103 D(x): 0.8609, D(G(z)): 0.1885 Epoch: [9/20], Batch Num: [585/600] Discriminator Loss: 0.5484, Generator Loss: 1.9998 D(x): 0.8577, D(G(z)): 0.2089 Epoch: [9/20], Batch Num: [586/600] Discriminator Loss: 0.5091, Generator Loss: 2.4755 D(x): 0.8583, D(G(z)): 0.1975 Epoch: [9/20], Batch Num: [587/600] Discriminator Loss: 0.5816, Generator Loss: 2.5686 D(x): 0.8639, D(G(z)): 0.2151 Epoch: [9/20], Batch Num: [588/600] Discriminator Loss: 0.6066, Generator Loss: 2.7365 D(x): 0.7999, D(G(z)): 0.1763 Epoch: [9/20], Batch Num: [589/600] Discriminator Loss: 0.6627, Generator Loss: 2.3772 D(x): 0.7501, D(G(z)): 0.1295 Epoch: [9/20], Batch Num: [590/600] Discriminator Loss: 0.5213, Generator Loss: 2.4607 D(x): 0.8515, D(G(z)): 0.1792 Epoch: [9/20], Batch Num: [591/600] Discriminator Loss: 0.5080, Generator Loss: 2.3844 D(x): 0.8447, D(G(z)): 0.1808 Epoch: [9/20], Batch Num: [592/600] Discriminator Loss: 0.4806, Generator Loss: 2.2722 D(x): 0.8872, D(G(z)): 0.1990 Epoch: [9/20], Batch Num: [593/600] Discriminator Loss: 0.4421, Generator Loss: 2.4464 D(x): 0.8650, D(G(z)): 0.1691 Epoch: [9/20], Batch Num: [594/600] Discriminator Loss: 0.5674, Generator Loss: 2.6185 D(x): 0.8411, D(G(z)): 0.2060 Epoch: [9/20], Batch Num: [595/600] Discriminator Loss: 0.6690, Generator Loss: 2.2853 D(x): 0.7673, D(G(z)): 0.1699 Epoch: [9/20], Batch Num: [596/600] Discriminator Loss: 0.8951, Generator Loss: 1.7816 D(x): 0.7436, D(G(z)): 0.2226 Epoch: [9/20], Batch Num: [597/600] Discriminator Loss: 0.7763, Generator Loss: 1.7405 D(x): 0.8197, D(G(z)): 0.2644 Epoch: [9/20], Batch Num: [598/600] Discriminator Loss: 0.8847, Generator Loss: 2.5218 D(x): 0.8370, D(G(z)): 0.3331 Epoch: [9/20], Batch Num: [599/600] Discriminator Loss: 0.8159, Generator Loss: 2.4921 D(x): 0.7127, D(G(z)): 0.1954 Epoch: 10, Batch Num: [0/600]
Epoch: [10/20], Batch Num: [0/600] Discriminator Loss: 0.9283, Generator Loss: 2.1273 D(x): 0.6943, D(G(z)): 0.1679 Epoch: [10/20], Batch Num: [1/600] Discriminator Loss: 0.5770, Generator Loss: 1.6858 D(x): 0.8065, D(G(z)): 0.1970 Epoch: [10/20], Batch Num: [2/600] Discriminator Loss: 0.8165, Generator Loss: 1.7393 D(x): 0.8297, D(G(z)): 0.3097 Epoch: [10/20], Batch Num: [3/600] Discriminator Loss: 0.8083, Generator Loss: 1.9790 D(x): 0.8345, D(G(z)): 0.2946 Epoch: [10/20], Batch Num: [4/600] Discriminator Loss: 0.7233, Generator Loss: 2.1770 D(x): 0.7623, D(G(z)): 0.1940 Epoch: [10/20], Batch Num: [5/600] Discriminator Loss: 0.7211, Generator Loss: 2.3544 D(x): 0.7646, D(G(z)): 0.2140 Epoch: [10/20], Batch Num: [6/600] Discriminator Loss: 0.8258, Generator Loss: 2.0821 D(x): 0.7078, D(G(z)): 0.1750 Epoch: [10/20], Batch Num: [7/600] Discriminator Loss: 0.8890, Generator Loss: 1.6155 D(x): 0.7676, D(G(z)): 0.2652 Epoch: [10/20], Batch Num: [8/600] Discriminator Loss: 0.8805, Generator Loss: 1.7107 D(x): 0.7634, D(G(z)): 0.2948 Epoch: [10/20], Batch Num: [9/600] Discriminator Loss: 0.7485, Generator Loss: 1.8209 D(x): 0.8290, D(G(z)): 0.2858 Epoch: [10/20], Batch Num: [10/600] Discriminator Loss: 0.8159, Generator Loss: 2.0178 D(x): 0.8169, D(G(z)): 0.2901 Epoch: [10/20], Batch Num: [11/600] Discriminator Loss: 0.7943, Generator Loss: 2.0003 D(x): 0.7511, D(G(z)): 0.2250 Epoch: [10/20], Batch Num: [12/600] Discriminator Loss: 0.6920, Generator Loss: 2.0912 D(x): 0.7604, D(G(z)): 0.1754 Epoch: [10/20], Batch Num: [13/600] Discriminator Loss: 0.7350, Generator Loss: 2.0461 D(x): 0.7831, D(G(z)): 0.2341 Epoch: [10/20], Batch Num: [14/600] Discriminator Loss: 0.7306, Generator Loss: 1.8737 D(x): 0.7977, D(G(z)): 0.2543 Epoch: [10/20], Batch Num: [15/600] Discriminator Loss: 0.6040, Generator Loss: 1.9183 D(x): 0.8231, D(G(z)): 0.2261 Epoch: [10/20], Batch Num: [16/600] Discriminator Loss: 0.5679, Generator Loss: 1.7480 D(x): 0.7939, D(G(z)): 0.1750 Epoch: [10/20], Batch Num: [17/600] Discriminator Loss: 0.6226, Generator Loss: 1.9964 D(x): 0.8693, D(G(z)): 0.2641 Epoch: [10/20], Batch Num: [18/600] Discriminator Loss: 0.6427, Generator Loss: 2.0510 D(x): 0.8055, D(G(z)): 0.2494 Epoch: [10/20], Batch Num: [19/600] Discriminator Loss: 0.6002, Generator Loss: 2.2909 D(x): 0.7918, D(G(z)): 0.1789 Epoch: [10/20], Batch Num: [20/600] Discriminator Loss: 0.5047, Generator Loss: 2.0194 D(x): 0.8384, D(G(z)): 0.1790 Epoch: [10/20], Batch Num: [21/600] Discriminator Loss: 0.4516, Generator Loss: 2.0331 D(x): 0.8583, D(G(z)): 0.1638 Epoch: [10/20], Batch Num: [22/600] Discriminator Loss: 0.4865, Generator Loss: 2.1486 D(x): 0.8894, D(G(z)): 0.2337 Epoch: [10/20], Batch Num: [23/600] Discriminator Loss: 0.5701, Generator Loss: 2.1979 D(x): 0.8325, D(G(z)): 0.2035 Epoch: [10/20], Batch Num: [24/600] Discriminator Loss: 0.6116, Generator Loss: 2.4316 D(x): 0.8163, D(G(z)): 0.2047 Epoch: [10/20], Batch Num: [25/600] Discriminator Loss: 0.5008, Generator Loss: 2.5190 D(x): 0.8526, D(G(z)): 0.1701 Epoch: [10/20], Batch Num: [26/600] Discriminator Loss: 0.5646, Generator Loss: 2.4881 D(x): 0.7811, D(G(z)): 0.1342 Epoch: [10/20], Batch Num: [27/600] Discriminator Loss: 0.4527, Generator Loss: 2.2129 D(x): 0.8425, D(G(z)): 0.1493 Epoch: [10/20], Batch Num: [28/600] Discriminator Loss: 0.5670, Generator Loss: 2.0976 D(x): 0.8196, D(G(z)): 0.1751 Epoch: [10/20], Batch Num: [29/600] Discriminator Loss: 0.5384, Generator Loss: 1.8765 D(x): 0.8590, D(G(z)): 0.2137 Epoch: [10/20], Batch Num: [30/600] Discriminator Loss: 0.4719, Generator Loss: 2.0532 D(x): 0.8870, D(G(z)): 0.2032 Epoch: [10/20], Batch Num: [31/600] Discriminator Loss: 0.3664, Generator Loss: 2.3773 D(x): 0.9059, D(G(z)): 0.1800 Epoch: [10/20], Batch Num: [32/600] Discriminator Loss: 0.4996, Generator Loss: 2.4566 D(x): 0.8381, D(G(z)): 0.1705 Epoch: [10/20], Batch Num: [33/600] Discriminator Loss: 0.3541, Generator Loss: 2.7821 D(x): 0.8937, D(G(z)): 0.1440 Epoch: [10/20], Batch Num: [34/600] Discriminator Loss: 0.3960, Generator Loss: 2.7545 D(x): 0.8313, D(G(z)): 0.1169 Epoch: [10/20], Batch Num: [35/600] Discriminator Loss: 0.4408, Generator Loss: 2.4391 D(x): 0.8103, D(G(z)): 0.1010 Epoch: [10/20], Batch Num: [36/600] Discriminator Loss: 0.6005, Generator Loss: 1.9390 D(x): 0.7959, D(G(z)): 0.1677 Epoch: [10/20], Batch Num: [37/600] Discriminator Loss: 0.5705, Generator Loss: 1.8428 D(x): 0.8616, D(G(z)): 0.2286 Epoch: [10/20], Batch Num: [38/600] Discriminator Loss: 0.5326, Generator Loss: 2.3163 D(x): 0.9278, D(G(z)): 0.2909 Epoch: [10/20], Batch Num: [39/600] Discriminator Loss: 0.5419, Generator Loss: 2.5950 D(x): 0.8660, D(G(z)): 0.2171 Epoch: [10/20], Batch Num: [40/600] Discriminator Loss: 0.3927, Generator Loss: 2.9718 D(x): 0.8295, D(G(z)): 0.1066 Epoch: [10/20], Batch Num: [41/600] Discriminator Loss: 0.6667, Generator Loss: 2.6527 D(x): 0.7387, D(G(z)): 0.1031 Epoch: [10/20], Batch Num: [42/600] Discriminator Loss: 0.5812, Generator Loss: 2.4574 D(x): 0.8243, D(G(z)): 0.1621 Epoch: [10/20], Batch Num: [43/600] Discriminator Loss: 0.6486, Generator Loss: 2.3045 D(x): 0.8042, D(G(z)): 0.1487 Epoch: [10/20], Batch Num: [44/600] Discriminator Loss: 0.5895, Generator Loss: 1.9322 D(x): 0.8469, D(G(z)): 0.1934 Epoch: [10/20], Batch Num: [45/600] Discriminator Loss: 0.5623, Generator Loss: 1.8106 D(x): 0.8363, D(G(z)): 0.1878 Epoch: [10/20], Batch Num: [46/600] Discriminator Loss: 0.7559, Generator Loss: 1.9038 D(x): 0.8025, D(G(z)): 0.2380 Epoch: [10/20], Batch Num: [47/600] Discriminator Loss: 0.5813, Generator Loss: 2.1665 D(x): 0.8653, D(G(z)): 0.2254 Epoch: [10/20], Batch Num: [48/600] Discriminator Loss: 0.5735, Generator Loss: 2.4254 D(x): 0.8329, D(G(z)): 0.1850 Epoch: [10/20], Batch Num: [49/600] Discriminator Loss: 0.5576, Generator Loss: 2.6395 D(x): 0.7921, D(G(z)): 0.1402 Epoch: [10/20], Batch Num: [50/600] Discriminator Loss: 0.6670, Generator Loss: 2.3939 D(x): 0.7639, D(G(z)): 0.1415 Epoch: [10/20], Batch Num: 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D(x): 0.8664, D(G(z)): 0.2894 Epoch: [10/20], Batch Num: [60/600] Discriminator Loss: 0.6159, Generator Loss: 2.3196 D(x): 0.7704, D(G(z)): 0.1473 Epoch: [10/20], Batch Num: [61/600] Discriminator Loss: 0.5152, Generator Loss: 2.5734 D(x): 0.8322, D(G(z)): 0.1621 Epoch: [10/20], Batch Num: [62/600] Discriminator Loss: 0.6355, Generator Loss: 2.4639 D(x): 0.7771, D(G(z)): 0.1558 Epoch: [10/20], Batch Num: [63/600] Discriminator Loss: 0.5812, Generator Loss: 2.2682 D(x): 0.8355, D(G(z)): 0.1579 Epoch: [10/20], Batch Num: [64/600] Discriminator Loss: 0.4842, Generator Loss: 2.3380 D(x): 0.8610, D(G(z)): 0.1970 Epoch: [10/20], Batch Num: [65/600] Discriminator Loss: 0.5216, Generator Loss: 2.3668 D(x): 0.8611, D(G(z)): 0.1964 Epoch: [10/20], Batch Num: [66/600] Discriminator Loss: 0.6353, Generator Loss: 2.2240 D(x): 0.7945, D(G(z)): 0.1778 Epoch: [10/20], Batch Num: [67/600] Discriminator Loss: 0.6068, Generator Loss: 2.4926 D(x): 0.8228, D(G(z)): 0.1635 Epoch: [10/20], Batch Num: 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D(x): 0.8307, D(G(z)): 0.1832 Epoch: [10/20], Batch Num: [77/600] Discriminator Loss: 0.5659, Generator Loss: 2.6982 D(x): 0.8172, D(G(z)): 0.1487 Epoch: [10/20], Batch Num: [78/600] Discriminator Loss: 0.8871, Generator Loss: 2.4601 D(x): 0.7038, D(G(z)): 0.1276 Epoch: [10/20], Batch Num: [79/600] Discriminator Loss: 0.7342, Generator Loss: 1.8648 D(x): 0.7493, D(G(z)): 0.1692 Epoch: [10/20], Batch Num: [80/600] Discriminator Loss: 0.7021, Generator Loss: 1.8610 D(x): 0.8419, D(G(z)): 0.2642 Epoch: [10/20], Batch Num: [81/600] Discriminator Loss: 0.5940, Generator Loss: 2.1750 D(x): 0.8810, D(G(z)): 0.2640 Epoch: [10/20], Batch Num: [82/600] Discriminator Loss: 0.7069, Generator Loss: 2.4071 D(x): 0.7925, D(G(z)): 0.2264 Epoch: [10/20], Batch Num: [83/600] Discriminator Loss: 0.6247, Generator Loss: 2.5616 D(x): 0.7733, D(G(z)): 0.1207 Epoch: [10/20], Batch Num: [84/600] Discriminator Loss: 0.8153, Generator Loss: 2.2807 D(x): 0.7232, D(G(z)): 0.1719 Epoch: [10/20], Batch Num: 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D(x): 0.7887, D(G(z)): 0.2867 Epoch: [10/20], Batch Num: [94/600] Discriminator Loss: 0.9002, Generator Loss: 1.8316 D(x): 0.7202, D(G(z)): 0.2293 Epoch: [10/20], Batch Num: [95/600] Discriminator Loss: 1.0103, Generator Loss: 1.7356 D(x): 0.6767, D(G(z)): 0.2646 Epoch: [10/20], Batch Num: [96/600] Discriminator Loss: 0.8837, Generator Loss: 1.7648 D(x): 0.7454, D(G(z)): 0.2955 Epoch: [10/20], Batch Num: [97/600] Discriminator Loss: 0.9109, Generator Loss: 1.6778 D(x): 0.7010, D(G(z)): 0.2651 Epoch: [10/20], Batch Num: [98/600] Discriminator Loss: 0.9701, Generator Loss: 1.6756 D(x): 0.6823, D(G(z)): 0.2594 Epoch: [10/20], Batch Num: [99/600] Discriminator Loss: 1.0063, Generator Loss: 1.5546 D(x): 0.6979, D(G(z)): 0.2839 Epoch: 10, Batch Num: [100/600]
Epoch: [10/20], Batch Num: [100/600] Discriminator Loss: 0.7479, Generator Loss: 1.6347 D(x): 0.7829, D(G(z)): 0.2523 Epoch: [10/20], Batch Num: [101/600] Discriminator Loss: 0.7637, Generator Loss: 1.7574 D(x): 0.7810, D(G(z)): 0.2678 Epoch: [10/20], Batch Num: [102/600] Discriminator Loss: 0.7878, Generator Loss: 1.7106 D(x): 0.7697, D(G(z)): 0.2632 Epoch: [10/20], Batch Num: [103/600] Discriminator Loss: 0.7756, Generator Loss: 1.8365 D(x): 0.7602, D(G(z)): 0.2398 Epoch: [10/20], Batch Num: [104/600] Discriminator Loss: 0.7138, Generator Loss: 1.9370 D(x): 0.7599, D(G(z)): 0.2245 Epoch: [10/20], Batch Num: [105/600] Discriminator Loss: 0.8269, Generator Loss: 1.8975 D(x): 0.7154, D(G(z)): 0.2059 Epoch: [10/20], Batch Num: [106/600] Discriminator Loss: 0.6498, Generator Loss: 1.7698 D(x): 0.7641, D(G(z)): 0.2019 Epoch: [10/20], Batch Num: [107/600] Discriminator Loss: 0.7146, Generator Loss: 1.5522 D(x): 0.7288, D(G(z)): 0.2208 Epoch: [10/20], Batch Num: [108/600] Discriminator Loss: 0.5487, Generator Loss: 1.5229 D(x): 0.8391, D(G(z)): 0.2421 Epoch: [10/20], Batch Num: [109/600] Discriminator Loss: 0.6795, Generator Loss: 1.6227 D(x): 0.8281, D(G(z)): 0.3001 Epoch: [10/20], Batch Num: [110/600] Discriminator Loss: 0.6503, Generator Loss: 1.7737 D(x): 0.8213, D(G(z)): 0.2737 Epoch: [10/20], Batch Num: [111/600] Discriminator Loss: 0.5933, Generator Loss: 2.0075 D(x): 0.8363, D(G(z)): 0.2478 Epoch: [10/20], Batch Num: [112/600] Discriminator Loss: 0.5457, Generator Loss: 2.2328 D(x): 0.8227, D(G(z)): 0.2085 Epoch: [10/20], Batch Num: [113/600] Discriminator Loss: 0.5746, Generator Loss: 2.3094 D(x): 0.7773, D(G(z)): 0.1671 Epoch: [10/20], Batch Num: [114/600] Discriminator Loss: 0.6219, Generator Loss: 2.2048 D(x): 0.7406, D(G(z)): 0.1427 Epoch: [10/20], Batch Num: [115/600] Discriminator Loss: 0.4861, Generator Loss: 1.9694 D(x): 0.8170, D(G(z)): 0.1689 Epoch: [10/20], Batch Num: [116/600] Discriminator Loss: 0.5663, Generator Loss: 1.8712 D(x): 0.8141, D(G(z)): 0.1927 Epoch: [10/20], Batch Num: [117/600] Discriminator Loss: 0.4920, Generator Loss: 1.8368 D(x): 0.8617, D(G(z)): 0.2152 Epoch: [10/20], Batch Num: [118/600] Discriminator Loss: 0.4742, Generator Loss: 1.8077 D(x): 0.8624, D(G(z)): 0.2135 Epoch: [10/20], Batch Num: [119/600] Discriminator Loss: 0.4827, Generator Loss: 2.0725 D(x): 0.8970, D(G(z)): 0.2503 Epoch: [10/20], Batch Num: [120/600] Discriminator Loss: 0.4616, Generator Loss: 2.3351 D(x): 0.8994, D(G(z)): 0.1937 Epoch: [10/20], Batch Num: [121/600] Discriminator Loss: 0.4945, Generator Loss: 2.4714 D(x): 0.8106, D(G(z)): 0.1360 Epoch: [10/20], Batch Num: [122/600] Discriminator Loss: 0.4540, Generator Loss: 2.5975 D(x): 0.7813, D(G(z)): 0.1081 Epoch: [10/20], Batch Num: [123/600] Discriminator Loss: 0.3648, Generator Loss: 2.3605 D(x): 0.8744, D(G(z)): 0.1348 Epoch: [10/20], Batch Num: [124/600] Discriminator Loss: 0.4378, Generator Loss: 2.2930 D(x): 0.8559, D(G(z)): 0.1670 Epoch: [10/20], Batch Num: [125/600] Discriminator Loss: 0.4770, Generator Loss: 2.5050 D(x): 0.8748, D(G(z)): 0.1723 Epoch: [10/20], Batch Num: [126/600] Discriminator Loss: 0.4549, Generator Loss: 2.2648 D(x): 0.8729, D(G(z)): 0.1902 Epoch: [10/20], Batch Num: [127/600] Discriminator Loss: 0.5032, Generator Loss: 2.3821 D(x): 0.8227, D(G(z)): 0.1511 Epoch: [10/20], Batch Num: [128/600] Discriminator Loss: 0.4393, Generator Loss: 2.3829 D(x): 0.8730, D(G(z)): 0.1666 Epoch: [10/20], Batch Num: [129/600] Discriminator Loss: 0.6268, Generator Loss: 2.2913 D(x): 0.8356, D(G(z)): 0.1941 Epoch: [10/20], Batch Num: [130/600] Discriminator Loss: 0.5244, Generator Loss: 2.4290 D(x): 0.8293, D(G(z)): 0.1794 Epoch: [10/20], Batch Num: [131/600] Discriminator Loss: 0.5162, Generator Loss: 2.5613 D(x): 0.8384, D(G(z)): 0.1762 Epoch: [10/20], Batch Num: [132/600] Discriminator Loss: 0.5573, Generator Loss: 2.5638 D(x): 0.8115, D(G(z)): 0.1798 Epoch: [10/20], Batch Num: [133/600] Discriminator Loss: 0.6623, Generator Loss: 2.2627 D(x): 0.7551, D(G(z)): 0.1858 Epoch: [10/20], Batch Num: [134/600] Discriminator Loss: 0.5052, Generator Loss: 2.2393 D(x): 0.8760, D(G(z)): 0.2105 Epoch: [10/20], Batch Num: [135/600] Discriminator Loss: 0.6073, Generator Loss: 2.6156 D(x): 0.8458, D(G(z)): 0.2153 Epoch: [10/20], Batch Num: [136/600] Discriminator Loss: 0.6571, Generator Loss: 2.5407 D(x): 0.7914, D(G(z)): 0.1994 Epoch: [10/20], Batch Num: [137/600] Discriminator Loss: 0.8878, Generator Loss: 2.1934 D(x): 0.6820, D(G(z)): 0.1734 Epoch: [10/20], Batch Num: [138/600] Discriminator Loss: 0.7135, Generator Loss: 1.9901 D(x): 0.7861, D(G(z)): 0.2155 Epoch: [10/20], Batch Num: [139/600] Discriminator Loss: 0.6944, Generator Loss: 1.7686 D(x): 0.7712, D(G(z)): 0.2106 Epoch: [10/20], Batch Num: [140/600] Discriminator Loss: 0.9245, Generator Loss: 1.9827 D(x): 0.7922, D(G(z)): 0.3216 Epoch: [10/20], Batch Num: [141/600] Discriminator Loss: 0.7363, Generator Loss: 2.1266 D(x): 0.7846, D(G(z)): 0.2295 Epoch: [10/20], Batch Num: [142/600] Discriminator Loss: 0.7552, Generator Loss: 2.2539 D(x): 0.7475, D(G(z)): 0.2296 Epoch: [10/20], Batch Num: [143/600] Discriminator Loss: 0.7517, Generator Loss: 2.1264 D(x): 0.7457, D(G(z)): 0.1987 Epoch: [10/20], Batch Num: [144/600] Discriminator Loss: 0.7564, Generator Loss: 1.9721 D(x): 0.7723, D(G(z)): 0.2423 Epoch: [10/20], Batch Num: [145/600] Discriminator Loss: 0.7482, Generator Loss: 1.8902 D(x): 0.7534, D(G(z)): 0.2056 Epoch: [10/20], Batch Num: [146/600] Discriminator Loss: 0.9610, Generator Loss: 1.7420 D(x): 0.7102, D(G(z)): 0.2712 Epoch: [10/20], Batch Num: [147/600] Discriminator Loss: 0.6108, Generator Loss: 1.9178 D(x): 0.8177, D(G(z)): 0.2296 Epoch: [10/20], Batch Num: [148/600] Discriminator Loss: 0.9044, Generator Loss: 1.7981 D(x): 0.7558, D(G(z)): 0.2903 Epoch: [10/20], Batch Num: [149/600] Discriminator Loss: 0.8873, Generator Loss: 2.2159 D(x): 0.7707, D(G(z)): 0.2936 Epoch: [10/20], Batch Num: [150/600] Discriminator Loss: 0.6071, Generator Loss: 2.3417 D(x): 0.8304, D(G(z)): 0.2062 Epoch: [10/20], Batch Num: [151/600] Discriminator Loss: 0.8862, Generator Loss: 2.1924 D(x): 0.6842, D(G(z)): 0.1808 Epoch: [10/20], Batch Num: [152/600] Discriminator Loss: 0.7833, Generator Loss: 2.0105 D(x): 0.7157, D(G(z)): 0.1850 Epoch: [10/20], Batch Num: [153/600] Discriminator Loss: 0.6553, Generator Loss: 1.8888 D(x): 0.8088, D(G(z)): 0.2453 Epoch: [10/20], Batch Num: [154/600] Discriminator Loss: 0.5940, Generator Loss: 2.0988 D(x): 0.8998, D(G(z)): 0.2815 Epoch: [10/20], Batch Num: [155/600] Discriminator Loss: 0.6443, Generator Loss: 2.6563 D(x): 0.7961, D(G(z)): 0.1985 Epoch: [10/20], Batch Num: [156/600] Discriminator Loss: 0.4979, Generator Loss: 2.5582 D(x): 0.7827, D(G(z)): 0.1291 Epoch: [10/20], Batch Num: [157/600] Discriminator Loss: 0.4763, Generator Loss: 2.2924 D(x): 0.8214, D(G(z)): 0.1450 Epoch: [10/20], Batch Num: [158/600] Discriminator Loss: 0.3327, Generator Loss: 2.4088 D(x): 0.8899, D(G(z)): 0.1447 Epoch: [10/20], Batch Num: [159/600] Discriminator Loss: 0.3416, Generator Loss: 2.5707 D(x): 0.9163, D(G(z)): 0.1674 Epoch: [10/20], Batch Num: [160/600] Discriminator Loss: 0.3583, Generator Loss: 2.7895 D(x): 0.8665, D(G(z)): 0.1285 Epoch: [10/20], Batch Num: [161/600] Discriminator Loss: 0.4263, Generator Loss: 2.8344 D(x): 0.8711, D(G(z)): 0.1646 Epoch: [10/20], Batch Num: [162/600] Discriminator Loss: 0.3720, Generator Loss: 2.7204 D(x): 0.8689, D(G(z)): 0.1277 Epoch: [10/20], Batch Num: [163/600] Discriminator Loss: 0.4607, Generator Loss: 2.5940 D(x): 0.8186, D(G(z)): 0.1417 Epoch: [10/20], Batch Num: [164/600] Discriminator Loss: 0.3766, Generator Loss: 2.6150 D(x): 0.9269, D(G(z)): 0.1755 Epoch: [10/20], Batch Num: [165/600] Discriminator Loss: 0.5354, Generator Loss: 2.9780 D(x): 0.8614, D(G(z)): 0.2114 Epoch: [10/20], Batch Num: [166/600] Discriminator Loss: 0.2462, Generator Loss: 3.2974 D(x): 0.8906, D(G(z)): 0.0945 Epoch: [10/20], Batch Num: [167/600] Discriminator Loss: 0.4045, Generator Loss: 3.1718 D(x): 0.8432, D(G(z)): 0.1068 Epoch: [10/20], Batch Num: [168/600] Discriminator Loss: 0.3925, Generator Loss: 2.5851 D(x): 0.8326, D(G(z)): 0.1121 Epoch: [10/20], Batch Num: [169/600] Discriminator Loss: 0.5271, Generator Loss: 2.2234 D(x): 0.8979, D(G(z)): 0.1930 Epoch: [10/20], Batch Num: [170/600] Discriminator Loss: 0.4169, Generator Loss: 2.4442 D(x): 0.8996, D(G(z)): 0.2019 Epoch: [10/20], Batch Num: [171/600] Discriminator Loss: 0.4779, Generator Loss: 2.9651 D(x): 0.9031, D(G(z)): 0.1947 Epoch: [10/20], Batch Num: [172/600] Discriminator Loss: 0.5744, Generator Loss: 3.2681 D(x): 0.8242, D(G(z)): 0.1588 Epoch: [10/20], Batch Num: [173/600] Discriminator Loss: 0.6179, Generator Loss: 3.3284 D(x): 0.7462, D(G(z)): 0.1194 Epoch: [10/20], Batch Num: [174/600] Discriminator Loss: 0.8275, Generator Loss: 2.6991 D(x): 0.7651, D(G(z)): 0.2161 Epoch: [10/20], Batch Num: [175/600] Discriminator Loss: 0.5999, Generator Loss: 2.1236 D(x): 0.7907, D(G(z)): 0.1550 Epoch: [10/20], Batch Num: [176/600] Discriminator Loss: 0.7117, Generator Loss: 2.2777 D(x): 0.8416, D(G(z)): 0.2808 Epoch: [10/20], Batch Num: [177/600] Discriminator Loss: 0.8323, Generator Loss: 2.2776 D(x): 0.7895, D(G(z)): 0.2614 Epoch: [10/20], Batch Num: [178/600] Discriminator Loss: 0.9788, Generator Loss: 2.4708 D(x): 0.7410, D(G(z)): 0.2293 Epoch: [10/20], Batch Num: [179/600] Discriminator Loss: 0.8739, Generator Loss: 2.4284 D(x): 0.7399, D(G(z)): 0.2177 Epoch: [10/20], Batch Num: [180/600] Discriminator Loss: 1.0457, Generator Loss: 2.0796 D(x): 0.6446, D(G(z)): 0.1952 Epoch: [10/20], Batch Num: [181/600] Discriminator Loss: 1.0704, Generator Loss: 1.8286 D(x): 0.7609, D(G(z)): 0.3094 Epoch: [10/20], Batch Num: [182/600] Discriminator Loss: 0.9301, Generator Loss: 1.9098 D(x): 0.8093, D(G(z)): 0.3205 Epoch: [10/20], Batch Num: [183/600] Discriminator Loss: 0.9354, Generator Loss: 2.3801 D(x): 0.7293, D(G(z)): 0.2591 Epoch: [10/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [10/20], Batch Num: [200/600] Discriminator Loss: 0.4927, Generator Loss: 2.3886 D(x): 0.8565, D(G(z)): 0.1888 Epoch: [10/20], Batch Num: [201/600] Discriminator Loss: 0.3267, Generator Loss: 2.7734 D(x): 0.9134, D(G(z)): 0.1436 Epoch: [10/20], Batch Num: [202/600] Discriminator Loss: 0.4929, Generator Loss: 2.6624 D(x): 0.8079, D(G(z)): 0.1090 Epoch: [10/20], Batch Num: [203/600] Discriminator Loss: 0.5559, Generator Loss: 2.4216 D(x): 0.7938, D(G(z)): 0.1439 Epoch: [10/20], Batch Num: [204/600] Discriminator Loss: 0.4953, Generator Loss: 1.9831 D(x): 0.8238, D(G(z)): 0.1678 Epoch: [10/20], Batch Num: [205/600] Discriminator Loss: 0.4067, Generator Loss: 1.9327 D(x): 0.9214, D(G(z)): 0.2212 Epoch: [10/20], Batch Num: [206/600] Discriminator Loss: 0.4304, Generator Loss: 1.9867 D(x): 0.8937, D(G(z)): 0.2120 Epoch: [10/20], Batch Num: [207/600] Discriminator Loss: 0.4252, Generator Loss: 2.3102 D(x): 0.8750, D(G(z)): 0.1856 Epoch: [10/20], Batch Num: [208/600] Discriminator Loss: 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Discriminator Loss: 1.0236, Generator Loss: 1.9906 D(x): 0.7235, D(G(z)): 0.2321 Epoch: [10/20], Batch Num: [226/600] Discriminator Loss: 0.9578, Generator Loss: 2.0689 D(x): 0.8161, D(G(z)): 0.3487 Epoch: [10/20], Batch Num: [227/600] Discriminator Loss: 0.8415, Generator Loss: 2.2489 D(x): 0.7760, D(G(z)): 0.2615 Epoch: [10/20], Batch Num: [228/600] Discriminator Loss: 0.8526, Generator Loss: 2.1976 D(x): 0.7355, D(G(z)): 0.1997 Epoch: [10/20], Batch Num: [229/600] Discriminator Loss: 0.7447, Generator Loss: 2.3455 D(x): 0.7811, D(G(z)): 0.2270 Epoch: [10/20], Batch Num: [230/600] Discriminator Loss: 0.8832, Generator Loss: 2.0167 D(x): 0.6928, D(G(z)): 0.1734 Epoch: [10/20], Batch Num: [231/600] Discriminator Loss: 0.7372, Generator Loss: 1.9084 D(x): 0.8057, D(G(z)): 0.2169 Epoch: [10/20], Batch Num: [232/600] Discriminator Loss: 0.8004, Generator Loss: 1.9542 D(x): 0.8079, D(G(z)): 0.2709 Epoch: [10/20], Batch Num: [233/600] Discriminator Loss: 0.7458, Generator Loss: 1.8957 D(x): 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2.1817 D(x): 0.6826, D(G(z)): 0.1567 Epoch: [10/20], Batch Num: [251/600] Discriminator Loss: 0.5305, Generator Loss: 1.8838 D(x): 0.8373, D(G(z)): 0.2009 Epoch: [10/20], Batch Num: [252/600] Discriminator Loss: 0.6936, Generator Loss: 1.7111 D(x): 0.8316, D(G(z)): 0.3011 Epoch: [10/20], Batch Num: [253/600] Discriminator Loss: 0.6845, Generator Loss: 2.0438 D(x): 0.8405, D(G(z)): 0.2769 Epoch: [10/20], Batch Num: [254/600] Discriminator Loss: 0.7348, Generator Loss: 2.3531 D(x): 0.7634, D(G(z)): 0.2278 Epoch: [10/20], Batch Num: [255/600] Discriminator Loss: 0.6656, Generator Loss: 2.3117 D(x): 0.7487, D(G(z)): 0.1562 Epoch: [10/20], Batch Num: [256/600] Discriminator Loss: 0.7937, Generator Loss: 1.9936 D(x): 0.7249, D(G(z)): 0.1992 Epoch: [10/20], Batch Num: [257/600] Discriminator Loss: 0.8579, Generator Loss: 1.4576 D(x): 0.7311, D(G(z)): 0.2452 Epoch: [10/20], Batch Num: [258/600] Discriminator Loss: 0.8876, Generator Loss: 1.6158 D(x): 0.8082, D(G(z)): 0.3500 Epoch: [10/20], Batch Num: [259/600] Discriminator Loss: 0.9854, Generator Loss: 1.7348 D(x): 0.7294, D(G(z)): 0.2928 Epoch: [10/20], Batch Num: [260/600] Discriminator Loss: 0.9651, Generator Loss: 2.1696 D(x): 0.6972, D(G(z)): 0.2687 Epoch: [10/20], Batch Num: [261/600] Discriminator Loss: 1.0890, Generator Loss: 2.2519 D(x): 0.6946, D(G(z)): 0.2829 Epoch: [10/20], Batch Num: [262/600] Discriminator Loss: 1.0514, Generator Loss: 1.8207 D(x): 0.6221, D(G(z)): 0.2042 Epoch: [10/20], Batch Num: [263/600] Discriminator Loss: 0.8796, Generator Loss: 1.4052 D(x): 0.7581, D(G(z)): 0.2797 Epoch: [10/20], Batch Num: [264/600] Discriminator Loss: 0.9637, Generator Loss: 1.4999 D(x): 0.7456, D(G(z)): 0.3187 Epoch: [10/20], Batch Num: [265/600] Discriminator Loss: 0.9886, Generator Loss: 1.6181 D(x): 0.7564, D(G(z)): 0.3483 Epoch: [10/20], Batch Num: [266/600] Discriminator Loss: 0.9976, Generator Loss: 1.8967 D(x): 0.7178, D(G(z)): 0.3180 Epoch: [10/20], Batch Num: [267/600] Discriminator Loss: 0.8907, Generator Loss: 1.9673 D(x): 0.6630, D(G(z)): 0.1823 Epoch: [10/20], Batch Num: [268/600] Discriminator Loss: 0.9003, Generator Loss: 1.6561 D(x): 0.6689, D(G(z)): 0.2007 Epoch: [10/20], Batch Num: [269/600] Discriminator Loss: 0.7617, Generator Loss: 1.6144 D(x): 0.7905, D(G(z)): 0.2792 Epoch: [10/20], Batch Num: [270/600] Discriminator Loss: 0.9319, Generator Loss: 1.8225 D(x): 0.7100, D(G(z)): 0.2842 Epoch: [10/20], Batch Num: [271/600] Discriminator Loss: 0.6735, Generator Loss: 1.6277 D(x): 0.8026, D(G(z)): 0.2464 Epoch: [10/20], Batch Num: [272/600] Discriminator Loss: 0.8460, Generator Loss: 1.8442 D(x): 0.7484, D(G(z)): 0.2933 Epoch: [10/20], Batch Num: [273/600] Discriminator Loss: 0.6211, Generator Loss: 2.1055 D(x): 0.8271, D(G(z)): 0.2680 Epoch: [10/20], Batch Num: [274/600] Discriminator Loss: 0.5267, Generator Loss: 2.4754 D(x): 0.8167, D(G(z)): 0.2048 Epoch: [10/20], Batch Num: [275/600] Discriminator Loss: 0.7692, Generator Loss: 2.5889 D(x): 0.7037, D(G(z)): 0.1480 Epoch: [10/20], Batch Num: [276/600] Discriminator Loss: 0.4280, Generator Loss: 2.6953 D(x): 0.8406, D(G(z)): 0.1362 Epoch: [10/20], Batch Num: [277/600] Discriminator Loss: 0.4805, Generator Loss: 2.2403 D(x): 0.8152, D(G(z)): 0.1402 Epoch: [10/20], Batch Num: [278/600] Discriminator Loss: 0.4979, Generator Loss: 2.2218 D(x): 0.8216, D(G(z)): 0.1704 Epoch: [10/20], Batch Num: [279/600] Discriminator Loss: 0.3814, Generator Loss: 2.1804 D(x): 0.8915, D(G(z)): 0.1758 Epoch: [10/20], Batch Num: [280/600] Discriminator Loss: 0.3851, Generator Loss: 2.4538 D(x): 0.9273, D(G(z)): 0.1858 Epoch: [10/20], Batch Num: [281/600] Discriminator Loss: 0.3430, Generator Loss: 2.6053 D(x): 0.8903, D(G(z)): 0.1584 Epoch: [10/20], Batch Num: [282/600] Discriminator Loss: 0.3429, Generator Loss: 2.6049 D(x): 0.8920, D(G(z)): 0.1508 Epoch: [10/20], Batch Num: [283/600] Discriminator Loss: 0.3920, Generator Loss: 2.8410 D(x): 0.8798, D(G(z)): 0.1379 Epoch: [10/20], Batch Num: [284/600] Discriminator Loss: 0.3959, Generator Loss: 3.1981 D(x): 0.8543, D(G(z)): 0.1221 Epoch: [10/20], Batch Num: [285/600] Discriminator Loss: 0.4682, Generator Loss: 2.8208 D(x): 0.8103, D(G(z)): 0.1094 Epoch: [10/20], Batch Num: [286/600] Discriminator Loss: 0.3578, Generator Loss: 2.6197 D(x): 0.8897, D(G(z)): 0.1170 Epoch: [10/20], Batch Num: [287/600] Discriminator Loss: 0.2856, Generator Loss: 2.4672 D(x): 0.9068, D(G(z)): 0.1147 Epoch: [10/20], Batch Num: [288/600] Discriminator Loss: 0.4387, Generator Loss: 2.3907 D(x): 0.8704, D(G(z)): 0.1666 Epoch: [10/20], Batch Num: [289/600] Discriminator Loss: 0.3907, Generator Loss: 2.3738 D(x): 0.9008, D(G(z)): 0.1787 Epoch: [10/20], Batch Num: [290/600] Discriminator Loss: 0.4444, Generator Loss: 2.6598 D(x): 0.9069, D(G(z)): 0.1834 Epoch: [10/20], Batch Num: [291/600] Discriminator Loss: 0.2977, Generator Loss: 2.8266 D(x): 0.9002, D(G(z)): 0.1329 Epoch: [10/20], Batch Num: [292/600] Discriminator Loss: 0.4496, Generator Loss: 2.8096 D(x): 0.8543, D(G(z)): 0.1353 Epoch: [10/20], Batch Num: [293/600] Discriminator Loss: 0.3984, Generator Loss: 2.8016 D(x): 0.8721, D(G(z)): 0.1367 Epoch: [10/20], Batch Num: [294/600] Discriminator Loss: 0.5907, Generator Loss: 2.8164 D(x): 0.8080, D(G(z)): 0.1707 Epoch: [10/20], Batch Num: [295/600] Discriminator Loss: 0.3884, Generator Loss: 3.0027 D(x): 0.8896, D(G(z)): 0.1569 Epoch: [10/20], Batch Num: [296/600] Discriminator Loss: 0.3060, Generator Loss: 2.9160 D(x): 0.9120, D(G(z)): 0.1387 Epoch: [10/20], Batch Num: [297/600] Discriminator Loss: 0.4437, Generator Loss: 2.7150 D(x): 0.8389, D(G(z)): 0.1309 Epoch: [10/20], Batch Num: [298/600] Discriminator Loss: 0.6119, Generator Loss: 2.6156 D(x): 0.7673, D(G(z)): 0.1292 Epoch: [10/20], Batch Num: [299/600] Discriminator Loss: 0.4723, Generator Loss: 2.4356 D(x): 0.8733, D(G(z)): 0.1784 Epoch: 10, Batch Num: [300/600]
Epoch: [10/20], Batch Num: [300/600] Discriminator Loss: 0.6542, Generator Loss: 2.4584 D(x): 0.8445, D(G(z)): 0.2352 Epoch: [10/20], Batch Num: [301/600] Discriminator Loss: 0.4369, Generator Loss: 2.6489 D(x): 0.9081, D(G(z)): 0.1854 Epoch: [10/20], Batch Num: [302/600] Discriminator Loss: 0.6945, Generator Loss: 2.9052 D(x): 0.7882, D(G(z)): 0.1610 Epoch: [10/20], Batch Num: [303/600] Discriminator Loss: 0.5873, Generator Loss: 2.7385 D(x): 0.7795, D(G(z)): 0.1269 Epoch: [10/20], Batch Num: [304/600] Discriminator Loss: 0.6456, Generator Loss: 2.3284 D(x): 0.8042, D(G(z)): 0.1455 Epoch: [10/20], Batch Num: [305/600] Discriminator Loss: 0.7349, Generator Loss: 1.9270 D(x): 0.7827, D(G(z)): 0.1996 Epoch: [10/20], Batch Num: [306/600] Discriminator Loss: 0.6915, Generator Loss: 1.6965 D(x): 0.8410, D(G(z)): 0.2583 Epoch: [10/20], Batch Num: [307/600] Discriminator Loss: 0.8437, Generator Loss: 2.5349 D(x): 0.8578, D(G(z)): 0.3281 Epoch: [10/20], Batch Num: [308/600] Discriminator Loss: 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0.1338 Epoch: [10/20], Batch Num: [317/600] Discriminator Loss: 0.6558, Generator Loss: 2.7286 D(x): 0.7651, D(G(z)): 0.1135 Epoch: [10/20], Batch Num: [318/600] Discriminator Loss: 0.5407, Generator Loss: 2.6758 D(x): 0.8178, D(G(z)): 0.1592 Epoch: [10/20], Batch Num: [319/600] Discriminator Loss: 0.4634, Generator Loss: 2.1022 D(x): 0.8616, D(G(z)): 0.1677 Epoch: [10/20], Batch Num: [320/600] Discriminator Loss: 0.6313, Generator Loss: 2.2324 D(x): 0.8361, D(G(z)): 0.2482 Epoch: [10/20], Batch Num: [321/600] Discriminator Loss: 0.4611, Generator Loss: 2.5011 D(x): 0.8945, D(G(z)): 0.2093 Epoch: [10/20], Batch Num: [322/600] Discriminator Loss: 0.4047, Generator Loss: 2.5080 D(x): 0.9100, D(G(z)): 0.1947 Epoch: [10/20], Batch Num: [323/600] Discriminator Loss: 0.4746, Generator Loss: 3.0356 D(x): 0.8634, D(G(z)): 0.1716 Epoch: [10/20], Batch Num: [324/600] Discriminator Loss: 0.6052, Generator Loss: 2.8180 D(x): 0.7986, D(G(z)): 0.1418 Epoch: [10/20], Batch Num: [325/600] Discriminator Loss: 0.5822, Generator Loss: 3.0282 D(x): 0.7798, D(G(z)): 0.1360 Epoch: [10/20], Batch Num: [326/600] Discriminator Loss: 0.5819, Generator Loss: 2.8120 D(x): 0.7538, D(G(z)): 0.1183 Epoch: [10/20], Batch Num: [327/600] Discriminator Loss: 0.6563, Generator Loss: 1.9815 D(x): 0.7823, D(G(z)): 0.1709 Epoch: [10/20], Batch Num: [328/600] Discriminator Loss: 0.5568, Generator Loss: 1.6423 D(x): 0.8205, D(G(z)): 0.1892 Epoch: [10/20], Batch Num: [329/600] Discriminator Loss: 0.6121, Generator Loss: 1.8556 D(x): 0.9335, D(G(z)): 0.3196 Epoch: [10/20], Batch Num: [330/600] Discriminator Loss: 0.7131, Generator Loss: 2.0566 D(x): 0.9052, D(G(z)): 0.3314 Epoch: [10/20], Batch Num: [331/600] Discriminator Loss: 0.6437, Generator Loss: 2.5928 D(x): 0.8402, D(G(z)): 0.2476 Epoch: [10/20], Batch Num: [332/600] Discriminator Loss: 0.6828, Generator Loss: 2.8655 D(x): 0.7319, D(G(z)): 0.1261 Epoch: [10/20], Batch Num: [333/600] Discriminator Loss: 0.7564, Generator Loss: 2.8301 D(x): 0.6865, D(G(z)): 0.0931 Epoch: [10/20], Batch Num: [334/600] Discriminator Loss: 0.8322, Generator Loss: 2.2484 D(x): 0.6933, D(G(z)): 0.1121 Epoch: [10/20], Batch Num: [335/600] Discriminator Loss: 0.6559, Generator Loss: 1.7506 D(x): 0.8218, D(G(z)): 0.2287 Epoch: [10/20], Batch Num: [336/600] Discriminator Loss: 0.6374, Generator Loss: 1.5573 D(x): 0.8817, D(G(z)): 0.2950 Epoch: [10/20], Batch Num: [337/600] Discriminator Loss: 0.7515, Generator Loss: 1.8517 D(x): 0.8889, D(G(z)): 0.3577 Epoch: [10/20], Batch Num: [338/600] Discriminator Loss: 0.8009, Generator Loss: 2.2185 D(x): 0.8378, D(G(z)): 0.3246 Epoch: [10/20], Batch Num: [339/600] Discriminator Loss: 0.6720, Generator Loss: 2.8572 D(x): 0.7885, D(G(z)): 0.2192 Epoch: [10/20], Batch Num: [340/600] Discriminator Loss: 0.8495, Generator Loss: 2.8258 D(x): 0.6713, D(G(z)): 0.1054 Epoch: [10/20], Batch Num: [341/600] Discriminator Loss: 0.8533, Generator Loss: 2.6914 D(x): 0.6350, D(G(z)): 0.1004 Epoch: [10/20], Batch Num: 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1.8847 D(x): 0.6783, D(G(z)): 0.1543 Epoch: [10/20], Batch Num: [351/600] Discriminator Loss: 0.6533, Generator Loss: 1.5021 D(x): 0.8060, D(G(z)): 0.2288 Epoch: [10/20], Batch Num: [352/600] Discriminator Loss: 0.8082, Generator Loss: 1.4849 D(x): 0.8685, D(G(z)): 0.3726 Epoch: [10/20], Batch Num: [353/600] Discriminator Loss: 0.7532, Generator Loss: 1.7199 D(x): 0.8601, D(G(z)): 0.3462 Epoch: [10/20], Batch Num: [354/600] Discriminator Loss: 0.7093, Generator Loss: 2.1231 D(x): 0.7923, D(G(z)): 0.2549 Epoch: [10/20], Batch Num: [355/600] Discriminator Loss: 0.7934, Generator Loss: 2.4242 D(x): 0.6793, D(G(z)): 0.1614 Epoch: [10/20], Batch Num: [356/600] Discriminator Loss: 0.9361, Generator Loss: 2.1595 D(x): 0.6465, D(G(z)): 0.1803 Epoch: [10/20], Batch Num: [357/600] Discriminator Loss: 0.7496, Generator Loss: 2.1000 D(x): 0.7275, D(G(z)): 0.1857 Epoch: [10/20], Batch Num: [358/600] Discriminator Loss: 0.8033, Generator Loss: 1.8180 D(x): 0.7478, D(G(z)): 0.2505 Epoch: [10/20], 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Generator Loss: 1.4139 D(x): 0.8111, D(G(z)): 0.2900 Epoch: [10/20], Batch Num: [368/600] Discriminator Loss: 0.7338, Generator Loss: 1.4957 D(x): 0.8398, D(G(z)): 0.3070 Epoch: [10/20], Batch Num: [369/600] Discriminator Loss: 0.7859, Generator Loss: 1.9400 D(x): 0.8262, D(G(z)): 0.3267 Epoch: [10/20], Batch Num: [370/600] Discriminator Loss: 0.8042, Generator Loss: 2.3090 D(x): 0.7090, D(G(z)): 0.2010 Epoch: [10/20], Batch Num: [371/600] Discriminator Loss: 0.7830, Generator Loss: 2.3404 D(x): 0.6903, D(G(z)): 0.1718 Epoch: [10/20], Batch Num: [372/600] Discriminator Loss: 0.9427, Generator Loss: 2.1840 D(x): 0.6328, D(G(z)): 0.1489 Epoch: [10/20], Batch Num: [373/600] Discriminator Loss: 0.9195, Generator Loss: 2.0213 D(x): 0.6836, D(G(z)): 0.2164 Epoch: [10/20], Batch Num: [374/600] Discriminator Loss: 0.7623, Generator Loss: 1.4129 D(x): 0.7149, D(G(z)): 0.2066 Epoch: [10/20], Batch Num: [375/600] Discriminator Loss: 0.7202, Generator Loss: 1.5073 D(x): 0.8219, D(G(z)): 0.2803 Epoch: [10/20], Batch Num: [376/600] Discriminator Loss: 0.7560, Generator Loss: 1.6509 D(x): 0.8614, D(G(z)): 0.3449 Epoch: [10/20], Batch Num: [377/600] Discriminator Loss: 0.7324, Generator Loss: 1.9691 D(x): 0.8024, D(G(z)): 0.2956 Epoch: [10/20], Batch Num: [378/600] Discriminator Loss: 0.6105, Generator Loss: 2.1212 D(x): 0.7676, D(G(z)): 0.1803 Epoch: [10/20], Batch Num: [379/600] Discriminator Loss: 0.6186, Generator Loss: 2.3449 D(x): 0.7560, D(G(z)): 0.1578 Epoch: [10/20], Batch Num: [380/600] Discriminator Loss: 0.6490, Generator Loss: 2.1329 D(x): 0.7111, D(G(z)): 0.1198 Epoch: [10/20], Batch Num: [381/600] Discriminator Loss: 0.6676, Generator Loss: 1.9742 D(x): 0.7571, D(G(z)): 0.1867 Epoch: [10/20], Batch Num: [382/600] Discriminator Loss: 0.5220, Generator Loss: 1.6984 D(x): 0.8200, D(G(z)): 0.1895 Epoch: [10/20], Batch Num: [383/600] Discriminator Loss: 0.7587, Generator Loss: 1.5783 D(x): 0.8329, D(G(z)): 0.2796 Epoch: [10/20], Batch Num: [384/600] Discriminator Loss: 0.5871, Generator Loss: 1.6982 D(x): 0.8476, D(G(z)): 0.2582 Epoch: [10/20], Batch Num: [385/600] Discriminator Loss: 0.6961, Generator Loss: 2.0278 D(x): 0.8309, D(G(z)): 0.2855 Epoch: [10/20], Batch Num: [386/600] Discriminator Loss: 0.5766, Generator Loss: 2.1564 D(x): 0.8057, D(G(z)): 0.1884 Epoch: [10/20], Batch Num: [387/600] Discriminator Loss: 0.6854, Generator Loss: 2.5237 D(x): 0.7251, D(G(z)): 0.1541 Epoch: [10/20], Batch Num: [388/600] Discriminator Loss: 0.6328, Generator Loss: 2.3610 D(x): 0.7405, D(G(z)): 0.1450 Epoch: [10/20], Batch Num: [389/600] Discriminator Loss: 0.6341, Generator Loss: 2.1296 D(x): 0.7384, D(G(z)): 0.1427 Epoch: [10/20], Batch Num: [390/600] Discriminator Loss: 0.6239, Generator Loss: 1.9114 D(x): 0.7986, D(G(z)): 0.1912 Epoch: [10/20], Batch Num: [391/600] Discriminator Loss: 0.5855, Generator Loss: 1.8441 D(x): 0.8244, D(G(z)): 0.2284 Epoch: [10/20], Batch Num: [392/600] Discriminator Loss: 0.5907, Generator Loss: 1.8045 D(x): 0.8350, D(G(z)): 0.2370 Epoch: [10/20], Batch Num: [393/600] Discriminator Loss: 0.5604, Generator Loss: 2.0139 D(x): 0.8895, D(G(z)): 0.2691 Epoch: [10/20], Batch Num: [394/600] Discriminator Loss: 0.5254, Generator Loss: 2.4471 D(x): 0.8126, D(G(z)): 0.1832 Epoch: [10/20], Batch Num: [395/600] Discriminator Loss: 0.5237, Generator Loss: 2.5273 D(x): 0.8104, D(G(z)): 0.1663 Epoch: [10/20], Batch Num: [396/600] Discriminator Loss: 0.5132, Generator Loss: 2.3986 D(x): 0.8205, D(G(z)): 0.1693 Epoch: [10/20], Batch Num: [397/600] Discriminator Loss: 0.6193, Generator Loss: 2.1852 D(x): 0.7637, D(G(z)): 0.1325 Epoch: [10/20], Batch Num: [398/600] Discriminator Loss: 0.5199, Generator Loss: 2.0870 D(x): 0.8310, D(G(z)): 0.1911 Epoch: [10/20], Batch Num: [399/600] Discriminator Loss: 0.5701, Generator Loss: 2.1900 D(x): 0.8190, D(G(z)): 0.1891 Epoch: 10, Batch Num: [400/600]
Epoch: [10/20], Batch Num: [400/600] Discriminator Loss: 0.4347, Generator Loss: 2.1671 D(x): 0.8670, D(G(z)): 0.1711 Epoch: [10/20], Batch Num: [401/600] Discriminator Loss: 0.4226, Generator Loss: 2.1621 D(x): 0.9014, D(G(z)): 0.1922 Epoch: [10/20], Batch Num: [402/600] Discriminator Loss: 0.4398, Generator Loss: 2.3613 D(x): 0.8570, D(G(z)): 0.1673 Epoch: [10/20], Batch Num: [403/600] Discriminator Loss: 0.4655, Generator Loss: 2.2714 D(x): 0.8235, D(G(z)): 0.1544 Epoch: [10/20], Batch Num: [404/600] Discriminator Loss: 0.5361, Generator Loss: 2.2002 D(x): 0.8082, D(G(z)): 0.1785 Epoch: [10/20], Batch Num: [405/600] Discriminator Loss: 0.7068, Generator Loss: 2.4206 D(x): 0.8049, D(G(z)): 0.2185 Epoch: [10/20], Batch Num: [406/600] Discriminator Loss: 0.6037, Generator Loss: 2.2958 D(x): 0.8557, D(G(z)): 0.2184 Epoch: [10/20], Batch Num: [407/600] Discriminator Loss: 0.6247, Generator Loss: 2.3326 D(x): 0.8124, D(G(z)): 0.2055 Epoch: [10/20], Batch Num: [408/600] Discriminator Loss: 0.5752, Generator Loss: 2.5699 D(x): 0.8190, D(G(z)): 0.1786 Epoch: [10/20], Batch Num: [409/600] Discriminator Loss: 0.4383, Generator Loss: 2.5451 D(x): 0.8248, D(G(z)): 0.1368 Epoch: [10/20], Batch Num: [410/600] Discriminator Loss: 0.6509, Generator Loss: 2.1475 D(x): 0.7613, D(G(z)): 0.1481 Epoch: [10/20], Batch Num: [411/600] Discriminator Loss: 0.6128, Generator Loss: 2.0666 D(x): 0.8682, D(G(z)): 0.2363 Epoch: [10/20], Batch Num: [412/600] Discriminator Loss: 0.6887, Generator Loss: 2.2881 D(x): 0.8141, D(G(z)): 0.2144 Epoch: [10/20], Batch Num: [413/600] Discriminator Loss: 0.5093, Generator Loss: 2.3194 D(x): 0.8517, D(G(z)): 0.2132 Epoch: [10/20], Batch Num: [414/600] Discriminator Loss: 0.7690, Generator Loss: 2.2336 D(x): 0.7286, D(G(z)): 0.1882 Epoch: [10/20], Batch Num: [415/600] Discriminator Loss: 0.6364, Generator Loss: 1.9814 D(x): 0.8270, D(G(z)): 0.2008 Epoch: [10/20], Batch Num: [416/600] Discriminator Loss: 0.6727, Generator Loss: 2.1304 D(x): 0.8126, D(G(z)): 0.2354 Epoch: [10/20], Batch Num: [417/600] Discriminator Loss: 0.5549, Generator Loss: 2.3580 D(x): 0.8341, D(G(z)): 0.1989 Epoch: [10/20], Batch Num: [418/600] Discriminator Loss: 0.7602, Generator Loss: 2.4213 D(x): 0.7775, D(G(z)): 0.2192 Epoch: [10/20], Batch Num: [419/600] Discriminator Loss: 0.8418, Generator Loss: 2.2410 D(x): 0.7236, D(G(z)): 0.2201 Epoch: [10/20], Batch Num: [420/600] Discriminator Loss: 0.8329, Generator Loss: 1.9360 D(x): 0.7319, D(G(z)): 0.1829 Epoch: [10/20], Batch Num: [421/600] Discriminator Loss: 0.7720, Generator Loss: 1.8758 D(x): 0.7627, D(G(z)): 0.2257 Epoch: [10/20], Batch Num: [422/600] Discriminator Loss: 0.7402, Generator Loss: 1.9391 D(x): 0.8013, D(G(z)): 0.2670 Epoch: [10/20], Batch Num: [423/600] Discriminator Loss: 0.7807, Generator Loss: 2.2516 D(x): 0.8352, D(G(z)): 0.2909 Epoch: [10/20], Batch Num: [424/600] Discriminator Loss: 0.7153, Generator Loss: 2.5062 D(x): 0.8189, D(G(z)): 0.2482 Epoch: [10/20], Batch Num: [425/600] Discriminator Loss: 0.6411, Generator Loss: 2.5754 D(x): 0.7623, D(G(z)): 0.1655 Epoch: [10/20], Batch Num: [426/600] Discriminator Loss: 0.8331, Generator Loss: 2.3110 D(x): 0.6318, D(G(z)): 0.1279 Epoch: [10/20], Batch Num: [427/600] Discriminator Loss: 0.7303, Generator Loss: 1.6863 D(x): 0.7226, D(G(z)): 0.1986 Epoch: [10/20], Batch Num: [428/600] Discriminator Loss: 0.6726, Generator Loss: 1.4825 D(x): 0.8289, D(G(z)): 0.2793 Epoch: [10/20], Batch Num: [429/600] Discriminator Loss: 0.5820, Generator Loss: 1.4996 D(x): 0.8710, D(G(z)): 0.2761 Epoch: [10/20], Batch Num: [430/600] Discriminator Loss: 0.7833, Generator Loss: 1.7906 D(x): 0.8656, D(G(z)): 0.3557 Epoch: [10/20], Batch Num: [431/600] Discriminator Loss: 0.6074, Generator Loss: 2.1233 D(x): 0.8096, D(G(z)): 0.2370 Epoch: [10/20], Batch Num: [432/600] Discriminator Loss: 0.5366, Generator Loss: 2.3654 D(x): 0.8127, D(G(z)): 0.1961 Epoch: [10/20], Batch Num: [433/600] Discriminator Loss: 0.7513, Generator Loss: 2.4344 D(x): 0.7059, D(G(z)): 0.1681 Epoch: [10/20], Batch Num: [434/600] Discriminator Loss: 0.5593, Generator Loss: 2.2606 D(x): 0.8005, D(G(z)): 0.1524 Epoch: [10/20], Batch Num: [435/600] Discriminator Loss: 0.6348, Generator Loss: 2.0288 D(x): 0.7989, D(G(z)): 0.2351 Epoch: [10/20], Batch Num: [436/600] Discriminator Loss: 0.6065, Generator Loss: 1.8872 D(x): 0.8081, D(G(z)): 0.2035 Epoch: [10/20], Batch Num: [437/600] Discriminator Loss: 0.5500, Generator Loss: 2.0362 D(x): 0.8553, D(G(z)): 0.2417 Epoch: [10/20], Batch Num: [438/600] Discriminator Loss: 0.5948, Generator Loss: 2.2854 D(x): 0.8346, D(G(z)): 0.2474 Epoch: [10/20], Batch Num: [439/600] Discriminator Loss: 0.5254, Generator Loss: 2.6284 D(x): 0.8780, D(G(z)): 0.2144 Epoch: [10/20], Batch Num: [440/600] Discriminator Loss: 0.4666, Generator Loss: 2.7183 D(x): 0.8147, D(G(z)): 0.1432 Epoch: [10/20], Batch Num: [441/600] Discriminator Loss: 0.5845, Generator Loss: 2.4231 D(x): 0.7846, D(G(z)): 0.1556 Epoch: [10/20], Batch Num: 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2.3784 D(x): 0.8325, D(G(z)): 0.2038 Epoch: [10/20], Batch Num: [451/600] Discriminator Loss: 0.5134, Generator Loss: 2.1521 D(x): 0.8457, D(G(z)): 0.1983 Epoch: [10/20], Batch Num: [452/600] Discriminator Loss: 0.5825, Generator Loss: 2.0449 D(x): 0.8674, D(G(z)): 0.2580 Epoch: [10/20], Batch Num: [453/600] Discriminator Loss: 0.7678, Generator Loss: 2.2799 D(x): 0.7918, D(G(z)): 0.2599 Epoch: [10/20], Batch Num: [454/600] Discriminator Loss: 0.8141, Generator Loss: 2.7418 D(x): 0.7757, D(G(z)): 0.2498 Epoch: [10/20], Batch Num: [455/600] Discriminator Loss: 0.6796, Generator Loss: 2.3923 D(x): 0.7787, D(G(z)): 0.1787 Epoch: [10/20], Batch Num: [456/600] Discriminator Loss: 0.8852, Generator Loss: 2.3670 D(x): 0.7635, D(G(z)): 0.2059 Epoch: [10/20], Batch Num: [457/600] Discriminator Loss: 0.8206, Generator Loss: 2.3610 D(x): 0.7354, D(G(z)): 0.2209 Epoch: [10/20], Batch Num: [458/600] Discriminator Loss: 0.7555, Generator Loss: 1.9974 D(x): 0.8065, D(G(z)): 0.2608 Epoch: [10/20], 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Generator Loss: 1.9812 D(x): 0.6632, D(G(z)): 0.1757 Epoch: [10/20], Batch Num: [468/600] Discriminator Loss: 0.6800, Generator Loss: 1.9170 D(x): 0.7736, D(G(z)): 0.2331 Epoch: [10/20], Batch Num: [469/600] Discriminator Loss: 0.6876, Generator Loss: 2.0690 D(x): 0.8434, D(G(z)): 0.2736 Epoch: [10/20], Batch Num: [470/600] Discriminator Loss: 0.6733, Generator Loss: 2.2622 D(x): 0.8193, D(G(z)): 0.2432 Epoch: [10/20], Batch Num: [471/600] Discriminator Loss: 0.8133, Generator Loss: 2.6349 D(x): 0.7694, D(G(z)): 0.2352 Epoch: [10/20], Batch Num: [472/600] Discriminator Loss: 0.5204, Generator Loss: 2.7803 D(x): 0.7973, D(G(z)): 0.1365 Epoch: [10/20], Batch Num: [473/600] Discriminator Loss: 0.6710, Generator Loss: 2.6963 D(x): 0.7365, D(G(z)): 0.1221 Epoch: [10/20], Batch Num: [474/600] Discriminator Loss: 0.5468, Generator Loss: 2.4495 D(x): 0.7966, D(G(z)): 0.1716 Epoch: [10/20], Batch Num: [475/600] Discriminator Loss: 0.6211, Generator Loss: 2.5140 D(x): 0.8288, D(G(z)): 0.1984 Epoch: [10/20], Batch Num: [476/600] Discriminator Loss: 0.5663, Generator Loss: 2.4258 D(x): 0.8730, D(G(z)): 0.2136 Epoch: [10/20], Batch Num: [477/600] Discriminator Loss: 0.6693, Generator Loss: 2.5294 D(x): 0.7504, D(G(z)): 0.1646 Epoch: [10/20], Batch Num: [478/600] Discriminator Loss: 0.5514, Generator Loss: 2.7264 D(x): 0.8335, D(G(z)): 0.1995 Epoch: [10/20], Batch Num: [479/600] Discriminator Loss: 0.5685, Generator Loss: 2.8663 D(x): 0.8374, D(G(z)): 0.1887 Epoch: [10/20], Batch Num: [480/600] Discriminator Loss: 0.5288, Generator Loss: 2.8260 D(x): 0.8179, D(G(z)): 0.1687 Epoch: [10/20], Batch Num: [481/600] Discriminator Loss: 0.4898, Generator Loss: 2.9014 D(x): 0.8262, D(G(z)): 0.1392 Epoch: [10/20], Batch Num: [482/600] Discriminator Loss: 0.7127, Generator Loss: 2.3715 D(x): 0.7177, D(G(z)): 0.1506 Epoch: [10/20], Batch Num: [483/600] Discriminator Loss: 0.5829, Generator Loss: 2.4780 D(x): 0.8095, D(G(z)): 0.1907 Epoch: [10/20], Batch Num: [484/600] Discriminator Loss: 0.4404, Generator Loss: 2.0749 D(x): 0.8854, D(G(z)): 0.1929 Epoch: [10/20], Batch Num: [485/600] Discriminator Loss: 0.7125, Generator Loss: 2.3207 D(x): 0.8306, D(G(z)): 0.2535 Epoch: [10/20], Batch Num: [486/600] Discriminator Loss: 0.7318, Generator Loss: 2.8253 D(x): 0.8249, D(G(z)): 0.2582 Epoch: [10/20], Batch Num: [487/600] Discriminator Loss: 0.7265, Generator Loss: 2.8968 D(x): 0.7733, D(G(z)): 0.1974 Epoch: [10/20], Batch Num: [488/600] Discriminator Loss: 0.7875, Generator Loss: 2.9610 D(x): 0.7469, D(G(z)): 0.1567 Epoch: [10/20], Batch Num: [489/600] Discriminator Loss: 0.8229, Generator Loss: 2.8745 D(x): 0.7145, D(G(z)): 0.1409 Epoch: [10/20], Batch Num: [490/600] Discriminator Loss: 0.6662, Generator Loss: 2.4789 D(x): 0.7586, D(G(z)): 0.1579 Epoch: [10/20], Batch Num: [491/600] Discriminator Loss: 0.7609, Generator Loss: 1.9881 D(x): 0.7936, D(G(z)): 0.2285 Epoch: [10/20], Batch Num: [492/600] Discriminator Loss: 1.0134, Generator Loss: 2.6990 D(x): 0.8242, D(G(z)): 0.3492 Epoch: [10/20], Batch Num: [493/600] Discriminator Loss: 0.8506, Generator Loss: 2.8949 D(x): 0.7277, D(G(z)): 0.1944 Epoch: [10/20], Batch Num: [494/600] Discriminator Loss: 0.7516, Generator Loss: 2.5527 D(x): 0.7379, D(G(z)): 0.1582 Epoch: [10/20], Batch Num: [495/600] Discriminator Loss: 0.9308, Generator Loss: 2.2126 D(x): 0.6895, D(G(z)): 0.1839 Epoch: [10/20], Batch Num: [496/600] Discriminator Loss: 0.8761, Generator Loss: 1.9083 D(x): 0.7731, D(G(z)): 0.2675 Epoch: [10/20], Batch Num: [497/600] Discriminator Loss: 0.8802, Generator Loss: 2.2768 D(x): 0.7565, D(G(z)): 0.2773 Epoch: [10/20], Batch Num: [498/600] Discriminator Loss: 0.8961, Generator Loss: 2.4566 D(x): 0.7594, D(G(z)): 0.2911 Epoch: [10/20], Batch Num: [499/600] Discriminator Loss: 0.9518, Generator Loss: 2.1791 D(x): 0.6675, D(G(z)): 0.2286 Epoch: 10, Batch Num: [500/600]
Epoch: [10/20], Batch Num: [500/600] Discriminator Loss: 1.0468, Generator Loss: 2.0711 D(x): 0.6562, D(G(z)): 0.2279 Epoch: [10/20], Batch Num: [501/600] Discriminator Loss: 0.9427, Generator Loss: 1.8403 D(x): 0.6713, D(G(z)): 0.2182 Epoch: [10/20], Batch Num: [502/600] Discriminator Loss: 0.9940, Generator Loss: 1.7546 D(x): 0.7319, D(G(z)): 0.3230 Epoch: [10/20], Batch Num: [503/600] Discriminator Loss: 0.9971, Generator Loss: 1.6715 D(x): 0.7247, D(G(z)): 0.2980 Epoch: [10/20], Batch Num: [504/600] Discriminator Loss: 1.0889, Generator Loss: 1.6817 D(x): 0.6512, D(G(z)): 0.3033 Epoch: [10/20], Batch Num: [505/600] Discriminator Loss: 0.9364, Generator Loss: 2.1501 D(x): 0.7547, D(G(z)): 0.3290 Epoch: [10/20], Batch Num: [506/600] Discriminator Loss: 0.9493, Generator Loss: 2.1446 D(x): 0.6668, D(G(z)): 0.2330 Epoch: [10/20], Batch Num: [507/600] Discriminator Loss: 0.8073, Generator Loss: 1.9942 D(x): 0.7292, D(G(z)): 0.2276 Epoch: [10/20], Batch Num: [508/600] Discriminator Loss: 0.9521, Generator Loss: 2.0088 D(x): 0.7073, D(G(z)): 0.2719 Epoch: [10/20], Batch Num: [509/600] Discriminator Loss: 0.8177, Generator Loss: 2.1146 D(x): 0.7419, D(G(z)): 0.2646 Epoch: [10/20], Batch Num: [510/600] Discriminator Loss: 0.7689, Generator Loss: 2.0686 D(x): 0.7315, D(G(z)): 0.2094 Epoch: [10/20], Batch Num: [511/600] Discriminator Loss: 0.8065, Generator Loss: 2.1052 D(x): 0.7294, D(G(z)): 0.2360 Epoch: [10/20], Batch Num: [512/600] Discriminator Loss: 0.6616, Generator Loss: 1.9152 D(x): 0.8015, D(G(z)): 0.2464 Epoch: [10/20], Batch Num: [513/600] Discriminator Loss: 0.7721, Generator Loss: 2.0455 D(x): 0.7258, D(G(z)): 0.2055 Epoch: [10/20], Batch Num: [514/600] Discriminator Loss: 0.7134, Generator Loss: 1.9191 D(x): 0.7756, D(G(z)): 0.2386 Epoch: [10/20], Batch Num: [515/600] Discriminator Loss: 0.8179, Generator Loss: 1.7094 D(x): 0.7259, D(G(z)): 0.2316 Epoch: [10/20], Batch Num: [516/600] Discriminator Loss: 0.7926, Generator Loss: 1.5734 D(x): 0.7655, D(G(z)): 0.2855 Epoch: [10/20], Batch Num: [517/600] Discriminator Loss: 0.7304, Generator Loss: 1.9638 D(x): 0.8147, D(G(z)): 0.2942 Epoch: [10/20], Batch Num: [518/600] Discriminator Loss: 0.7407, Generator Loss: 2.2749 D(x): 0.7829, D(G(z)): 0.2640 Epoch: [10/20], Batch Num: [519/600] Discriminator Loss: 0.6392, Generator Loss: 2.4040 D(x): 0.7820, D(G(z)): 0.1886 Epoch: [10/20], Batch Num: [520/600] Discriminator Loss: 0.5906, Generator Loss: 2.4384 D(x): 0.7723, D(G(z)): 0.1330 Epoch: [10/20], Batch Num: [521/600] Discriminator Loss: 0.7307, Generator Loss: 2.4152 D(x): 0.7218, D(G(z)): 0.1620 Epoch: [10/20], Batch Num: [522/600] Discriminator Loss: 0.6609, Generator Loss: 1.9524 D(x): 0.7948, D(G(z)): 0.1918 Epoch: [10/20], Batch Num: [523/600] Discriminator Loss: 0.5646, Generator Loss: 2.1107 D(x): 0.8525, D(G(z)): 0.2317 Epoch: [10/20], Batch Num: [524/600] Discriminator Loss: 0.6685, Generator Loss: 2.3270 D(x): 0.8348, D(G(z)): 0.2362 Epoch: [10/20], Batch Num: [525/600] Discriminator Loss: 0.6440, Generator Loss: 2.4884 D(x): 0.7877, D(G(z)): 0.1968 Epoch: [10/20], Batch Num: [526/600] Discriminator Loss: 0.7769, Generator Loss: 2.5628 D(x): 0.8127, D(G(z)): 0.2650 Epoch: [10/20], Batch Num: [527/600] Discriminator Loss: 0.5272, Generator Loss: 2.6757 D(x): 0.8278, D(G(z)): 0.1747 Epoch: [10/20], Batch Num: [528/600] Discriminator Loss: 0.7311, Generator Loss: 2.8230 D(x): 0.7765, D(G(z)): 0.1695 Epoch: [10/20], Batch Num: [529/600] Discriminator Loss: 0.8285, Generator Loss: 2.6767 D(x): 0.7259, D(G(z)): 0.1739 Epoch: [10/20], Batch Num: [530/600] Discriminator Loss: 0.6090, Generator Loss: 2.5606 D(x): 0.8107, D(G(z)): 0.1770 Epoch: [10/20], Batch Num: [531/600] Discriminator Loss: 0.7174, Generator Loss: 2.3484 D(x): 0.8250, D(G(z)): 0.2340 Epoch: [10/20], Batch Num: [532/600] Discriminator Loss: 0.7302, Generator Loss: 2.5694 D(x): 0.8154, D(G(z)): 0.2307 Epoch: [10/20], Batch Num: [533/600] Discriminator Loss: 0.7099, Generator Loss: 2.5246 D(x): 0.8226, D(G(z)): 0.2446 Epoch: [10/20], Batch Num: [534/600] Discriminator Loss: 0.6328, Generator Loss: 2.3508 D(x): 0.7564, D(G(z)): 0.1218 Epoch: [10/20], Batch Num: [535/600] Discriminator Loss: 0.6252, Generator Loss: 2.5944 D(x): 0.8213, D(G(z)): 0.1983 Epoch: [10/20], Batch Num: [536/600] Discriminator Loss: 0.8098, Generator Loss: 2.4033 D(x): 0.7902, D(G(z)): 0.2357 Epoch: [10/20], Batch Num: [537/600] Discriminator Loss: 0.6808, Generator Loss: 2.2144 D(x): 0.8285, D(G(z)): 0.2377 Epoch: [10/20], Batch Num: [538/600] Discriminator Loss: 0.7418, Generator Loss: 2.5440 D(x): 0.8324, D(G(z)): 0.2416 Epoch: [10/20], Batch Num: [539/600] Discriminator Loss: 0.6626, Generator Loss: 2.5729 D(x): 0.8081, D(G(z)): 0.2157 Epoch: [10/20], Batch Num: [540/600] Discriminator Loss: 0.7902, Generator Loss: 2.4750 D(x): 0.7626, D(G(z)): 0.2337 Epoch: [10/20], Batch Num: [541/600] Discriminator Loss: 0.6797, Generator Loss: 2.6481 D(x): 0.7496, D(G(z)): 0.1812 Epoch: [10/20], Batch Num: [542/600] Discriminator Loss: 0.6266, Generator Loss: 2.2477 D(x): 0.7646, D(G(z)): 0.1694 Epoch: [10/20], Batch Num: [543/600] Discriminator Loss: 0.8534, Generator Loss: 2.1298 D(x): 0.7461, D(G(z)): 0.2322 Epoch: [10/20], Batch Num: [544/600] Discriminator Loss: 0.6505, Generator Loss: 2.0143 D(x): 0.7960, D(G(z)): 0.2082 Epoch: [10/20], Batch Num: [545/600] Discriminator Loss: 0.7651, Generator Loss: 2.0859 D(x): 0.7829, D(G(z)): 0.2595 Epoch: [10/20], Batch Num: [546/600] Discriminator Loss: 0.8389, Generator Loss: 1.9750 D(x): 0.8017, D(G(z)): 0.2900 Epoch: [10/20], Batch Num: [547/600] Discriminator Loss: 0.7776, Generator Loss: 2.2560 D(x): 0.8345, D(G(z)): 0.2913 Epoch: [10/20], Batch Num: [548/600] Discriminator Loss: 0.7033, Generator Loss: 2.4797 D(x): 0.7677, D(G(z)): 0.2194 Epoch: [10/20], Batch Num: [549/600] Discriminator Loss: 0.6503, Generator Loss: 2.0794 D(x): 0.7394, D(G(z)): 0.1717 Epoch: [10/20], Batch Num: [550/600] Discriminator Loss: 0.6246, Generator Loss: 2.0805 D(x): 0.8078, D(G(z)): 0.2290 Epoch: [10/20], Batch Num: [551/600] Discriminator Loss: 0.7860, Generator Loss: 2.1393 D(x): 0.7271, D(G(z)): 0.2412 Epoch: [10/20], Batch Num: [552/600] Discriminator Loss: 0.8408, Generator Loss: 1.9304 D(x): 0.7296, D(G(z)): 0.2114 Epoch: [10/20], Batch Num: [553/600] Discriminator Loss: 0.6399, Generator Loss: 1.9127 D(x): 0.8404, D(G(z)): 0.2706 Epoch: [10/20], Batch Num: [554/600] Discriminator Loss: 0.6695, Generator Loss: 1.8570 D(x): 0.8326, D(G(z)): 0.2622 Epoch: [10/20], Batch Num: [555/600] Discriminator Loss: 0.7089, Generator Loss: 1.6582 D(x): 0.7890, D(G(z)): 0.2672 Epoch: [10/20], Batch Num: [556/600] Discriminator Loss: 0.7462, Generator Loss: 1.8342 D(x): 0.7692, D(G(z)): 0.2767 Epoch: [10/20], Batch Num: [557/600] Discriminator Loss: 0.8670, Generator Loss: 1.8985 D(x): 0.7168, D(G(z)): 0.2575 Epoch: [10/20], Batch Num: [558/600] Discriminator Loss: 0.8020, Generator Loss: 1.8722 D(x): 0.7775, D(G(z)): 0.2721 Epoch: [10/20], Batch Num: [559/600] Discriminator Loss: 0.8924, Generator Loss: 1.8333 D(x): 0.7487, D(G(z)): 0.3097 Epoch: [10/20], Batch Num: [560/600] Discriminator Loss: 0.7261, Generator Loss: 1.9499 D(x): 0.7325, D(G(z)): 0.2135 Epoch: [10/20], Batch Num: [561/600] Discriminator Loss: 0.8147, Generator Loss: 1.9511 D(x): 0.7514, D(G(z)): 0.2611 Epoch: [10/20], Batch Num: [562/600] Discriminator Loss: 0.7756, Generator Loss: 1.7340 D(x): 0.7320, D(G(z)): 0.2351 Epoch: [10/20], Batch Num: [563/600] Discriminator Loss: 0.7676, Generator Loss: 1.6717 D(x): 0.7749, D(G(z)): 0.2789 Epoch: [10/20], Batch Num: [564/600] Discriminator Loss: 0.7572, Generator Loss: 1.7213 D(x): 0.7794, D(G(z)): 0.2774 Epoch: [10/20], Batch Num: [565/600] Discriminator Loss: 0.6260, Generator Loss: 1.7325 D(x): 0.8434, D(G(z)): 0.2903 Epoch: [10/20], Batch Num: [566/600] Discriminator Loss: 0.7413, Generator Loss: 1.7547 D(x): 0.7936, D(G(z)): 0.2759 Epoch: [10/20], Batch Num: [567/600] Discriminator Loss: 0.6901, Generator Loss: 2.0219 D(x): 0.7826, D(G(z)): 0.2616 Epoch: [10/20], Batch Num: [568/600] Discriminator Loss: 0.9118, Generator Loss: 1.7271 D(x): 0.7434, D(G(z)): 0.2839 Epoch: [10/20], Batch Num: [569/600] Discriminator Loss: 0.6227, Generator Loss: 1.9299 D(x): 0.7939, D(G(z)): 0.2361 Epoch: [10/20], Batch Num: [570/600] Discriminator Loss: 0.6923, Generator Loss: 2.0667 D(x): 0.7772, D(G(z)): 0.2271 Epoch: [10/20], Batch Num: [571/600] Discriminator Loss: 0.6274, Generator Loss: 1.9390 D(x): 0.7738, D(G(z)): 0.1919 Epoch: [10/20], Batch Num: [572/600] Discriminator Loss: 0.6883, Generator Loss: 1.9309 D(x): 0.7719, D(G(z)): 0.2307 Epoch: [10/20], Batch Num: [573/600] Discriminator Loss: 0.7139, Generator Loss: 1.7184 D(x): 0.7723, D(G(z)): 0.2317 Epoch: [10/20], Batch Num: [574/600] Discriminator Loss: 0.6980, Generator Loss: 1.9808 D(x): 0.8688, D(G(z)): 0.3127 Epoch: [10/20], Batch Num: [575/600] Discriminator Loss: 0.5485, Generator Loss: 2.4793 D(x): 0.8329, D(G(z)): 0.2110 Epoch: [10/20], Batch Num: [576/600] Discriminator Loss: 0.5467, Generator Loss: 2.7095 D(x): 0.7911, D(G(z)): 0.1659 Epoch: [10/20], Batch Num: [577/600] Discriminator Loss: 0.4968, Generator Loss: 2.3860 D(x): 0.7907, D(G(z)): 0.1462 Epoch: [10/20], Batch Num: [578/600] Discriminator Loss: 0.5190, Generator Loss: 2.6302 D(x): 0.8002, D(G(z)): 0.1553 Epoch: [10/20], Batch Num: [579/600] Discriminator Loss: 0.5615, Generator Loss: 2.4050 D(x): 0.7902, D(G(z)): 0.1608 Epoch: [10/20], Batch Num: [580/600] Discriminator Loss: 0.5452, Generator Loss: 2.3536 D(x): 0.8463, D(G(z)): 0.2146 Epoch: [10/20], Batch Num: [581/600] Discriminator Loss: 0.5038, Generator Loss: 2.1757 D(x): 0.8506, D(G(z)): 0.1854 Epoch: [10/20], Batch Num: [582/600] Discriminator Loss: 0.4679, Generator Loss: 2.3697 D(x): 0.8912, D(G(z)): 0.2072 Epoch: [10/20], Batch Num: [583/600] Discriminator Loss: 0.4673, Generator Loss: 2.7139 D(x): 0.8601, D(G(z)): 0.1731 Epoch: [10/20], Batch Num: [584/600] Discriminator Loss: 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Epoch: [11/20], Batch Num: [0/600] Discriminator Loss: 0.6647, Generator Loss: 2.4376 D(x): 0.7976, D(G(z)): 0.2079 Epoch: [11/20], Batch Num: [1/600] Discriminator Loss: 0.9287, Generator Loss: 2.0628 D(x): 0.7229, D(G(z)): 0.1963 Epoch: [11/20], Batch Num: [2/600] Discriminator Loss: 0.8049, Generator Loss: 1.9645 D(x): 0.7593, D(G(z)): 0.2363 Epoch: [11/20], Batch Num: [3/600] Discriminator Loss: 0.7259, Generator Loss: 1.8717 D(x): 0.8025, D(G(z)): 0.2272 Epoch: [11/20], Batch Num: [4/600] Discriminator Loss: 0.9079, Generator Loss: 1.7935 D(x): 0.7554, D(G(z)): 0.2594 Epoch: [11/20], Batch Num: [5/600] Discriminator Loss: 0.9061, Generator Loss: 1.9384 D(x): 0.7432, D(G(z)): 0.2650 Epoch: [11/20], Batch Num: [6/600] Discriminator Loss: 1.0875, Generator Loss: 1.8146 D(x): 0.6992, D(G(z)): 0.3018 Epoch: [11/20], Batch Num: [7/600] Discriminator Loss: 1.0357, Generator Loss: 1.8382 D(x): 0.7145, D(G(z)): 0.2875 Epoch: [11/20], Batch Num: [8/600] Discriminator Loss: 0.9776, Generator 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Num: [17/600] Discriminator Loss: 0.8157, Generator Loss: 1.6621 D(x): 0.8087, D(G(z)): 0.3114 Epoch: [11/20], Batch Num: [18/600] Discriminator Loss: 0.5811, Generator Loss: 1.9393 D(x): 0.8508, D(G(z)): 0.2746 Epoch: [11/20], Batch Num: [19/600] Discriminator Loss: 0.7039, Generator Loss: 2.1681 D(x): 0.8414, D(G(z)): 0.2697 Epoch: [11/20], Batch Num: [20/600] Discriminator Loss: 0.6451, Generator Loss: 2.3185 D(x): 0.7730, D(G(z)): 0.1964 Epoch: [11/20], Batch Num: [21/600] Discriminator Loss: 0.4880, Generator Loss: 2.7098 D(x): 0.8090, D(G(z)): 0.1602 Epoch: [11/20], Batch Num: [22/600] Discriminator Loss: 0.6491, Generator Loss: 2.2954 D(x): 0.7456, D(G(z)): 0.1437 Epoch: [11/20], Batch Num: [23/600] Discriminator Loss: 0.5473, Generator Loss: 2.3042 D(x): 0.7990, D(G(z)): 0.1504 Epoch: [11/20], Batch Num: [24/600] Discriminator Loss: 0.4934, Generator Loss: 2.0648 D(x): 0.8012, D(G(z)): 0.1549 Epoch: [11/20], Batch Num: [25/600] Discriminator Loss: 0.5262, Generator Loss: 2.0839 D(x): 0.8468, D(G(z)): 0.1874 Epoch: [11/20], Batch Num: [26/600] Discriminator Loss: 0.4340, Generator Loss: 2.1401 D(x): 0.8911, D(G(z)): 0.1951 Epoch: [11/20], Batch Num: [27/600] Discriminator Loss: 0.4101, Generator Loss: 2.2193 D(x): 0.8812, D(G(z)): 0.1779 Epoch: [11/20], Batch Num: [28/600] Discriminator Loss: 0.4074, Generator Loss: 2.3702 D(x): 0.8951, D(G(z)): 0.1881 Epoch: [11/20], Batch Num: [29/600] Discriminator Loss: 0.5613, Generator Loss: 2.8136 D(x): 0.8953, D(G(z)): 0.1904 Epoch: [11/20], Batch Num: [30/600] Discriminator Loss: 0.5031, Generator Loss: 2.9159 D(x): 0.8299, D(G(z)): 0.1298 Epoch: [11/20], Batch Num: [31/600] Discriminator Loss: 0.4779, Generator Loss: 3.1137 D(x): 0.8054, D(G(z)): 0.0980 Epoch: [11/20], Batch Num: [32/600] Discriminator Loss: 0.4239, Generator Loss: 2.9393 D(x): 0.8191, D(G(z)): 0.0991 Epoch: [11/20], Batch Num: [33/600] Discriminator Loss: 0.3654, Generator Loss: 2.5325 D(x): 0.8560, D(G(z)): 0.1068 Epoch: [11/20], Batch Num: [34/600] Discriminator Loss: 0.4643, Generator Loss: 2.4304 D(x): 0.8696, D(G(z)): 0.1746 Epoch: [11/20], Batch Num: [35/600] Discriminator Loss: 0.6391, Generator Loss: 2.2929 D(x): 0.8577, D(G(z)): 0.2143 Epoch: [11/20], Batch Num: [36/600] Discriminator Loss: 0.5668, Generator Loss: 2.6735 D(x): 0.8956, D(G(z)): 0.2382 Epoch: [11/20], Batch Num: [37/600] Discriminator Loss: 0.6022, Generator Loss: 2.9315 D(x): 0.8112, D(G(z)): 0.1524 Epoch: [11/20], Batch Num: [38/600] Discriminator Loss: 0.6086, Generator Loss: 2.9625 D(x): 0.7733, D(G(z)): 0.1410 Epoch: [11/20], Batch Num: [39/600] Discriminator Loss: 0.4830, Generator Loss: 2.8520 D(x): 0.8161, D(G(z)): 0.1333 Epoch: [11/20], Batch Num: [40/600] Discriminator Loss: 0.6291, Generator Loss: 2.7695 D(x): 0.8045, D(G(z)): 0.1852 Epoch: [11/20], Batch Num: [41/600] Discriminator Loss: 0.6322, Generator Loss: 2.3642 D(x): 0.8424, D(G(z)): 0.2084 Epoch: [11/20], Batch Num: [42/600] Discriminator Loss: 0.6985, Generator Loss: 2.4340 D(x): 0.7835, D(G(z)): 0.1915 Epoch: [11/20], Batch Num: [43/600] Discriminator Loss: 0.5541, Generator Loss: 2.3396 D(x): 0.8184, D(G(z)): 0.1999 Epoch: [11/20], Batch Num: [44/600] Discriminator Loss: 0.6516, Generator Loss: 2.2116 D(x): 0.8072, D(G(z)): 0.2148 Epoch: [11/20], Batch Num: [45/600] Discriminator Loss: 0.9225, Generator Loss: 2.4917 D(x): 0.8081, D(G(z)): 0.2855 Epoch: [11/20], Batch Num: [46/600] Discriminator Loss: 0.9586, Generator Loss: 2.3786 D(x): 0.6816, D(G(z)): 0.2064 Epoch: [11/20], Batch Num: [47/600] Discriminator Loss: 0.7444, Generator Loss: 2.3296 D(x): 0.7581, D(G(z)): 0.1956 Epoch: [11/20], Batch Num: [48/600] Discriminator Loss: 0.8304, Generator Loss: 1.9851 D(x): 0.7536, D(G(z)): 0.1819 Epoch: [11/20], Batch Num: [49/600] Discriminator Loss: 0.9201, Generator Loss: 1.7721 D(x): 0.7661, D(G(z)): 0.2771 Epoch: [11/20], Batch Num: [50/600] Discriminator Loss: 0.9540, Generator Loss: 1.7261 D(x): 0.7825, D(G(z)): 0.2946 Epoch: [11/20], Batch Num: [51/600] Discriminator Loss: 1.0995, Generator Loss: 2.0270 D(x): 0.7625, D(G(z)): 0.3307 Epoch: [11/20], Batch Num: [52/600] Discriminator Loss: 0.8963, Generator Loss: 1.8508 D(x): 0.6893, D(G(z)): 0.2171 Epoch: [11/20], Batch Num: [53/600] Discriminator Loss: 0.8767, Generator Loss: 1.9288 D(x): 0.7452, D(G(z)): 0.2695 Epoch: [11/20], Batch Num: [54/600] Discriminator Loss: 0.8468, Generator Loss: 1.7825 D(x): 0.6958, D(G(z)): 0.2054 Epoch: [11/20], Batch Num: [55/600] Discriminator Loss: 0.9011, Generator Loss: 1.6144 D(x): 0.7247, D(G(z)): 0.2660 Epoch: [11/20], Batch Num: [56/600] Discriminator Loss: 0.9979, Generator Loss: 1.6809 D(x): 0.7722, D(G(z)): 0.3232 Epoch: [11/20], Batch Num: [57/600] Discriminator Loss: 1.1628, Generator Loss: 1.6449 D(x): 0.7061, D(G(z)): 0.3519 Epoch: [11/20], Batch Num: [58/600] Discriminator Loss: 1.0633, Generator Loss: 1.8086 D(x): 0.7023, D(G(z)): 0.3100 Epoch: [11/20], Batch Num: [59/600] Discriminator Loss: 0.9123, Generator Loss: 1.7468 D(x): 0.6766, D(G(z)): 0.2280 Epoch: [11/20], Batch Num: [60/600] Discriminator Loss: 1.1105, Generator Loss: 1.5726 D(x): 0.6173, D(G(z)): 0.2329 Epoch: [11/20], Batch Num: [61/600] Discriminator Loss: 0.9041, Generator Loss: 1.5077 D(x): 0.6898, D(G(z)): 0.2448 Epoch: [11/20], Batch Num: [62/600] Discriminator Loss: 1.0150, Generator Loss: 1.3615 D(x): 0.7275, D(G(z)): 0.3540 Epoch: [11/20], Batch Num: [63/600] Discriminator Loss: 0.9018, Generator Loss: 1.3565 D(x): 0.7635, D(G(z)): 0.3361 Epoch: [11/20], Batch Num: [64/600] Discriminator Loss: 0.8076, Generator Loss: 1.5652 D(x): 0.7822, D(G(z)): 0.3164 Epoch: [11/20], Batch Num: [65/600] Discriminator Loss: 0.8866, Generator Loss: 1.8736 D(x): 0.7309, D(G(z)): 0.3111 Epoch: [11/20], Batch Num: [66/600] Discriminator Loss: 0.7018, Generator Loss: 1.8892 D(x): 0.7225, D(G(z)): 0.2099 Epoch: [11/20], Batch Num: [67/600] Discriminator Loss: 0.7917, Generator Loss: 1.9081 D(x): 0.7139, D(G(z)): 0.2119 Epoch: [11/20], Batch Num: [68/600] Discriminator Loss: 0.7824, Generator Loss: 1.8656 D(x): 0.7177, D(G(z)): 0.2269 Epoch: [11/20], Batch Num: [69/600] Discriminator Loss: 0.6807, Generator Loss: 1.7135 D(x): 0.7512, D(G(z)): 0.2015 Epoch: [11/20], Batch Num: [70/600] Discriminator Loss: 0.5678, Generator Loss: 1.8685 D(x): 0.8138, D(G(z)): 0.2244 Epoch: [11/20], Batch Num: [71/600] Discriminator Loss: 0.7273, Generator Loss: 1.7078 D(x): 0.7019, D(G(z)): 0.2137 Epoch: [11/20], Batch Num: [72/600] Discriminator Loss: 0.6407, Generator Loss: 1.5405 D(x): 0.8048, D(G(z)): 0.2502 Epoch: [11/20], Batch Num: [73/600] Discriminator Loss: 0.6139, Generator Loss: 1.5982 D(x): 0.8130, D(G(z)): 0.2690 Epoch: [11/20], Batch Num: [74/600] Discriminator Loss: 0.6120, Generator Loss: 1.7126 D(x): 0.8283, D(G(z)): 0.2746 Epoch: [11/20], Batch Num: [75/600] Discriminator Loss: 0.5270, Generator Loss: 1.8707 D(x): 0.8479, D(G(z)): 0.2417 Epoch: [11/20], Batch Num: [76/600] Discriminator Loss: 0.4764, Generator Loss: 2.0657 D(x): 0.8196, D(G(z)): 0.1809 Epoch: [11/20], Batch Num: [77/600] Discriminator Loss: 0.4514, Generator Loss: 2.0885 D(x): 0.8465, D(G(z)): 0.1958 Epoch: [11/20], Batch Num: [78/600] Discriminator Loss: 0.4539, Generator Loss: 2.1095 D(x): 0.8257, D(G(z)): 0.1589 Epoch: [11/20], Batch Num: [79/600] Discriminator Loss: 0.4279, Generator Loss: 2.1167 D(x): 0.8324, D(G(z)): 0.1404 Epoch: [11/20], Batch Num: [80/600] Discriminator Loss: 0.4420, Generator Loss: 2.2303 D(x): 0.8656, D(G(z)): 0.1948 Epoch: [11/20], Batch Num: [81/600] Discriminator Loss: 0.4275, Generator Loss: 2.1391 D(x): 0.8609, D(G(z)): 0.1718 Epoch: [11/20], Batch Num: [82/600] Discriminator Loss: 0.4055, Generator Loss: 2.1513 D(x): 0.8787, D(G(z)): 0.1727 Epoch: [11/20], Batch Num: [83/600] Discriminator Loss: 0.4290, Generator Loss: 2.1263 D(x): 0.8688, D(G(z)): 0.1816 Epoch: [11/20], Batch Num: [84/600] Discriminator Loss: 0.3324, Generator Loss: 2.2267 D(x): 0.8920, D(G(z)): 0.1512 Epoch: [11/20], Batch Num: [85/600] Discriminator Loss: 0.4221, Generator Loss: 2.4858 D(x): 0.8925, D(G(z)): 0.1856 Epoch: [11/20], Batch Num: [86/600] Discriminator Loss: 0.4831, Generator Loss: 2.7493 D(x): 0.8426, D(G(z)): 0.1591 Epoch: [11/20], Batch Num: [87/600] Discriminator Loss: 0.3198, Generator Loss: 2.5063 D(x): 0.8677, D(G(z)): 0.1151 Epoch: [11/20], Batch Num: [88/600] Discriminator Loss: 0.4312, Generator Loss: 2.6645 D(x): 0.8543, D(G(z)): 0.1686 Epoch: [11/20], Batch Num: [89/600] Discriminator Loss: 0.3552, Generator Loss: 2.6931 D(x): 0.8901, D(G(z)): 0.1503 Epoch: [11/20], Batch Num: [90/600] Discriminator Loss: 0.3523, Generator Loss: 2.5598 D(x): 0.9044, D(G(z)): 0.1556 Epoch: [11/20], Batch Num: [91/600] Discriminator Loss: 0.3462, Generator Loss: 2.6361 D(x): 0.9013, D(G(z)): 0.1615 Epoch: [11/20], Batch Num: [92/600] Discriminator Loss: 0.4626, Generator Loss: 2.8781 D(x): 0.8431, D(G(z)): 0.1432 Epoch: [11/20], Batch Num: [93/600] Discriminator Loss: 0.5104, Generator Loss: 2.7197 D(x): 0.8225, D(G(z)): 0.1404 Epoch: [11/20], Batch Num: [94/600] Discriminator Loss: 0.4137, Generator Loss: 2.5499 D(x): 0.8507, D(G(z)): 0.1179 Epoch: [11/20], Batch Num: [95/600] Discriminator Loss: 0.4851, Generator Loss: 2.1837 D(x): 0.8235, D(G(z)): 0.1596 Epoch: [11/20], Batch Num: [96/600] Discriminator Loss: 0.3652, Generator Loss: 2.0755 D(x): 0.9055, D(G(z)): 0.1725 Epoch: [11/20], Batch Num: [97/600] Discriminator Loss: 0.4824, Generator Loss: 2.2919 D(x): 0.9072, D(G(z)): 0.2316 Epoch: [11/20], Batch Num: [98/600] Discriminator Loss: 0.5818, Generator Loss: 2.6348 D(x): 0.8512, D(G(z)): 0.2435 Epoch: [11/20], Batch Num: [99/600] Discriminator Loss: 0.5259, Generator Loss: 2.8699 D(x): 0.7927, D(G(z)): 0.1229 Epoch: 11, Batch Num: [100/600]
Epoch: [11/20], Batch Num: [100/600] Discriminator Loss: 0.7273, Generator Loss: 2.5740 D(x): 0.7382, D(G(z)): 0.1486 Epoch: [11/20], Batch Num: [101/600] Discriminator Loss: 0.7016, Generator Loss: 2.3493 D(x): 0.8127, D(G(z)): 0.2153 Epoch: [11/20], Batch Num: [102/600] Discriminator Loss: 0.7406, Generator Loss: 2.1240 D(x): 0.7974, D(G(z)): 0.2253 Epoch: [11/20], Batch Num: [103/600] Discriminator Loss: 0.8578, Generator Loss: 1.9001 D(x): 0.7615, D(G(z)): 0.2362 Epoch: [11/20], Batch Num: [104/600] Discriminator Loss: 0.7950, Generator Loss: 1.9040 D(x): 0.7790, D(G(z)): 0.2274 Epoch: [11/20], Batch Num: [105/600] Discriminator Loss: 0.7353, Generator Loss: 1.8224 D(x): 0.8164, D(G(z)): 0.2516 Epoch: [11/20], Batch Num: [106/600] Discriminator Loss: 0.7508, Generator Loss: 2.1929 D(x): 0.7885, D(G(z)): 0.2224 Epoch: [11/20], Batch Num: [107/600] Discriminator Loss: 0.7776, Generator Loss: 2.2218 D(x): 0.7882, D(G(z)): 0.2229 Epoch: [11/20], Batch Num: [108/600] Discriminator Loss: 0.8674, Generator Loss: 1.8576 D(x): 0.7508, D(G(z)): 0.2447 Epoch: [11/20], Batch Num: [109/600] Discriminator Loss: 0.8494, Generator Loss: 2.0330 D(x): 0.7712, D(G(z)): 0.2512 Epoch: [11/20], Batch Num: [110/600] Discriminator Loss: 0.9222, Generator Loss: 2.1181 D(x): 0.7550, D(G(z)): 0.2886 Epoch: [11/20], Batch Num: [111/600] Discriminator Loss: 1.0289, Generator Loss: 2.0149 D(x): 0.6945, D(G(z)): 0.2579 Epoch: [11/20], Batch Num: [112/600] Discriminator Loss: 0.8695, Generator Loss: 2.0437 D(x): 0.7381, D(G(z)): 0.2458 Epoch: [11/20], Batch Num: [113/600] Discriminator Loss: 0.9103, Generator Loss: 1.9003 D(x): 0.7157, D(G(z)): 0.2337 Epoch: [11/20], Batch Num: [114/600] Discriminator Loss: 0.9714, Generator Loss: 2.0781 D(x): 0.7616, D(G(z)): 0.3040 Epoch: [11/20], Batch Num: [115/600] Discriminator Loss: 0.9829, Generator Loss: 1.7236 D(x): 0.7355, D(G(z)): 0.2771 Epoch: [11/20], Batch Num: [116/600] Discriminator Loss: 0.7585, Generator Loss: 1.9328 D(x): 0.7924, D(G(z)): 0.2688 Epoch: [11/20], Batch Num: [117/600] Discriminator Loss: 0.9295, Generator Loss: 2.0149 D(x): 0.7320, D(G(z)): 0.2805 Epoch: [11/20], Batch Num: [118/600] Discriminator Loss: 0.7432, Generator Loss: 2.1875 D(x): 0.7431, D(G(z)): 0.1996 Epoch: [11/20], Batch Num: [119/600] Discriminator Loss: 0.8529, Generator Loss: 2.2289 D(x): 0.7086, D(G(z)): 0.2191 Epoch: [11/20], Batch Num: [120/600] Discriminator Loss: 0.8123, Generator Loss: 2.0925 D(x): 0.7044, D(G(z)): 0.2055 Epoch: [11/20], Batch Num: [121/600] Discriminator Loss: 0.6798, Generator Loss: 1.8838 D(x): 0.7843, D(G(z)): 0.2069 Epoch: [11/20], Batch Num: [122/600] Discriminator Loss: 0.6269, Generator Loss: 1.9322 D(x): 0.8244, D(G(z)): 0.2424 Epoch: [11/20], Batch Num: [123/600] Discriminator Loss: 0.5397, Generator Loss: 1.9452 D(x): 0.8404, D(G(z)): 0.2016 Epoch: [11/20], Batch Num: [124/600] Discriminator Loss: 0.6171, Generator Loss: 1.8735 D(x): 0.8257, D(G(z)): 0.2144 Epoch: [11/20], Batch Num: [125/600] Discriminator Loss: 0.5948, Generator Loss: 2.1090 D(x): 0.8548, D(G(z)): 0.2449 Epoch: [11/20], Batch Num: [126/600] Discriminator Loss: 0.6186, Generator Loss: 2.3453 D(x): 0.8451, D(G(z)): 0.2144 Epoch: [11/20], Batch Num: [127/600] Discriminator Loss: 0.5330, Generator Loss: 2.5655 D(x): 0.8081, D(G(z)): 0.1702 Epoch: [11/20], Batch Num: [128/600] Discriminator Loss: 0.6132, Generator Loss: 2.4753 D(x): 0.7674, D(G(z)): 0.1674 Epoch: [11/20], Batch Num: [129/600] Discriminator Loss: 0.4840, Generator Loss: 2.3426 D(x): 0.8016, D(G(z)): 0.1257 Epoch: [11/20], Batch Num: [130/600] Discriminator Loss: 0.4507, Generator Loss: 2.2564 D(x): 0.8465, D(G(z)): 0.1770 Epoch: [11/20], Batch Num: [131/600] Discriminator Loss: 0.5454, Generator Loss: 2.0136 D(x): 0.8325, D(G(z)): 0.1878 Epoch: [11/20], Batch Num: [132/600] Discriminator Loss: 0.4212, Generator Loss: 1.9745 D(x): 0.9038, D(G(z)): 0.2015 Epoch: [11/20], Batch Num: [133/600] Discriminator Loss: 0.4426, Generator Loss: 1.9759 D(x): 0.9011, D(G(z)): 0.2115 Epoch: [11/20], Batch Num: [134/600] Discriminator Loss: 0.4478, Generator Loss: 2.4403 D(x): 0.8744, D(G(z)): 0.1998 Epoch: [11/20], Batch Num: [135/600] Discriminator Loss: 0.5544, Generator Loss: 2.4950 D(x): 0.8258, D(G(z)): 0.1835 Epoch: [11/20], Batch Num: [136/600] Discriminator Loss: 0.4257, Generator Loss: 2.5979 D(x): 0.8609, D(G(z)): 0.1531 Epoch: [11/20], Batch Num: [137/600] Discriminator Loss: 0.4080, Generator Loss: 2.4570 D(x): 0.8497, D(G(z)): 0.1440 Epoch: [11/20], Batch Num: [138/600] Discriminator Loss: 0.3927, Generator Loss: 2.4108 D(x): 0.8605, D(G(z)): 0.1242 Epoch: [11/20], Batch Num: [139/600] Discriminator Loss: 0.4712, Generator Loss: 2.4937 D(x): 0.8438, D(G(z)): 0.1596 Epoch: [11/20], Batch Num: [140/600] Discriminator Loss: 0.6069, Generator Loss: 2.4583 D(x): 0.8501, D(G(z)): 0.2102 Epoch: [11/20], Batch Num: [141/600] Discriminator Loss: 0.4507, Generator Loss: 2.5484 D(x): 0.8573, D(G(z)): 0.1824 Epoch: [11/20], Batch Num: [142/600] Discriminator Loss: 0.5037, Generator Loss: 2.5468 D(x): 0.8455, D(G(z)): 0.1764 Epoch: [11/20], Batch Num: [143/600] Discriminator Loss: 0.5428, Generator Loss: 2.3580 D(x): 0.7862, D(G(z)): 0.1352 Epoch: [11/20], Batch Num: [144/600] Discriminator Loss: 0.5739, Generator Loss: 2.4939 D(x): 0.8466, D(G(z)): 0.1960 Epoch: [11/20], Batch Num: [145/600] Discriminator Loss: 0.6642, Generator Loss: 2.4290 D(x): 0.7648, D(G(z)): 0.1637 Epoch: [11/20], Batch Num: [146/600] Discriminator Loss: 0.6978, Generator Loss: 2.0877 D(x): 0.8388, D(G(z)): 0.2251 Epoch: [11/20], Batch Num: [147/600] Discriminator Loss: 0.6451, Generator Loss: 2.2410 D(x): 0.8275, D(G(z)): 0.2317 Epoch: [11/20], Batch Num: [148/600] Discriminator Loss: 0.7253, Generator Loss: 2.4385 D(x): 0.8083, D(G(z)): 0.2391 Epoch: [11/20], Batch Num: [149/600] Discriminator Loss: 0.7248, Generator Loss: 2.4557 D(x): 0.7847, D(G(z)): 0.2052 Epoch: [11/20], Batch Num: [150/600] Discriminator Loss: 0.7434, Generator Loss: 2.3119 D(x): 0.7332, D(G(z)): 0.1795 Epoch: [11/20], Batch Num: [151/600] Discriminator Loss: 0.7431, Generator Loss: 1.9806 D(x): 0.7688, D(G(z)): 0.1963 Epoch: [11/20], Batch Num: [152/600] Discriminator Loss: 0.8831, Generator Loss: 1.6591 D(x): 0.7541, D(G(z)): 0.2602 Epoch: [11/20], Batch Num: [153/600] Discriminator Loss: 0.7998, Generator Loss: 1.6934 D(x): 0.8400, D(G(z)): 0.3274 Epoch: [11/20], Batch Num: [154/600] Discriminator Loss: 0.8783, Generator Loss: 1.9882 D(x): 0.7831, D(G(z)): 0.2935 Epoch: [11/20], Batch Num: [155/600] Discriminator Loss: 0.7340, Generator Loss: 2.3938 D(x): 0.8262, D(G(z)): 0.2645 Epoch: [11/20], Batch Num: [156/600] Discriminator Loss: 0.8437, Generator Loss: 2.5385 D(x): 0.7103, D(G(z)): 0.1951 Epoch: [11/20], Batch Num: [157/600] Discriminator Loss: 1.0176, Generator Loss: 2.1852 D(x): 0.6393, D(G(z)): 0.1627 Epoch: [11/20], Batch Num: [158/600] Discriminator Loss: 1.0048, Generator Loss: 1.9565 D(x): 0.7109, D(G(z)): 0.2478 Epoch: [11/20], Batch Num: [159/600] Discriminator Loss: 0.9816, Generator Loss: 1.5057 D(x): 0.7082, D(G(z)): 0.2736 Epoch: [11/20], Batch Num: [160/600] Discriminator Loss: 0.7484, Generator Loss: 1.4545 D(x): 0.8038, D(G(z)): 0.3053 Epoch: [11/20], Batch Num: [161/600] Discriminator Loss: 0.9943, Generator Loss: 1.7867 D(x): 0.7919, D(G(z)): 0.3546 Epoch: [11/20], Batch Num: [162/600] Discriminator Loss: 0.8308, Generator Loss: 2.0870 D(x): 0.7689, D(G(z)): 0.2738 Epoch: [11/20], Batch Num: [163/600] Discriminator Loss: 0.6886, Generator Loss: 2.2749 D(x): 0.7287, D(G(z)): 0.1757 Epoch: [11/20], Batch Num: [164/600] Discriminator Loss: 0.7060, Generator Loss: 2.0228 D(x): 0.7115, D(G(z)): 0.1790 Epoch: [11/20], Batch Num: [165/600] Discriminator Loss: 0.7956, Generator Loss: 2.0069 D(x): 0.6961, D(G(z)): 0.1715 Epoch: [11/20], Batch Num: [166/600] Discriminator Loss: 0.6874, Generator Loss: 1.5764 D(x): 0.7808, D(G(z)): 0.2496 Epoch: [11/20], Batch Num: [167/600] Discriminator Loss: 0.7120, Generator Loss: 1.5832 D(x): 0.8307, D(G(z)): 0.3104 Epoch: [11/20], Batch Num: [168/600] Discriminator Loss: 0.5955, Generator Loss: 1.9274 D(x): 0.8412, D(G(z)): 0.2621 Epoch: [11/20], Batch Num: [169/600] Discriminator Loss: 0.6661, Generator Loss: 2.2929 D(x): 0.8085, D(G(z)): 0.2612 Epoch: [11/20], Batch Num: [170/600] Discriminator Loss: 0.6111, Generator Loss: 2.3909 D(x): 0.7762, D(G(z)): 0.1770 Epoch: [11/20], Batch Num: [171/600] Discriminator Loss: 0.6591, Generator Loss: 2.5605 D(x): 0.7328, D(G(z)): 0.1540 Epoch: [11/20], Batch Num: [172/600] Discriminator Loss: 0.7303, Generator Loss: 2.4573 D(x): 0.6864, D(G(z)): 0.1217 Epoch: [11/20], Batch Num: [173/600] Discriminator Loss: 0.6167, Generator Loss: 1.6904 D(x): 0.7384, D(G(z)): 0.1657 Epoch: [11/20], Batch Num: [174/600] Discriminator Loss: 0.5367, Generator Loss: 1.4542 D(x): 0.8743, D(G(z)): 0.2492 Epoch: [11/20], Batch Num: [175/600] Discriminator Loss: 0.6219, Generator Loss: 1.8670 D(x): 0.9159, D(G(z)): 0.3246 Epoch: [11/20], Batch Num: [176/600] Discriminator Loss: 0.5890, Generator Loss: 2.0709 D(x): 0.9042, D(G(z)): 0.2758 Epoch: [11/20], Batch Num: [177/600] Discriminator Loss: 0.5453, Generator Loss: 2.8436 D(x): 0.8177, D(G(z)): 0.2022 Epoch: [11/20], Batch Num: [178/600] Discriminator Loss: 0.6216, Generator Loss: 2.8909 D(x): 0.7172, D(G(z)): 0.1115 Epoch: [11/20], Batch Num: [179/600] Discriminator Loss: 0.5850, Generator Loss: 2.6513 D(x): 0.7486, D(G(z)): 0.1117 Epoch: [11/20], Batch Num: [180/600] Discriminator Loss: 0.4462, Generator Loss: 2.1390 D(x): 0.8111, D(G(z)): 0.1257 Epoch: [11/20], Batch Num: [181/600] Discriminator Loss: 0.5014, Generator Loss: 1.8564 D(x): 0.8433, D(G(z)): 0.1722 Epoch: [11/20], Batch Num: [182/600] Discriminator Loss: 0.4945, Generator Loss: 1.5903 D(x): 0.8873, D(G(z)): 0.2357 Epoch: [11/20], Batch Num: [183/600] Discriminator Loss: 0.6063, Generator Loss: 1.8901 D(x): 0.8628, D(G(z)): 0.2577 Epoch: [11/20], Batch Num: [184/600] Discriminator Loss: 0.6041, Generator Loss: 2.2415 D(x): 0.8673, D(G(z)): 0.2572 Epoch: [11/20], Batch Num: [185/600] Discriminator Loss: 0.6336, Generator Loss: 2.6189 D(x): 0.8009, D(G(z)): 0.1827 Epoch: [11/20], Batch Num: [186/600] Discriminator Loss: 0.5859, Generator Loss: 2.4096 D(x): 0.7642, D(G(z)): 0.1420 Epoch: [11/20], Batch Num: [187/600] Discriminator Loss: 0.7932, Generator Loss: 2.1804 D(x): 0.7027, D(G(z)): 0.1605 Epoch: [11/20], Batch Num: [188/600] Discriminator Loss: 0.5803, Generator Loss: 1.7328 D(x): 0.8096, D(G(z)): 0.1912 Epoch: [11/20], Batch Num: [189/600] Discriminator Loss: 0.6009, Generator Loss: 1.9242 D(x): 0.8710, D(G(z)): 0.2451 Epoch: [11/20], Batch Num: [190/600] Discriminator Loss: 0.7253, Generator Loss: 2.0726 D(x): 0.8229, D(G(z)): 0.2597 Epoch: [11/20], Batch Num: [191/600] Discriminator Loss: 0.6881, Generator Loss: 2.3476 D(x): 0.8000, D(G(z)): 0.2186 Epoch: [11/20], Batch Num: [192/600] Discriminator Loss: 0.6350, Generator Loss: 2.3368 D(x): 0.7487, D(G(z)): 0.1604 Epoch: [11/20], Batch Num: [193/600] Discriminator Loss: 0.6086, Generator Loss: 2.3433 D(x): 0.7833, D(G(z)): 0.1736 Epoch: [11/20], Batch Num: [194/600] Discriminator Loss: 0.7142, Generator Loss: 1.9705 D(x): 0.7564, D(G(z)): 0.1942 Epoch: [11/20], Batch Num: [195/600] Discriminator Loss: 0.7050, Generator Loss: 1.8082 D(x): 0.8100, D(G(z)): 0.2642 Epoch: [11/20], Batch Num: [196/600] Discriminator Loss: 0.7815, Generator Loss: 1.9308 D(x): 0.8077, D(G(z)): 0.2745 Epoch: [11/20], Batch Num: [197/600] Discriminator Loss: 0.7357, Generator Loss: 2.0639 D(x): 0.7524, D(G(z)): 0.2241 Epoch: [11/20], Batch Num: [198/600] Discriminator Loss: 0.6640, Generator Loss: 2.3083 D(x): 0.8030, D(G(z)): 0.2235 Epoch: [11/20], Batch Num: [199/600] Discriminator Loss: 0.8673, Generator Loss: 2.1445 D(x): 0.7024, D(G(z)): 0.2127 Epoch: 11, Batch Num: [200/600]
Epoch: [11/20], Batch Num: [200/600] Discriminator Loss: 0.7082, Generator Loss: 1.8718 D(x): 0.7541, D(G(z)): 0.1971 Epoch: [11/20], Batch Num: [201/600] Discriminator Loss: 0.8504, Generator Loss: 1.6625 D(x): 0.7399, D(G(z)): 0.2353 Epoch: [11/20], Batch Num: [202/600] Discriminator Loss: 0.7066, Generator Loss: 1.7424 D(x): 0.8174, D(G(z)): 0.2749 Epoch: [11/20], Batch Num: [203/600] Discriminator Loss: 0.8291, Generator Loss: 2.0351 D(x): 0.7673, D(G(z)): 0.2755 Epoch: [11/20], Batch Num: [204/600] Discriminator Loss: 0.7754, Generator Loss: 2.1046 D(x): 0.7447, D(G(z)): 0.2074 Epoch: [11/20], Batch Num: [205/600] Discriminator Loss: 0.9337, Generator Loss: 1.5807 D(x): 0.7158, D(G(z)): 0.2265 Epoch: [11/20], Batch Num: [206/600] Discriminator Loss: 0.6962, Generator Loss: 1.8472 D(x): 0.8049, D(G(z)): 0.2571 Epoch: [11/20], Batch Num: [207/600] Discriminator Loss: 0.8216, Generator Loss: 2.0347 D(x): 0.7528, D(G(z)): 0.2548 Epoch: [11/20], Batch Num: [208/600] Discriminator Loss: 0.8669, Generator Loss: 2.0172 D(x): 0.7223, D(G(z)): 0.2270 Epoch: [11/20], Batch Num: [209/600] Discriminator Loss: 0.7848, Generator Loss: 1.8838 D(x): 0.7424, D(G(z)): 0.2462 Epoch: [11/20], Batch Num: [210/600] Discriminator Loss: 0.8542, Generator Loss: 1.8519 D(x): 0.7135, D(G(z)): 0.2430 Epoch: [11/20], Batch Num: [211/600] Discriminator Loss: 0.8429, Generator Loss: 1.8190 D(x): 0.7631, D(G(z)): 0.2711 Epoch: [11/20], Batch Num: [212/600] Discriminator Loss: 0.6428, Generator Loss: 1.9074 D(x): 0.7892, D(G(z)): 0.2264 Epoch: [11/20], Batch Num: [213/600] Discriminator Loss: 0.7459, Generator Loss: 2.1200 D(x): 0.8088, D(G(z)): 0.2887 Epoch: [11/20], Batch Num: [214/600] Discriminator Loss: 0.6725, Generator Loss: 2.3135 D(x): 0.7792, D(G(z)): 0.1921 Epoch: [11/20], Batch Num: [215/600] Discriminator Loss: 0.7637, Generator Loss: 2.1758 D(x): 0.7057, D(G(z)): 0.1690 Epoch: [11/20], Batch Num: [216/600] Discriminator Loss: 0.6800, Generator Loss: 1.8648 D(x): 0.7632, D(G(z)): 0.2032 Epoch: [11/20], Batch Num: [217/600] Discriminator Loss: 0.6437, Generator Loss: 1.7286 D(x): 0.7765, D(G(z)): 0.2022 Epoch: [11/20], Batch Num: [218/600] Discriminator Loss: 0.7949, Generator Loss: 1.7670 D(x): 0.7850, D(G(z)): 0.2892 Epoch: [11/20], Batch Num: [219/600] Discriminator Loss: 0.6392, Generator Loss: 1.7885 D(x): 0.8431, D(G(z)): 0.2714 Epoch: [11/20], Batch Num: [220/600] Discriminator Loss: 0.8375, Generator Loss: 1.8602 D(x): 0.7727, D(G(z)): 0.2783 Epoch: [11/20], Batch Num: [221/600] Discriminator Loss: 0.7263, Generator Loss: 2.1034 D(x): 0.7889, D(G(z)): 0.2474 Epoch: [11/20], Batch Num: [222/600] Discriminator Loss: 0.7723, Generator Loss: 2.2584 D(x): 0.7131, D(G(z)): 0.1967 Epoch: [11/20], Batch Num: [223/600] Discriminator Loss: 0.6846, Generator Loss: 1.9120 D(x): 0.7481, D(G(z)): 0.1994 Epoch: [11/20], Batch Num: [224/600] Discriminator Loss: 0.7530, Generator Loss: 1.9741 D(x): 0.7920, D(G(z)): 0.2561 Epoch: [11/20], Batch Num: [225/600] Discriminator Loss: 0.7771, Generator Loss: 2.1938 D(x): 0.7835, D(G(z)): 0.2437 Epoch: [11/20], Batch Num: [226/600] Discriminator Loss: 0.7445, Generator Loss: 2.2228 D(x): 0.7550, D(G(z)): 0.1860 Epoch: [11/20], Batch Num: [227/600] Discriminator Loss: 0.6423, Generator Loss: 2.2327 D(x): 0.7803, D(G(z)): 0.2171 Epoch: [11/20], Batch Num: [228/600] Discriminator Loss: 0.6328, Generator Loss: 1.9399 D(x): 0.7834, D(G(z)): 0.2018 Epoch: [11/20], Batch Num: [229/600] Discriminator Loss: 0.8121, Generator Loss: 1.8353 D(x): 0.8084, D(G(z)): 0.2968 Epoch: [11/20], Batch Num: [230/600] Discriminator Loss: 0.6112, Generator Loss: 2.0538 D(x): 0.8063, D(G(z)): 0.2113 Epoch: [11/20], Batch Num: [231/600] Discriminator Loss: 0.6151, Generator Loss: 1.9657 D(x): 0.7491, D(G(z)): 0.1660 Epoch: [11/20], Batch Num: [232/600] Discriminator Loss: 0.7219, Generator Loss: 1.7443 D(x): 0.7725, D(G(z)): 0.2472 Epoch: [11/20], Batch Num: [233/600] Discriminator Loss: 0.7959, Generator Loss: 1.9130 D(x): 0.7371, D(G(z)): 0.2412 Epoch: [11/20], Batch Num: [234/600] Discriminator Loss: 0.7565, Generator Loss: 1.7874 D(x): 0.8009, D(G(z)): 0.2932 Epoch: [11/20], Batch Num: [235/600] Discriminator Loss: 0.8609, Generator Loss: 2.0246 D(x): 0.7645, D(G(z)): 0.2657 Epoch: [11/20], Batch Num: [236/600] Discriminator Loss: 0.7111, Generator Loss: 2.1250 D(x): 0.7649, D(G(z)): 0.2187 Epoch: [11/20], Batch Num: [237/600] Discriminator Loss: 0.7919, Generator Loss: 2.1002 D(x): 0.7429, D(G(z)): 0.2269 Epoch: [11/20], Batch Num: [238/600] Discriminator Loss: 0.7098, Generator Loss: 2.1160 D(x): 0.7442, D(G(z)): 0.1931 Epoch: [11/20], Batch Num: [239/600] Discriminator Loss: 0.8995, Generator Loss: 1.8979 D(x): 0.6992, D(G(z)): 0.2250 Epoch: [11/20], Batch Num: [240/600] Discriminator Loss: 0.7404, Generator Loss: 1.7140 D(x): 0.7908, D(G(z)): 0.2674 Epoch: [11/20], Batch Num: [241/600] Discriminator Loss: 0.7497, Generator Loss: 1.9003 D(x): 0.7955, D(G(z)): 0.2643 Epoch: [11/20], Batch Num: 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1.9177 D(x): 0.7296, D(G(z)): 0.2235 Epoch: [11/20], Batch Num: [251/600] Discriminator Loss: 0.7658, Generator Loss: 1.8702 D(x): 0.7541, D(G(z)): 0.2494 Epoch: [11/20], Batch Num: [252/600] Discriminator Loss: 0.9216, Generator Loss: 1.8140 D(x): 0.6902, D(G(z)): 0.2775 Epoch: [11/20], Batch Num: [253/600] Discriminator Loss: 0.8943, Generator Loss: 1.7760 D(x): 0.7329, D(G(z)): 0.2924 Epoch: [11/20], Batch Num: [254/600] Discriminator Loss: 0.9515, Generator Loss: 1.9396 D(x): 0.7575, D(G(z)): 0.3251 Epoch: [11/20], Batch Num: [255/600] Discriminator Loss: 0.8831, Generator Loss: 1.8696 D(x): 0.7417, D(G(z)): 0.2794 Epoch: [11/20], Batch Num: [256/600] Discriminator Loss: 0.8160, Generator Loss: 2.1945 D(x): 0.7819, D(G(z)): 0.2756 Epoch: [11/20], Batch Num: [257/600] Discriminator Loss: 0.7358, Generator Loss: 2.1111 D(x): 0.7105, D(G(z)): 0.2041 Epoch: [11/20], Batch Num: [258/600] Discriminator Loss: 0.9204, Generator Loss: 2.0058 D(x): 0.6928, D(G(z)): 0.2399 Epoch: [11/20], 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Generator Loss: 1.8334 D(x): 0.7657, D(G(z)): 0.3003 Epoch: [11/20], Batch Num: [268/600] Discriminator Loss: 0.7609, Generator Loss: 1.8516 D(x): 0.8267, D(G(z)): 0.3056 Epoch: [11/20], Batch Num: [269/600] Discriminator Loss: 0.9852, Generator Loss: 2.0285 D(x): 0.6851, D(G(z)): 0.2700 Epoch: [11/20], Batch Num: [270/600] Discriminator Loss: 0.8597, Generator Loss: 2.1332 D(x): 0.6940, D(G(z)): 0.2186 Epoch: [11/20], Batch Num: [271/600] Discriminator Loss: 0.9227, Generator Loss: 1.8853 D(x): 0.7065, D(G(z)): 0.2310 Epoch: [11/20], Batch Num: [272/600] Discriminator Loss: 0.9610, Generator Loss: 1.5793 D(x): 0.7023, D(G(z)): 0.2796 Epoch: [11/20], Batch Num: [273/600] Discriminator Loss: 0.8487, Generator Loss: 1.6182 D(x): 0.7860, D(G(z)): 0.2977 Epoch: [11/20], Batch Num: [274/600] Discriminator Loss: 0.7219, Generator Loss: 1.7746 D(x): 0.8216, D(G(z)): 0.3038 Epoch: [11/20], Batch Num: [275/600] Discriminator Loss: 0.7827, Generator Loss: 2.1419 D(x): 0.8198, D(G(z)): 0.3107 Epoch: [11/20], Batch Num: [276/600] Discriminator Loss: 0.7762, Generator Loss: 2.1171 D(x): 0.7071, D(G(z)): 0.1920 Epoch: [11/20], Batch Num: [277/600] Discriminator Loss: 0.8931, Generator Loss: 2.0471 D(x): 0.6679, D(G(z)): 0.2049 Epoch: [11/20], Batch Num: [278/600] Discriminator Loss: 0.8001, Generator Loss: 2.0193 D(x): 0.7287, D(G(z)): 0.2601 Epoch: [11/20], Batch Num: [279/600] Discriminator Loss: 0.7738, Generator Loss: 1.7446 D(x): 0.7705, D(G(z)): 0.2576 Epoch: [11/20], Batch Num: [280/600] Discriminator Loss: 0.8687, Generator Loss: 1.8734 D(x): 0.7217, D(G(z)): 0.2451 Epoch: [11/20], Batch Num: [281/600] Discriminator Loss: 0.7203, Generator Loss: 1.7321 D(x): 0.7781, D(G(z)): 0.2606 Epoch: [11/20], Batch Num: [282/600] Discriminator Loss: 0.6553, Generator Loss: 1.8625 D(x): 0.8226, D(G(z)): 0.2674 Epoch: [11/20], Batch Num: [283/600] Discriminator Loss: 0.8155, Generator Loss: 1.8996 D(x): 0.7468, D(G(z)): 0.2663 Epoch: [11/20], Batch Num: [284/600] Discriminator Loss: 0.6834, Generator Loss: 2.3522 D(x): 0.7800, D(G(z)): 0.2248 Epoch: [11/20], Batch Num: [285/600] Discriminator Loss: 0.7530, Generator Loss: 2.1396 D(x): 0.7190, D(G(z)): 0.1979 Epoch: [11/20], Batch Num: [286/600] Discriminator Loss: 0.7930, Generator Loss: 2.2475 D(x): 0.7343, D(G(z)): 0.2266 Epoch: [11/20], Batch Num: [287/600] Discriminator Loss: 0.7859, Generator Loss: 2.1213 D(x): 0.7774, D(G(z)): 0.2643 Epoch: [11/20], Batch Num: [288/600] Discriminator Loss: 0.6364, Generator Loss: 2.0797 D(x): 0.8119, D(G(z)): 0.2317 Epoch: [11/20], Batch Num: [289/600] Discriminator Loss: 0.6220, Generator Loss: 1.9958 D(x): 0.7879, D(G(z)): 0.2269 Epoch: [11/20], Batch Num: [290/600] Discriminator Loss: 0.6538, Generator Loss: 2.1703 D(x): 0.7633, D(G(z)): 0.2021 Epoch: [11/20], Batch Num: [291/600] Discriminator Loss: 0.7842, Generator Loss: 1.9857 D(x): 0.7129, D(G(z)): 0.2169 Epoch: [11/20], Batch Num: [292/600] Discriminator Loss: 0.7068, Generator Loss: 2.0462 D(x): 0.7626, D(G(z)): 0.2232 Epoch: [11/20], Batch Num: [293/600] Discriminator Loss: 0.7538, Generator Loss: 2.1055 D(x): 0.8303, D(G(z)): 0.2792 Epoch: [11/20], Batch Num: [294/600] Discriminator Loss: 0.6780, Generator Loss: 2.2822 D(x): 0.8162, D(G(z)): 0.2409 Epoch: [11/20], Batch Num: [295/600] Discriminator Loss: 0.6746, Generator Loss: 2.3747 D(x): 0.7903, D(G(z)): 0.2394 Epoch: [11/20], Batch Num: [296/600] Discriminator Loss: 0.6687, Generator Loss: 2.4578 D(x): 0.7759, D(G(z)): 0.1979 Epoch: [11/20], Batch Num: [297/600] Discriminator Loss: 0.5158, Generator Loss: 2.3285 D(x): 0.8164, D(G(z)): 0.1836 Epoch: [11/20], Batch Num: [298/600] Discriminator Loss: 0.7309, Generator Loss: 2.1869 D(x): 0.7671, D(G(z)): 0.2235 Epoch: [11/20], Batch Num: [299/600] Discriminator Loss: 0.6954, Generator Loss: 2.1637 D(x): 0.7552, D(G(z)): 0.1849 Epoch: 11, Batch Num: [300/600]
Epoch: [11/20], Batch Num: [300/600] Discriminator Loss: 0.5531, Generator Loss: 2.0452 D(x): 0.8493, D(G(z)): 0.2140 Epoch: [11/20], Batch Num: [301/600] Discriminator Loss: 0.7822, Generator Loss: 1.8982 D(x): 0.7684, D(G(z)): 0.2493 Epoch: [11/20], Batch Num: [302/600] Discriminator Loss: 0.8508, Generator Loss: 1.9826 D(x): 0.7713, D(G(z)): 0.2898 Epoch: [11/20], Batch Num: [303/600] Discriminator Loss: 0.7893, Generator Loss: 2.4055 D(x): 0.8047, D(G(z)): 0.2718 Epoch: [11/20], Batch Num: [304/600] Discriminator Loss: 0.7822, Generator Loss: 2.0308 D(x): 0.7293, D(G(z)): 0.2083 Epoch: [11/20], Batch Num: [305/600] Discriminator Loss: 0.8537, Generator Loss: 2.1334 D(x): 0.7094, D(G(z)): 0.2284 Epoch: [11/20], Batch Num: [306/600] Discriminator Loss: 0.8059, Generator Loss: 1.9263 D(x): 0.7332, D(G(z)): 0.2356 Epoch: [11/20], Batch Num: [307/600] Discriminator Loss: 0.8532, Generator Loss: 1.8573 D(x): 0.7687, D(G(z)): 0.2828 Epoch: [11/20], Batch Num: [308/600] Discriminator Loss: 0.9715, Generator Loss: 1.7226 D(x): 0.7141, D(G(z)): 0.2857 Epoch: [11/20], Batch Num: [309/600] Discriminator Loss: 1.1368, Generator Loss: 1.9462 D(x): 0.6650, D(G(z)): 0.3416 Epoch: [11/20], Batch Num: [310/600] Discriminator Loss: 1.1365, Generator Loss: 1.8668 D(x): 0.7061, D(G(z)): 0.3079 Epoch: [11/20], Batch Num: [311/600] Discriminator Loss: 0.9636, Generator Loss: 1.7882 D(x): 0.6747, D(G(z)): 0.2389 Epoch: [11/20], Batch Num: [312/600] Discriminator Loss: 1.0961, Generator Loss: 1.5543 D(x): 0.6859, D(G(z)): 0.3078 Epoch: [11/20], Batch Num: [313/600] Discriminator Loss: 1.0167, Generator Loss: 1.6912 D(x): 0.6934, D(G(z)): 0.2800 Epoch: [11/20], Batch Num: [314/600] Discriminator Loss: 1.0118, Generator Loss: 1.6723 D(x): 0.7328, D(G(z)): 0.2894 Epoch: [11/20], Batch Num: [315/600] Discriminator Loss: 1.1332, Generator Loss: 1.6336 D(x): 0.6616, D(G(z)): 0.2922 Epoch: [11/20], Batch Num: [316/600] Discriminator Loss: 1.0701, Generator Loss: 1.7057 D(x): 0.7051, D(G(z)): 0.2999 Epoch: [11/20], Batch Num: [317/600] Discriminator Loss: 1.1276, Generator Loss: 1.8774 D(x): 0.6667, D(G(z)): 0.3161 Epoch: [11/20], Batch Num: [318/600] Discriminator Loss: 1.1119, Generator Loss: 1.6281 D(x): 0.6281, D(G(z)): 0.2740 Epoch: [11/20], Batch Num: [319/600] Discriminator Loss: 0.9334, Generator Loss: 1.6853 D(x): 0.7259, D(G(z)): 0.2877 Epoch: [11/20], Batch Num: [320/600] Discriminator Loss: 1.0581, Generator Loss: 1.8833 D(x): 0.6947, D(G(z)): 0.2836 Epoch: [11/20], Batch Num: [321/600] Discriminator Loss: 0.9065, Generator Loss: 1.6927 D(x): 0.7017, D(G(z)): 0.2677 Epoch: [11/20], Batch Num: [322/600] Discriminator Loss: 0.8569, Generator Loss: 1.8604 D(x): 0.7056, D(G(z)): 0.2607 Epoch: [11/20], Batch Num: [323/600] Discriminator Loss: 0.9106, Generator Loss: 1.6661 D(x): 0.7023, D(G(z)): 0.2361 Epoch: [11/20], Batch Num: [324/600] Discriminator Loss: 0.8838, Generator Loss: 1.9156 D(x): 0.7270, D(G(z)): 0.2741 Epoch: [11/20], Batch Num: [325/600] Discriminator Loss: 0.8871, Generator Loss: 1.6361 D(x): 0.7144, D(G(z)): 0.2678 Epoch: [11/20], Batch Num: [326/600] Discriminator Loss: 0.9231, Generator Loss: 1.7748 D(x): 0.7340, D(G(z)): 0.2944 Epoch: [11/20], Batch Num: [327/600] Discriminator Loss: 0.8337, Generator Loss: 1.9124 D(x): 0.7151, D(G(z)): 0.2775 Epoch: [11/20], Batch Num: [328/600] Discriminator Loss: 0.8159, Generator Loss: 1.6467 D(x): 0.7071, D(G(z)): 0.2609 Epoch: [11/20], Batch Num: [329/600] Discriminator Loss: 0.8155, Generator Loss: 1.8835 D(x): 0.7552, D(G(z)): 0.2867 Epoch: [11/20], Batch Num: [330/600] Discriminator Loss: 0.8736, Generator Loss: 1.9142 D(x): 0.7322, D(G(z)): 0.2787 Epoch: [11/20], Batch Num: [331/600] Discriminator Loss: 0.8797, Generator Loss: 2.1288 D(x): 0.6935, D(G(z)): 0.2489 Epoch: [11/20], Batch Num: [332/600] Discriminator Loss: 0.7655, Generator Loss: 1.8286 D(x): 0.7415, D(G(z)): 0.2489 Epoch: [11/20], Batch Num: [333/600] Discriminator Loss: 0.7365, Generator Loss: 1.8576 D(x): 0.7495, D(G(z)): 0.2458 Epoch: [11/20], Batch Num: [334/600] Discriminator Loss: 0.7834, Generator Loss: 1.8790 D(x): 0.7227, D(G(z)): 0.2388 Epoch: [11/20], Batch Num: [335/600] Discriminator Loss: 0.5969, Generator Loss: 1.8789 D(x): 0.7948, D(G(z)): 0.2109 Epoch: [11/20], Batch Num: [336/600] Discriminator Loss: 0.7319, Generator Loss: 1.7637 D(x): 0.7544, D(G(z)): 0.2508 Epoch: [11/20], Batch Num: [337/600] Discriminator Loss: 0.6932, Generator Loss: 1.8527 D(x): 0.7744, D(G(z)): 0.2459 Epoch: [11/20], Batch Num: [338/600] Discriminator Loss: 0.7107, Generator Loss: 1.7437 D(x): 0.7496, D(G(z)): 0.2386 Epoch: [11/20], Batch Num: [339/600] Discriminator Loss: 0.5134, Generator Loss: 1.8988 D(x): 0.8502, D(G(z)): 0.2271 Epoch: [11/20], Batch Num: [340/600] Discriminator Loss: 0.5274, Generator Loss: 2.0794 D(x): 0.8079, D(G(z)): 0.1984 Epoch: [11/20], Batch Num: [341/600] Discriminator Loss: 0.5497, Generator Loss: 2.0959 D(x): 0.8231, D(G(z)): 0.2361 Epoch: [11/20], Batch Num: [342/600] Discriminator Loss: 0.6219, Generator Loss: 2.2426 D(x): 0.7743, D(G(z)): 0.1925 Epoch: [11/20], Batch Num: [343/600] Discriminator Loss: 0.7627, Generator Loss: 2.0033 D(x): 0.7313, D(G(z)): 0.2281 Epoch: [11/20], Batch Num: [344/600] Discriminator Loss: 0.5583, Generator Loss: 1.8390 D(x): 0.8109, D(G(z)): 0.2136 Epoch: [11/20], Batch Num: [345/600] Discriminator Loss: 0.6643, Generator Loss: 2.1912 D(x): 0.8537, D(G(z)): 0.2719 Epoch: [11/20], Batch Num: [346/600] Discriminator Loss: 0.6811, Generator Loss: 2.2307 D(x): 0.8238, D(G(z)): 0.2457 Epoch: [11/20], Batch Num: [347/600] Discriminator Loss: 0.8984, Generator Loss: 2.2531 D(x): 0.7149, D(G(z)): 0.2441 Epoch: [11/20], Batch Num: [348/600] Discriminator Loss: 0.7225, Generator Loss: 2.2670 D(x): 0.8112, D(G(z)): 0.2615 Epoch: [11/20], Batch Num: [349/600] Discriminator Loss: 0.8401, Generator Loss: 2.3572 D(x): 0.7081, D(G(z)): 0.2153 Epoch: [11/20], Batch Num: [350/600] Discriminator Loss: 0.8726, Generator Loss: 2.0701 D(x): 0.7202, D(G(z)): 0.2012 Epoch: [11/20], Batch Num: [351/600] Discriminator Loss: 1.0830, Generator Loss: 1.9583 D(x): 0.7526, D(G(z)): 0.3259 Epoch: [11/20], Batch Num: [352/600] Discriminator Loss: 0.8917, Generator Loss: 1.7738 D(x): 0.7245, D(G(z)): 0.2530 Epoch: [11/20], Batch Num: [353/600] Discriminator Loss: 0.8565, Generator Loss: 1.7432 D(x): 0.7965, D(G(z)): 0.3143 Epoch: [11/20], Batch Num: [354/600] Discriminator Loss: 0.7438, Generator Loss: 1.7550 D(x): 0.7557, D(G(z)): 0.2516 Epoch: [11/20], Batch Num: [355/600] Discriminator Loss: 0.7104, Generator Loss: 1.9246 D(x): 0.8030, D(G(z)): 0.2773 Epoch: [11/20], Batch Num: [356/600] Discriminator Loss: 0.8137, Generator Loss: 2.0127 D(x): 0.7407, D(G(z)): 0.2703 Epoch: [11/20], Batch Num: [357/600] Discriminator Loss: 1.1034, Generator Loss: 2.0041 D(x): 0.6841, D(G(z)): 0.2759 Epoch: [11/20], Batch Num: [358/600] Discriminator Loss: 1.0020, Generator Loss: 1.7087 D(x): 0.6657, D(G(z)): 0.2435 Epoch: [11/20], Batch Num: [359/600] Discriminator Loss: 0.9904, Generator Loss: 1.5141 D(x): 0.6806, D(G(z)): 0.2702 Epoch: [11/20], Batch Num: [360/600] Discriminator Loss: 0.9735, Generator Loss: 1.3827 D(x): 0.7570, D(G(z)): 0.3384 Epoch: [11/20], Batch Num: [361/600] Discriminator Loss: 1.0083, Generator Loss: 1.6017 D(x): 0.7466, D(G(z)): 0.3537 Epoch: [11/20], Batch Num: [362/600] Discriminator Loss: 0.7677, Generator Loss: 1.6816 D(x): 0.7718, D(G(z)): 0.2809 Epoch: [11/20], Batch Num: [363/600] Discriminator Loss: 0.8532, Generator Loss: 1.6557 D(x): 0.7060, D(G(z)): 0.2443 Epoch: [11/20], Batch Num: [364/600] Discriminator Loss: 0.9269, Generator Loss: 1.8053 D(x): 0.6705, D(G(z)): 0.2487 Epoch: [11/20], Batch Num: [365/600] Discriminator Loss: 0.9179, Generator Loss: 1.6138 D(x): 0.6794, D(G(z)): 0.2387 Epoch: [11/20], Batch Num: [366/600] Discriminator Loss: 1.0163, Generator Loss: 1.6871 D(x): 0.6656, D(G(z)): 0.3000 Epoch: [11/20], Batch Num: [367/600] Discriminator Loss: 0.7207, Generator Loss: 1.5677 D(x): 0.7823, D(G(z)): 0.2696 Epoch: [11/20], Batch Num: [368/600] Discriminator Loss: 0.8393, Generator Loss: 1.6359 D(x): 0.7738, D(G(z)): 0.3224 Epoch: [11/20], Batch Num: [369/600] Discriminator Loss: 0.8885, Generator Loss: 1.8177 D(x): 0.7621, D(G(z)): 0.3190 Epoch: [11/20], Batch Num: [370/600] Discriminator Loss: 0.7665, Generator Loss: 1.6949 D(x): 0.7176, D(G(z)): 0.2250 Epoch: [11/20], Batch Num: [371/600] Discriminator Loss: 0.7107, Generator Loss: 1.8411 D(x): 0.7548, D(G(z)): 0.2443 Epoch: [11/20], Batch Num: [372/600] Discriminator Loss: 0.9512, Generator Loss: 1.8131 D(x): 0.6467, D(G(z)): 0.2407 Epoch: [11/20], Batch Num: [373/600] Discriminator Loss: 0.7993, Generator Loss: 1.4672 D(x): 0.7056, D(G(z)): 0.2441 Epoch: [11/20], Batch Num: [374/600] Discriminator Loss: 0.7740, Generator Loss: 1.4657 D(x): 0.7521, D(G(z)): 0.2708 Epoch: [11/20], Batch Num: [375/600] Discriminator Loss: 0.9060, Generator Loss: 1.5028 D(x): 0.7401, D(G(z)): 0.3376 Epoch: [11/20], Batch Num: [376/600] Discriminator Loss: 0.7227, Generator Loss: 1.5129 D(x): 0.8270, D(G(z)): 0.3038 Epoch: [11/20], Batch Num: [377/600] Discriminator Loss: 0.6877, Generator Loss: 1.7022 D(x): 0.7775, D(G(z)): 0.2664 Epoch: [11/20], Batch Num: [378/600] Discriminator Loss: 0.6761, Generator Loss: 1.8395 D(x): 0.7928, D(G(z)): 0.2638 Epoch: [11/20], Batch Num: [379/600] Discriminator Loss: 0.7301, Generator Loss: 1.6890 D(x): 0.7425, D(G(z)): 0.2452 Epoch: [11/20], Batch Num: [380/600] Discriminator Loss: 0.7689, Generator Loss: 1.8615 D(x): 0.7684, D(G(z)): 0.2624 Epoch: [11/20], Batch Num: [381/600] Discriminator Loss: 0.7625, Generator Loss: 1.9881 D(x): 0.7530, D(G(z)): 0.2734 Epoch: [11/20], Batch Num: [382/600] Discriminator Loss: 0.7192, Generator Loss: 1.9673 D(x): 0.7527, D(G(z)): 0.2395 Epoch: [11/20], Batch Num: [383/600] Discriminator Loss: 0.6609, Generator Loss: 1.8384 D(x): 0.7439, D(G(z)): 0.1940 Epoch: [11/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [11/20], Batch Num: [400/600] Discriminator Loss: 0.8100, Generator Loss: 1.7036 D(x): 0.7325, D(G(z)): 0.2682 Epoch: [11/20], Batch Num: [401/600] Discriminator Loss: 1.0026, Generator Loss: 1.7363 D(x): 0.7150, D(G(z)): 0.3291 Epoch: [11/20], Batch Num: [402/600] Discriminator Loss: 0.8045, Generator Loss: 1.5776 D(x): 0.7527, D(G(z)): 0.2711 Epoch: [11/20], Batch Num: [403/600] Discriminator Loss: 0.9200, Generator Loss: 1.6292 D(x): 0.7049, D(G(z)): 0.2869 Epoch: [11/20], Batch Num: [404/600] Discriminator Loss: 0.9474, Generator Loss: 1.6546 D(x): 0.7056, D(G(z)): 0.2910 Epoch: [11/20], Batch Num: [405/600] Discriminator Loss: 0.8329, Generator Loss: 1.5445 D(x): 0.7470, D(G(z)): 0.2955 Epoch: [11/20], Batch Num: [406/600] Discriminator Loss: 0.7642, Generator Loss: 1.7638 D(x): 0.8008, D(G(z)): 0.3144 Epoch: [11/20], Batch Num: [407/600] Discriminator Loss: 0.7859, Generator Loss: 1.6806 D(x): 0.7328, D(G(z)): 0.2465 Epoch: [11/20], Batch Num: [408/600] Discriminator Loss: 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Discriminator Loss: 0.7246, Generator Loss: 2.0811 D(x): 0.7504, D(G(z)): 0.2538 Epoch: [11/20], Batch Num: [426/600] Discriminator Loss: 0.7151, Generator Loss: 2.0152 D(x): 0.7209, D(G(z)): 0.2098 Epoch: [11/20], Batch Num: [427/600] Discriminator Loss: 0.7295, Generator Loss: 1.9867 D(x): 0.7265, D(G(z)): 0.1894 Epoch: [11/20], Batch Num: [428/600] Discriminator Loss: 0.6903, Generator Loss: 1.9377 D(x): 0.7447, D(G(z)): 0.2112 Epoch: [11/20], Batch Num: [429/600] Discriminator Loss: 0.7725, Generator Loss: 1.8123 D(x): 0.7718, D(G(z)): 0.2702 Epoch: [11/20], Batch Num: [430/600] Discriminator Loss: 0.7011, Generator Loss: 1.7044 D(x): 0.7842, D(G(z)): 0.2534 Epoch: [11/20], Batch Num: [431/600] Discriminator Loss: 0.6544, Generator Loss: 1.8156 D(x): 0.8370, D(G(z)): 0.2849 Epoch: [11/20], Batch Num: [432/600] Discriminator Loss: 0.6867, Generator Loss: 2.0168 D(x): 0.8338, D(G(z)): 0.2837 Epoch: [11/20], Batch Num: [433/600] Discriminator Loss: 0.6554, Generator Loss: 2.1820 D(x): 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[442/600] Discriminator Loss: 0.7022, Generator Loss: 2.2714 D(x): 0.7350, D(G(z)): 0.1815 Epoch: [11/20], Batch Num: [443/600] Discriminator Loss: 0.7110, Generator Loss: 1.8603 D(x): 0.7820, D(G(z)): 0.2305 Epoch: [11/20], Batch Num: [444/600] Discriminator Loss: 0.6806, Generator Loss: 1.9948 D(x): 0.8489, D(G(z)): 0.2858 Epoch: [11/20], Batch Num: [445/600] Discriminator Loss: 0.6033, Generator Loss: 1.9410 D(x): 0.8364, D(G(z)): 0.2239 Epoch: [11/20], Batch Num: [446/600] Discriminator Loss: 0.7817, Generator Loss: 2.0870 D(x): 0.7653, D(G(z)): 0.2482 Epoch: [11/20], Batch Num: [447/600] Discriminator Loss: 0.7683, Generator Loss: 2.2552 D(x): 0.7232, D(G(z)): 0.1962 Epoch: [11/20], Batch Num: [448/600] Discriminator Loss: 0.7262, Generator Loss: 2.2722 D(x): 0.8110, D(G(z)): 0.2528 Epoch: [11/20], Batch Num: [449/600] Discriminator Loss: 0.8323, Generator Loss: 2.1164 D(x): 0.6949, D(G(z)): 0.2177 Epoch: [11/20], Batch Num: [450/600] Discriminator Loss: 0.8842, Generator Loss: 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Generator Loss: 2.1101 D(x): 0.7696, D(G(z)): 0.2506 Epoch: [11/20], Batch Num: [468/600] Discriminator Loss: 0.7604, Generator Loss: 2.2014 D(x): 0.7375, D(G(z)): 0.2283 Epoch: [11/20], Batch Num: [469/600] Discriminator Loss: 0.8147, Generator Loss: 2.0634 D(x): 0.6808, D(G(z)): 0.1912 Epoch: [11/20], Batch Num: [470/600] Discriminator Loss: 0.7063, Generator Loss: 2.0738 D(x): 0.7747, D(G(z)): 0.2280 Epoch: [11/20], Batch Num: [471/600] Discriminator Loss: 0.6463, Generator Loss: 1.8891 D(x): 0.7939, D(G(z)): 0.2303 Epoch: [11/20], Batch Num: [472/600] Discriminator Loss: 0.6977, Generator Loss: 2.0996 D(x): 0.7936, D(G(z)): 0.2397 Epoch: [11/20], Batch Num: [473/600] Discriminator Loss: 0.6198, Generator Loss: 1.7506 D(x): 0.7780, D(G(z)): 0.2083 Epoch: [11/20], Batch Num: [474/600] Discriminator Loss: 0.7327, Generator Loss: 1.7926 D(x): 0.8133, D(G(z)): 0.2743 Epoch: [11/20], Batch Num: [475/600] Discriminator Loss: 0.7248, Generator Loss: 1.8937 D(x): 0.8262, D(G(z)): 0.3098 Epoch: [11/20], Batch Num: [476/600] Discriminator Loss: 0.6587, Generator Loss: 2.5225 D(x): 0.8306, D(G(z)): 0.2643 Epoch: [11/20], Batch Num: [477/600] Discriminator Loss: 0.6143, Generator Loss: 2.4111 D(x): 0.7471, D(G(z)): 0.1446 Epoch: [11/20], Batch Num: [478/600] Discriminator Loss: 0.7681, Generator Loss: 2.3130 D(x): 0.6885, D(G(z)): 0.1382 Epoch: [11/20], Batch Num: [479/600] Discriminator Loss: 0.6411, Generator Loss: 2.0779 D(x): 0.7644, D(G(z)): 0.2006 Epoch: [11/20], Batch Num: [480/600] Discriminator Loss: 0.6832, Generator Loss: 1.9278 D(x): 0.7705, D(G(z)): 0.2158 Epoch: [11/20], Batch Num: [481/600] Discriminator Loss: 0.6888, Generator Loss: 1.9057 D(x): 0.7949, D(G(z)): 0.2549 Epoch: [11/20], Batch Num: [482/600] Discriminator Loss: 0.5219, Generator Loss: 1.9873 D(x): 0.8714, D(G(z)): 0.2389 Epoch: [11/20], Batch Num: [483/600] Discriminator Loss: 0.7112, Generator Loss: 2.1334 D(x): 0.8291, D(G(z)): 0.2788 Epoch: [11/20], Batch Num: [484/600] Discriminator Loss: 0.6439, Generator Loss: 2.3232 D(x): 0.7532, D(G(z)): 0.1838 Epoch: [11/20], Batch Num: [485/600] Discriminator Loss: 0.7526, Generator Loss: 2.2603 D(x): 0.6840, D(G(z)): 0.1661 Epoch: [11/20], Batch Num: [486/600] Discriminator Loss: 0.7320, Generator Loss: 1.7230 D(x): 0.7632, D(G(z)): 0.2147 Epoch: [11/20], Batch Num: [487/600] Discriminator Loss: 0.6632, Generator Loss: 1.7561 D(x): 0.7769, D(G(z)): 0.2120 Epoch: [11/20], Batch Num: [488/600] Discriminator Loss: 0.7296, Generator Loss: 1.7520 D(x): 0.8143, D(G(z)): 0.3005 Epoch: [11/20], Batch Num: [489/600] Discriminator Loss: 0.6898, Generator Loss: 1.9892 D(x): 0.8466, D(G(z)): 0.2864 Epoch: [11/20], Batch Num: [490/600] Discriminator Loss: 0.6776, Generator Loss: 2.5305 D(x): 0.8213, D(G(z)): 0.2824 Epoch: [11/20], Batch Num: [491/600] Discriminator Loss: 0.6296, Generator Loss: 2.5991 D(x): 0.7663, D(G(z)): 0.1629 Epoch: [11/20], Batch Num: [492/600] Discriminator Loss: 0.7626, Generator Loss: 2.7179 D(x): 0.6799, D(G(z)): 0.1373 Epoch: [11/20], Batch Num: [493/600] Discriminator Loss: 0.7217, Generator Loss: 1.9943 D(x): 0.7054, D(G(z)): 0.1498 Epoch: [11/20], Batch Num: [494/600] Discriminator Loss: 0.7277, Generator Loss: 1.4006 D(x): 0.7449, D(G(z)): 0.2003 Epoch: [11/20], Batch Num: [495/600] Discriminator Loss: 0.9073, Generator Loss: 1.3090 D(x): 0.8302, D(G(z)): 0.3955 Epoch: [11/20], Batch Num: [496/600] Discriminator Loss: 0.8091, Generator Loss: 1.6690 D(x): 0.8397, D(G(z)): 0.3416 Epoch: [11/20], Batch Num: [497/600] Discriminator Loss: 0.7040, Generator Loss: 2.2603 D(x): 0.7805, D(G(z)): 0.2724 Epoch: [11/20], Batch Num: [498/600] Discriminator Loss: 0.8176, Generator Loss: 2.1417 D(x): 0.6903, D(G(z)): 0.1826 Epoch: [11/20], Batch Num: [499/600] Discriminator Loss: 0.7058, Generator Loss: 2.0577 D(x): 0.7332, D(G(z)): 0.1921 Epoch: 11, Batch Num: [500/600]
Epoch: [11/20], Batch Num: [500/600] Discriminator Loss: 0.7991, Generator Loss: 1.8217 D(x): 0.7117, D(G(z)): 0.1920 Epoch: [11/20], Batch Num: [501/600] Discriminator Loss: 0.7759, Generator Loss: 1.6114 D(x): 0.7675, D(G(z)): 0.2562 Epoch: [11/20], Batch Num: [502/600] Discriminator Loss: 0.6907, Generator Loss: 1.7614 D(x): 0.8432, D(G(z)): 0.2966 Epoch: [11/20], Batch Num: [503/600] Discriminator Loss: 0.6868, Generator Loss: 1.7351 D(x): 0.7968, D(G(z)): 0.2738 Epoch: [11/20], Batch Num: [504/600] Discriminator Loss: 0.8167, Generator Loss: 1.8377 D(x): 0.7059, D(G(z)): 0.2260 Epoch: [11/20], Batch Num: [505/600] Discriminator Loss: 0.9773, Generator Loss: 1.9627 D(x): 0.7014, D(G(z)): 0.2775 Epoch: [11/20], Batch Num: [506/600] Discriminator Loss: 0.7319, Generator Loss: 2.0728 D(x): 0.7705, D(G(z)): 0.2410 Epoch: [11/20], Batch Num: [507/600] Discriminator Loss: 0.8898, Generator Loss: 1.7619 D(x): 0.6811, D(G(z)): 0.2221 Epoch: [11/20], Batch Num: [508/600] Discriminator Loss: 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Discriminator Loss: 0.7140, Generator Loss: 1.6847 D(x): 0.8050, D(G(z)): 0.2948 Epoch: [11/20], Batch Num: [526/600] Discriminator Loss: 0.7553, Generator Loss: 1.6982 D(x): 0.7869, D(G(z)): 0.2880 Epoch: [11/20], Batch Num: [527/600] Discriminator Loss: 0.7641, Generator Loss: 1.9394 D(x): 0.7868, D(G(z)): 0.2688 Epoch: [11/20], Batch Num: [528/600] Discriminator Loss: 0.7436, Generator Loss: 1.8713 D(x): 0.6994, D(G(z)): 0.1754 Epoch: [11/20], Batch Num: [529/600] Discriminator Loss: 0.7709, Generator Loss: 1.7777 D(x): 0.7735, D(G(z)): 0.2574 Epoch: [11/20], Batch Num: [530/600] Discriminator Loss: 0.8709, Generator Loss: 1.7061 D(x): 0.7230, D(G(z)): 0.2579 Epoch: [11/20], Batch Num: [531/600] Discriminator Loss: 0.8281, Generator Loss: 1.5968 D(x): 0.7213, D(G(z)): 0.2698 Epoch: [11/20], Batch Num: [532/600] Discriminator Loss: 0.8051, Generator Loss: 1.8712 D(x): 0.7870, D(G(z)): 0.3055 Epoch: [11/20], Batch Num: [533/600] Discriminator Loss: 0.6767, Generator Loss: 1.8969 D(x): 0.7609, D(G(z)): 0.2150 Epoch: [11/20], Batch Num: [534/600] Discriminator Loss: 0.6452, Generator Loss: 1.9054 D(x): 0.7713, D(G(z)): 0.2239 Epoch: [11/20], Batch Num: [535/600] Discriminator Loss: 0.7346, Generator Loss: 1.7275 D(x): 0.7485, D(G(z)): 0.2429 Epoch: [11/20], Batch Num: [536/600] Discriminator Loss: 0.7177, Generator Loss: 1.6127 D(x): 0.7359, D(G(z)): 0.1955 Epoch: [11/20], Batch Num: [537/600] Discriminator Loss: 0.8110, Generator Loss: 1.4487 D(x): 0.7809, D(G(z)): 0.3220 Epoch: [11/20], Batch Num: [538/600] Discriminator Loss: 0.7435, Generator Loss: 1.5779 D(x): 0.7707, D(G(z)): 0.2641 Epoch: [11/20], Batch Num: [539/600] Discriminator Loss: 0.7391, Generator Loss: 1.7973 D(x): 0.7625, D(G(z)): 0.2583 Epoch: [11/20], Batch Num: [540/600] Discriminator Loss: 0.8124, Generator Loss: 1.9372 D(x): 0.7749, D(G(z)): 0.3090 Epoch: [11/20], Batch Num: [541/600] Discriminator Loss: 0.7976, Generator Loss: 1.8668 D(x): 0.7107, D(G(z)): 0.2164 Epoch: [11/20], Batch Num: 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1.9058 D(x): 0.7147, D(G(z)): 0.3234 Epoch: [11/20], Batch Num: [551/600] Discriminator Loss: 0.8887, Generator Loss: 1.9399 D(x): 0.6921, D(G(z)): 0.2534 Epoch: [11/20], Batch Num: [552/600] Discriminator Loss: 0.9062, Generator Loss: 1.7251 D(x): 0.6528, D(G(z)): 0.2247 Epoch: [11/20], Batch Num: [553/600] Discriminator Loss: 0.9200, Generator Loss: 1.4192 D(x): 0.6933, D(G(z)): 0.2667 Epoch: [11/20], Batch Num: [554/600] Discriminator Loss: 0.9572, Generator Loss: 1.3270 D(x): 0.7431, D(G(z)): 0.3387 Epoch: [11/20], Batch Num: [555/600] Discriminator Loss: 0.9059, Generator Loss: 1.5649 D(x): 0.7621, D(G(z)): 0.3614 Epoch: [11/20], Batch Num: [556/600] Discriminator Loss: 0.9216, Generator Loss: 1.5888 D(x): 0.7110, D(G(z)): 0.3030 Epoch: [11/20], Batch Num: [557/600] Discriminator Loss: 0.9504, Generator Loss: 1.4761 D(x): 0.6972, D(G(z)): 0.3111 Epoch: [11/20], Batch Num: [558/600] Discriminator Loss: 0.7935, Generator Loss: 1.7147 D(x): 0.7369, D(G(z)): 0.2800 Epoch: [11/20], Batch Num: [559/600] Discriminator Loss: 0.8902, Generator Loss: 1.6316 D(x): 0.7306, D(G(z)): 0.2757 Epoch: [11/20], Batch Num: [560/600] Discriminator Loss: 0.8688, Generator Loss: 1.6147 D(x): 0.7149, D(G(z)): 0.2726 Epoch: [11/20], Batch Num: [561/600] Discriminator Loss: 0.8483, Generator Loss: 1.7752 D(x): 0.7467, D(G(z)): 0.3115 Epoch: [11/20], Batch Num: [562/600] Discriminator Loss: 0.9333, Generator Loss: 1.6391 D(x): 0.7074, D(G(z)): 0.3096 Epoch: [11/20], Batch Num: [563/600] Discriminator Loss: 0.7326, Generator Loss: 1.6551 D(x): 0.7617, D(G(z)): 0.2688 Epoch: [11/20], Batch Num: [564/600] Discriminator Loss: 0.6853, Generator Loss: 1.8358 D(x): 0.7647, D(G(z)): 0.2544 Epoch: [11/20], Batch Num: [565/600] Discriminator Loss: 0.6047, Generator Loss: 1.9715 D(x): 0.7873, D(G(z)): 0.2297 Epoch: [11/20], Batch Num: [566/600] Discriminator Loss: 0.7156, Generator Loss: 1.7593 D(x): 0.7655, D(G(z)): 0.2662 Epoch: [11/20], Batch Num: [567/600] Discriminator Loss: 0.7605, Generator Loss: 1.6814 D(x): 0.7370, D(G(z)): 0.2552 Epoch: [11/20], Batch Num: [568/600] Discriminator Loss: 0.6583, Generator Loss: 1.6408 D(x): 0.7882, D(G(z)): 0.2697 Epoch: [11/20], Batch Num: [569/600] Discriminator Loss: 0.6300, Generator Loss: 1.8473 D(x): 0.7863, D(G(z)): 0.2398 Epoch: [11/20], Batch Num: [570/600] Discriminator Loss: 0.5609, Generator Loss: 2.1583 D(x): 0.8600, D(G(z)): 0.2511 Epoch: [11/20], Batch Num: [571/600] Discriminator Loss: 0.6357, Generator Loss: 2.0812 D(x): 0.7884, D(G(z)): 0.2066 Epoch: [11/20], Batch Num: [572/600] Discriminator Loss: 0.6677, Generator Loss: 2.2035 D(x): 0.7317, D(G(z)): 0.1965 Epoch: [11/20], Batch Num: [573/600] Discriminator Loss: 0.6604, Generator Loss: 1.9891 D(x): 0.7334, D(G(z)): 0.1833 Epoch: [11/20], Batch Num: [574/600] Discriminator Loss: 0.5359, Generator Loss: 1.9075 D(x): 0.8360, D(G(z)): 0.2145 Epoch: [11/20], Batch Num: [575/600] Discriminator Loss: 0.5605, Generator Loss: 2.0375 D(x): 0.8487, D(G(z)): 0.2350 Epoch: [11/20], Batch Num: [576/600] Discriminator Loss: 0.5627, Generator Loss: 2.1398 D(x): 0.7871, D(G(z)): 0.1956 Epoch: [11/20], Batch Num: [577/600] Discriminator Loss: 0.5975, Generator Loss: 2.0314 D(x): 0.8388, D(G(z)): 0.2487 Epoch: [11/20], Batch Num: [578/600] Discriminator Loss: 0.6459, Generator Loss: 2.1301 D(x): 0.7991, D(G(z)): 0.2189 Epoch: [11/20], Batch Num: [579/600] Discriminator Loss: 0.5183, Generator Loss: 2.1782 D(x): 0.8114, D(G(z)): 0.1802 Epoch: [11/20], Batch Num: [580/600] Discriminator Loss: 0.5394, Generator Loss: 2.0423 D(x): 0.8098, D(G(z)): 0.1883 Epoch: [11/20], Batch Num: [581/600] Discriminator Loss: 0.6245, Generator Loss: 2.0591 D(x): 0.7852, D(G(z)): 0.2205 Epoch: [11/20], Batch Num: [582/600] Discriminator Loss: 0.5948, Generator Loss: 1.9359 D(x): 0.8567, D(G(z)): 0.2586 Epoch: [11/20], Batch Num: [583/600] Discriminator Loss: 0.7681, Generator Loss: 2.2508 D(x): 0.7863, D(G(z)): 0.2504 Epoch: [11/20], Batch Num: [584/600] Discriminator Loss: 0.7197, Generator Loss: 2.1428 D(x): 0.7892, D(G(z)): 0.2194 Epoch: [11/20], Batch Num: [585/600] Discriminator Loss: 0.5939, Generator Loss: 2.2444 D(x): 0.7927, D(G(z)): 0.1985 Epoch: [11/20], Batch Num: [586/600] Discriminator Loss: 0.6224, Generator Loss: 1.7497 D(x): 0.7930, D(G(z)): 0.2036 Epoch: [11/20], Batch Num: [587/600] Discriminator Loss: 0.6652, Generator Loss: 2.0635 D(x): 0.8458, D(G(z)): 0.2670 Epoch: [11/20], Batch Num: [588/600] Discriminator Loss: 0.8305, Generator Loss: 2.1399 D(x): 0.7860, D(G(z)): 0.2482 Epoch: [11/20], Batch Num: [589/600] Discriminator Loss: 0.7569, Generator Loss: 1.8719 D(x): 0.7390, D(G(z)): 0.2155 Epoch: [11/20], Batch Num: [590/600] Discriminator Loss: 0.7142, Generator Loss: 1.8280 D(x): 0.7720, D(G(z)): 0.2335 Epoch: [11/20], Batch Num: [591/600] Discriminator Loss: 0.8587, Generator Loss: 1.8915 D(x): 0.7630, D(G(z)): 0.2618 Epoch: [11/20], Batch Num: [592/600] Discriminator Loss: 0.9331, Generator Loss: 1.8684 D(x): 0.7384, D(G(z)): 0.2645 Epoch: [11/20], Batch Num: [593/600] Discriminator Loss: 0.9724, Generator Loss: 1.9500 D(x): 0.7285, D(G(z)): 0.3134 Epoch: [11/20], Batch Num: [594/600] Discriminator Loss: 0.9572, Generator Loss: 2.0539 D(x): 0.7101, D(G(z)): 0.2635 Epoch: [11/20], Batch Num: [595/600] Discriminator Loss: 0.8605, Generator Loss: 1.8916 D(x): 0.6844, D(G(z)): 0.2147 Epoch: [11/20], Batch Num: [596/600] Discriminator Loss: 0.7884, Generator Loss: 1.3154 D(x): 0.7477, D(G(z)): 0.2701 Epoch: [11/20], Batch Num: [597/600] Discriminator Loss: 0.9467, Generator Loss: 1.3641 D(x): 0.7539, D(G(z)): 0.3393 Epoch: [11/20], Batch Num: [598/600] Discriminator Loss: 0.9843, Generator Loss: 1.5756 D(x): 0.7470, D(G(z)): 0.3654 Epoch: [11/20], Batch Num: [599/600] Discriminator Loss: 0.8486, Generator Loss: 1.7855 D(x): 0.7343, D(G(z)): 0.2670 Epoch: 12, Batch Num: [0/600]
Epoch: [12/20], Batch Num: [0/600] Discriminator Loss: 0.9191, Generator Loss: 1.7381 D(x): 0.6946, D(G(z)): 0.2825 Epoch: [12/20], Batch Num: [1/600] Discriminator Loss: 0.8100, Generator Loss: 1.8386 D(x): 0.7435, D(G(z)): 0.2696 Epoch: [12/20], Batch Num: [2/600] Discriminator Loss: 0.8761, Generator Loss: 1.8893 D(x): 0.7419, D(G(z)): 0.2761 Epoch: [12/20], Batch Num: [3/600] Discriminator Loss: 0.7353, Generator Loss: 1.9979 D(x): 0.7859, D(G(z)): 0.2601 Epoch: [12/20], Batch Num: [4/600] Discriminator Loss: 0.7543, Generator Loss: 2.0915 D(x): 0.7378, D(G(z)): 0.2266 Epoch: [12/20], Batch Num: [5/600] Discriminator Loss: 0.6675, Generator Loss: 1.9495 D(x): 0.7296, D(G(z)): 0.1853 Epoch: [12/20], Batch Num: [6/600] Discriminator Loss: 0.7600, Generator Loss: 1.4974 D(x): 0.7275, D(G(z)): 0.2160 Epoch: [12/20], Batch Num: [7/600] Discriminator Loss: 0.6545, Generator Loss: 1.7925 D(x): 0.8116, D(G(z)): 0.2627 Epoch: [12/20], Batch Num: [8/600] Discriminator Loss: 0.6043, Generator Loss: 1.9680 D(x): 0.9244, D(G(z)): 0.3231 Epoch: [12/20], Batch Num: [9/600] Discriminator Loss: 0.4780, Generator Loss: 2.5310 D(x): 0.8301, D(G(z)): 0.1845 Epoch: [12/20], Batch Num: [10/600] Discriminator Loss: 0.4472, Generator Loss: 2.7498 D(x): 0.8239, D(G(z)): 0.1472 Epoch: [12/20], Batch Num: [11/600] Discriminator Loss: 0.5926, Generator Loss: 2.2996 D(x): 0.7301, D(G(z)): 0.1328 Epoch: [12/20], Batch Num: [12/600] Discriminator Loss: 0.5447, Generator Loss: 2.1405 D(x): 0.7874, D(G(z)): 0.1503 Epoch: [12/20], Batch Num: [13/600] Discriminator Loss: 0.4112, Generator Loss: 2.0352 D(x): 0.8737, D(G(z)): 0.1791 Epoch: [12/20], Batch Num: [14/600] Discriminator Loss: 0.4159, Generator Loss: 2.1362 D(x): 0.9186, D(G(z)): 0.2119 Epoch: [12/20], Batch Num: [15/600] Discriminator Loss: 0.5559, Generator Loss: 2.4095 D(x): 0.8723, D(G(z)): 0.2493 Epoch: [12/20], Batch Num: [16/600] Discriminator Loss: 0.5085, Generator Loss: 2.8218 D(x): 0.8433, D(G(z)): 0.1735 Epoch: [12/20], Batch Num: [17/600] Discriminator Loss: 0.5209, Generator Loss: 2.7721 D(x): 0.8202, D(G(z)): 0.1585 Epoch: [12/20], Batch Num: [18/600] Discriminator Loss: 0.3896, Generator Loss: 2.8844 D(x): 0.8407, D(G(z)): 0.1260 Epoch: [12/20], Batch Num: [19/600] Discriminator Loss: 0.5380, Generator Loss: 2.6622 D(x): 0.8216, D(G(z)): 0.1299 Epoch: [12/20], Batch Num: [20/600] Discriminator Loss: 0.6193, Generator Loss: 2.4720 D(x): 0.8078, D(G(z)): 0.1774 Epoch: [12/20], Batch Num: [21/600] Discriminator Loss: 0.5849, Generator Loss: 2.1594 D(x): 0.8379, D(G(z)): 0.2167 Epoch: [12/20], Batch Num: [22/600] Discriminator Loss: 0.7029, Generator Loss: 2.1949 D(x): 0.8476, D(G(z)): 0.2518 Epoch: [12/20], Batch Num: [23/600] Discriminator Loss: 0.6344, Generator Loss: 2.2421 D(x): 0.8627, D(G(z)): 0.2365 Epoch: [12/20], Batch Num: [24/600] Discriminator Loss: 0.6903, Generator Loss: 2.4781 D(x): 0.7762, D(G(z)): 0.1844 Epoch: [12/20], Batch Num: [25/600] Discriminator Loss: 0.4634, Generator Loss: 2.6189 D(x): 0.8292, D(G(z)): 0.1579 Epoch: [12/20], Batch Num: [26/600] Discriminator Loss: 0.8178, Generator Loss: 1.9735 D(x): 0.7584, D(G(z)): 0.2346 Epoch: [12/20], Batch Num: [27/600] Discriminator Loss: 0.8219, Generator Loss: 1.8875 D(x): 0.8111, D(G(z)): 0.2558 Epoch: [12/20], Batch Num: [28/600] Discriminator Loss: 1.0107, Generator Loss: 2.2939 D(x): 0.7868, D(G(z)): 0.3582 Epoch: [12/20], Batch Num: [29/600] Discriminator Loss: 0.8050, Generator Loss: 2.2826 D(x): 0.7638, D(G(z)): 0.2292 Epoch: [12/20], Batch Num: [30/600] Discriminator Loss: 0.8481, Generator Loss: 2.0784 D(x): 0.7082, D(G(z)): 0.2067 Epoch: [12/20], Batch Num: [31/600] Discriminator Loss: 0.8494, Generator Loss: 1.8871 D(x): 0.7695, D(G(z)): 0.2654 Epoch: [12/20], Batch Num: [32/600] Discriminator Loss: 0.8961, Generator Loss: 1.8475 D(x): 0.7765, D(G(z)): 0.2979 Epoch: [12/20], Batch Num: [33/600] Discriminator Loss: 1.1403, Generator Loss: 1.9792 D(x): 0.7161, D(G(z)): 0.2912 Epoch: [12/20], Batch Num: [34/600] Discriminator Loss: 0.7874, Generator Loss: 2.0401 D(x): 0.7680, D(G(z)): 0.2538 Epoch: [12/20], Batch Num: [35/600] Discriminator Loss: 0.8590, Generator Loss: 1.8005 D(x): 0.7284, D(G(z)): 0.2716 Epoch: [12/20], Batch Num: [36/600] Discriminator Loss: 1.2510, Generator Loss: 1.6695 D(x): 0.6555, D(G(z)): 0.3007 Epoch: [12/20], Batch Num: [37/600] Discriminator Loss: 1.0768, Generator Loss: 1.7296 D(x): 0.7148, D(G(z)): 0.3193 Epoch: [12/20], Batch Num: [38/600] Discriminator Loss: 0.9908, Generator Loss: 1.6708 D(x): 0.7267, D(G(z)): 0.3002 Epoch: [12/20], Batch Num: [39/600] Discriminator Loss: 1.0121, Generator Loss: 1.9190 D(x): 0.7690, D(G(z)): 0.3258 Epoch: [12/20], Batch Num: [40/600] Discriminator Loss: 0.8313, Generator Loss: 2.1548 D(x): 0.7411, D(G(z)): 0.2451 Epoch: [12/20], Batch Num: [41/600] Discriminator Loss: 0.7931, Generator Loss: 2.0582 D(x): 0.7320, D(G(z)): 0.2433 Epoch: [12/20], Batch Num: [42/600] Discriminator Loss: 0.8055, Generator Loss: 2.0770 D(x): 0.7457, D(G(z)): 0.2454 Epoch: [12/20], Batch Num: [43/600] Discriminator Loss: 0.7575, Generator Loss: 1.9702 D(x): 0.7083, D(G(z)): 0.2057 Epoch: [12/20], Batch Num: [44/600] Discriminator Loss: 0.6122, Generator Loss: 1.6568 D(x): 0.7974, D(G(z)): 0.2279 Epoch: [12/20], Batch Num: [45/600] Discriminator Loss: 0.8158, Generator Loss: 2.0564 D(x): 0.8101, D(G(z)): 0.3311 Epoch: [12/20], Batch Num: [46/600] Discriminator Loss: 0.8427, Generator Loss: 2.0116 D(x): 0.7561, D(G(z)): 0.2666 Epoch: [12/20], Batch Num: [47/600] Discriminator Loss: 0.6889, Generator Loss: 2.3194 D(x): 0.7655, D(G(z)): 0.2217 Epoch: [12/20], Batch Num: [48/600] Discriminator Loss: 0.6235, Generator Loss: 2.2693 D(x): 0.7798, D(G(z)): 0.1995 Epoch: [12/20], Batch Num: [49/600] Discriminator Loss: 0.6839, Generator Loss: 1.9662 D(x): 0.7166, D(G(z)): 0.1674 Epoch: [12/20], Batch Num: [50/600] Discriminator Loss: 0.6714, Generator Loss: 1.9406 D(x): 0.7444, D(G(z)): 0.1711 Epoch: [12/20], Batch Num: 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D(x): 0.8538, D(G(z)): 0.3180 Epoch: [12/20], Batch Num: [60/600] Discriminator Loss: 0.7522, Generator Loss: 2.3222 D(x): 0.7621, D(G(z)): 0.2143 Epoch: [12/20], Batch Num: [61/600] Discriminator Loss: 0.7765, Generator Loss: 2.5650 D(x): 0.7558, D(G(z)): 0.2328 Epoch: [12/20], Batch Num: [62/600] Discriminator Loss: 0.6596, Generator Loss: 2.5111 D(x): 0.7685, D(G(z)): 0.1722 Epoch: [12/20], Batch Num: [63/600] Discriminator Loss: 0.8126, Generator Loss: 2.3201 D(x): 0.7083, D(G(z)): 0.2125 Epoch: [12/20], Batch Num: [64/600] Discriminator Loss: 0.8943, Generator Loss: 1.8912 D(x): 0.7071, D(G(z)): 0.2261 Epoch: [12/20], Batch Num: [65/600] Discriminator Loss: 1.1198, Generator Loss: 1.6115 D(x): 0.6846, D(G(z)): 0.3195 Epoch: [12/20], Batch Num: [66/600] Discriminator Loss: 0.9998, Generator Loss: 1.8569 D(x): 0.7596, D(G(z)): 0.3392 Epoch: [12/20], Batch Num: [67/600] Discriminator Loss: 0.9566, Generator Loss: 1.6364 D(x): 0.6989, D(G(z)): 0.2407 Epoch: [12/20], Batch Num: [68/600] Discriminator Loss: 1.2275, Generator Loss: 1.8113 D(x): 0.6537, D(G(z)): 0.3006 Epoch: [12/20], Batch Num: [69/600] Discriminator Loss: 1.0487, Generator Loss: 1.8395 D(x): 0.6746, D(G(z)): 0.3058 Epoch: [12/20], Batch Num: [70/600] Discriminator Loss: 1.4139, Generator Loss: 1.7697 D(x): 0.6561, D(G(z)): 0.3541 Epoch: [12/20], Batch Num: [71/600] Discriminator Loss: 1.2834, Generator Loss: 1.6167 D(x): 0.6080, D(G(z)): 0.2973 Epoch: [12/20], Batch Num: [72/600] Discriminator Loss: 1.1666, Generator Loss: 1.5006 D(x): 0.6056, D(G(z)): 0.2912 Epoch: [12/20], Batch Num: [73/600] Discriminator Loss: 1.3073, Generator Loss: 1.4782 D(x): 0.6549, D(G(z)): 0.3505 Epoch: [12/20], Batch Num: [74/600] Discriminator Loss: 1.0515, Generator Loss: 1.4680 D(x): 0.6902, D(G(z)): 0.3434 Epoch: [12/20], Batch Num: [75/600] Discriminator Loss: 1.2102, Generator Loss: 1.4986 D(x): 0.6179, D(G(z)): 0.3340 Epoch: [12/20], Batch Num: [76/600] Discriminator Loss: 1.2611, Generator Loss: 1.4291 D(x): 0.5834, D(G(z)): 0.3232 Epoch: [12/20], Batch Num: [77/600] Discriminator Loss: 1.1498, Generator Loss: 1.3737 D(x): 0.6377, D(G(z)): 0.3035 Epoch: [12/20], Batch Num: [78/600] Discriminator Loss: 1.0079, Generator Loss: 1.4273 D(x): 0.6880, D(G(z)): 0.3175 Epoch: [12/20], Batch Num: [79/600] Discriminator Loss: 1.1048, Generator Loss: 1.6559 D(x): 0.6626, D(G(z)): 0.3590 Epoch: [12/20], Batch Num: [80/600] Discriminator Loss: 1.0053, Generator Loss: 1.7366 D(x): 0.6446, D(G(z)): 0.2887 Epoch: [12/20], Batch Num: [81/600] Discriminator Loss: 0.9505, Generator Loss: 1.5786 D(x): 0.6498, D(G(z)): 0.2657 Epoch: [12/20], Batch Num: [82/600] Discriminator Loss: 0.8123, Generator Loss: 1.6915 D(x): 0.6579, D(G(z)): 0.2213 Epoch: [12/20], Batch Num: [83/600] Discriminator Loss: 0.8420, Generator Loss: 1.6284 D(x): 0.6742, D(G(z)): 0.2531 Epoch: [12/20], Batch Num: [84/600] Discriminator Loss: 0.7259, Generator Loss: 1.4567 D(x): 0.7679, D(G(z)): 0.2913 Epoch: [12/20], Batch Num: [85/600] Discriminator Loss: 0.7871, Generator Loss: 1.6351 D(x): 0.7637, D(G(z)): 0.3097 Epoch: [12/20], Batch Num: [86/600] Discriminator Loss: 0.7479, Generator Loss: 1.5124 D(x): 0.7299, D(G(z)): 0.2747 Epoch: [12/20], Batch Num: [87/600] Discriminator Loss: 0.6444, Generator Loss: 1.6485 D(x): 0.7507, D(G(z)): 0.2348 Epoch: [12/20], Batch Num: [88/600] Discriminator Loss: 0.7352, Generator Loss: 1.6751 D(x): 0.7541, D(G(z)): 0.2854 Epoch: [12/20], Batch Num: [89/600] Discriminator Loss: 0.7202, Generator Loss: 1.7057 D(x): 0.7299, D(G(z)): 0.2455 Epoch: [12/20], Batch Num: [90/600] Discriminator Loss: 0.6509, Generator Loss: 1.8143 D(x): 0.7863, D(G(z)): 0.2684 Epoch: [12/20], Batch Num: [91/600] Discriminator Loss: 0.5470, Generator Loss: 1.7979 D(x): 0.8034, D(G(z)): 0.2296 Epoch: [12/20], Batch Num: [92/600] Discriminator Loss: 0.6666, Generator Loss: 2.0020 D(x): 0.7526, D(G(z)): 0.2167 Epoch: [12/20], Batch Num: [93/600] Discriminator Loss: 0.5041, Generator Loss: 1.8243 D(x): 0.7976, D(G(z)): 0.1922 Epoch: [12/20], Batch Num: [94/600] Discriminator Loss: 0.4754, Generator Loss: 1.9273 D(x): 0.8378, D(G(z)): 0.2019 Epoch: [12/20], Batch Num: [95/600] Discriminator Loss: 0.5060, Generator Loss: 1.9965 D(x): 0.8556, D(G(z)): 0.2349 Epoch: [12/20], Batch Num: [96/600] Discriminator Loss: 0.5194, Generator Loss: 2.1437 D(x): 0.8096, D(G(z)): 0.2109 Epoch: [12/20], Batch Num: [97/600] Discriminator Loss: 0.4567, Generator Loss: 2.3385 D(x): 0.8201, D(G(z)): 0.1744 Epoch: [12/20], Batch Num: [98/600] Discriminator Loss: 0.5039, Generator Loss: 2.2269 D(x): 0.7930, D(G(z)): 0.1725 Epoch: [12/20], Batch Num: [99/600] Discriminator Loss: 0.6389, Generator Loss: 2.2531 D(x): 0.7674, D(G(z)): 0.2115 Epoch: 12, Batch Num: [100/600]
Epoch: [12/20], Batch Num: [100/600] Discriminator Loss: 0.4498, Generator Loss: 2.1647 D(x): 0.8750, D(G(z)): 0.2080 Epoch: [12/20], Batch Num: [101/600] Discriminator Loss: 0.6163, Generator Loss: 2.1229 D(x): 0.8249, D(G(z)): 0.2433 Epoch: [12/20], Batch Num: [102/600] Discriminator Loss: 0.6539, Generator Loss: 2.2204 D(x): 0.8013, D(G(z)): 0.2307 Epoch: [12/20], Batch Num: [103/600] Discriminator Loss: 0.6641, Generator Loss: 2.3236 D(x): 0.8279, D(G(z)): 0.2712 Epoch: [12/20], Batch Num: [104/600] Discriminator Loss: 0.7536, Generator Loss: 2.4364 D(x): 0.7480, D(G(z)): 0.2240 Epoch: [12/20], Batch Num: [105/600] Discriminator Loss: 0.7315, Generator Loss: 2.2828 D(x): 0.7346, D(G(z)): 0.1955 Epoch: [12/20], Batch Num: [106/600] Discriminator Loss: 0.7674, Generator Loss: 2.0556 D(x): 0.7781, D(G(z)): 0.2500 Epoch: [12/20], Batch Num: [107/600] Discriminator Loss: 0.8710, Generator Loss: 1.8701 D(x): 0.7742, D(G(z)): 0.3151 Epoch: [12/20], Batch Num: [108/600] Discriminator Loss: 0.8488, Generator Loss: 1.7131 D(x): 0.7724, D(G(z)): 0.3033 Epoch: [12/20], Batch Num: [109/600] Discriminator Loss: 0.9413, Generator Loss: 1.7328 D(x): 0.7580, D(G(z)): 0.3164 Epoch: [12/20], Batch Num: [110/600] Discriminator Loss: 0.8042, Generator Loss: 2.0915 D(x): 0.8217, D(G(z)): 0.3223 Epoch: [12/20], Batch Num: [111/600] Discriminator Loss: 0.9806, Generator Loss: 2.0593 D(x): 0.7147, D(G(z)): 0.2533 Epoch: [12/20], Batch Num: [112/600] Discriminator Loss: 1.0378, Generator Loss: 1.6842 D(x): 0.6563, D(G(z)): 0.2386 Epoch: [12/20], Batch Num: [113/600] Discriminator Loss: 1.2633, Generator Loss: 1.3707 D(x): 0.6429, D(G(z)): 0.3357 Epoch: [12/20], Batch Num: [114/600] Discriminator Loss: 1.3586, Generator Loss: 1.7141 D(x): 0.7465, D(G(z)): 0.4333 Epoch: [12/20], Batch Num: [115/600] Discriminator Loss: 1.2180, Generator Loss: 2.4305 D(x): 0.8096, D(G(z)): 0.4391 Epoch: [12/20], Batch Num: [116/600] Discriminator Loss: 1.0183, Generator Loss: 2.3334 D(x): 0.7205, D(G(z)): 0.3196 Epoch: [12/20], Batch Num: [117/600] Discriminator Loss: 1.1657, Generator Loss: 3.0194 D(x): 0.6650, D(G(z)): 0.3122 Epoch: [12/20], Batch Num: [118/600] Discriminator Loss: 1.2466, Generator Loss: 2.3834 D(x): 0.6365, D(G(z)): 0.3089 Epoch: [12/20], Batch Num: [119/600] Discriminator Loss: 1.1172, Generator Loss: 1.8669 D(x): 0.7201, D(G(z)): 0.3303 Epoch: [12/20], Batch Num: [120/600] Discriminator Loss: 1.0532, Generator Loss: 2.2910 D(x): 0.7486, D(G(z)): 0.3665 Epoch: [12/20], Batch Num: [121/600] Discriminator Loss: 1.1735, Generator Loss: 2.0180 D(x): 0.7079, D(G(z)): 0.3868 Epoch: [12/20], Batch Num: [122/600] Discriminator Loss: 1.2690, Generator Loss: 1.8904 D(x): 0.6367, D(G(z)): 0.3703 Epoch: [12/20], Batch Num: [123/600] Discriminator Loss: 0.9971, Generator Loss: 1.7649 D(x): 0.6693, D(G(z)): 0.2981 Epoch: [12/20], Batch Num: [124/600] Discriminator Loss: 0.9639, Generator Loss: 1.8292 D(x): 0.6787, D(G(z)): 0.3211 Epoch: [12/20], Batch Num: [125/600] Discriminator Loss: 1.0371, Generator Loss: 1.5309 D(x): 0.6492, D(G(z)): 0.3043 Epoch: [12/20], Batch Num: [126/600] Discriminator Loss: 0.8099, Generator Loss: 1.2203 D(x): 0.7454, D(G(z)): 0.2911 Epoch: [12/20], Batch Num: [127/600] Discriminator Loss: 0.8524, Generator Loss: 1.2827 D(x): 0.7398, D(G(z)): 0.3215 Epoch: [12/20], Batch Num: [128/600] Discriminator Loss: 1.0517, Generator Loss: 1.6265 D(x): 0.7094, D(G(z)): 0.3913 Epoch: [12/20], Batch Num: [129/600] Discriminator Loss: 0.9837, Generator Loss: 1.6428 D(x): 0.6805, D(G(z)): 0.3277 Epoch: [12/20], Batch Num: [130/600] Discriminator Loss: 0.8559, Generator Loss: 1.8059 D(x): 0.7152, D(G(z)): 0.2912 Epoch: [12/20], Batch Num: [131/600] Discriminator Loss: 0.8810, Generator Loss: 1.9273 D(x): 0.6923, D(G(z)): 0.2568 Epoch: [12/20], Batch Num: [132/600] Discriminator Loss: 0.9662, Generator Loss: 1.8210 D(x): 0.6565, D(G(z)): 0.2787 Epoch: [12/20], Batch Num: [133/600] Discriminator Loss: 0.7552, Generator Loss: 1.5899 D(x): 0.7517, D(G(z)): 0.2811 Epoch: [12/20], Batch Num: [134/600] Discriminator Loss: 0.6594, Generator Loss: 1.7436 D(x): 0.7893, D(G(z)): 0.2713 Epoch: [12/20], Batch Num: [135/600] Discriminator Loss: 0.7174, Generator Loss: 1.5604 D(x): 0.7487, D(G(z)): 0.2452 Epoch: [12/20], Batch Num: [136/600] Discriminator Loss: 0.6978, Generator Loss: 1.6247 D(x): 0.7704, D(G(z)): 0.2652 Epoch: [12/20], Batch Num: [137/600] Discriminator Loss: 0.6939, Generator Loss: 1.8435 D(x): 0.7477, D(G(z)): 0.2477 Epoch: [12/20], Batch Num: [138/600] Discriminator Loss: 0.6114, Generator Loss: 1.8951 D(x): 0.7787, D(G(z)): 0.2248 Epoch: [12/20], Batch Num: [139/600] Discriminator Loss: 0.7157, Generator Loss: 2.1346 D(x): 0.8020, D(G(z)): 0.2954 Epoch: [12/20], Batch Num: [140/600] Discriminator Loss: 0.6158, Generator Loss: 2.3918 D(x): 0.7693, D(G(z)): 0.2134 Epoch: [12/20], Batch Num: [141/600] Discriminator Loss: 0.6711, Generator Loss: 2.3238 D(x): 0.7438, D(G(z)): 0.2024 Epoch: [12/20], Batch Num: [142/600] Discriminator Loss: 0.7366, Generator Loss: 2.2519 D(x): 0.7058, D(G(z)): 0.1966 Epoch: [12/20], Batch Num: [143/600] Discriminator Loss: 0.5063, Generator Loss: 2.2394 D(x): 0.8281, D(G(z)): 0.1855 Epoch: [12/20], Batch Num: [144/600] Discriminator Loss: 0.6949, Generator Loss: 2.1020 D(x): 0.8181, D(G(z)): 0.2677 Epoch: [12/20], Batch Num: [145/600] Discriminator Loss: 0.7173, Generator Loss: 2.2394 D(x): 0.8025, D(G(z)): 0.2644 Epoch: [12/20], Batch Num: [146/600] Discriminator Loss: 0.5533, Generator Loss: 2.7215 D(x): 0.8035, D(G(z)): 0.1929 Epoch: [12/20], Batch Num: [147/600] Discriminator Loss: 0.8123, Generator Loss: 2.4253 D(x): 0.6765, D(G(z)): 0.1682 Epoch: [12/20], Batch Num: [148/600] Discriminator Loss: 0.7687, Generator Loss: 2.1184 D(x): 0.7406, D(G(z)): 0.2245 Epoch: [12/20], Batch Num: [149/600] Discriminator Loss: 0.7339, Generator Loss: 1.8908 D(x): 0.8025, D(G(z)): 0.2332 Epoch: [12/20], Batch Num: [150/600] Discriminator Loss: 0.7392, Generator Loss: 2.3328 D(x): 0.8490, D(G(z)): 0.3357 Epoch: [12/20], Batch Num: [151/600] Discriminator Loss: 0.7945, Generator Loss: 2.7363 D(x): 0.7525, D(G(z)): 0.2211 Epoch: [12/20], Batch Num: [152/600] Discriminator Loss: 0.8271, Generator Loss: 2.7648 D(x): 0.7274, D(G(z)): 0.2100 Epoch: [12/20], Batch Num: [153/600] Discriminator Loss: 0.9510, Generator Loss: 2.0792 D(x): 0.6715, D(G(z)): 0.2009 Epoch: [12/20], Batch Num: [154/600] Discriminator Loss: 0.7793, Generator Loss: 1.6772 D(x): 0.7986, D(G(z)): 0.2603 Epoch: [12/20], Batch Num: [155/600] Discriminator Loss: 0.9368, Generator Loss: 1.9276 D(x): 0.7881, D(G(z)): 0.3144 Epoch: [12/20], Batch Num: [156/600] Discriminator Loss: 1.2210, Generator Loss: 2.0288 D(x): 0.6320, D(G(z)): 0.3179 Epoch: [12/20], Batch Num: [157/600] Discriminator Loss: 1.1003, Generator Loss: 1.8622 D(x): 0.7185, D(G(z)): 0.3258 Epoch: [12/20], Batch Num: [158/600] Discriminator Loss: 1.0349, Generator Loss: 1.7922 D(x): 0.7220, D(G(z)): 0.2979 Epoch: [12/20], Batch Num: [159/600] Discriminator Loss: 1.0536, Generator Loss: 1.8508 D(x): 0.7102, D(G(z)): 0.2812 Epoch: [12/20], Batch Num: [160/600] Discriminator Loss: 0.9027, Generator Loss: 1.8679 D(x): 0.7357, D(G(z)): 0.2590 Epoch: [12/20], Batch Num: [161/600] Discriminator Loss: 0.8446, Generator Loss: 1.7434 D(x): 0.7408, D(G(z)): 0.2470 Epoch: [12/20], Batch Num: [162/600] Discriminator Loss: 0.8464, Generator Loss: 1.8413 D(x): 0.7379, D(G(z)): 0.2821 Epoch: [12/20], Batch Num: [163/600] Discriminator Loss: 0.8860, Generator Loss: 1.6339 D(x): 0.6779, D(G(z)): 0.2260 Epoch: [12/20], Batch Num: [164/600] Discriminator Loss: 0.7391, Generator Loss: 1.4687 D(x): 0.7816, D(G(z)): 0.2730 Epoch: [12/20], Batch Num: [165/600] Discriminator Loss: 0.8690, Generator Loss: 1.5177 D(x): 0.7508, D(G(z)): 0.3068 Epoch: [12/20], Batch Num: [166/600] Discriminator Loss: 0.6659, Generator Loss: 1.6899 D(x): 0.8170, D(G(z)): 0.2990 Epoch: [12/20], Batch Num: [167/600] Discriminator Loss: 0.8161, Generator Loss: 2.0122 D(x): 0.7690, D(G(z)): 0.2736 Epoch: [12/20], Batch Num: [168/600] Discriminator Loss: 0.6794, Generator Loss: 2.0785 D(x): 0.7534, D(G(z)): 0.2115 Epoch: [12/20], Batch Num: [169/600] Discriminator Loss: 0.6477, Generator Loss: 2.1045 D(x): 0.7211, D(G(z)): 0.1686 Epoch: [12/20], Batch Num: [170/600] Discriminator Loss: 0.6990, Generator Loss: 2.0447 D(x): 0.7314, D(G(z)): 0.1983 Epoch: [12/20], Batch Num: [171/600] Discriminator Loss: 0.5981, Generator Loss: 1.8375 D(x): 0.8069, D(G(z)): 0.2021 Epoch: [12/20], Batch Num: [172/600] Discriminator Loss: 0.4711, Generator Loss: 2.0725 D(x): 0.8684, D(G(z)): 0.2144 Epoch: [12/20], Batch Num: [173/600] Discriminator Loss: 0.6191, Generator Loss: 2.1604 D(x): 0.8576, D(G(z)): 0.2650 Epoch: [12/20], Batch Num: [174/600] Discriminator Loss: 0.6076, Generator Loss: 2.2763 D(x): 0.7961, D(G(z)): 0.2022 Epoch: [12/20], Batch Num: [175/600] Discriminator Loss: 0.5036, Generator Loss: 2.7934 D(x): 0.8408, D(G(z)): 0.1778 Epoch: [12/20], Batch Num: [176/600] Discriminator Loss: 0.6116, Generator Loss: 2.5817 D(x): 0.7506, D(G(z)): 0.1657 Epoch: [12/20], Batch Num: [177/600] Discriminator Loss: 0.5459, Generator Loss: 2.3608 D(x): 0.7761, D(G(z)): 0.1407 Epoch: [12/20], Batch Num: [178/600] Discriminator Loss: 0.4172, Generator Loss: 2.3124 D(x): 0.8602, D(G(z)): 0.1526 Epoch: [12/20], Batch Num: [179/600] Discriminator Loss: 0.5546, Generator Loss: 2.1074 D(x): 0.8622, D(G(z)): 0.2245 Epoch: [12/20], Batch Num: [180/600] Discriminator Loss: 0.5249, Generator Loss: 2.2658 D(x): 0.8302, D(G(z)): 0.1775 Epoch: [12/20], Batch Num: [181/600] Discriminator Loss: 0.4737, Generator Loss: 2.2650 D(x): 0.8698, D(G(z)): 0.1865 Epoch: [12/20], Batch Num: [182/600] Discriminator Loss: 0.4859, Generator Loss: 2.5196 D(x): 0.8860, D(G(z)): 0.2067 Epoch: [12/20], Batch Num: [183/600] Discriminator Loss: 0.6361, Generator Loss: 2.7435 D(x): 0.7938, D(G(z)): 0.2046 Epoch: [12/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [12/20], Batch Num: [200/600] Discriminator Loss: 1.0241, Generator Loss: 1.4520 D(x): 0.8303, D(G(z)): 0.3773 Epoch: [12/20], Batch Num: [201/600] Discriminator Loss: 0.8270, Generator Loss: 1.6288 D(x): 0.8308, D(G(z)): 0.3552 Epoch: [12/20], Batch Num: [202/600] Discriminator Loss: 0.7742, Generator Loss: 2.0896 D(x): 0.8108, D(G(z)): 0.2928 Epoch: [12/20], Batch Num: [203/600] Discriminator Loss: 0.8469, Generator Loss: 2.2795 D(x): 0.6946, D(G(z)): 0.2054 Epoch: [12/20], Batch Num: [204/600] Discriminator Loss: 0.8630, Generator Loss: 1.9880 D(x): 0.6721, D(G(z)): 0.1822 Epoch: [12/20], Batch Num: [205/600] Discriminator Loss: 0.8606, Generator Loss: 1.8322 D(x): 0.6891, D(G(z)): 0.2008 Epoch: [12/20], Batch Num: [206/600] Discriminator Loss: 0.6416, Generator Loss: 1.4402 D(x): 0.7879, D(G(z)): 0.2368 Epoch: [12/20], Batch Num: [207/600] Discriminator Loss: 0.8648, Generator Loss: 1.3998 D(x): 0.8096, D(G(z)): 0.3471 Epoch: [12/20], Batch Num: [208/600] Discriminator Loss: 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0.2425 Epoch: [12/20], Batch Num: [217/600] Discriminator Loss: 0.5350, Generator Loss: 2.0453 D(x): 0.8332, D(G(z)): 0.2174 Epoch: [12/20], Batch Num: [218/600] Discriminator Loss: 0.5561, Generator Loss: 2.2662 D(x): 0.8203, D(G(z)): 0.2059 Epoch: [12/20], Batch Num: [219/600] Discriminator Loss: 0.6937, Generator Loss: 2.3292 D(x): 0.7470, D(G(z)): 0.1765 Epoch: [12/20], Batch Num: [220/600] Discriminator Loss: 0.5695, Generator Loss: 2.0758 D(x): 0.7936, D(G(z)): 0.1722 Epoch: [12/20], Batch Num: [221/600] Discriminator Loss: 0.6334, Generator Loss: 2.0000 D(x): 0.7583, D(G(z)): 0.1936 Epoch: [12/20], Batch Num: [222/600] Discriminator Loss: 0.5946, Generator Loss: 1.7544 D(x): 0.8119, D(G(z)): 0.2318 Epoch: [12/20], Batch Num: [223/600] Discriminator Loss: 0.6340, Generator Loss: 1.9187 D(x): 0.8746, D(G(z)): 0.3071 Epoch: [12/20], Batch Num: [224/600] Discriminator Loss: 0.7129, Generator Loss: 2.0269 D(x): 0.8119, D(G(z)): 0.2545 Epoch: [12/20], Batch Num: [225/600] Discriminator Loss: 0.6660, Generator Loss: 2.1901 D(x): 0.7952, D(G(z)): 0.2391 Epoch: [12/20], Batch Num: [226/600] Discriminator Loss: 0.7725, Generator Loss: 2.4090 D(x): 0.7170, D(G(z)): 0.2031 Epoch: [12/20], Batch Num: [227/600] Discriminator Loss: 0.6861, Generator Loss: 2.2950 D(x): 0.7430, D(G(z)): 0.1940 Epoch: [12/20], Batch Num: [228/600] Discriminator Loss: 0.5409, Generator Loss: 2.1522 D(x): 0.8011, D(G(z)): 0.1751 Epoch: [12/20], Batch Num: [229/600] Discriminator Loss: 0.7420, Generator Loss: 1.8114 D(x): 0.7586, D(G(z)): 0.2345 Epoch: [12/20], Batch Num: [230/600] Discriminator Loss: 0.6640, Generator Loss: 1.6292 D(x): 0.8012, D(G(z)): 0.2522 Epoch: [12/20], Batch Num: [231/600] Discriminator Loss: 0.6934, Generator Loss: 1.6832 D(x): 0.8247, D(G(z)): 0.2780 Epoch: [12/20], Batch Num: [232/600] Discriminator Loss: 0.7132, Generator Loss: 2.0157 D(x): 0.8057, D(G(z)): 0.2733 Epoch: [12/20], Batch Num: [233/600] Discriminator Loss: 0.8149, Generator Loss: 2.1071 D(x): 0.7369, D(G(z)): 0.2487 Epoch: [12/20], Batch Num: [234/600] Discriminator Loss: 0.6305, Generator Loss: 2.0743 D(x): 0.7527, D(G(z)): 0.1866 Epoch: [12/20], Batch Num: [235/600] Discriminator Loss: 0.9240, Generator Loss: 1.9963 D(x): 0.6933, D(G(z)): 0.2462 Epoch: [12/20], Batch Num: [236/600] Discriminator Loss: 0.7084, Generator Loss: 1.9541 D(x): 0.8113, D(G(z)): 0.2590 Epoch: [12/20], Batch Num: [237/600] Discriminator Loss: 0.7999, Generator Loss: 2.3443 D(x): 0.7616, D(G(z)): 0.2482 Epoch: [12/20], Batch Num: [238/600] Discriminator Loss: 0.8735, Generator Loss: 2.1102 D(x): 0.7329, D(G(z)): 0.2030 Epoch: [12/20], Batch Num: [239/600] Discriminator Loss: 0.7929, Generator Loss: 2.2438 D(x): 0.7749, D(G(z)): 0.2658 Epoch: [12/20], Batch Num: [240/600] Discriminator Loss: 0.8476, Generator Loss: 2.3123 D(x): 0.7441, D(G(z)): 0.2629 Epoch: [12/20], Batch Num: [241/600] Discriminator Loss: 0.9400, Generator Loss: 2.3283 D(x): 0.6971, D(G(z)): 0.2641 Epoch: [12/20], Batch Num: [242/600] Discriminator Loss: 0.6421, Generator Loss: 2.2086 D(x): 0.7677, D(G(z)): 0.2121 Epoch: [12/20], Batch Num: [243/600] Discriminator Loss: 0.8877, Generator Loss: 2.2705 D(x): 0.7298, D(G(z)): 0.2878 Epoch: [12/20], Batch Num: [244/600] Discriminator Loss: 0.6660, Generator Loss: 2.2013 D(x): 0.7642, D(G(z)): 0.1890 Epoch: [12/20], Batch Num: [245/600] Discriminator Loss: 0.6890, Generator Loss: 2.0059 D(x): 0.7332, D(G(z)): 0.1794 Epoch: [12/20], Batch Num: [246/600] Discriminator Loss: 0.7223, Generator Loss: 2.2877 D(x): 0.8099, D(G(z)): 0.2768 Epoch: [12/20], Batch Num: [247/600] Discriminator Loss: 0.7628, Generator Loss: 2.1338 D(x): 0.7432, D(G(z)): 0.2257 Epoch: [12/20], Batch Num: [248/600] Discriminator Loss: 0.5209, Generator Loss: 2.1511 D(x): 0.8244, D(G(z)): 0.1712 Epoch: [12/20], Batch Num: [249/600] Discriminator Loss: 0.6637, Generator Loss: 2.3502 D(x): 0.7926, D(G(z)): 0.2439 Epoch: [12/20], Batch Num: [250/600] Discriminator Loss: 0.4793, Generator Loss: 2.5338 D(x): 0.8512, D(G(z)): 0.1931 Epoch: [12/20], Batch Num: [251/600] Discriminator Loss: 0.6457, Generator Loss: 2.2780 D(x): 0.7568, D(G(z)): 0.1770 Epoch: [12/20], Batch Num: [252/600] Discriminator Loss: 0.5741, Generator Loss: 2.1105 D(x): 0.7557, D(G(z)): 0.1700 Epoch: [12/20], Batch Num: [253/600] Discriminator Loss: 0.6184, Generator Loss: 2.1368 D(x): 0.8118, D(G(z)): 0.2279 Epoch: [12/20], Batch Num: [254/600] Discriminator Loss: 0.6057, Generator Loss: 2.0760 D(x): 0.8108, D(G(z)): 0.2296 Epoch: [12/20], Batch Num: [255/600] Discriminator Loss: 0.5336, Generator Loss: 2.1237 D(x): 0.8262, D(G(z)): 0.2277 Epoch: [12/20], Batch Num: [256/600] Discriminator Loss: 0.5875, Generator Loss: 2.1661 D(x): 0.8257, D(G(z)): 0.2265 Epoch: [12/20], Batch Num: [257/600] Discriminator Loss: 0.8061, Generator Loss: 2.8463 D(x): 0.7341, D(G(z)): 0.2239 Epoch: [12/20], Batch Num: [258/600] Discriminator Loss: 0.8131, Generator Loss: 2.4620 D(x): 0.7175, D(G(z)): 0.2100 Epoch: [12/20], Batch Num: [259/600] Discriminator Loss: 0.6124, Generator Loss: 2.6297 D(x): 0.7822, D(G(z)): 0.1701 Epoch: [12/20], Batch Num: [260/600] Discriminator Loss: 0.6641, Generator Loss: 2.5246 D(x): 0.8273, D(G(z)): 0.2436 Epoch: [12/20], Batch Num: [261/600] Discriminator Loss: 0.7835, Generator Loss: 2.2012 D(x): 0.7348, D(G(z)): 0.2166 Epoch: [12/20], Batch Num: [262/600] Discriminator Loss: 0.8148, Generator Loss: 2.1366 D(x): 0.7233, D(G(z)): 0.2095 Epoch: [12/20], Batch Num: [263/600] Discriminator Loss: 0.7848, Generator Loss: 1.8258 D(x): 0.7469, D(G(z)): 0.2815 Epoch: [12/20], Batch Num: [264/600] Discriminator Loss: 0.8359, Generator Loss: 1.9737 D(x): 0.8063, D(G(z)): 0.3234 Epoch: [12/20], Batch Num: [265/600] Discriminator Loss: 0.8379, Generator Loss: 1.9736 D(x): 0.7451, D(G(z)): 0.2681 Epoch: [12/20], Batch Num: [266/600] Discriminator Loss: 0.7996, Generator Loss: 2.3185 D(x): 0.7666, D(G(z)): 0.2679 Epoch: [12/20], Batch Num: [267/600] Discriminator Loss: 1.2181, Generator Loss: 2.1372 D(x): 0.6066, D(G(z)): 0.2239 Epoch: [12/20], Batch Num: [268/600] Discriminator Loss: 1.1216, Generator Loss: 1.5706 D(x): 0.6706, D(G(z)): 0.2639 Epoch: [12/20], Batch Num: [269/600] Discriminator Loss: 1.2033, Generator Loss: 1.2182 D(x): 0.6838, D(G(z)): 0.3693 Epoch: [12/20], Batch Num: [270/600] Discriminator Loss: 1.3619, Generator Loss: 1.6591 D(x): 0.6957, D(G(z)): 0.4148 Epoch: [12/20], Batch Num: [271/600] Discriminator Loss: 1.1758, Generator Loss: 1.7603 D(x): 0.6741, D(G(z)): 0.3562 Epoch: [12/20], Batch Num: [272/600] Discriminator Loss: 1.0275, Generator Loss: 1.8143 D(x): 0.6614, D(G(z)): 0.2932 Epoch: [12/20], Batch Num: [273/600] Discriminator Loss: 1.0231, Generator Loss: 1.6898 D(x): 0.6417, D(G(z)): 0.2547 Epoch: [12/20], Batch Num: [274/600] Discriminator Loss: 1.2018, Generator Loss: 1.4807 D(x): 0.5712, D(G(z)): 0.2539 Epoch: [12/20], Batch Num: [275/600] Discriminator Loss: 1.0020, Generator Loss: 1.3917 D(x): 0.7114, D(G(z)): 0.3265 Epoch: [12/20], Batch Num: [276/600] Discriminator Loss: 0.9551, Generator Loss: 1.4173 D(x): 0.7757, D(G(z)): 0.3994 Epoch: [12/20], Batch Num: [277/600] Discriminator Loss: 0.7363, Generator Loss: 1.8809 D(x): 0.8012, D(G(z)): 0.3141 Epoch: [12/20], Batch Num: [278/600] Discriminator Loss: 0.8197, Generator Loss: 2.0887 D(x): 0.6762, D(G(z)): 0.2124 Epoch: [12/20], Batch Num: [279/600] Discriminator Loss: 0.7608, Generator Loss: 2.2102 D(x): 0.6825, D(G(z)): 0.1691 Epoch: [12/20], Batch Num: [280/600] Discriminator Loss: 0.7177, Generator Loss: 1.9907 D(x): 0.7120, D(G(z)): 0.1821 Epoch: [12/20], Batch Num: [281/600] Discriminator Loss: 0.7209, Generator Loss: 1.6932 D(x): 0.7342, D(G(z)): 0.2011 Epoch: [12/20], Batch Num: [282/600] Discriminator Loss: 0.6876, Generator Loss: 1.5286 D(x): 0.7860, D(G(z)): 0.2589 Epoch: [12/20], Batch Num: [283/600] Discriminator Loss: 0.6108, Generator Loss: 1.5059 D(x): 0.8490, D(G(z)): 0.2811 Epoch: [12/20], Batch Num: [284/600] Discriminator Loss: 0.5794, Generator Loss: 1.9411 D(x): 0.8988, D(G(z)): 0.2940 Epoch: [12/20], Batch Num: [285/600] Discriminator Loss: 0.5668, Generator Loss: 2.4593 D(x): 0.8377, D(G(z)): 0.2347 Epoch: [12/20], Batch Num: [286/600] Discriminator Loss: 0.5298, Generator Loss: 2.8148 D(x): 0.7858, D(G(z)): 0.1463 Epoch: [12/20], Batch Num: [287/600] Discriminator Loss: 0.5325, Generator Loss: 2.8338 D(x): 0.7645, D(G(z)): 0.1213 Epoch: [12/20], Batch Num: [288/600] Discriminator Loss: 0.5395, Generator Loss: 2.6519 D(x): 0.7464, D(G(z)): 0.1163 Epoch: [12/20], Batch Num: [289/600] Discriminator Loss: 0.5505, Generator Loss: 2.3775 D(x): 0.8011, D(G(z)): 0.1445 Epoch: [12/20], Batch Num: [290/600] Discriminator Loss: 0.4993, Generator Loss: 2.1081 D(x): 0.8550, D(G(z)): 0.1987 Epoch: [12/20], Batch Num: [291/600] Discriminator Loss: 0.4800, Generator Loss: 2.2629 D(x): 0.8991, D(G(z)): 0.2360 Epoch: [12/20], Batch Num: [292/600] Discriminator Loss: 0.4667, Generator Loss: 2.4991 D(x): 0.9037, D(G(z)): 0.2105 Epoch: [12/20], Batch Num: [293/600] Discriminator Loss: 0.5153, Generator Loss: 2.6038 D(x): 0.8663, D(G(z)): 0.1780 Epoch: [12/20], Batch Num: [294/600] Discriminator Loss: 0.5148, Generator Loss: 3.1176 D(x): 0.8122, D(G(z)): 0.1498 Epoch: [12/20], Batch Num: [295/600] Discriminator Loss: 0.5246, Generator Loss: 2.7318 D(x): 0.7972, D(G(z)): 0.1383 Epoch: [12/20], Batch Num: [296/600] Discriminator Loss: 0.5653, Generator Loss: 2.4927 D(x): 0.8244, D(G(z)): 0.1545 Epoch: [12/20], Batch Num: [297/600] Discriminator Loss: 0.5454, Generator Loss: 2.3806 D(x): 0.8407, D(G(z)): 0.1968 Epoch: [12/20], Batch Num: [298/600] Discriminator Loss: 0.6898, Generator Loss: 2.0548 D(x): 0.8064, D(G(z)): 0.2017 Epoch: [12/20], Batch Num: [299/600] Discriminator Loss: 0.9041, Generator Loss: 2.1677 D(x): 0.7685, D(G(z)): 0.2590 Epoch: 12, Batch Num: [300/600]
Epoch: [12/20], Batch Num: [300/600] Discriminator Loss: 0.6319, Generator Loss: 2.2783 D(x): 0.8429, D(G(z)): 0.2345 Epoch: [12/20], Batch Num: [301/600] Discriminator Loss: 0.8171, Generator Loss: 2.3726 D(x): 0.7419, D(G(z)): 0.2483 Epoch: [12/20], Batch Num: [302/600] Discriminator Loss: 0.6769, Generator Loss: 2.2288 D(x): 0.7870, D(G(z)): 0.2098 Epoch: [12/20], Batch Num: [303/600] Discriminator Loss: 0.7979, Generator Loss: 2.4811 D(x): 0.7603, D(G(z)): 0.2063 Epoch: [12/20], Batch Num: [304/600] Discriminator Loss: 0.8967, Generator Loss: 2.1345 D(x): 0.7402, D(G(z)): 0.2271 Epoch: [12/20], Batch Num: [305/600] Discriminator Loss: 0.6353, Generator Loss: 1.9585 D(x): 0.8075, D(G(z)): 0.1839 Epoch: [12/20], Batch Num: [306/600] Discriminator Loss: 0.8551, Generator Loss: 1.9262 D(x): 0.7978, D(G(z)): 0.2687 Epoch: [12/20], Batch Num: [307/600] Discriminator Loss: 0.9097, Generator Loss: 1.8457 D(x): 0.7682, D(G(z)): 0.2949 Epoch: [12/20], Batch Num: [308/600] Discriminator Loss: 1.0464, Generator Loss: 2.0253 D(x): 0.7732, D(G(z)): 0.3242 Epoch: [12/20], Batch Num: [309/600] Discriminator Loss: 0.9076, Generator Loss: 2.1490 D(x): 0.7466, D(G(z)): 0.2472 Epoch: [12/20], Batch Num: [310/600] Discriminator Loss: 0.8141, Generator Loss: 2.1710 D(x): 0.7456, D(G(z)): 0.2298 Epoch: [12/20], Batch Num: [311/600] Discriminator Loss: 0.9243, Generator Loss: 2.0653 D(x): 0.7182, D(G(z)): 0.2334 Epoch: [12/20], Batch Num: [312/600] Discriminator Loss: 1.0000, Generator Loss: 1.6606 D(x): 0.6871, D(G(z)): 0.2582 Epoch: [12/20], Batch Num: [313/600] Discriminator Loss: 0.9411, Generator Loss: 1.5351 D(x): 0.7803, D(G(z)): 0.3114 Epoch: [12/20], Batch Num: [314/600] Discriminator Loss: 0.7824, Generator Loss: 1.8863 D(x): 0.8180, D(G(z)): 0.3338 Epoch: [12/20], Batch Num: [315/600] Discriminator Loss: 0.7763, Generator Loss: 2.1204 D(x): 0.7532, D(G(z)): 0.2411 Epoch: [12/20], Batch Num: [316/600] Discriminator Loss: 0.7015, Generator Loss: 2.3288 D(x): 0.7597, D(G(z)): 0.1949 Epoch: [12/20], Batch Num: [317/600] Discriminator Loss: 0.6782, Generator Loss: 2.1829 D(x): 0.7756, D(G(z)): 0.2113 Epoch: [12/20], Batch Num: [318/600] Discriminator Loss: 0.7695, Generator Loss: 2.0154 D(x): 0.7171, D(G(z)): 0.2122 Epoch: [12/20], Batch Num: [319/600] Discriminator Loss: 0.5541, Generator Loss: 2.0555 D(x): 0.8115, D(G(z)): 0.2118 Epoch: [12/20], Batch Num: [320/600] Discriminator Loss: 0.6753, Generator Loss: 2.2558 D(x): 0.7806, D(G(z)): 0.2356 Epoch: [12/20], Batch Num: [321/600] Discriminator Loss: 0.6053, Generator Loss: 1.9839 D(x): 0.8409, D(G(z)): 0.2563 Epoch: [12/20], Batch Num: [322/600] Discriminator Loss: 0.5733, Generator Loss: 2.1596 D(x): 0.8183, D(G(z)): 0.2358 Epoch: [12/20], Batch Num: [323/600] Discriminator Loss: 0.6490, Generator Loss: 1.9440 D(x): 0.7785, D(G(z)): 0.1807 Epoch: [12/20], Batch Num: [324/600] Discriminator Loss: 0.5450, Generator Loss: 2.0718 D(x): 0.8212, D(G(z)): 0.1939 Epoch: [12/20], Batch Num: [325/600] Discriminator Loss: 0.5451, Generator Loss: 2.1812 D(x): 0.8125, D(G(z)): 0.2041 Epoch: [12/20], Batch Num: [326/600] Discriminator Loss: 0.4315, Generator Loss: 2.5871 D(x): 0.8437, D(G(z)): 0.1741 Epoch: [12/20], Batch Num: [327/600] Discriminator Loss: 0.6053, Generator Loss: 2.5828 D(x): 0.8163, D(G(z)): 0.2346 Epoch: [12/20], Batch Num: [328/600] Discriminator Loss: 0.5012, Generator Loss: 2.6196 D(x): 0.8221, D(G(z)): 0.1767 Epoch: [12/20], Batch Num: [329/600] Discriminator Loss: 0.6306, Generator Loss: 2.8099 D(x): 0.8454, D(G(z)): 0.2307 Epoch: [12/20], Batch Num: [330/600] Discriminator Loss: 0.6136, Generator Loss: 2.7908 D(x): 0.7834, D(G(z)): 0.1583 Epoch: [12/20], Batch Num: [331/600] Discriminator Loss: 0.5853, Generator Loss: 2.6828 D(x): 0.7675, D(G(z)): 0.1300 Epoch: [12/20], Batch Num: [332/600] Discriminator Loss: 0.4953, Generator Loss: 2.2717 D(x): 0.8400, D(G(z)): 0.1732 Epoch: [12/20], Batch Num: [333/600] Discriminator Loss: 0.5226, Generator Loss: 2.3551 D(x): 0.8584, D(G(z)): 0.2102 Epoch: [12/20], Batch Num: [334/600] Discriminator Loss: 0.7053, Generator Loss: 2.4339 D(x): 0.8062, D(G(z)): 0.2361 Epoch: [12/20], Batch Num: [335/600] Discriminator Loss: 0.5597, Generator Loss: 2.4448 D(x): 0.8185, D(G(z)): 0.2069 Epoch: [12/20], Batch Num: [336/600] Discriminator Loss: 0.5185, Generator Loss: 2.7607 D(x): 0.8504, D(G(z)): 0.2052 Epoch: [12/20], Batch Num: [337/600] Discriminator Loss: 0.6069, Generator Loss: 3.2642 D(x): 0.8582, D(G(z)): 0.2119 Epoch: [12/20], Batch Num: [338/600] Discriminator Loss: 0.8035, Generator Loss: 3.1124 D(x): 0.7028, D(G(z)): 0.1616 Epoch: [12/20], Batch Num: [339/600] Discriminator Loss: 0.6867, Generator Loss: 2.5798 D(x): 0.7505, D(G(z)): 0.1451 Epoch: [12/20], Batch Num: [340/600] Discriminator Loss: 0.6338, Generator Loss: 2.5999 D(x): 0.8249, D(G(z)): 0.2064 Epoch: [12/20], Batch Num: [341/600] Discriminator Loss: 0.6831, Generator Loss: 2.3900 D(x): 0.8189, D(G(z)): 0.2410 Epoch: [12/20], Batch Num: 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1.9511 D(x): 0.7756, D(G(z)): 0.3293 Epoch: [12/20], Batch Num: [351/600] Discriminator Loss: 0.8959, Generator Loss: 2.0080 D(x): 0.7670, D(G(z)): 0.3336 Epoch: [12/20], Batch Num: [352/600] Discriminator Loss: 0.8345, Generator Loss: 1.9269 D(x): 0.7512, D(G(z)): 0.2616 Epoch: [12/20], Batch Num: [353/600] Discriminator Loss: 0.9239, Generator Loss: 1.9966 D(x): 0.7159, D(G(z)): 0.2513 Epoch: [12/20], Batch Num: [354/600] Discriminator Loss: 0.7629, Generator Loss: 2.2996 D(x): 0.7550, D(G(z)): 0.2760 Epoch: [12/20], Batch Num: [355/600] Discriminator Loss: 0.8619, Generator Loss: 1.9870 D(x): 0.7146, D(G(z)): 0.2694 Epoch: [12/20], Batch Num: [356/600] Discriminator Loss: 0.8216, Generator Loss: 1.9086 D(x): 0.7479, D(G(z)): 0.2786 Epoch: [12/20], Batch Num: [357/600] Discriminator Loss: 0.8509, Generator Loss: 1.9692 D(x): 0.7137, D(G(z)): 0.2409 Epoch: [12/20], Batch Num: [358/600] Discriminator Loss: 0.9927, Generator Loss: 1.8874 D(x): 0.6704, D(G(z)): 0.2821 Epoch: [12/20], Batch Num: [359/600] Discriminator Loss: 0.8817, Generator Loss: 1.7568 D(x): 0.7180, D(G(z)): 0.2800 Epoch: [12/20], Batch Num: [360/600] Discriminator Loss: 0.8478, Generator Loss: 1.6926 D(x): 0.7448, D(G(z)): 0.3216 Epoch: [12/20], Batch Num: [361/600] Discriminator Loss: 0.8742, Generator Loss: 1.6649 D(x): 0.7407, D(G(z)): 0.3109 Epoch: [12/20], Batch Num: [362/600] Discriminator Loss: 0.9965, Generator Loss: 1.7067 D(x): 0.7092, D(G(z)): 0.3154 Epoch: [12/20], Batch Num: [363/600] Discriminator Loss: 0.9221, Generator Loss: 1.8712 D(x): 0.7287, D(G(z)): 0.3215 Epoch: [12/20], Batch Num: [364/600] Discriminator Loss: 0.8421, Generator Loss: 1.7076 D(x): 0.7407, D(G(z)): 0.2841 Epoch: [12/20], Batch Num: [365/600] Discriminator Loss: 0.9052, Generator Loss: 1.9911 D(x): 0.6956, D(G(z)): 0.2646 Epoch: [12/20], Batch Num: [366/600] Discriminator Loss: 0.9104, Generator Loss: 1.6592 D(x): 0.6678, D(G(z)): 0.2477 Epoch: [12/20], Batch Num: [367/600] Discriminator Loss: 0.8618, Generator Loss: 1.5879 D(x): 0.6528, D(G(z)): 0.2172 Epoch: [12/20], Batch Num: [368/600] Discriminator Loss: 0.8055, Generator Loss: 1.0824 D(x): 0.7591, D(G(z)): 0.3198 Epoch: [12/20], Batch Num: [369/600] Discriminator Loss: 1.1877, Generator Loss: 1.1232 D(x): 0.7499, D(G(z)): 0.4619 Epoch: [12/20], Batch Num: [370/600] Discriminator Loss: 0.8668, Generator Loss: 1.5787 D(x): 0.8054, D(G(z)): 0.3792 Epoch: [12/20], Batch Num: [371/600] Discriminator Loss: 0.8151, Generator Loss: 2.0852 D(x): 0.7401, D(G(z)): 0.3007 Epoch: [12/20], Batch Num: [372/600] Discriminator Loss: 0.8008, Generator Loss: 2.0343 D(x): 0.6771, D(G(z)): 0.2163 Epoch: [12/20], Batch Num: [373/600] Discriminator Loss: 0.9786, Generator Loss: 2.0749 D(x): 0.6189, D(G(z)): 0.2108 Epoch: [12/20], Batch Num: [374/600] Discriminator Loss: 0.7688, Generator Loss: 1.8423 D(x): 0.6760, D(G(z)): 0.1878 Epoch: [12/20], Batch Num: [375/600] Discriminator Loss: 0.8645, Generator Loss: 1.5814 D(x): 0.7036, D(G(z)): 0.2735 Epoch: [12/20], Batch Num: [376/600] Discriminator Loss: 0.7684, Generator Loss: 1.3906 D(x): 0.7819, D(G(z)): 0.3090 Epoch: [12/20], Batch Num: [377/600] Discriminator Loss: 0.7552, Generator Loss: 1.3900 D(x): 0.8120, D(G(z)): 0.3333 Epoch: [12/20], Batch Num: [378/600] Discriminator Loss: 0.7710, Generator Loss: 1.7570 D(x): 0.8248, D(G(z)): 0.3524 Epoch: [12/20], Batch Num: [379/600] Discriminator Loss: 0.6811, Generator Loss: 1.7939 D(x): 0.7959, D(G(z)): 0.2817 Epoch: [12/20], Batch Num: [380/600] Discriminator Loss: 0.7042, Generator Loss: 2.1629 D(x): 0.7237, D(G(z)): 0.2034 Epoch: [12/20], Batch Num: [381/600] Discriminator Loss: 0.6943, Generator Loss: 2.3443 D(x): 0.7193, D(G(z)): 0.1892 Epoch: [12/20], Batch Num: [382/600] Discriminator Loss: 0.8533, Generator Loss: 2.0672 D(x): 0.6697, D(G(z)): 0.1859 Epoch: [12/20], Batch Num: [383/600] Discriminator Loss: 0.7469, Generator Loss: 1.7338 D(x): 0.7000, D(G(z)): 0.1892 Epoch: [12/20], Batch Num: [384/600] Discriminator Loss: 0.5693, Generator Loss: 1.5059 D(x): 0.8227, D(G(z)): 0.2342 Epoch: [12/20], Batch Num: [385/600] Discriminator Loss: 0.7006, Generator Loss: 1.5388 D(x): 0.8286, D(G(z)): 0.3015 Epoch: [12/20], Batch Num: [386/600] Discriminator Loss: 0.9254, Generator Loss: 1.6253 D(x): 0.8046, D(G(z)): 0.3554 Epoch: [12/20], Batch Num: [387/600] Discriminator Loss: 0.7776, Generator Loss: 1.9473 D(x): 0.7502, D(G(z)): 0.2577 Epoch: [12/20], Batch Num: [388/600] Discriminator Loss: 0.6438, Generator Loss: 2.1339 D(x): 0.7757, D(G(z)): 0.2250 Epoch: [12/20], Batch Num: [389/600] Discriminator Loss: 0.6816, Generator Loss: 2.3228 D(x): 0.7808, D(G(z)): 0.2173 Epoch: [12/20], Batch Num: [390/600] Discriminator Loss: 0.8408, Generator Loss: 2.0591 D(x): 0.6918, D(G(z)): 0.2234 Epoch: [12/20], Batch Num: [391/600] Discriminator Loss: 0.9048, Generator Loss: 2.2122 D(x): 0.6861, D(G(z)): 0.2205 Epoch: [12/20], Batch Num: [392/600] Discriminator Loss: 0.7979, Generator Loss: 1.8480 D(x): 0.7167, D(G(z)): 0.2347 Epoch: [12/20], Batch Num: [393/600] Discriminator Loss: 0.7847, Generator Loss: 1.6047 D(x): 0.7678, D(G(z)): 0.2767 Epoch: [12/20], Batch Num: [394/600] Discriminator Loss: 0.8790, Generator Loss: 1.8887 D(x): 0.7619, D(G(z)): 0.2971 Epoch: [12/20], Batch Num: [395/600] Discriminator Loss: 0.8232, Generator Loss: 1.7559 D(x): 0.7898, D(G(z)): 0.3316 Epoch: [12/20], Batch Num: [396/600] Discriminator Loss: 0.8488, Generator Loss: 1.9659 D(x): 0.7180, D(G(z)): 0.2710 Epoch: [12/20], Batch Num: [397/600] Discriminator Loss: 0.7854, Generator Loss: 1.9445 D(x): 0.7099, D(G(z)): 0.2183 Epoch: [12/20], Batch Num: [398/600] Discriminator Loss: 0.8607, Generator Loss: 1.7607 D(x): 0.7003, D(G(z)): 0.2412 Epoch: [12/20], Batch Num: [399/600] Discriminator Loss: 0.8458, Generator Loss: 1.6335 D(x): 0.7419, D(G(z)): 0.2611 Epoch: 12, Batch Num: [400/600]
Epoch: [12/20], Batch Num: [400/600] Discriminator Loss: 0.9269, Generator Loss: 1.5301 D(x): 0.7033, D(G(z)): 0.2866 Epoch: [12/20], Batch Num: [401/600] Discriminator Loss: 0.8515, Generator Loss: 1.6665 D(x): 0.8181, D(G(z)): 0.3414 Epoch: [12/20], Batch Num: [402/600] Discriminator Loss: 1.0764, Generator Loss: 1.5787 D(x): 0.7062, D(G(z)): 0.3437 Epoch: [12/20], Batch Num: [403/600] Discriminator Loss: 0.9649, Generator Loss: 1.8656 D(x): 0.7214, D(G(z)): 0.3059 Epoch: [12/20], Batch Num: [404/600] Discriminator Loss: 0.7796, Generator Loss: 2.0488 D(x): 0.7198, D(G(z)): 0.2353 Epoch: [12/20], Batch Num: [405/600] Discriminator Loss: 0.9006, Generator Loss: 1.6834 D(x): 0.6585, D(G(z)): 0.2089 Epoch: [12/20], Batch Num: [406/600] Discriminator Loss: 0.8504, Generator Loss: 1.7217 D(x): 0.7068, D(G(z)): 0.2251 Epoch: [12/20], Batch Num: [407/600] Discriminator Loss: 0.8149, Generator Loss: 1.5541 D(x): 0.7368, D(G(z)): 0.2613 Epoch: [12/20], Batch Num: [408/600] Discriminator Loss: 1.0822, Generator Loss: 1.4156 D(x): 0.7334, D(G(z)): 0.3395 Epoch: [12/20], Batch Num: [409/600] Discriminator Loss: 0.8650, Generator Loss: 1.4795 D(x): 0.7732, D(G(z)): 0.3101 Epoch: [12/20], Batch Num: [410/600] Discriminator Loss: 0.8616, Generator Loss: 1.5460 D(x): 0.7772, D(G(z)): 0.3331 Epoch: [12/20], Batch Num: [411/600] Discriminator Loss: 0.8696, Generator Loss: 1.8808 D(x): 0.7502, D(G(z)): 0.3115 Epoch: [12/20], Batch Num: [412/600] Discriminator Loss: 0.6955, Generator Loss: 1.8072 D(x): 0.7429, D(G(z)): 0.2262 Epoch: [12/20], Batch Num: [413/600] Discriminator Loss: 0.9780, Generator Loss: 2.1047 D(x): 0.6644, D(G(z)): 0.2517 Epoch: [12/20], Batch Num: [414/600] Discriminator Loss: 0.8325, Generator Loss: 1.8939 D(x): 0.6845, D(G(z)): 0.1906 Epoch: [12/20], Batch Num: [415/600] Discriminator Loss: 0.7378, Generator Loss: 1.9845 D(x): 0.7590, D(G(z)): 0.2391 Epoch: [12/20], Batch Num: [416/600] Discriminator Loss: 0.7473, Generator Loss: 1.6421 D(x): 0.7491, D(G(z)): 0.2424 Epoch: [12/20], Batch Num: [417/600] Discriminator Loss: 0.7228, Generator Loss: 1.7648 D(x): 0.8107, D(G(z)): 0.3004 Epoch: [12/20], Batch Num: [418/600] Discriminator Loss: 0.5914, Generator Loss: 1.9064 D(x): 0.8542, D(G(z)): 0.2671 Epoch: [12/20], Batch Num: [419/600] Discriminator Loss: 0.6380, Generator Loss: 2.0379 D(x): 0.7717, D(G(z)): 0.1977 Epoch: [12/20], Batch Num: [420/600] Discriminator Loss: 0.6168, Generator Loss: 2.1072 D(x): 0.7943, D(G(z)): 0.2103 Epoch: [12/20], Batch Num: [421/600] Discriminator Loss: 0.6513, Generator Loss: 2.3862 D(x): 0.7771, D(G(z)): 0.1961 Epoch: [12/20], Batch Num: [422/600] Discriminator Loss: 0.4820, Generator Loss: 2.0979 D(x): 0.8007, D(G(z)): 0.1594 Epoch: [12/20], Batch Num: [423/600] Discriminator Loss: 0.6088, Generator Loss: 1.9348 D(x): 0.7439, D(G(z)): 0.1668 Epoch: [12/20], Batch Num: [424/600] Discriminator Loss: 0.6240, Generator Loss: 1.9484 D(x): 0.8004, D(G(z)): 0.2308 Epoch: [12/20], Batch Num: [425/600] Discriminator Loss: 0.7476, Generator Loss: 1.7997 D(x): 0.7974, D(G(z)): 0.2807 Epoch: [12/20], Batch Num: [426/600] Discriminator Loss: 0.6472, Generator Loss: 2.0203 D(x): 0.8513, D(G(z)): 0.2746 Epoch: [12/20], Batch Num: [427/600] Discriminator Loss: 0.5952, Generator Loss: 2.4145 D(x): 0.8228, D(G(z)): 0.2233 Epoch: [12/20], Batch Num: [428/600] Discriminator Loss: 0.5660, Generator Loss: 2.5407 D(x): 0.8156, D(G(z)): 0.2071 Epoch: [12/20], Batch Num: [429/600] Discriminator Loss: 0.5915, Generator Loss: 2.3419 D(x): 0.8014, D(G(z)): 0.2032 Epoch: [12/20], Batch Num: [430/600] Discriminator Loss: 0.6333, Generator Loss: 2.2062 D(x): 0.7620, D(G(z)): 0.1722 Epoch: [12/20], Batch Num: [431/600] Discriminator Loss: 0.6783, Generator Loss: 2.0737 D(x): 0.7499, D(G(z)): 0.1803 Epoch: [12/20], Batch Num: [432/600] Discriminator Loss: 0.5914, Generator Loss: 1.9602 D(x): 0.7767, D(G(z)): 0.1684 Epoch: [12/20], Batch Num: [433/600] Discriminator Loss: 0.6397, Generator Loss: 1.7622 D(x): 0.8434, D(G(z)): 0.2506 Epoch: [12/20], Batch Num: [434/600] Discriminator Loss: 0.8130, Generator Loss: 2.1798 D(x): 0.8401, D(G(z)): 0.3312 Epoch: [12/20], Batch Num: [435/600] Discriminator Loss: 0.6269, Generator Loss: 2.5492 D(x): 0.8548, D(G(z)): 0.2553 Epoch: [12/20], Batch Num: [436/600] Discriminator Loss: 0.6773, Generator Loss: 2.3911 D(x): 0.7817, D(G(z)): 0.2161 Epoch: [12/20], Batch Num: [437/600] Discriminator Loss: 0.6244, Generator Loss: 2.7814 D(x): 0.7807, D(G(z)): 0.1820 Epoch: [12/20], Batch Num: [438/600] Discriminator Loss: 0.7206, Generator Loss: 2.7445 D(x): 0.7607, D(G(z)): 0.1789 Epoch: [12/20], Batch Num: [439/600] Discriminator Loss: 0.8425, Generator Loss: 2.1358 D(x): 0.6542, D(G(z)): 0.1540 Epoch: [12/20], Batch Num: [440/600] Discriminator Loss: 0.8606, Generator Loss: 1.6724 D(x): 0.6977, D(G(z)): 0.2052 Epoch: [12/20], Batch Num: [441/600] Discriminator Loss: 0.8354, Generator Loss: 1.3619 D(x): 0.8591, D(G(z)): 0.3286 Epoch: [12/20], Batch Num: 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2.0019 D(x): 0.7967, D(G(z)): 0.3529 Epoch: [12/20], Batch Num: [451/600] Discriminator Loss: 0.7570, Generator Loss: 2.0914 D(x): 0.7486, D(G(z)): 0.2295 Epoch: [12/20], Batch Num: [452/600] Discriminator Loss: 1.0648, Generator Loss: 2.0499 D(x): 0.6268, D(G(z)): 0.2372 Epoch: [12/20], Batch Num: [453/600] Discriminator Loss: 1.0251, Generator Loss: 1.8497 D(x): 0.6269, D(G(z)): 0.2002 Epoch: [12/20], Batch Num: [454/600] Discriminator Loss: 1.0597, Generator Loss: 1.6994 D(x): 0.6617, D(G(z)): 0.2729 Epoch: [12/20], Batch Num: [455/600] Discriminator Loss: 0.9726, Generator Loss: 1.7122 D(x): 0.7433, D(G(z)): 0.3230 Epoch: [12/20], Batch Num: [456/600] Discriminator Loss: 0.8829, Generator Loss: 1.5922 D(x): 0.7716, D(G(z)): 0.3172 Epoch: [12/20], Batch Num: [457/600] Discriminator Loss: 0.8744, Generator Loss: 1.4756 D(x): 0.7703, D(G(z)): 0.3203 Epoch: [12/20], Batch Num: [458/600] Discriminator Loss: 0.9659, Generator Loss: 1.8068 D(x): 0.7222, D(G(z)): 0.3379 Epoch: [12/20], 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Generator Loss: 1.8948 D(x): 0.7074, D(G(z)): 0.3008 Epoch: [12/20], Batch Num: [468/600] Discriminator Loss: 0.7148, Generator Loss: 1.9897 D(x): 0.7268, D(G(z)): 0.2007 Epoch: [12/20], Batch Num: [469/600] Discriminator Loss: 0.7351, Generator Loss: 1.7503 D(x): 0.6996, D(G(z)): 0.2025 Epoch: [12/20], Batch Num: [470/600] Discriminator Loss: 0.7894, Generator Loss: 1.6776 D(x): 0.7567, D(G(z)): 0.2601 Epoch: [12/20], Batch Num: [471/600] Discriminator Loss: 0.6882, Generator Loss: 1.8259 D(x): 0.7912, D(G(z)): 0.2633 Epoch: [12/20], Batch Num: [472/600] Discriminator Loss: 0.6421, Generator Loss: 1.7727 D(x): 0.8077, D(G(z)): 0.2565 Epoch: [12/20], Batch Num: [473/600] Discriminator Loss: 0.6930, Generator Loss: 1.8170 D(x): 0.7870, D(G(z)): 0.2663 Epoch: [12/20], Batch Num: [474/600] Discriminator Loss: 0.5801, Generator Loss: 2.0006 D(x): 0.8017, D(G(z)): 0.2362 Epoch: [12/20], Batch Num: [475/600] Discriminator Loss: 0.5480, Generator Loss: 2.1444 D(x): 0.7780, D(G(z)): 0.1741 Epoch: [12/20], Batch Num: [476/600] Discriminator Loss: 0.6267, Generator Loss: 1.7490 D(x): 0.7248, D(G(z)): 0.1725 Epoch: [12/20], Batch Num: [477/600] Discriminator Loss: 0.6684, Generator Loss: 1.7008 D(x): 0.7604, D(G(z)): 0.2213 Epoch: [12/20], Batch Num: [478/600] Discriminator Loss: 0.7061, Generator Loss: 1.8064 D(x): 0.7757, D(G(z)): 0.2603 Epoch: [12/20], Batch Num: [479/600] Discriminator Loss: 0.6353, Generator Loss: 1.5445 D(x): 0.8149, D(G(z)): 0.2557 Epoch: [12/20], Batch Num: [480/600] Discriminator Loss: 0.7443, Generator Loss: 1.8153 D(x): 0.8076, D(G(z)): 0.2987 Epoch: [12/20], Batch Num: [481/600] Discriminator Loss: 0.7695, Generator Loss: 2.1695 D(x): 0.7917, D(G(z)): 0.2826 Epoch: [12/20], Batch Num: [482/600] Discriminator Loss: 0.7068, Generator Loss: 2.2637 D(x): 0.7592, D(G(z)): 0.2162 Epoch: [12/20], Batch Num: [483/600] Discriminator Loss: 0.8142, Generator Loss: 2.0984 D(x): 0.6867, D(G(z)): 0.2131 Epoch: [12/20], Batch Num: [484/600] Discriminator Loss: 0.8700, Generator Loss: 1.7693 D(x): 0.6682, D(G(z)): 0.1964 Epoch: [12/20], Batch Num: [485/600] Discriminator Loss: 0.8923, Generator Loss: 1.7135 D(x): 0.7360, D(G(z)): 0.2687 Epoch: [12/20], Batch Num: [486/600] Discriminator Loss: 0.8612, Generator Loss: 1.4289 D(x): 0.7683, D(G(z)): 0.3106 Epoch: [12/20], Batch Num: [487/600] Discriminator Loss: 0.9307, Generator Loss: 1.6161 D(x): 0.7832, D(G(z)): 0.3633 Epoch: [12/20], Batch Num: [488/600] Discriminator Loss: 1.1852, Generator Loss: 1.9645 D(x): 0.6759, D(G(z)): 0.3411 Epoch: [12/20], Batch Num: [489/600] Discriminator Loss: 1.1468, Generator Loss: 1.8335 D(x): 0.6004, D(G(z)): 0.2324 Epoch: [12/20], Batch Num: [490/600] Discriminator Loss: 1.1336, Generator Loss: 1.6789 D(x): 0.6276, D(G(z)): 0.3215 Epoch: [12/20], Batch Num: [491/600] Discriminator Loss: 1.3036, Generator Loss: 1.5123 D(x): 0.6775, D(G(z)): 0.3998 Epoch: [12/20], Batch Num: [492/600] Discriminator Loss: 1.0452, Generator Loss: 1.5453 D(x): 0.6703, D(G(z)): 0.3060 Epoch: [12/20], Batch Num: [493/600] Discriminator Loss: 1.0666, Generator Loss: 1.6102 D(x): 0.6906, D(G(z)): 0.3576 Epoch: [12/20], Batch Num: [494/600] Discriminator Loss: 1.0771, Generator Loss: 1.5217 D(x): 0.6828, D(G(z)): 0.3197 Epoch: [12/20], Batch Num: [495/600] Discriminator Loss: 1.0878, Generator Loss: 1.5596 D(x): 0.6339, D(G(z)): 0.2928 Epoch: [12/20], Batch Num: [496/600] Discriminator Loss: 1.1994, Generator Loss: 1.3419 D(x): 0.6448, D(G(z)): 0.3807 Epoch: [12/20], Batch Num: [497/600] Discriminator Loss: 1.2416, Generator Loss: 1.4188 D(x): 0.6291, D(G(z)): 0.3777 Epoch: [12/20], Batch Num: [498/600] Discriminator Loss: 1.1631, Generator Loss: 1.4148 D(x): 0.6386, D(G(z)): 0.3551 Epoch: [12/20], Batch Num: [499/600] Discriminator Loss: 1.0701, Generator Loss: 1.2829 D(x): 0.6373, D(G(z)): 0.3364 Epoch: 12, Batch Num: [500/600]
Epoch: [12/20], Batch Num: [500/600] Discriminator Loss: 1.1141, Generator Loss: 1.1755 D(x): 0.6398, D(G(z)): 0.3314 Epoch: [12/20], Batch Num: [501/600] Discriminator Loss: 1.1678, Generator Loss: 1.3680 D(x): 0.6889, D(G(z)): 0.4059 Epoch: [12/20], Batch Num: [502/600] Discriminator Loss: 1.0477, Generator Loss: 1.2909 D(x): 0.6652, D(G(z)): 0.3339 Epoch: [12/20], Batch Num: [503/600] Discriminator Loss: 0.8514, Generator Loss: 1.4035 D(x): 0.7023, D(G(z)): 0.2841 Epoch: [12/20], Batch Num: [504/600] Discriminator Loss: 0.9761, Generator Loss: 1.4277 D(x): 0.6502, D(G(z)): 0.2955 Epoch: [12/20], Batch Num: [505/600] Discriminator Loss: 0.8171, Generator Loss: 1.5085 D(x): 0.7456, D(G(z)): 0.3207 Epoch: [12/20], Batch Num: [506/600] Discriminator Loss: 0.8747, Generator Loss: 1.5417 D(x): 0.6908, D(G(z)): 0.2910 Epoch: [12/20], Batch Num: [507/600] Discriminator Loss: 0.8909, Generator Loss: 1.5102 D(x): 0.7031, D(G(z)): 0.3179 Epoch: [12/20], Batch Num: [508/600] Discriminator Loss: 0.7185, Generator Loss: 1.7396 D(x): 0.7420, D(G(z)): 0.2674 Epoch: [12/20], Batch Num: [509/600] Discriminator Loss: 0.8680, Generator Loss: 1.6479 D(x): 0.6892, D(G(z)): 0.2814 Epoch: [12/20], Batch Num: [510/600] Discriminator Loss: 0.6268, Generator Loss: 1.7170 D(x): 0.7592, D(G(z)): 0.2272 Epoch: [12/20], Batch Num: [511/600] Discriminator Loss: 0.5066, Generator Loss: 1.6426 D(x): 0.8395, D(G(z)): 0.2341 Epoch: [12/20], Batch Num: [512/600] Discriminator Loss: 0.6312, Generator Loss: 1.8283 D(x): 0.7895, D(G(z)): 0.2554 Epoch: [12/20], Batch Num: [513/600] Discriminator Loss: 0.6271, Generator Loss: 1.9136 D(x): 0.7761, D(G(z)): 0.2275 Epoch: [12/20], Batch Num: [514/600] Discriminator Loss: 0.6202, Generator Loss: 2.0643 D(x): 0.7896, D(G(z)): 0.2224 Epoch: [12/20], Batch Num: [515/600] Discriminator Loss: 0.6071, Generator Loss: 2.0937 D(x): 0.7687, D(G(z)): 0.2006 Epoch: [12/20], Batch Num: [516/600] Discriminator Loss: 0.5740, Generator Loss: 2.0956 D(x): 0.8098, D(G(z)): 0.2106 Epoch: [12/20], Batch Num: [517/600] Discriminator Loss: 0.5038, Generator Loss: 2.1951 D(x): 0.8134, D(G(z)): 0.1872 Epoch: [12/20], Batch Num: [518/600] Discriminator Loss: 0.5326, Generator Loss: 2.3784 D(x): 0.8050, D(G(z)): 0.1904 Epoch: [12/20], Batch Num: [519/600] Discriminator Loss: 0.6018, Generator Loss: 2.3283 D(x): 0.7936, D(G(z)): 0.2087 Epoch: [12/20], Batch Num: [520/600] Discriminator Loss: 0.5585, Generator Loss: 2.3394 D(x): 0.8459, D(G(z)): 0.2214 Epoch: [12/20], Batch Num: [521/600] Discriminator Loss: 0.6025, Generator Loss: 2.4826 D(x): 0.7688, D(G(z)): 0.1719 Epoch: [12/20], Batch Num: [522/600] Discriminator Loss: 0.5165, Generator Loss: 2.5852 D(x): 0.8190, D(G(z)): 0.1778 Epoch: [12/20], Batch Num: [523/600] Discriminator Loss: 0.4814, Generator Loss: 2.4918 D(x): 0.8253, D(G(z)): 0.1645 Epoch: [12/20], Batch Num: [524/600] Discriminator Loss: 0.6543, Generator Loss: 2.3264 D(x): 0.7521, D(G(z)): 0.1842 Epoch: [12/20], Batch Num: [525/600] Discriminator Loss: 0.6289, Generator Loss: 2.0658 D(x): 0.8051, D(G(z)): 0.2307 Epoch: [12/20], Batch Num: [526/600] Discriminator Loss: 0.5360, Generator Loss: 2.4193 D(x): 0.8683, D(G(z)): 0.2320 Epoch: [12/20], Batch Num: [527/600] Discriminator Loss: 0.5226, Generator Loss: 2.5208 D(x): 0.8416, D(G(z)): 0.2032 Epoch: [12/20], Batch Num: [528/600] Discriminator Loss: 0.8114, Generator Loss: 2.3603 D(x): 0.7718, D(G(z)): 0.2449 Epoch: [12/20], Batch Num: [529/600] Discriminator Loss: 0.6766, Generator Loss: 3.0123 D(x): 0.7999, D(G(z)): 0.2271 Epoch: [12/20], Batch Num: [530/600] Discriminator Loss: 0.7778, Generator Loss: 2.3926 D(x): 0.7349, D(G(z)): 0.1781 Epoch: [12/20], Batch Num: [531/600] Discriminator Loss: 0.7546, Generator Loss: 2.1044 D(x): 0.7340, D(G(z)): 0.2010 Epoch: [12/20], Batch Num: [532/600] Discriminator Loss: 0.7453, Generator Loss: 1.9259 D(x): 0.7920, D(G(z)): 0.2337 Epoch: [12/20], Batch Num: [533/600] Discriminator Loss: 0.9765, Generator Loss: 1.9546 D(x): 0.7587, D(G(z)): 0.3013 Epoch: [12/20], Batch Num: [534/600] Discriminator Loss: 1.1846, Generator Loss: 1.6618 D(x): 0.6574, D(G(z)): 0.3204 Epoch: [12/20], Batch Num: [535/600] Discriminator Loss: 0.8817, Generator Loss: 1.7993 D(x): 0.7355, D(G(z)): 0.2765 Epoch: [12/20], Batch Num: [536/600] Discriminator Loss: 0.8896, Generator Loss: 1.8177 D(x): 0.7414, D(G(z)): 0.3065 Epoch: [12/20], Batch Num: [537/600] Discriminator Loss: 0.9177, Generator Loss: 1.9823 D(x): 0.7309, D(G(z)): 0.2506 Epoch: [12/20], Batch Num: [538/600] Discriminator Loss: 1.0588, Generator Loss: 1.7782 D(x): 0.6596, D(G(z)): 0.2944 Epoch: [12/20], Batch Num: [539/600] Discriminator Loss: 0.9888, Generator Loss: 1.5515 D(x): 0.6923, D(G(z)): 0.2925 Epoch: [12/20], Batch Num: [540/600] Discriminator Loss: 1.1433, Generator Loss: 1.5485 D(x): 0.6759, D(G(z)): 0.3708 Epoch: [12/20], Batch Num: [541/600] Discriminator Loss: 1.0654, Generator Loss: 1.4913 D(x): 0.6941, D(G(z)): 0.3423 Epoch: [12/20], Batch Num: [542/600] Discriminator Loss: 1.1160, Generator Loss: 1.7074 D(x): 0.6642, D(G(z)): 0.3245 Epoch: [12/20], Batch Num: [543/600] Discriminator Loss: 1.2064, Generator Loss: 1.3439 D(x): 0.5772, D(G(z)): 0.2830 Epoch: [12/20], Batch Num: [544/600] Discriminator Loss: 1.1538, Generator Loss: 1.4519 D(x): 0.7053, D(G(z)): 0.3874 Epoch: [12/20], Batch Num: [545/600] Discriminator Loss: 0.9716, Generator Loss: 1.5578 D(x): 0.7155, D(G(z)): 0.3311 Epoch: [12/20], Batch Num: [546/600] Discriminator Loss: 1.0063, Generator Loss: 1.7491 D(x): 0.6439, D(G(z)): 0.3015 Epoch: [12/20], Batch Num: [547/600] Discriminator Loss: 1.0379, Generator Loss: 1.4917 D(x): 0.6857, D(G(z)): 0.3186 Epoch: [12/20], Batch Num: [548/600] Discriminator Loss: 0.9208, Generator Loss: 1.5607 D(x): 0.7010, D(G(z)): 0.3005 Epoch: [12/20], Batch Num: [549/600] Discriminator Loss: 0.7206, Generator Loss: 1.5725 D(x): 0.7710, D(G(z)): 0.2796 Epoch: [12/20], Batch Num: [550/600] Discriminator Loss: 0.7595, Generator Loss: 1.6276 D(x): 0.7615, D(G(z)): 0.2712 Epoch: [12/20], Batch Num: [551/600] Discriminator Loss: 0.7766, Generator Loss: 1.9347 D(x): 0.7170, D(G(z)): 0.2493 Epoch: [12/20], Batch Num: [552/600] Discriminator Loss: 0.6435, Generator Loss: 1.8031 D(x): 0.7653, D(G(z)): 0.2241 Epoch: [12/20], Batch Num: [553/600] Discriminator Loss: 0.5915, Generator Loss: 1.6410 D(x): 0.7856, D(G(z)): 0.2096 Epoch: [12/20], Batch Num: [554/600] Discriminator Loss: 0.6931, Generator Loss: 1.7688 D(x): 0.7627, D(G(z)): 0.2473 Epoch: [12/20], Batch Num: [555/600] Discriminator Loss: 0.6884, Generator Loss: 2.1008 D(x): 0.7805, D(G(z)): 0.2573 Epoch: [12/20], Batch Num: [556/600] Discriminator Loss: 0.6417, Generator Loss: 2.0472 D(x): 0.8062, D(G(z)): 0.2299 Epoch: [12/20], Batch Num: [557/600] Discriminator Loss: 0.5223, Generator Loss: 2.4659 D(x): 0.8401, D(G(z)): 0.2142 Epoch: [12/20], Batch Num: [558/600] Discriminator Loss: 0.5335, Generator Loss: 2.4753 D(x): 0.7741, D(G(z)): 0.1644 Epoch: [12/20], Batch Num: [559/600] Discriminator Loss: 0.5827, Generator Loss: 2.1368 D(x): 0.7486, D(G(z)): 0.1318 Epoch: [12/20], Batch Num: [560/600] Discriminator Loss: 0.5153, Generator Loss: 2.1472 D(x): 0.8117, D(G(z)): 0.1824 Epoch: [12/20], Batch Num: [561/600] Discriminator Loss: 0.4658, Generator Loss: 1.9394 D(x): 0.8815, D(G(z)): 0.2178 Epoch: [12/20], Batch Num: [562/600] Discriminator Loss: 0.5243, Generator Loss: 2.3708 D(x): 0.8686, D(G(z)): 0.2282 Epoch: [12/20], Batch Num: [563/600] Discriminator Loss: 0.5486, Generator Loss: 2.6174 D(x): 0.8731, D(G(z)): 0.2430 Epoch: [12/20], Batch Num: [564/600] Discriminator Loss: 0.5977, Generator Loss: 3.1893 D(x): 0.7694, D(G(z)): 0.1580 Epoch: [12/20], Batch Num: [565/600] Discriminator Loss: 0.5843, Generator Loss: 2.8035 D(x): 0.7447, D(G(z)): 0.0954 Epoch: [12/20], Batch Num: [566/600] Discriminator Loss: 0.5151, Generator Loss: 2.3413 D(x): 0.8020, D(G(z)): 0.1424 Epoch: [12/20], Batch Num: [567/600] Discriminator Loss: 0.5101, Generator Loss: 2.0211 D(x): 0.8531, D(G(z)): 0.1921 Epoch: [12/20], Batch Num: [568/600] Discriminator Loss: 0.5989, Generator Loss: 1.9934 D(x): 0.8494, D(G(z)): 0.2296 Epoch: [12/20], Batch Num: [569/600] Discriminator Loss: 0.5879, Generator Loss: 2.5065 D(x): 0.8810, D(G(z)): 0.2548 Epoch: [12/20], Batch Num: [570/600] Discriminator Loss: 0.5582, Generator Loss: 2.6174 D(x): 0.8469, D(G(z)): 0.2128 Epoch: [12/20], Batch Num: [571/600] Discriminator Loss: 0.6983, Generator Loss: 2.6751 D(x): 0.7306, D(G(z)): 0.1495 Epoch: [12/20], Batch Num: [572/600] Discriminator Loss: 0.6504, Generator Loss: 2.1384 D(x): 0.7250, D(G(z)): 0.1344 Epoch: [12/20], Batch Num: [573/600] Discriminator Loss: 0.7454, Generator Loss: 1.7509 D(x): 0.7543, D(G(z)): 0.2092 Epoch: [12/20], Batch Num: [574/600] Discriminator Loss: 0.6702, Generator Loss: 1.6754 D(x): 0.8612, D(G(z)): 0.2968 Epoch: [12/20], Batch Num: [575/600] Discriminator Loss: 1.1440, Generator Loss: 2.3501 D(x): 0.8033, D(G(z)): 0.4246 Epoch: [12/20], Batch Num: [576/600] Discriminator Loss: 0.8601, Generator Loss: 2.5849 D(x): 0.7056, D(G(z)): 0.2231 Epoch: [12/20], Batch Num: [577/600] Discriminator Loss: 1.1812, Generator Loss: 2.2336 D(x): 0.5855, D(G(z)): 0.1908 Epoch: [12/20], Batch Num: [578/600] Discriminator Loss: 1.0553, Generator Loss: 1.5779 D(x): 0.6327, D(G(z)): 0.2496 Epoch: [12/20], Batch Num: [579/600] Discriminator Loss: 1.3030, Generator Loss: 1.2572 D(x): 0.6957, D(G(z)): 0.3868 Epoch: [12/20], Batch Num: [580/600] Discriminator Loss: 1.1362, Generator Loss: 1.5075 D(x): 0.7877, D(G(z)): 0.4402 Epoch: [12/20], Batch Num: [581/600] Discriminator Loss: 1.2020, Generator Loss: 1.7172 D(x): 0.6596, D(G(z)): 0.3671 Epoch: [12/20], Batch Num: [582/600] Discriminator Loss: 1.1255, Generator Loss: 1.9861 D(x): 0.6672, D(G(z)): 0.3270 Epoch: [12/20], Batch Num: [583/600] Discriminator Loss: 1.2107, Generator Loss: 1.7347 D(x): 0.5508, D(G(z)): 0.2431 Epoch: [12/20], Batch Num: [584/600] Discriminator Loss: 1.0900, Generator Loss: 1.3390 D(x): 0.6139, D(G(z)): 0.2567 Epoch: [12/20], Batch Num: [585/600] Discriminator Loss: 1.0674, Generator Loss: 1.2789 D(x): 0.7008, D(G(z)): 0.3765 Epoch: [12/20], Batch Num: [586/600] Discriminator Loss: 1.0800, Generator Loss: 1.1557 D(x): 0.7377, D(G(z)): 0.4055 Epoch: [12/20], Batch Num: [587/600] Discriminator Loss: 1.1653, Generator Loss: 1.4896 D(x): 0.7296, D(G(z)): 0.4226 Epoch: [12/20], Batch Num: [588/600] Discriminator Loss: 0.9568, Generator Loss: 1.4804 D(x): 0.6869, D(G(z)): 0.3202 Epoch: [12/20], Batch Num: [589/600] Discriminator Loss: 1.0505, Generator Loss: 1.7429 D(x): 0.6357, D(G(z)): 0.3264 Epoch: [12/20], Batch Num: [590/600] Discriminator Loss: 0.9816, Generator Loss: 1.6066 D(x): 0.6440, D(G(z)): 0.2481 Epoch: [12/20], Batch Num: [591/600] Discriminator Loss: 0.9919, Generator Loss: 1.6490 D(x): 0.6259, D(G(z)): 0.2547 Epoch: [12/20], Batch Num: [592/600] Discriminator Loss: 0.8993, Generator Loss: 1.5748 D(x): 0.6983, D(G(z)): 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Epoch: [13/20], Batch Num: [0/600] Discriminator Loss: 0.7309, Generator Loss: 1.9369 D(x): 0.6823, D(G(z)): 0.1875 Epoch: [13/20], Batch Num: [1/600] Discriminator Loss: 0.6599, Generator Loss: 1.7224 D(x): 0.7306, D(G(z)): 0.1910 Epoch: [13/20], Batch Num: [2/600] Discriminator Loss: 0.5599, Generator Loss: 1.4337 D(x): 0.8548, D(G(z)): 0.2680 Epoch: [13/20], Batch Num: [3/600] Discriminator Loss: 0.5996, Generator Loss: 1.6253 D(x): 0.8439, D(G(z)): 0.2797 Epoch: [13/20], Batch Num: [4/600] Discriminator Loss: 0.6018, Generator Loss: 1.8687 D(x): 0.8475, D(G(z)): 0.2852 Epoch: [13/20], Batch Num: [5/600] Discriminator Loss: 0.5725, Generator Loss: 2.0601 D(x): 0.8044, D(G(z)): 0.2170 Epoch: [13/20], Batch Num: [6/600] Discriminator Loss: 0.5556, Generator Loss: 2.3274 D(x): 0.8311, D(G(z)): 0.2263 Epoch: [13/20], Batch Num: [7/600] Discriminator Loss: 0.5464, Generator Loss: 2.2025 D(x): 0.7711, D(G(z)): 0.1691 Epoch: [13/20], Batch Num: [8/600] Discriminator Loss: 0.5532, Generator 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D(x): 0.7013, D(G(z)): 0.3529 Epoch: [13/20], Batch Num: [26/600] Discriminator Loss: 0.8116, Generator Loss: 2.1854 D(x): 0.7499, D(G(z)): 0.2352 Epoch: [13/20], Batch Num: [27/600] Discriminator Loss: 1.1357, Generator Loss: 1.8482 D(x): 0.6313, D(G(z)): 0.2619 Epoch: [13/20], Batch Num: [28/600] Discriminator Loss: 1.0705, Generator Loss: 1.6664 D(x): 0.7197, D(G(z)): 0.3534 Epoch: [13/20], Batch Num: [29/600] Discriminator Loss: 1.0997, Generator Loss: 1.4620 D(x): 0.7220, D(G(z)): 0.3569 Epoch: [13/20], Batch Num: [30/600] Discriminator Loss: 1.2655, Generator Loss: 1.8029 D(x): 0.6717, D(G(z)): 0.3640 Epoch: [13/20], Batch Num: [31/600] Discriminator Loss: 1.2400, Generator Loss: 1.5873 D(x): 0.6190, D(G(z)): 0.3102 Epoch: [13/20], Batch Num: [32/600] Discriminator Loss: 1.2111, Generator Loss: 1.4112 D(x): 0.6386, D(G(z)): 0.2978 Epoch: [13/20], Batch Num: [33/600] Discriminator Loss: 1.2386, Generator Loss: 1.3436 D(x): 0.6965, D(G(z)): 0.3712 Epoch: [13/20], Batch Num: [34/600] Discriminator Loss: 1.2862, Generator Loss: 1.5029 D(x): 0.6636, D(G(z)): 0.3893 Epoch: [13/20], Batch Num: [35/600] Discriminator Loss: 1.3044, Generator Loss: 1.4553 D(x): 0.6289, D(G(z)): 0.3758 Epoch: [13/20], Batch Num: [36/600] Discriminator Loss: 1.1712, Generator Loss: 1.1806 D(x): 0.6306, D(G(z)): 0.3355 Epoch: [13/20], Batch Num: [37/600] Discriminator Loss: 1.1579, Generator Loss: 1.2077 D(x): 0.6598, D(G(z)): 0.3472 Epoch: [13/20], Batch Num: [38/600] Discriminator Loss: 1.3065, Generator Loss: 1.2151 D(x): 0.6308, D(G(z)): 0.3812 Epoch: [13/20], Batch Num: [39/600] Discriminator Loss: 1.1470, Generator Loss: 1.3772 D(x): 0.6588, D(G(z)): 0.3551 Epoch: [13/20], Batch Num: [40/600] Discriminator Loss: 1.0175, Generator Loss: 1.3817 D(x): 0.7130, D(G(z)): 0.3791 Epoch: [13/20], Batch Num: [41/600] Discriminator Loss: 0.9226, Generator Loss: 1.4961 D(x): 0.7114, D(G(z)): 0.3278 Epoch: [13/20], Batch Num: [42/600] Discriminator Loss: 0.8852, Generator Loss: 1.5592 D(x): 0.7047, D(G(z)): 0.2914 Epoch: [13/20], Batch Num: [43/600] Discriminator Loss: 1.0001, Generator Loss: 1.4756 D(x): 0.6513, D(G(z)): 0.2903 Epoch: [13/20], Batch Num: [44/600] Discriminator Loss: 0.9055, Generator Loss: 1.5300 D(x): 0.6903, D(G(z)): 0.3108 Epoch: [13/20], Batch Num: [45/600] Discriminator Loss: 0.8643, Generator Loss: 1.3567 D(x): 0.6805, D(G(z)): 0.2843 Epoch: [13/20], Batch Num: [46/600] Discriminator Loss: 0.8356, Generator Loss: 1.4027 D(x): 0.7247, D(G(z)): 0.3185 Epoch: [13/20], Batch Num: [47/600] Discriminator Loss: 0.7588, Generator Loss: 1.3412 D(x): 0.7971, D(G(z)): 0.3362 Epoch: [13/20], Batch Num: [48/600] Discriminator Loss: 0.7845, Generator Loss: 1.5497 D(x): 0.7657, D(G(z)): 0.3236 Epoch: [13/20], Batch Num: [49/600] Discriminator Loss: 0.7946, Generator Loss: 1.6624 D(x): 0.7180, D(G(z)): 0.2838 Epoch: [13/20], Batch Num: [50/600] Discriminator Loss: 0.6369, Generator Loss: 1.8499 D(x): 0.7786, D(G(z)): 0.2510 Epoch: [13/20], Batch Num: [51/600] Discriminator Loss: 0.6318, Generator Loss: 1.9375 D(x): 0.7485, D(G(z)): 0.2112 Epoch: [13/20], Batch Num: [52/600] Discriminator Loss: 0.6254, Generator Loss: 1.7932 D(x): 0.7624, D(G(z)): 0.2300 Epoch: [13/20], Batch Num: [53/600] Discriminator Loss: 0.5545, Generator Loss: 1.9158 D(x): 0.7900, D(G(z)): 0.2089 Epoch: [13/20], Batch Num: [54/600] Discriminator Loss: 0.5818, Generator Loss: 1.8366 D(x): 0.7827, D(G(z)): 0.2034 Epoch: [13/20], Batch Num: [55/600] Discriminator Loss: 0.6960, Generator Loss: 1.7097 D(x): 0.7465, D(G(z)): 0.2612 Epoch: [13/20], Batch Num: [56/600] Discriminator Loss: 0.6199, Generator Loss: 1.5924 D(x): 0.7965, D(G(z)): 0.2599 Epoch: [13/20], Batch Num: [57/600] Discriminator Loss: 0.4978, Generator Loss: 1.7187 D(x): 0.8689, D(G(z)): 0.2506 Epoch: [13/20], Batch Num: [58/600] Discriminator Loss: 0.5190, Generator Loss: 1.9549 D(x): 0.8627, D(G(z)): 0.2442 Epoch: [13/20], Batch Num: [59/600] Discriminator Loss: 0.5864, Generator Loss: 2.2955 D(x): 0.8292, D(G(z)): 0.2583 Epoch: [13/20], Batch Num: [60/600] Discriminator Loss: 0.5537, Generator Loss: 2.3210 D(x): 0.7467, D(G(z)): 0.1417 Epoch: [13/20], Batch Num: [61/600] Discriminator Loss: 0.6742, Generator Loss: 2.1647 D(x): 0.7062, D(G(z)): 0.1508 Epoch: [13/20], Batch Num: [62/600] Discriminator Loss: 0.5332, Generator Loss: 1.9563 D(x): 0.8096, D(G(z)): 0.1735 Epoch: [13/20], Batch Num: [63/600] Discriminator Loss: 0.5956, Generator Loss: 1.9087 D(x): 0.8262, D(G(z)): 0.2437 Epoch: [13/20], Batch Num: [64/600] Discriminator Loss: 0.5949, Generator Loss: 1.9539 D(x): 0.8657, D(G(z)): 0.2756 Epoch: [13/20], Batch Num: [65/600] Discriminator Loss: 0.6300, Generator Loss: 2.1953 D(x): 0.8312, D(G(z)): 0.2468 Epoch: [13/20], Batch Num: [66/600] Discriminator Loss: 0.6084, Generator Loss: 2.3692 D(x): 0.7970, D(G(z)): 0.2082 Epoch: [13/20], Batch Num: [67/600] Discriminator Loss: 0.6724, Generator Loss: 2.5844 D(x): 0.7447, D(G(z)): 0.1948 Epoch: [13/20], Batch Num: [68/600] Discriminator Loss: 0.7502, Generator Loss: 2.0603 D(x): 0.7235, D(G(z)): 0.1947 Epoch: [13/20], Batch Num: [69/600] Discriminator Loss: 0.6552, Generator Loss: 1.9133 D(x): 0.7946, D(G(z)): 0.2491 Epoch: [13/20], Batch Num: [70/600] Discriminator Loss: 0.6248, Generator Loss: 1.9085 D(x): 0.7860, D(G(z)): 0.2173 Epoch: [13/20], Batch Num: [71/600] Discriminator Loss: 0.8647, Generator Loss: 2.0230 D(x): 0.8165, D(G(z)): 0.3452 Epoch: [13/20], Batch Num: [72/600] Discriminator Loss: 0.8145, Generator Loss: 2.4738 D(x): 0.7563, D(G(z)): 0.2710 Epoch: [13/20], Batch Num: [73/600] Discriminator Loss: 0.9767, Generator Loss: 2.5374 D(x): 0.6450, D(G(z)): 0.2268 Epoch: [13/20], Batch Num: [74/600] Discriminator Loss: 0.8392, Generator Loss: 1.7496 D(x): 0.6759, D(G(z)): 0.1592 Epoch: [13/20], Batch Num: [75/600] Discriminator Loss: 1.0757, Generator Loss: 1.3473 D(x): 0.6643, D(G(z)): 0.2621 Epoch: [13/20], Batch Num: [76/600] Discriminator Loss: 1.0387, Generator Loss: 1.4665 D(x): 0.8168, D(G(z)): 0.4294 Epoch: [13/20], Batch Num: [77/600] Discriminator Loss: 1.1549, Generator Loss: 2.1913 D(x): 0.7733, D(G(z)): 0.4246 Epoch: [13/20], Batch Num: [78/600] Discriminator Loss: 0.9545, Generator Loss: 2.6667 D(x): 0.6400, D(G(z)): 0.2147 Epoch: [13/20], Batch Num: [79/600] Discriminator Loss: 1.0309, Generator Loss: 2.4124 D(x): 0.6006, D(G(z)): 0.1439 Epoch: [13/20], Batch Num: [80/600] Discriminator Loss: 0.9766, Generator Loss: 1.6412 D(x): 0.5983, D(G(z)): 0.1729 Epoch: [13/20], Batch Num: [81/600] Discriminator Loss: 1.0393, Generator Loss: 1.1944 D(x): 0.7061, D(G(z)): 0.3127 Epoch: [13/20], Batch Num: [82/600] Discriminator Loss: 1.1784, Generator Loss: 1.5096 D(x): 0.8149, D(G(z)): 0.4683 Epoch: [13/20], Batch Num: [83/600] Discriminator Loss: 0.9188, Generator Loss: 1.8747 D(x): 0.7479, D(G(z)): 0.3330 Epoch: [13/20], Batch Num: [84/600] Discriminator Loss: 0.9068, Generator Loss: 2.0509 D(x): 0.6412, D(G(z)): 0.2079 Epoch: [13/20], Batch Num: [85/600] Discriminator Loss: 0.9097, Generator Loss: 1.6813 D(x): 0.6562, D(G(z)): 0.2153 Epoch: [13/20], Batch Num: [86/600] Discriminator Loss: 0.6735, Generator Loss: 1.8062 D(x): 0.7581, D(G(z)): 0.2249 Epoch: [13/20], Batch Num: [87/600] Discriminator Loss: 0.8679, Generator Loss: 1.6912 D(x): 0.7693, D(G(z)): 0.3042 Epoch: [13/20], Batch Num: [88/600] Discriminator Loss: 0.6903, Generator Loss: 1.9175 D(x): 0.8193, D(G(z)): 0.2859 Epoch: [13/20], Batch Num: [89/600] Discriminator Loss: 0.7855, Generator Loss: 1.8839 D(x): 0.7338, D(G(z)): 0.2636 Epoch: [13/20], Batch Num: [90/600] Discriminator Loss: 0.7750, Generator Loss: 2.1543 D(x): 0.7398, D(G(z)): 0.2296 Epoch: [13/20], Batch Num: [91/600] Discriminator Loss: 0.7092, Generator Loss: 2.1271 D(x): 0.7147, D(G(z)): 0.1767 Epoch: [13/20], Batch Num: [92/600] Discriminator Loss: 0.8386, Generator Loss: 1.8826 D(x): 0.7138, D(G(z)): 0.2443 Epoch: [13/20], Batch Num: [93/600] Discriminator Loss: 0.8106, Generator Loss: 1.8554 D(x): 0.7328, D(G(z)): 0.2531 Epoch: [13/20], Batch Num: [94/600] Discriminator Loss: 0.7056, Generator Loss: 1.4562 D(x): 0.7778, D(G(z)): 0.2527 Epoch: [13/20], Batch Num: [95/600] Discriminator Loss: 0.7992, Generator Loss: 1.8707 D(x): 0.8391, D(G(z)): 0.3424 Epoch: [13/20], Batch Num: [96/600] Discriminator Loss: 0.7349, Generator Loss: 1.9867 D(x): 0.7572, D(G(z)): 0.2506 Epoch: [13/20], Batch Num: [97/600] Discriminator Loss: 0.7548, Generator Loss: 2.4534 D(x): 0.7676, D(G(z)): 0.2712 Epoch: [13/20], Batch Num: [98/600] Discriminator Loss: 0.7320, Generator Loss: 2.3441 D(x): 0.6870, D(G(z)): 0.1487 Epoch: [13/20], Batch Num: [99/600] Discriminator Loss: 0.7362, Generator Loss: 1.9674 D(x): 0.6885, D(G(z)): 0.1741 Epoch: 13, Batch Num: [100/600]
Epoch: [13/20], Batch Num: [100/600] Discriminator Loss: 0.7463, Generator Loss: 1.6247 D(x): 0.7355, D(G(z)): 0.2268 Epoch: [13/20], Batch Num: [101/600] Discriminator Loss: 0.7620, Generator Loss: 1.6551 D(x): 0.8438, D(G(z)): 0.3586 Epoch: [13/20], Batch Num: [102/600] Discriminator Loss: 0.7437, Generator Loss: 1.9338 D(x): 0.8473, D(G(z)): 0.3135 Epoch: [13/20], Batch Num: [103/600] Discriminator Loss: 0.6733, Generator Loss: 2.2018 D(x): 0.7540, D(G(z)): 0.2214 Epoch: [13/20], Batch Num: [104/600] Discriminator Loss: 0.7663, Generator Loss: 2.0756 D(x): 0.7032, D(G(z)): 0.1839 Epoch: [13/20], Batch Num: [105/600] Discriminator Loss: 0.7533, Generator Loss: 1.9783 D(x): 0.7150, D(G(z)): 0.1923 Epoch: [13/20], Batch Num: [106/600] Discriminator Loss: 0.6801, Generator Loss: 1.7042 D(x): 0.7658, D(G(z)): 0.2251 Epoch: [13/20], Batch Num: [107/600] Discriminator Loss: 0.6594, Generator Loss: 1.8234 D(x): 0.8192, D(G(z)): 0.2664 Epoch: [13/20], Batch Num: [108/600] Discriminator Loss: 0.6208, Generator Loss: 1.9950 D(x): 0.8054, D(G(z)): 0.2212 Epoch: [13/20], Batch Num: [109/600] Discriminator Loss: 0.7042, Generator Loss: 2.0118 D(x): 0.8089, D(G(z)): 0.2854 Epoch: [13/20], Batch Num: [110/600] Discriminator Loss: 0.7895, Generator Loss: 2.4188 D(x): 0.7756, D(G(z)): 0.2489 Epoch: [13/20], Batch Num: [111/600] Discriminator Loss: 0.7157, Generator Loss: 2.1262 D(x): 0.7522, D(G(z)): 0.2234 Epoch: [13/20], Batch Num: [112/600] Discriminator Loss: 0.6419, Generator Loss: 2.2060 D(x): 0.7457, D(G(z)): 0.1637 Epoch: [13/20], Batch Num: [113/600] Discriminator Loss: 0.7715, Generator Loss: 1.9205 D(x): 0.7413, D(G(z)): 0.2189 Epoch: [13/20], Batch Num: [114/600] Discriminator Loss: 0.7307, Generator Loss: 1.7110 D(x): 0.7769, D(G(z)): 0.2457 Epoch: [13/20], Batch Num: [115/600] Discriminator Loss: 0.8588, Generator Loss: 1.9134 D(x): 0.7616, D(G(z)): 0.2713 Epoch: [13/20], Batch Num: [116/600] Discriminator Loss: 0.6334, Generator Loss: 2.1341 D(x): 0.8385, D(G(z)): 0.2822 Epoch: [13/20], Batch Num: [117/600] Discriminator Loss: 0.8155, Generator Loss: 2.1569 D(x): 0.7441, D(G(z)): 0.2317 Epoch: [13/20], Batch Num: [118/600] Discriminator Loss: 0.6830, Generator Loss: 2.2755 D(x): 0.7544, D(G(z)): 0.1932 Epoch: [13/20], Batch Num: [119/600] Discriminator Loss: 0.6666, Generator Loss: 2.2874 D(x): 0.7879, D(G(z)): 0.2195 Epoch: [13/20], Batch Num: [120/600] Discriminator Loss: 0.8608, Generator Loss: 1.9638 D(x): 0.7145, D(G(z)): 0.2089 Epoch: [13/20], Batch Num: [121/600] Discriminator Loss: 0.6861, Generator Loss: 1.9632 D(x): 0.7819, D(G(z)): 0.2126 Epoch: [13/20], Batch Num: [122/600] Discriminator Loss: 0.8357, Generator Loss: 2.0833 D(x): 0.7707, D(G(z)): 0.2744 Epoch: [13/20], Batch Num: [123/600] Discriminator Loss: 0.6940, Generator Loss: 1.9655 D(x): 0.8255, D(G(z)): 0.2530 Epoch: [13/20], Batch Num: [124/600] Discriminator Loss: 0.8291, Generator Loss: 2.1244 D(x): 0.7656, D(G(z)): 0.2588 Epoch: [13/20], Batch Num: [125/600] Discriminator Loss: 0.6804, Generator Loss: 2.3184 D(x): 0.8020, D(G(z)): 0.2530 Epoch: [13/20], Batch Num: [126/600] Discriminator Loss: 0.8252, Generator Loss: 2.3797 D(x): 0.7199, D(G(z)): 0.2149 Epoch: [13/20], Batch Num: [127/600] Discriminator Loss: 0.7734, Generator Loss: 2.2889 D(x): 0.7317, D(G(z)): 0.2025 Epoch: [13/20], Batch Num: [128/600] Discriminator Loss: 0.5838, Generator Loss: 2.2500 D(x): 0.8091, D(G(z)): 0.2019 Epoch: [13/20], Batch Num: [129/600] Discriminator Loss: 0.5274, Generator Loss: 2.0320 D(x): 0.8137, D(G(z)): 0.1856 Epoch: [13/20], Batch Num: [130/600] Discriminator Loss: 0.9185, Generator Loss: 2.0611 D(x): 0.7838, D(G(z)): 0.2701 Epoch: [13/20], Batch Num: [131/600] Discriminator Loss: 0.7181, Generator Loss: 2.0978 D(x): 0.8062, D(G(z)): 0.2540 Epoch: [13/20], Batch Num: [132/600] Discriminator Loss: 0.8242, Generator Loss: 2.1773 D(x): 0.7456, D(G(z)): 0.2188 Epoch: [13/20], Batch Num: [133/600] Discriminator Loss: 0.6831, Generator Loss: 2.2895 D(x): 0.8095, D(G(z)): 0.2496 Epoch: [13/20], Batch Num: [134/600] Discriminator Loss: 0.6918, Generator Loss: 2.2412 D(x): 0.7893, D(G(z)): 0.2254 Epoch: [13/20], Batch Num: [135/600] Discriminator Loss: 0.8376, Generator Loss: 2.2186 D(x): 0.7579, D(G(z)): 0.2568 Epoch: [13/20], Batch Num: [136/600] Discriminator Loss: 0.8638, Generator Loss: 2.1425 D(x): 0.7220, D(G(z)): 0.2301 Epoch: [13/20], Batch Num: [137/600] Discriminator Loss: 0.7565, Generator Loss: 2.1959 D(x): 0.7463, D(G(z)): 0.2276 Epoch: [13/20], Batch Num: [138/600] Discriminator Loss: 0.8012, Generator Loss: 1.9797 D(x): 0.7842, D(G(z)): 0.2636 Epoch: [13/20], Batch Num: [139/600] Discriminator Loss: 0.8725, Generator Loss: 2.1643 D(x): 0.7847, D(G(z)): 0.2950 Epoch: [13/20], Batch Num: [140/600] Discriminator Loss: 0.8622, Generator Loss: 1.8982 D(x): 0.7551, D(G(z)): 0.2550 Epoch: [13/20], Batch Num: [141/600] Discriminator Loss: 0.8716, Generator Loss: 2.1515 D(x): 0.7217, D(G(z)): 0.2423 Epoch: [13/20], Batch Num: [142/600] Discriminator Loss: 1.1069, Generator Loss: 1.9562 D(x): 0.6205, D(G(z)): 0.2651 Epoch: [13/20], Batch Num: [143/600] Discriminator Loss: 0.8192, Generator Loss: 1.7592 D(x): 0.7504, D(G(z)): 0.2788 Epoch: [13/20], Batch Num: [144/600] Discriminator Loss: 0.9362, Generator Loss: 1.7045 D(x): 0.7423, D(G(z)): 0.3015 Epoch: [13/20], Batch Num: [145/600] Discriminator Loss: 1.0074, Generator Loss: 1.5376 D(x): 0.7385, D(G(z)): 0.3184 Epoch: [13/20], Batch Num: [146/600] Discriminator Loss: 1.0736, Generator Loss: 1.6062 D(x): 0.7509, D(G(z)): 0.3704 Epoch: [13/20], Batch Num: [147/600] Discriminator Loss: 0.8869, Generator Loss: 1.9783 D(x): 0.7116, D(G(z)): 0.2583 Epoch: [13/20], Batch Num: [148/600] Discriminator Loss: 0.8630, Generator Loss: 2.0910 D(x): 0.7076, D(G(z)): 0.2387 Epoch: [13/20], Batch Num: [149/600] Discriminator Loss: 0.9061, Generator Loss: 2.0839 D(x): 0.7054, D(G(z)): 0.2299 Epoch: [13/20], Batch Num: [150/600] Discriminator Loss: 0.9601, Generator Loss: 1.9373 D(x): 0.6454, D(G(z)): 0.2103 Epoch: [13/20], Batch Num: [151/600] Discriminator Loss: 0.8914, Generator Loss: 1.5676 D(x): 0.6757, D(G(z)): 0.2543 Epoch: [13/20], Batch Num: [152/600] Discriminator Loss: 0.8657, Generator Loss: 1.5828 D(x): 0.7906, D(G(z)): 0.3236 Epoch: [13/20], Batch Num: [153/600] Discriminator Loss: 0.8357, Generator Loss: 1.5784 D(x): 0.8222, D(G(z)): 0.3529 Epoch: [13/20], Batch Num: [154/600] Discriminator Loss: 0.8193, Generator Loss: 1.9901 D(x): 0.7750, D(G(z)): 0.3027 Epoch: [13/20], Batch Num: [155/600] Discriminator Loss: 1.0445, Generator Loss: 1.9365 D(x): 0.6383, D(G(z)): 0.2833 Epoch: [13/20], Batch Num: [156/600] Discriminator Loss: 0.7058, Generator Loss: 1.9193 D(x): 0.6974, D(G(z)): 0.2002 Epoch: [13/20], Batch Num: [157/600] Discriminator Loss: 0.8139, Generator Loss: 2.0958 D(x): 0.6826, D(G(z)): 0.2373 Epoch: [13/20], Batch Num: [158/600] Discriminator Loss: 0.8292, Generator Loss: 1.8093 D(x): 0.7443, D(G(z)): 0.2868 Epoch: [13/20], Batch Num: [159/600] Discriminator Loss: 0.6912, Generator Loss: 1.6423 D(x): 0.7488, D(G(z)): 0.2288 Epoch: [13/20], Batch Num: [160/600] Discriminator Loss: 0.8118, Generator Loss: 1.5561 D(x): 0.7721, D(G(z)): 0.2930 Epoch: [13/20], Batch Num: [161/600] Discriminator Loss: 0.7725, Generator Loss: 1.7896 D(x): 0.7836, D(G(z)): 0.2882 Epoch: [13/20], Batch Num: [162/600] Discriminator Loss: 0.8817, Generator Loss: 2.1060 D(x): 0.7608, D(G(z)): 0.3221 Epoch: [13/20], Batch Num: [163/600] Discriminator Loss: 0.6495, Generator Loss: 2.2804 D(x): 0.7777, D(G(z)): 0.2372 Epoch: [13/20], Batch Num: [164/600] Discriminator Loss: 0.6298, Generator Loss: 2.4147 D(x): 0.7712, D(G(z)): 0.2049 Epoch: [13/20], Batch Num: [165/600] Discriminator Loss: 0.5963, Generator Loss: 2.4196 D(x): 0.7489, D(G(z)): 0.1794 Epoch: [13/20], Batch Num: [166/600] Discriminator Loss: 0.5974, Generator Loss: 2.4771 D(x): 0.7738, D(G(z)): 0.1820 Epoch: [13/20], Batch Num: [167/600] Discriminator Loss: 0.5196, Generator Loss: 2.2577 D(x): 0.8076, D(G(z)): 0.1959 Epoch: [13/20], Batch Num: [168/600] Discriminator Loss: 0.5584, Generator Loss: 2.3617 D(x): 0.8046, D(G(z)): 0.2071 Epoch: [13/20], Batch Num: [169/600] Discriminator Loss: 0.5927, Generator Loss: 2.2755 D(x): 0.8165, D(G(z)): 0.2270 Epoch: [13/20], Batch Num: [170/600] Discriminator Loss: 0.6918, Generator Loss: 2.2372 D(x): 0.7760, D(G(z)): 0.2260 Epoch: [13/20], Batch Num: [171/600] Discriminator Loss: 0.6610, Generator Loss: 2.1800 D(x): 0.7880, D(G(z)): 0.2305 Epoch: [13/20], Batch Num: [172/600] Discriminator Loss: 0.5703, Generator Loss: 2.3882 D(x): 0.8298, D(G(z)): 0.2157 Epoch: [13/20], Batch Num: [173/600] Discriminator Loss: 0.6688, Generator Loss: 2.4159 D(x): 0.7924, D(G(z)): 0.2508 Epoch: [13/20], Batch Num: [174/600] Discriminator Loss: 0.6212, Generator Loss: 2.4501 D(x): 0.7511, D(G(z)): 0.1685 Epoch: [13/20], Batch Num: [175/600] Discriminator Loss: 0.8287, Generator Loss: 2.2563 D(x): 0.7284, D(G(z)): 0.2441 Epoch: [13/20], Batch Num: [176/600] Discriminator Loss: 0.5187, Generator Loss: 2.2332 D(x): 0.7865, D(G(z)): 0.1644 Epoch: [13/20], Batch Num: [177/600] Discriminator Loss: 0.7661, Generator Loss: 1.9660 D(x): 0.7655, D(G(z)): 0.2463 Epoch: [13/20], Batch Num: [178/600] Discriminator Loss: 0.7445, Generator Loss: 1.9715 D(x): 0.7679, D(G(z)): 0.2470 Epoch: [13/20], Batch Num: [179/600] Discriminator Loss: 0.9389, Generator Loss: 1.6522 D(x): 0.7034, D(G(z)): 0.2888 Epoch: [13/20], Batch Num: [180/600] Discriminator Loss: 0.8852, Generator Loss: 2.0805 D(x): 0.7758, D(G(z)): 0.3124 Epoch: [13/20], Batch Num: [181/600] Discriminator Loss: 0.8534, Generator Loss: 2.3257 D(x): 0.7751, D(G(z)): 0.2901 Epoch: [13/20], Batch Num: [182/600] Discriminator Loss: 0.9633, Generator Loss: 2.3156 D(x): 0.6446, D(G(z)): 0.2084 Epoch: [13/20], Batch Num: [183/600] Discriminator Loss: 1.1209, Generator Loss: 1.8190 D(x): 0.6283, D(G(z)): 0.2516 Epoch: [13/20], Batch Num: [184/600] Discriminator Loss: 1.0105, Generator Loss: 1.7983 D(x): 0.7415, D(G(z)): 0.3315 Epoch: [13/20], Batch Num: [185/600] Discriminator Loss: 1.0798, Generator Loss: 1.9512 D(x): 0.7182, D(G(z)): 0.3680 Epoch: [13/20], Batch Num: [186/600] Discriminator Loss: 1.0904, Generator Loss: 1.8320 D(x): 0.6836, D(G(z)): 0.3111 Epoch: [13/20], Batch Num: [187/600] Discriminator Loss: 1.0598, Generator Loss: 1.9233 D(x): 0.6563, D(G(z)): 0.2534 Epoch: [13/20], Batch Num: [188/600] Discriminator Loss: 1.2745, Generator Loss: 1.5563 D(x): 0.6033, D(G(z)): 0.2698 Epoch: [13/20], Batch Num: [189/600] Discriminator Loss: 1.1030, Generator Loss: 1.3089 D(x): 0.6725, D(G(z)): 0.3316 Epoch: [13/20], Batch Num: [190/600] Discriminator Loss: 1.1161, Generator Loss: 1.2645 D(x): 0.7412, D(G(z)): 0.3913 Epoch: [13/20], Batch Num: [191/600] Discriminator Loss: 1.2123, Generator Loss: 1.6141 D(x): 0.7060, D(G(z)): 0.4292 Epoch: [13/20], Batch Num: [192/600] Discriminator Loss: 1.1190, Generator Loss: 1.9265 D(x): 0.6491, D(G(z)): 0.3183 Epoch: [13/20], Batch Num: [193/600] Discriminator Loss: 1.0974, Generator Loss: 1.8797 D(x): 0.5960, D(G(z)): 0.2449 Epoch: [13/20], Batch Num: [194/600] Discriminator Loss: 1.0211, Generator Loss: 1.7224 D(x): 0.6263, D(G(z)): 0.2391 Epoch: [13/20], Batch Num: [195/600] Discriminator Loss: 1.1472, Generator Loss: 1.5096 D(x): 0.6152, D(G(z)): 0.2928 Epoch: [13/20], Batch Num: [196/600] Discriminator Loss: 0.9056, Generator Loss: 1.3254 D(x): 0.7087, D(G(z)): 0.2983 Epoch: [13/20], Batch Num: [197/600] Discriminator Loss: 0.9007, Generator Loss: 1.5691 D(x): 0.7980, D(G(z)): 0.3760 Epoch: [13/20], Batch Num: [198/600] Discriminator Loss: 1.0489, Generator Loss: 1.6209 D(x): 0.6981, D(G(z)): 0.3724 Epoch: [13/20], Batch Num: [199/600] Discriminator Loss: 0.8058, Generator Loss: 1.7533 D(x): 0.7305, D(G(z)): 0.2903 Epoch: 13, Batch Num: [200/600]
Epoch: [13/20], Batch Num: [200/600] Discriminator Loss: 0.8855, Generator Loss: 1.8121 D(x): 0.6537, D(G(z)): 0.2479 Epoch: [13/20], Batch Num: [201/600] Discriminator Loss: 0.8559, Generator Loss: 1.8300 D(x): 0.6724, D(G(z)): 0.2402 Epoch: [13/20], Batch Num: [202/600] Discriminator Loss: 0.8055, Generator Loss: 1.6491 D(x): 0.7388, D(G(z)): 0.2906 Epoch: [13/20], Batch Num: [203/600] Discriminator Loss: 0.6968, Generator Loss: 1.8158 D(x): 0.7678, D(G(z)): 0.2556 Epoch: [13/20], Batch Num: [204/600] Discriminator Loss: 0.7072, Generator Loss: 1.7265 D(x): 0.7527, D(G(z)): 0.2468 Epoch: [13/20], Batch Num: [205/600] Discriminator Loss: 0.5633, Generator Loss: 1.9321 D(x): 0.8273, D(G(z)): 0.2427 Epoch: [13/20], Batch Num: [206/600] Discriminator Loss: 0.5267, Generator Loss: 1.8224 D(x): 0.8311, D(G(z)): 0.2303 Epoch: [13/20], Batch Num: [207/600] Discriminator Loss: 0.6445, Generator Loss: 2.2529 D(x): 0.8152, D(G(z)): 0.2482 Epoch: [13/20], Batch Num: [208/600] Discriminator Loss: 0.5972, Generator Loss: 1.9293 D(x): 0.7179, D(G(z)): 0.1494 Epoch: [13/20], Batch Num: [209/600] Discriminator Loss: 0.6955, Generator Loss: 2.1865 D(x): 0.7599, D(G(z)): 0.2370 Epoch: [13/20], Batch Num: [210/600] Discriminator Loss: 0.5561, Generator Loss: 2.1419 D(x): 0.8002, D(G(z)): 0.2054 Epoch: [13/20], Batch Num: [211/600] Discriminator Loss: 0.5787, Generator Loss: 2.1863 D(x): 0.8224, D(G(z)): 0.2410 Epoch: [13/20], Batch Num: [212/600] Discriminator Loss: 0.5557, Generator Loss: 2.3829 D(x): 0.8187, D(G(z)): 0.2158 Epoch: [13/20], Batch Num: [213/600] Discriminator Loss: 0.6115, Generator Loss: 2.4633 D(x): 0.8127, D(G(z)): 0.2214 Epoch: [13/20], Batch Num: [214/600] Discriminator Loss: 0.6854, Generator Loss: 2.4100 D(x): 0.7510, D(G(z)): 0.2036 Epoch: [13/20], Batch Num: [215/600] Discriminator Loss: 0.5414, Generator Loss: 2.2930 D(x): 0.7893, D(G(z)): 0.1783 Epoch: [13/20], Batch Num: [216/600] Discriminator Loss: 0.5512, Generator Loss: 2.3562 D(x): 0.8319, D(G(z)): 0.2062 Epoch: [13/20], Batch Num: [217/600] Discriminator Loss: 0.5707, Generator Loss: 2.2905 D(x): 0.7706, D(G(z)): 0.1617 Epoch: [13/20], Batch Num: [218/600] Discriminator Loss: 0.5954, Generator Loss: 2.2537 D(x): 0.8173, D(G(z)): 0.2240 Epoch: [13/20], Batch Num: [219/600] Discriminator Loss: 0.6896, Generator Loss: 2.2786 D(x): 0.8271, D(G(z)): 0.2692 Epoch: [13/20], Batch Num: [220/600] Discriminator Loss: 0.6043, Generator Loss: 2.4722 D(x): 0.8067, D(G(z)): 0.2135 Epoch: [13/20], Batch Num: [221/600] Discriminator Loss: 0.7086, Generator Loss: 2.5964 D(x): 0.7436, D(G(z)): 0.1692 Epoch: [13/20], Batch Num: [222/600] Discriminator Loss: 0.6658, Generator Loss: 2.2951 D(x): 0.7520, D(G(z)): 0.1711 Epoch: [13/20], Batch Num: [223/600] Discriminator Loss: 0.8021, Generator Loss: 2.0782 D(x): 0.7834, D(G(z)): 0.2576 Epoch: [13/20], Batch Num: [224/600] Discriminator Loss: 0.7045, Generator Loss: 1.8990 D(x): 0.7992, D(G(z)): 0.2391 Epoch: [13/20], Batch Num: [225/600] Discriminator Loss: 0.6965, Generator Loss: 2.2066 D(x): 0.8150, D(G(z)): 0.2435 Epoch: [13/20], Batch Num: [226/600] Discriminator Loss: 0.8681, Generator Loss: 2.5555 D(x): 0.7743, D(G(z)): 0.2777 Epoch: [13/20], Batch Num: [227/600] Discriminator Loss: 0.8227, Generator Loss: 2.5176 D(x): 0.7182, D(G(z)): 0.1807 Epoch: [13/20], Batch Num: [228/600] Discriminator Loss: 0.8520, Generator Loss: 2.3541 D(x): 0.7133, D(G(z)): 0.2163 Epoch: [13/20], Batch Num: [229/600] Discriminator Loss: 0.7433, Generator Loss: 1.6741 D(x): 0.7724, D(G(z)): 0.2728 Epoch: [13/20], Batch Num: [230/600] Discriminator Loss: 0.7726, Generator Loss: 1.6816 D(x): 0.7822, D(G(z)): 0.2786 Epoch: [13/20], Batch Num: [231/600] Discriminator Loss: 0.7735, Generator Loss: 2.2239 D(x): 0.8123, D(G(z)): 0.2698 Epoch: [13/20], Batch Num: [232/600] Discriminator Loss: 0.8421, Generator Loss: 2.6595 D(x): 0.7694, D(G(z)): 0.2722 Epoch: [13/20], Batch Num: [233/600] Discriminator Loss: 0.9163, Generator Loss: 2.2369 D(x): 0.6467, D(G(z)): 0.1794 Epoch: [13/20], Batch Num: [234/600] Discriminator Loss: 1.0732, Generator Loss: 1.6086 D(x): 0.6232, D(G(z)): 0.2121 Epoch: [13/20], Batch Num: [235/600] Discriminator Loss: 0.8601, Generator Loss: 1.3047 D(x): 0.7419, D(G(z)): 0.2754 Epoch: [13/20], Batch Num: [236/600] Discriminator Loss: 1.1542, Generator Loss: 1.2112 D(x): 0.8032, D(G(z)): 0.4346 Epoch: [13/20], Batch Num: [237/600] Discriminator Loss: 0.8953, Generator Loss: 1.8313 D(x): 0.8632, D(G(z)): 0.4018 Epoch: [13/20], Batch Num: [238/600] Discriminator Loss: 0.9489, Generator Loss: 2.4534 D(x): 0.6831, D(G(z)): 0.2585 Epoch: [13/20], Batch Num: [239/600] Discriminator Loss: 0.9832, Generator Loss: 2.0320 D(x): 0.6279, D(G(z)): 0.1816 Epoch: [13/20], Batch Num: [240/600] Discriminator Loss: 1.1152, Generator Loss: 1.4793 D(x): 0.5758, D(G(z)): 0.1828 Epoch: [13/20], Batch Num: [241/600] Discriminator Loss: 0.8998, Generator Loss: 1.1798 D(x): 0.6846, D(G(z)): 0.2604 Epoch: [13/20], Batch Num: 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1.4190 D(x): 0.7773, D(G(z)): 0.3655 Epoch: [13/20], Batch Num: [251/600] Discriminator Loss: 0.8927, Generator Loss: 1.3681 D(x): 0.7766, D(G(z)): 0.3575 Epoch: [13/20], Batch Num: [252/600] Discriminator Loss: 0.9408, Generator Loss: 1.3384 D(x): 0.7952, D(G(z)): 0.3954 Epoch: [13/20], Batch Num: [253/600] Discriminator Loss: 0.7865, Generator Loss: 1.7121 D(x): 0.7586, D(G(z)): 0.2918 Epoch: [13/20], Batch Num: [254/600] Discriminator Loss: 0.7753, Generator Loss: 1.9350 D(x): 0.7045, D(G(z)): 0.2438 Epoch: [13/20], Batch Num: [255/600] Discriminator Loss: 0.8019, Generator Loss: 1.8111 D(x): 0.6688, D(G(z)): 0.2038 Epoch: [13/20], Batch Num: [256/600] Discriminator Loss: 0.7908, Generator Loss: 1.7989 D(x): 0.6910, D(G(z)): 0.2381 Epoch: [13/20], Batch Num: [257/600] Discriminator Loss: 0.7433, Generator Loss: 1.6932 D(x): 0.7075, D(G(z)): 0.2300 Epoch: [13/20], Batch Num: [258/600] Discriminator Loss: 0.7627, Generator Loss: 1.4788 D(x): 0.7415, D(G(z)): 0.2853 Epoch: [13/20], Batch Num: [259/600] Discriminator Loss: 0.7478, Generator Loss: 1.4404 D(x): 0.8228, D(G(z)): 0.3234 Epoch: [13/20], Batch Num: [260/600] Discriminator Loss: 0.7355, Generator Loss: 1.4986 D(x): 0.8134, D(G(z)): 0.3198 Epoch: [13/20], Batch Num: [261/600] Discriminator Loss: 0.6438, Generator Loss: 1.6966 D(x): 0.8181, D(G(z)): 0.2691 Epoch: [13/20], Batch Num: [262/600] Discriminator Loss: 0.7518, Generator Loss: 1.7680 D(x): 0.7716, D(G(z)): 0.2969 Epoch: [13/20], Batch Num: [263/600] Discriminator Loss: 0.6700, Generator Loss: 2.1702 D(x): 0.7661, D(G(z)): 0.2560 Epoch: [13/20], Batch Num: [264/600] Discriminator Loss: 0.7051, Generator Loss: 2.3424 D(x): 0.7332, D(G(z)): 0.2052 Epoch: [13/20], Batch Num: [265/600] Discriminator Loss: 0.7326, Generator Loss: 1.9754 D(x): 0.6744, D(G(z)): 0.1757 Epoch: [13/20], Batch Num: [266/600] Discriminator Loss: 0.5812, Generator Loss: 1.7775 D(x): 0.8003, D(G(z)): 0.2125 Epoch: [13/20], Batch Num: [267/600] Discriminator Loss: 0.6504, Generator Loss: 1.9687 D(x): 0.8229, D(G(z)): 0.2674 Epoch: [13/20], Batch Num: [268/600] Discriminator Loss: 0.6076, Generator Loss: 1.8656 D(x): 0.8146, D(G(z)): 0.2368 Epoch: [13/20], Batch Num: [269/600] Discriminator Loss: 0.6197, Generator Loss: 2.0465 D(x): 0.7905, D(G(z)): 0.2241 Epoch: [13/20], Batch Num: [270/600] Discriminator Loss: 0.5391, Generator Loss: 1.9559 D(x): 0.8177, D(G(z)): 0.2219 Epoch: [13/20], Batch Num: [271/600] Discriminator Loss: 0.6197, Generator Loss: 2.0236 D(x): 0.7732, D(G(z)): 0.2125 Epoch: [13/20], Batch Num: [272/600] Discriminator Loss: 0.5691, Generator Loss: 2.3686 D(x): 0.8314, D(G(z)): 0.2357 Epoch: [13/20], Batch Num: [273/600] Discriminator Loss: 0.7096, Generator Loss: 2.2022 D(x): 0.7321, D(G(z)): 0.1954 Epoch: [13/20], Batch Num: [274/600] Discriminator Loss: 0.5701, Generator Loss: 2.3262 D(x): 0.8139, D(G(z)): 0.2131 Epoch: [13/20], Batch Num: [275/600] Discriminator Loss: 0.6596, Generator Loss: 2.1177 D(x): 0.7230, D(G(z)): 0.1666 Epoch: [13/20], Batch Num: [276/600] Discriminator Loss: 0.5695, Generator Loss: 1.7898 D(x): 0.8048, D(G(z)): 0.1958 Epoch: [13/20], Batch Num: [277/600] Discriminator Loss: 0.7250, Generator Loss: 1.7222 D(x): 0.7679, D(G(z)): 0.2630 Epoch: [13/20], Batch Num: [278/600] Discriminator Loss: 0.7215, Generator Loss: 1.8178 D(x): 0.8435, D(G(z)): 0.3179 Epoch: [13/20], Batch Num: [279/600] Discriminator Loss: 0.6827, Generator Loss: 2.2641 D(x): 0.8255, D(G(z)): 0.2679 Epoch: [13/20], Batch Num: [280/600] Discriminator Loss: 0.6571, Generator Loss: 2.2800 D(x): 0.7650, D(G(z)): 0.1969 Epoch: [13/20], Batch Num: [281/600] Discriminator Loss: 0.6524, Generator Loss: 2.2813 D(x): 0.8000, D(G(z)): 0.2184 Epoch: [13/20], Batch Num: [282/600] Discriminator Loss: 0.8268, Generator Loss: 2.2751 D(x): 0.7036, D(G(z)): 0.1774 Epoch: [13/20], Batch Num: [283/600] Discriminator Loss: 0.7716, Generator Loss: 2.1109 D(x): 0.7720, D(G(z)): 0.2543 Epoch: [13/20], Batch Num: [284/600] Discriminator Loss: 0.6280, Generator Loss: 1.9795 D(x): 0.8024, D(G(z)): 0.2389 Epoch: [13/20], Batch Num: [285/600] Discriminator Loss: 0.6185, Generator Loss: 2.0974 D(x): 0.7644, D(G(z)): 0.1772 Epoch: [13/20], Batch Num: [286/600] Discriminator Loss: 0.7724, Generator Loss: 1.7285 D(x): 0.7745, D(G(z)): 0.2859 Epoch: [13/20], Batch Num: [287/600] Discriminator Loss: 0.7747, Generator Loss: 2.0395 D(x): 0.8099, D(G(z)): 0.3042 Epoch: [13/20], Batch Num: [288/600] Discriminator Loss: 0.7880, Generator Loss: 2.1483 D(x): 0.7239, D(G(z)): 0.2293 Epoch: [13/20], Batch Num: [289/600] Discriminator Loss: 0.9177, Generator Loss: 1.8542 D(x): 0.6507, D(G(z)): 0.2044 Epoch: [13/20], Batch Num: [290/600] Discriminator Loss: 0.7384, Generator Loss: 1.7990 D(x): 0.8051, D(G(z)): 0.2764 Epoch: [13/20], Batch Num: [291/600] Discriminator Loss: 0.8517, Generator Loss: 1.8330 D(x): 0.7659, D(G(z)): 0.3075 Epoch: [13/20], Batch Num: [292/600] Discriminator Loss: 0.9969, Generator Loss: 1.8780 D(x): 0.7166, D(G(z)): 0.2917 Epoch: [13/20], Batch Num: [293/600] Discriminator Loss: 1.0209, Generator Loss: 2.0172 D(x): 0.7106, D(G(z)): 0.3038 Epoch: [13/20], Batch Num: [294/600] Discriminator Loss: 0.8924, Generator Loss: 2.0749 D(x): 0.7134, D(G(z)): 0.2477 Epoch: [13/20], Batch Num: [295/600] Discriminator Loss: 0.9710, Generator Loss: 1.6679 D(x): 0.6662, D(G(z)): 0.2244 Epoch: [13/20], Batch Num: [296/600] Discriminator Loss: 0.8297, Generator Loss: 1.6721 D(x): 0.7943, D(G(z)): 0.2937 Epoch: [13/20], Batch Num: [297/600] Discriminator Loss: 0.8826, Generator Loss: 1.6826 D(x): 0.7528, D(G(z)): 0.3058 Epoch: [13/20], Batch Num: [298/600] Discriminator Loss: 1.0500, Generator Loss: 1.8538 D(x): 0.7124, D(G(z)): 0.3472 Epoch: [13/20], Batch Num: [299/600] Discriminator Loss: 0.7978, Generator Loss: 2.0520 D(x): 0.7681, D(G(z)): 0.2644 Epoch: 13, Batch Num: [300/600]
Epoch: [13/20], Batch Num: [300/600] Discriminator Loss: 1.0699, Generator Loss: 1.8763 D(x): 0.6568, D(G(z)): 0.2674 Epoch: [13/20], Batch Num: [301/600] Discriminator Loss: 1.1025, Generator Loss: 1.8324 D(x): 0.6272, D(G(z)): 0.2488 Epoch: [13/20], Batch Num: [302/600] Discriminator Loss: 0.7692, Generator Loss: 1.7580 D(x): 0.7656, D(G(z)): 0.2791 Epoch: [13/20], Batch Num: [303/600] Discriminator Loss: 0.8696, Generator Loss: 1.8240 D(x): 0.7647, D(G(z)): 0.3288 Epoch: [13/20], Batch Num: [304/600] Discriminator Loss: 0.8386, Generator Loss: 1.8043 D(x): 0.7617, D(G(z)): 0.2833 Epoch: [13/20], Batch Num: [305/600] Discriminator Loss: 0.8681, Generator Loss: 1.7862 D(x): 0.7368, D(G(z)): 0.2603 Epoch: [13/20], Batch Num: [306/600] Discriminator Loss: 0.8072, Generator Loss: 1.8866 D(x): 0.6909, D(G(z)): 0.2148 Epoch: [13/20], Batch Num: [307/600] Discriminator Loss: 0.7996, Generator Loss: 1.8239 D(x): 0.7364, D(G(z)): 0.2523 Epoch: [13/20], Batch Num: [308/600] Discriminator Loss: 0.8191, Generator Loss: 1.8001 D(x): 0.7450, D(G(z)): 0.2758 Epoch: [13/20], Batch Num: [309/600] Discriminator Loss: 0.9446, Generator Loss: 1.7131 D(x): 0.6954, D(G(z)): 0.2628 Epoch: [13/20], Batch Num: [310/600] Discriminator Loss: 0.7271, Generator Loss: 1.8290 D(x): 0.7853, D(G(z)): 0.2691 Epoch: [13/20], Batch Num: [311/600] Discriminator Loss: 0.7228, Generator Loss: 1.8117 D(x): 0.7367, D(G(z)): 0.2272 Epoch: [13/20], Batch Num: [312/600] Discriminator Loss: 0.7365, Generator Loss: 1.8352 D(x): 0.8024, D(G(z)): 0.2993 Epoch: [13/20], Batch Num: [313/600] Discriminator Loss: 0.7107, Generator Loss: 1.8982 D(x): 0.7844, D(G(z)): 0.2644 Epoch: [13/20], Batch Num: [314/600] Discriminator Loss: 0.5567, Generator Loss: 2.2711 D(x): 0.8216, D(G(z)): 0.2216 Epoch: [13/20], Batch Num: [315/600] Discriminator Loss: 0.7310, Generator Loss: 2.3446 D(x): 0.7659, D(G(z)): 0.2246 Epoch: [13/20], Batch Num: [316/600] Discriminator Loss: 0.6315, Generator Loss: 2.4496 D(x): 0.7454, D(G(z)): 0.1615 Epoch: [13/20], Batch Num: [317/600] Discriminator Loss: 0.7057, Generator Loss: 2.0554 D(x): 0.7186, D(G(z)): 0.1659 Epoch: [13/20], Batch Num: [318/600] Discriminator Loss: 0.6441, Generator Loss: 1.9576 D(x): 0.8043, D(G(z)): 0.2064 Epoch: [13/20], Batch Num: [319/600] Discriminator Loss: 0.6044, Generator Loss: 1.9278 D(x): 0.8528, D(G(z)): 0.2640 Epoch: [13/20], Batch Num: [320/600] Discriminator Loss: 0.6184, Generator Loss: 2.1029 D(x): 0.8540, D(G(z)): 0.2591 Epoch: [13/20], Batch Num: [321/600] Discriminator Loss: 0.5351, Generator Loss: 2.3209 D(x): 0.8547, D(G(z)): 0.2327 Epoch: [13/20], Batch Num: [322/600] Discriminator Loss: 0.5503, Generator Loss: 2.6277 D(x): 0.8291, D(G(z)): 0.2088 Epoch: [13/20], Batch Num: [323/600] Discriminator Loss: 0.4434, Generator Loss: 2.8608 D(x): 0.8236, D(G(z)): 0.1422 Epoch: [13/20], Batch Num: [324/600] Discriminator Loss: 0.7228, Generator Loss: 2.4028 D(x): 0.7181, D(G(z)): 0.1491 Epoch: [13/20], Batch Num: [325/600] Discriminator Loss: 0.5741, Generator Loss: 2.2859 D(x): 0.7826, D(G(z)): 0.1579 Epoch: [13/20], Batch Num: [326/600] Discriminator Loss: 0.6723, Generator Loss: 2.1603 D(x): 0.8264, D(G(z)): 0.2371 Epoch: [13/20], Batch Num: [327/600] Discriminator Loss: 0.4751, Generator Loss: 2.0877 D(x): 0.8724, D(G(z)): 0.2239 Epoch: [13/20], Batch Num: [328/600] Discriminator Loss: 0.5890, Generator Loss: 1.9585 D(x): 0.8623, D(G(z)): 0.2388 Epoch: [13/20], Batch Num: [329/600] Discriminator Loss: 0.6480, Generator Loss: 2.4098 D(x): 0.8146, D(G(z)): 0.2076 Epoch: [13/20], Batch Num: [330/600] Discriminator Loss: 0.5259, Generator Loss: 2.4340 D(x): 0.8332, D(G(z)): 0.1995 Epoch: [13/20], Batch Num: [331/600] Discriminator Loss: 0.7417, Generator Loss: 2.6276 D(x): 0.7568, D(G(z)): 0.2054 Epoch: [13/20], Batch Num: [332/600] Discriminator Loss: 0.7047, Generator Loss: 2.4380 D(x): 0.7427, D(G(z)): 0.1470 Epoch: [13/20], Batch Num: [333/600] Discriminator Loss: 0.6508, Generator Loss: 2.1252 D(x): 0.8002, D(G(z)): 0.1978 Epoch: [13/20], Batch Num: [334/600] Discriminator Loss: 0.6916, Generator Loss: 1.8319 D(x): 0.7555, D(G(z)): 0.1966 Epoch: [13/20], Batch Num: [335/600] Discriminator Loss: 0.6251, Generator Loss: 1.8083 D(x): 0.8705, D(G(z)): 0.2864 Epoch: [13/20], Batch Num: [336/600] Discriminator Loss: 0.6818, Generator Loss: 2.0430 D(x): 0.8641, D(G(z)): 0.2807 Epoch: [13/20], Batch Num: [337/600] Discriminator Loss: 0.9521, Generator Loss: 2.3881 D(x): 0.7908, D(G(z)): 0.3206 Epoch: [13/20], Batch Num: [338/600] Discriminator Loss: 0.8021, Generator Loss: 2.6058 D(x): 0.7364, D(G(z)): 0.2051 Epoch: [13/20], Batch Num: [339/600] Discriminator Loss: 0.8794, Generator Loss: 2.5946 D(x): 0.6833, D(G(z)): 0.1864 Epoch: [13/20], Batch Num: [340/600] Discriminator Loss: 1.0153, Generator Loss: 2.1674 D(x): 0.6628, D(G(z)): 0.2316 Epoch: [13/20], Batch Num: [341/600] Discriminator Loss: 0.7942, Generator Loss: 1.6033 D(x): 0.7423, D(G(z)): 0.2503 Epoch: [13/20], Batch Num: [342/600] Discriminator Loss: 0.9151, Generator Loss: 1.4644 D(x): 0.8029, D(G(z)): 0.3651 Epoch: [13/20], Batch Num: [343/600] Discriminator Loss: 0.8336, Generator Loss: 1.4301 D(x): 0.8052, D(G(z)): 0.3080 Epoch: [13/20], Batch Num: [344/600] Discriminator Loss: 1.0621, Generator Loss: 1.8467 D(x): 0.7335, D(G(z)): 0.3375 Epoch: [13/20], Batch Num: [345/600] Discriminator Loss: 1.0086, Generator Loss: 1.9572 D(x): 0.6893, D(G(z)): 0.2489 Epoch: [13/20], Batch Num: [346/600] Discriminator Loss: 1.0468, Generator Loss: 1.8756 D(x): 0.6153, D(G(z)): 0.2293 Epoch: [13/20], Batch Num: [347/600] Discriminator Loss: 0.9436, Generator Loss: 1.5852 D(x): 0.6694, D(G(z)): 0.2599 Epoch: [13/20], Batch Num: [348/600] Discriminator Loss: 0.7245, Generator Loss: 1.4982 D(x): 0.7500, D(G(z)): 0.2499 Epoch: [13/20], Batch Num: [349/600] Discriminator Loss: 0.8273, Generator Loss: 1.6214 D(x): 0.7986, D(G(z)): 0.3304 Epoch: [13/20], Batch Num: [350/600] Discriminator Loss: 0.9042, Generator Loss: 1.5619 D(x): 0.7626, D(G(z)): 0.3294 Epoch: [13/20], Batch Num: [351/600] Discriminator Loss: 0.8794, Generator Loss: 1.7782 D(x): 0.7609, D(G(z)): 0.3166 Epoch: [13/20], Batch Num: [352/600] Discriminator Loss: 0.8639, Generator Loss: 2.0251 D(x): 0.6974, D(G(z)): 0.2625 Epoch: [13/20], Batch Num: [353/600] Discriminator Loss: 0.7316, Generator Loss: 1.9593 D(x): 0.7204, D(G(z)): 0.2140 Epoch: [13/20], Batch Num: [354/600] Discriminator Loss: 0.6030, Generator Loss: 1.8265 D(x): 0.7614, D(G(z)): 0.1805 Epoch: [13/20], Batch Num: [355/600] Discriminator Loss: 0.7519, Generator Loss: 1.8613 D(x): 0.7656, D(G(z)): 0.2528 Epoch: [13/20], Batch Num: [356/600] Discriminator Loss: 0.7662, Generator Loss: 1.9012 D(x): 0.7658, D(G(z)): 0.2691 Epoch: [13/20], Batch Num: [357/600] Discriminator Loss: 0.6166, Generator Loss: 2.0688 D(x): 0.8327, D(G(z)): 0.2726 Epoch: [13/20], Batch Num: [358/600] Discriminator Loss: 0.7229, Generator Loss: 2.1116 D(x): 0.7651, D(G(z)): 0.2517 Epoch: [13/20], Batch Num: [359/600] Discriminator Loss: 0.5636, Generator Loss: 2.4048 D(x): 0.8327, D(G(z)): 0.2210 Epoch: [13/20], Batch Num: [360/600] Discriminator Loss: 0.5365, Generator Loss: 2.3481 D(x): 0.7871, D(G(z)): 0.1760 Epoch: [13/20], Batch Num: [361/600] Discriminator Loss: 0.8148, Generator Loss: 2.4673 D(x): 0.6880, D(G(z)): 0.2082 Epoch: [13/20], Batch Num: [362/600] Discriminator Loss: 0.6210, Generator Loss: 2.2978 D(x): 0.7708, D(G(z)): 0.1900 Epoch: [13/20], Batch Num: [363/600] Discriminator Loss: 0.5738, Generator Loss: 2.3142 D(x): 0.7885, D(G(z)): 0.1798 Epoch: [13/20], Batch Num: [364/600] Discriminator Loss: 0.4961, Generator Loss: 2.1873 D(x): 0.8623, D(G(z)): 0.2061 Epoch: [13/20], Batch Num: [365/600] Discriminator Loss: 0.6739, Generator Loss: 2.2022 D(x): 0.8118, D(G(z)): 0.2538 Epoch: [13/20], Batch Num: [366/600] Discriminator Loss: 0.5672, Generator Loss: 2.2840 D(x): 0.8347, D(G(z)): 0.2261 Epoch: [13/20], Batch Num: [367/600] Discriminator Loss: 0.5230, Generator Loss: 2.6210 D(x): 0.8342, D(G(z)): 0.1918 Epoch: [13/20], Batch Num: [368/600] Discriminator Loss: 0.6221, Generator Loss: 2.5831 D(x): 0.8174, D(G(z)): 0.2515 Epoch: [13/20], Batch Num: [369/600] Discriminator Loss: 0.6839, Generator Loss: 2.7891 D(x): 0.7114, D(G(z)): 0.1466 Epoch: [13/20], Batch Num: [370/600] Discriminator Loss: 0.5602, Generator Loss: 2.4369 D(x): 0.7624, D(G(z)): 0.1336 Epoch: [13/20], Batch Num: [371/600] Discriminator Loss: 0.5439, Generator Loss: 2.2670 D(x): 0.8090, D(G(z)): 0.1656 Epoch: [13/20], Batch Num: [372/600] Discriminator Loss: 0.6480, Generator Loss: 1.9394 D(x): 0.8285, D(G(z)): 0.2177 Epoch: [13/20], Batch Num: [373/600] Discriminator Loss: 0.6260, Generator Loss: 2.0253 D(x): 0.8605, D(G(z)): 0.2587 Epoch: [13/20], Batch Num: [374/600] Discriminator Loss: 0.6716, Generator Loss: 2.2376 D(x): 0.8304, D(G(z)): 0.2707 Epoch: [13/20], Batch Num: [375/600] Discriminator Loss: 0.7130, Generator Loss: 2.4456 D(x): 0.7776, D(G(z)): 0.2341 Epoch: [13/20], Batch Num: [376/600] Discriminator Loss: 0.6737, Generator Loss: 2.6232 D(x): 0.7320, D(G(z)): 0.1626 Epoch: [13/20], Batch Num: [377/600] Discriminator Loss: 0.7272, Generator Loss: 2.3375 D(x): 0.7356, D(G(z)): 0.1792 Epoch: [13/20], Batch Num: [378/600] Discriminator Loss: 0.6857, Generator Loss: 2.0241 D(x): 0.7689, D(G(z)): 0.2340 Epoch: [13/20], Batch Num: [379/600] Discriminator Loss: 0.6851, Generator Loss: 1.9997 D(x): 0.8218, D(G(z)): 0.2746 Epoch: [13/20], Batch Num: [380/600] Discriminator Loss: 0.7427, Generator Loss: 2.2312 D(x): 0.8156, D(G(z)): 0.2818 Epoch: [13/20], Batch Num: [381/600] Discriminator Loss: 0.8563, Generator Loss: 2.1698 D(x): 0.7146, D(G(z)): 0.2290 Epoch: [13/20], Batch Num: [382/600] Discriminator Loss: 0.6874, Generator Loss: 2.1089 D(x): 0.7690, D(G(z)): 0.2186 Epoch: [13/20], Batch Num: [383/600] Discriminator Loss: 0.7749, Generator Loss: 1.9098 D(x): 0.7738, D(G(z)): 0.2474 Epoch: [13/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [13/20], Batch Num: [400/600] Discriminator Loss: 1.0152, Generator Loss: 1.2225 D(x): 0.6632, D(G(z)): 0.3151 Epoch: [13/20], Batch Num: [401/600] Discriminator Loss: 0.8344, Generator Loss: 1.3701 D(x): 0.8073, D(G(z)): 0.3672 Epoch: [13/20], Batch Num: [402/600] Discriminator Loss: 0.8279, Generator Loss: 1.3824 D(x): 0.8149, D(G(z)): 0.3737 Epoch: [13/20], Batch Num: [403/600] Discriminator Loss: 0.9578, Generator Loss: 1.8329 D(x): 0.7201, D(G(z)): 0.3457 Epoch: [13/20], Batch Num: [404/600] Discriminator Loss: 0.8687, Generator Loss: 1.9682 D(x): 0.7181, D(G(z)): 0.3017 Epoch: [13/20], Batch Num: [405/600] Discriminator Loss: 0.7331, Generator Loss: 2.0636 D(x): 0.6920, D(G(z)): 0.2014 Epoch: [13/20], Batch Num: [406/600] Discriminator Loss: 0.8166, Generator Loss: 2.1211 D(x): 0.6740, D(G(z)): 0.2054 Epoch: [13/20], Batch Num: [407/600] Discriminator Loss: 0.7457, Generator Loss: 1.9304 D(x): 0.7297, D(G(z)): 0.2320 Epoch: [13/20], Batch Num: [408/600] Discriminator Loss: 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Discriminator Loss: 0.4382, Generator Loss: 2.8020 D(x): 0.8351, D(G(z)): 0.1417 Epoch: [13/20], Batch Num: [426/600] Discriminator Loss: 0.4345, Generator Loss: 2.6617 D(x): 0.8512, D(G(z)): 0.1618 Epoch: [13/20], Batch Num: [427/600] Discriminator Loss: 0.4789, Generator Loss: 2.5244 D(x): 0.8220, D(G(z)): 0.1414 Epoch: [13/20], Batch Num: [428/600] Discriminator Loss: 0.5292, Generator Loss: 2.4204 D(x): 0.8187, D(G(z)): 0.1775 Epoch: [13/20], Batch Num: [429/600] Discriminator Loss: 0.5992, Generator Loss: 2.4249 D(x): 0.8158, D(G(z)): 0.2189 Epoch: [13/20], Batch Num: [430/600] Discriminator Loss: 0.5745, Generator Loss: 2.5450 D(x): 0.8610, D(G(z)): 0.2518 Epoch: [13/20], Batch Num: [431/600] Discriminator Loss: 0.5528, Generator Loss: 2.7960 D(x): 0.7881, D(G(z)): 0.1434 Epoch: [13/20], Batch Num: [432/600] Discriminator Loss: 0.6861, Generator Loss: 2.4653 D(x): 0.7355, D(G(z)): 0.1740 Epoch: [13/20], Batch Num: [433/600] Discriminator Loss: 0.6105, Generator Loss: 1.9340 D(x): 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[442/600] Discriminator Loss: 0.9621, Generator Loss: 1.7491 D(x): 0.6549, D(G(z)): 0.2183 Epoch: [13/20], Batch Num: [443/600] Discriminator Loss: 1.1868, Generator Loss: 1.6592 D(x): 0.7131, D(G(z)): 0.3720 Epoch: [13/20], Batch Num: [444/600] Discriminator Loss: 1.0933, Generator Loss: 1.8412 D(x): 0.6946, D(G(z)): 0.3294 Epoch: [13/20], Batch Num: [445/600] Discriminator Loss: 1.1942, Generator Loss: 1.7297 D(x): 0.6675, D(G(z)): 0.3087 Epoch: [13/20], Batch Num: [446/600] Discriminator Loss: 0.9809, Generator Loss: 1.7396 D(x): 0.6775, D(G(z)): 0.2602 Epoch: [13/20], Batch Num: [447/600] Discriminator Loss: 1.2112, Generator Loss: 1.7168 D(x): 0.6161, D(G(z)): 0.2654 Epoch: [13/20], Batch Num: [448/600] Discriminator Loss: 1.0863, Generator Loss: 1.5032 D(x): 0.6545, D(G(z)): 0.3047 Epoch: [13/20], Batch Num: [449/600] Discriminator Loss: 1.1562, Generator Loss: 1.4374 D(x): 0.6787, D(G(z)): 0.3397 Epoch: [13/20], Batch Num: [450/600] Discriminator Loss: 1.2597, Generator Loss: 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Generator Loss: 1.8785 D(x): 0.8181, D(G(z)): 0.2770 Epoch: [13/20], Batch Num: [468/600] Discriminator Loss: 0.4713, Generator Loss: 2.1121 D(x): 0.8531, D(G(z)): 0.2164 Epoch: [13/20], Batch Num: [469/600] Discriminator Loss: 0.5842, Generator Loss: 2.4642 D(x): 0.7637, D(G(z)): 0.1740 Epoch: [13/20], Batch Num: [470/600] Discriminator Loss: 0.4314, Generator Loss: 2.4585 D(x): 0.8205, D(G(z)): 0.1506 Epoch: [13/20], Batch Num: [471/600] Discriminator Loss: 0.4544, Generator Loss: 2.4813 D(x): 0.8211, D(G(z)): 0.1547 Epoch: [13/20], Batch Num: [472/600] Discriminator Loss: 0.4189, Generator Loss: 2.2926 D(x): 0.8533, D(G(z)): 0.1597 Epoch: [13/20], Batch Num: [473/600] Discriminator Loss: 0.5175, Generator Loss: 2.2587 D(x): 0.8119, D(G(z)): 0.1814 Epoch: [13/20], Batch Num: [474/600] Discriminator Loss: 0.5861, Generator Loss: 2.3838 D(x): 0.7938, D(G(z)): 0.1791 Epoch: [13/20], Batch Num: [475/600] Discriminator Loss: 0.4523, Generator Loss: 2.2439 D(x): 0.8471, D(G(z)): 0.1645 Epoch: [13/20], Batch Num: [476/600] Discriminator Loss: 0.4853, Generator Loss: 2.3612 D(x): 0.8713, D(G(z)): 0.2055 Epoch: [13/20], Batch Num: [477/600] Discriminator Loss: 0.5640, Generator Loss: 2.2407 D(x): 0.8082, D(G(z)): 0.1785 Epoch: [13/20], Batch Num: [478/600] Discriminator Loss: 0.4792, Generator Loss: 2.3741 D(x): 0.8432, D(G(z)): 0.1940 Epoch: [13/20], Batch Num: [479/600] Discriminator Loss: 0.4884, Generator Loss: 2.5185 D(x): 0.8288, D(G(z)): 0.1726 Epoch: [13/20], Batch Num: [480/600] Discriminator Loss: 0.5712, Generator Loss: 2.4101 D(x): 0.8380, D(G(z)): 0.1960 Epoch: [13/20], Batch Num: [481/600] Discriminator Loss: 0.4387, Generator Loss: 2.3466 D(x): 0.8330, D(G(z)): 0.1565 Epoch: [13/20], Batch Num: [482/600] Discriminator Loss: 0.5143, Generator Loss: 2.4557 D(x): 0.8435, D(G(z)): 0.1866 Epoch: [13/20], Batch Num: [483/600] Discriminator Loss: 0.5324, Generator Loss: 2.5925 D(x): 0.8220, D(G(z)): 0.1955 Epoch: [13/20], Batch Num: [484/600] Discriminator Loss: 0.8621, Generator Loss: 2.6034 D(x): 0.7477, D(G(z)): 0.2291 Epoch: [13/20], Batch Num: [485/600] Discriminator Loss: 0.6960, Generator Loss: 2.4401 D(x): 0.7826, D(G(z)): 0.2057 Epoch: [13/20], Batch Num: [486/600] Discriminator Loss: 0.6100, Generator Loss: 2.0266 D(x): 0.8127, D(G(z)): 0.2223 Epoch: [13/20], Batch Num: [487/600] Discriminator Loss: 0.6597, Generator Loss: 2.0190 D(x): 0.8131, D(G(z)): 0.2122 Epoch: [13/20], Batch Num: [488/600] Discriminator Loss: 0.8498, Generator Loss: 2.0634 D(x): 0.7883, D(G(z)): 0.2943 Epoch: [13/20], Batch Num: [489/600] Discriminator Loss: 0.9354, Generator Loss: 2.2667 D(x): 0.7829, D(G(z)): 0.2908 Epoch: [13/20], Batch Num: [490/600] Discriminator Loss: 0.9281, Generator Loss: 2.4103 D(x): 0.7096, D(G(z)): 0.2326 Epoch: [13/20], Batch Num: [491/600] Discriminator Loss: 1.2917, Generator Loss: 1.9582 D(x): 0.5885, D(G(z)): 0.2601 Epoch: [13/20], Batch Num: [492/600] Discriminator Loss: 1.0752, Generator Loss: 1.6532 D(x): 0.6721, D(G(z)): 0.2996 Epoch: [13/20], Batch Num: [493/600] Discriminator Loss: 1.1710, Generator Loss: 1.4515 D(x): 0.7069, D(G(z)): 0.3427 Epoch: [13/20], Batch Num: [494/600] Discriminator Loss: 1.2877, Generator Loss: 1.4613 D(x): 0.6603, D(G(z)): 0.3536 Epoch: [13/20], Batch Num: [495/600] Discriminator Loss: 1.4855, Generator Loss: 1.5987 D(x): 0.6332, D(G(z)): 0.4021 Epoch: [13/20], Batch Num: [496/600] Discriminator Loss: 1.2400, Generator Loss: 1.7762 D(x): 0.6502, D(G(z)): 0.3428 Epoch: [13/20], Batch Num: [497/600] Discriminator Loss: 1.3761, Generator Loss: 1.5795 D(x): 0.5443, D(G(z)): 0.2617 Epoch: [13/20], Batch Num: [498/600] Discriminator Loss: 1.3563, Generator Loss: 1.4696 D(x): 0.6268, D(G(z)): 0.3517 Epoch: [13/20], Batch Num: [499/600] Discriminator Loss: 1.4625, Generator Loss: 1.3488 D(x): 0.5470, D(G(z)): 0.3276 Epoch: 13, Batch Num: [500/600]
Epoch: [13/20], Batch Num: [500/600] Discriminator Loss: 1.4140, Generator Loss: 1.1911 D(x): 0.6111, D(G(z)): 0.4081 Epoch: [13/20], Batch Num: [501/600] Discriminator Loss: 1.2924, Generator Loss: 1.2708 D(x): 0.6451, D(G(z)): 0.4054 Epoch: [13/20], Batch Num: [502/600] Discriminator Loss: 1.1841, Generator Loss: 1.2185 D(x): 0.6507, D(G(z)): 0.3787 Epoch: [13/20], Batch Num: [503/600] Discriminator Loss: 1.1860, Generator Loss: 1.3655 D(x): 0.6613, D(G(z)): 0.3906 Epoch: [13/20], Batch Num: [504/600] Discriminator Loss: 1.0643, Generator Loss: 1.4207 D(x): 0.6294, D(G(z)): 0.3021 Epoch: [13/20], Batch Num: [505/600] Discriminator Loss: 1.0579, Generator Loss: 1.4361 D(x): 0.6510, D(G(z)): 0.3396 Epoch: [13/20], Batch Num: [506/600] Discriminator Loss: 0.8963, Generator Loss: 1.4572 D(x): 0.6981, D(G(z)): 0.3005 Epoch: [13/20], Batch Num: [507/600] Discriminator Loss: 0.9449, Generator Loss: 1.6563 D(x): 0.6583, D(G(z)): 0.3024 Epoch: [13/20], Batch Num: [508/600] Discriminator Loss: 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0.2381 Epoch: [13/20], Batch Num: [517/600] Discriminator Loss: 0.6072, Generator Loss: 1.7386 D(x): 0.7959, D(G(z)): 0.2463 Epoch: [13/20], Batch Num: [518/600] Discriminator Loss: 0.6750, Generator Loss: 1.8419 D(x): 0.7956, D(G(z)): 0.2858 Epoch: [13/20], Batch Num: [519/600] Discriminator Loss: 0.5136, Generator Loss: 2.0295 D(x): 0.8047, D(G(z)): 0.1902 Epoch: [13/20], Batch Num: [520/600] Discriminator Loss: 0.5446, Generator Loss: 2.0331 D(x): 0.8059, D(G(z)): 0.2055 Epoch: [13/20], Batch Num: [521/600] Discriminator Loss: 0.5304, Generator Loss: 2.0657 D(x): 0.8145, D(G(z)): 0.2180 Epoch: [13/20], Batch Num: [522/600] Discriminator Loss: 0.5992, Generator Loss: 2.3966 D(x): 0.8074, D(G(z)): 0.2280 Epoch: [13/20], Batch Num: [523/600] Discriminator Loss: 0.4603, Generator Loss: 2.1662 D(x): 0.8173, D(G(z)): 0.1673 Epoch: [13/20], Batch Num: [524/600] Discriminator Loss: 0.6080, Generator Loss: 2.2478 D(x): 0.7530, D(G(z)): 0.1866 Epoch: [13/20], Batch Num: [525/600] Discriminator Loss: 0.4142, Generator Loss: 2.3097 D(x): 0.8893, D(G(z)): 0.2097 Epoch: [13/20], Batch Num: [526/600] Discriminator Loss: 0.5927, Generator Loss: 2.5563 D(x): 0.8242, D(G(z)): 0.2246 Epoch: [13/20], Batch Num: [527/600] Discriminator Loss: 0.5329, Generator Loss: 2.4938 D(x): 0.8602, D(G(z)): 0.2153 Epoch: [13/20], Batch Num: [528/600] Discriminator Loss: 0.5229, Generator Loss: 2.5082 D(x): 0.8333, D(G(z)): 0.2127 Epoch: [13/20], Batch Num: [529/600] Discriminator Loss: 0.6281, Generator Loss: 2.6261 D(x): 0.7577, D(G(z)): 0.1768 Epoch: [13/20], Batch Num: [530/600] Discriminator Loss: 0.6399, Generator Loss: 2.8219 D(x): 0.7655, D(G(z)): 0.1667 Epoch: [13/20], Batch Num: [531/600] Discriminator Loss: 0.7011, Generator Loss: 2.2086 D(x): 0.7304, D(G(z)): 0.1665 Epoch: [13/20], Batch Num: [532/600] Discriminator Loss: 0.5875, Generator Loss: 1.8362 D(x): 0.8112, D(G(z)): 0.2124 Epoch: [13/20], Batch Num: [533/600] Discriminator Loss: 0.6002, Generator Loss: 1.9341 D(x): 0.8624, D(G(z)): 0.2676 Epoch: [13/20], Batch Num: [534/600] Discriminator Loss: 0.7402, Generator Loss: 2.3742 D(x): 0.8403, D(G(z)): 0.2837 Epoch: [13/20], Batch Num: [535/600] Discriminator Loss: 0.8277, Generator Loss: 2.3972 D(x): 0.7215, D(G(z)): 0.2192 Epoch: [13/20], Batch Num: [536/600] Discriminator Loss: 0.9277, Generator Loss: 2.4148 D(x): 0.6762, D(G(z)): 0.2106 Epoch: [13/20], Batch Num: [537/600] Discriminator Loss: 0.8771, Generator Loss: 2.2019 D(x): 0.7130, D(G(z)): 0.2716 Epoch: [13/20], Batch Num: [538/600] Discriminator Loss: 0.9547, Generator Loss: 1.8889 D(x): 0.7217, D(G(z)): 0.2630 Epoch: [13/20], Batch Num: [539/600] Discriminator Loss: 1.1951, Generator Loss: 1.9252 D(x): 0.7189, D(G(z)): 0.3533 Epoch: [13/20], Batch Num: [540/600] Discriminator Loss: 1.0920, Generator Loss: 1.6206 D(x): 0.6777, D(G(z)): 0.3130 Epoch: [13/20], Batch Num: [541/600] Discriminator Loss: 1.2745, Generator Loss: 1.4903 D(x): 0.6444, D(G(z)): 0.3559 Epoch: [13/20], Batch Num: 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1.2908 D(x): 0.6841, D(G(z)): 0.3856 Epoch: [13/20], Batch Num: [551/600] Discriminator Loss: 1.1654, Generator Loss: 1.3402 D(x): 0.6284, D(G(z)): 0.3744 Epoch: [13/20], Batch Num: [552/600] Discriminator Loss: 1.0917, Generator Loss: 1.3840 D(x): 0.6205, D(G(z)): 0.3083 Epoch: [13/20], Batch Num: [553/600] Discriminator Loss: 0.9867, Generator Loss: 1.3080 D(x): 0.6604, D(G(z)): 0.3391 Epoch: [13/20], Batch Num: [554/600] Discriminator Loss: 0.9620, Generator Loss: 1.2200 D(x): 0.6685, D(G(z)): 0.3289 Epoch: [13/20], Batch Num: [555/600] Discriminator Loss: 0.8967, Generator Loss: 1.3679 D(x): 0.6918, D(G(z)): 0.3334 Epoch: [13/20], Batch Num: [556/600] Discriminator Loss: 0.7354, Generator Loss: 1.4143 D(x): 0.7421, D(G(z)): 0.2784 Epoch: [13/20], Batch Num: [557/600] Discriminator Loss: 0.7828, Generator Loss: 1.5731 D(x): 0.7633, D(G(z)): 0.3022 Epoch: [13/20], Batch Num: [558/600] Discriminator Loss: 0.7011, Generator Loss: 1.6482 D(x): 0.7709, D(G(z)): 0.2774 Epoch: [13/20], Batch Num: [559/600] Discriminator Loss: 0.6882, Generator Loss: 1.7000 D(x): 0.7243, D(G(z)): 0.2380 Epoch: [13/20], Batch Num: [560/600] Discriminator Loss: 0.8129, Generator Loss: 1.7345 D(x): 0.7131, D(G(z)): 0.2742 Epoch: [13/20], Batch Num: [561/600] Discriminator Loss: 0.6707, Generator Loss: 1.4940 D(x): 0.7608, D(G(z)): 0.2627 Epoch: [13/20], Batch Num: [562/600] Discriminator Loss: 0.6473, Generator Loss: 1.8662 D(x): 0.7801, D(G(z)): 0.2630 Epoch: [13/20], Batch Num: [563/600] Discriminator Loss: 0.6702, Generator Loss: 1.8020 D(x): 0.7883, D(G(z)): 0.2491 Epoch: [13/20], Batch Num: [564/600] Discriminator Loss: 0.6283, Generator Loss: 2.0236 D(x): 0.7924, D(G(z)): 0.2411 Epoch: [13/20], Batch Num: [565/600] Discriminator Loss: 0.6846, Generator Loss: 1.9870 D(x): 0.7572, D(G(z)): 0.2259 Epoch: [13/20], Batch Num: [566/600] Discriminator Loss: 0.5779, Generator Loss: 2.1001 D(x): 0.7940, D(G(z)): 0.2102 Epoch: [13/20], Batch Num: [567/600] Discriminator Loss: 0.6307, Generator Loss: 2.0530 D(x): 0.7794, D(G(z)): 0.2406 Epoch: [13/20], Batch Num: [568/600] Discriminator Loss: 0.5656, Generator Loss: 2.1848 D(x): 0.7990, D(G(z)): 0.2093 Epoch: [13/20], Batch Num: [569/600] Discriminator Loss: 0.5865, Generator Loss: 1.9984 D(x): 0.8164, D(G(z)): 0.2330 Epoch: [13/20], Batch Num: [570/600] Discriminator Loss: 0.5332, Generator Loss: 2.1316 D(x): 0.7965, D(G(z)): 0.1877 Epoch: [13/20], Batch Num: [571/600] Discriminator Loss: 0.6260, Generator Loss: 2.1387 D(x): 0.7838, D(G(z)): 0.2193 Epoch: [13/20], Batch Num: [572/600] Discriminator Loss: 0.4641, Generator Loss: 2.3049 D(x): 0.8688, D(G(z)): 0.2197 Epoch: [13/20], Batch Num: [573/600] Discriminator Loss: 0.5994, Generator Loss: 2.3408 D(x): 0.7979, D(G(z)): 0.2083 Epoch: [13/20], Batch Num: [574/600] Discriminator Loss: 0.5612, Generator Loss: 2.3078 D(x): 0.8075, D(G(z)): 0.1833 Epoch: [13/20], Batch Num: [575/600] Discriminator Loss: 0.5032, Generator Loss: 2.2435 D(x): 0.7819, D(G(z)): 0.1414 Epoch: [13/20], Batch Num: [576/600] Discriminator Loss: 0.4596, Generator Loss: 2.1078 D(x): 0.8370, D(G(z)): 0.1848 Epoch: [13/20], Batch Num: [577/600] Discriminator Loss: 0.4882, Generator Loss: 1.9871 D(x): 0.8539, D(G(z)): 0.1972 Epoch: [13/20], Batch Num: [578/600] Discriminator Loss: 0.5586, Generator Loss: 2.1638 D(x): 0.8487, D(G(z)): 0.2276 Epoch: [13/20], Batch Num: [579/600] Discriminator Loss: 0.5522, Generator Loss: 2.4506 D(x): 0.8418, D(G(z)): 0.2029 Epoch: [13/20], Batch Num: [580/600] Discriminator Loss: 0.6406, Generator Loss: 2.7751 D(x): 0.7915, D(G(z)): 0.2099 Epoch: [13/20], Batch Num: [581/600] Discriminator Loss: 0.5447, Generator Loss: 2.4084 D(x): 0.7856, D(G(z)): 0.1589 Epoch: [13/20], Batch Num: [582/600] Discriminator Loss: 0.6574, Generator Loss: 2.3518 D(x): 0.7598, D(G(z)): 0.1744 Epoch: [13/20], Batch Num: [583/600] Discriminator Loss: 0.6924, Generator Loss: 2.1934 D(x): 0.7405, D(G(z)): 0.2039 Epoch: [13/20], Batch Num: [584/600] Discriminator Loss: 0.5566, Generator Loss: 2.0762 D(x): 0.8744, D(G(z)): 0.2546 Epoch: [13/20], Batch Num: [585/600] Discriminator Loss: 0.7066, Generator Loss: 2.4603 D(x): 0.8070, D(G(z)): 0.2546 Epoch: [13/20], Batch Num: [586/600] Discriminator Loss: 0.8905, Generator Loss: 2.3495 D(x): 0.7087, D(G(z)): 0.2025 Epoch: [13/20], Batch Num: [587/600] Discriminator Loss: 0.8538, Generator Loss: 2.2545 D(x): 0.7214, D(G(z)): 0.2511 Epoch: [13/20], Batch Num: [588/600] Discriminator Loss: 0.8660, Generator Loss: 2.0503 D(x): 0.7658, D(G(z)): 0.2737 Epoch: [13/20], Batch Num: [589/600] Discriminator Loss: 0.8691, Generator Loss: 1.8693 D(x): 0.7001, D(G(z)): 0.2499 Epoch: [13/20], Batch Num: [590/600] Discriminator Loss: 0.9923, Generator Loss: 1.7592 D(x): 0.7034, D(G(z)): 0.2912 Epoch: [13/20], Batch Num: [591/600] Discriminator Loss: 0.9557, Generator Loss: 2.1698 D(x): 0.7612, D(G(z)): 0.3296 Epoch: [13/20], Batch Num: [592/600] Discriminator Loss: 0.8130, Generator Loss: 2.1601 D(x): 0.7184, D(G(z)): 0.2165 Epoch: [13/20], Batch Num: [593/600] Discriminator Loss: 0.9687, Generator Loss: 1.7618 D(x): 0.6729, D(G(z)): 0.2165 Epoch: [13/20], Batch Num: [594/600] Discriminator Loss: 1.0910, Generator Loss: 1.4380 D(x): 0.6644, D(G(z)): 0.3085 Epoch: [13/20], Batch Num: [595/600] Discriminator Loss: 1.2155, Generator Loss: 1.3782 D(x): 0.6814, D(G(z)): 0.3881 Epoch: [13/20], Batch Num: [596/600] Discriminator Loss: 1.0526, Generator Loss: 1.5508 D(x): 0.7419, D(G(z)): 0.3768 Epoch: [13/20], Batch Num: [597/600] Discriminator Loss: 1.2022, Generator Loss: 1.6740 D(x): 0.6293, D(G(z)): 0.3186 Epoch: [13/20], Batch Num: [598/600] Discriminator Loss: 1.1980, Generator Loss: 1.5590 D(x): 0.6209, D(G(z)): 0.2946 Epoch: [13/20], Batch Num: [599/600] Discriminator Loss: 1.0672, Generator Loss: 1.2423 D(x): 0.6296, D(G(z)): 0.2704 Epoch: 14, Batch Num: [0/600]
Epoch: [14/20], Batch Num: [0/600] Discriminator Loss: 1.0477, Generator Loss: 1.4197 D(x): 0.7352, D(G(z)): 0.3643 Epoch: [14/20], Batch Num: [1/600] Discriminator Loss: 1.0544, Generator Loss: 1.2611 D(x): 0.7109, D(G(z)): 0.3549 Epoch: [14/20], Batch Num: [2/600] Discriminator Loss: 0.9002, Generator Loss: 1.7149 D(x): 0.7399, D(G(z)): 0.3190 Epoch: [14/20], Batch Num: [3/600] Discriminator Loss: 1.0458, Generator Loss: 1.5883 D(x): 0.6427, D(G(z)): 0.2660 Epoch: [14/20], Batch Num: [4/600] Discriminator Loss: 0.9988, Generator Loss: 1.4784 D(x): 0.6572, D(G(z)): 0.2733 Epoch: [14/20], Batch Num: [5/600] Discriminator Loss: 0.9335, Generator Loss: 1.3975 D(x): 0.7226, D(G(z)): 0.3063 Epoch: [14/20], Batch Num: [6/600] Discriminator Loss: 0.8766, Generator Loss: 1.5327 D(x): 0.7817, D(G(z)): 0.3378 Epoch: [14/20], Batch Num: [7/600] Discriminator Loss: 0.9039, Generator Loss: 1.6265 D(x): 0.7348, D(G(z)): 0.3214 Epoch: [14/20], Batch Num: [8/600] Discriminator Loss: 0.7511, Generator Loss: 1.9110 D(x): 0.7468, D(G(z)): 0.2707 Epoch: [14/20], Batch Num: [9/600] Discriminator Loss: 0.6745, Generator Loss: 1.7537 D(x): 0.7580, D(G(z)): 0.2347 Epoch: [14/20], Batch Num: [10/600] Discriminator Loss: 0.7786, Generator Loss: 1.7312 D(x): 0.6688, D(G(z)): 0.2011 Epoch: [14/20], Batch Num: [11/600] Discriminator Loss: 0.6575, Generator Loss: 1.6458 D(x): 0.7855, D(G(z)): 0.2567 Epoch: [14/20], Batch Num: [12/600] Discriminator Loss: 0.7457, Generator Loss: 1.5152 D(x): 0.7648, D(G(z)): 0.2704 Epoch: [14/20], Batch Num: [13/600] Discriminator Loss: 0.6561, Generator Loss: 1.4950 D(x): 0.8132, D(G(z)): 0.2712 Epoch: [14/20], Batch Num: [14/600] Discriminator Loss: 0.6538, Generator Loss: 1.6864 D(x): 0.8058, D(G(z)): 0.2629 Epoch: [14/20], Batch Num: [15/600] Discriminator Loss: 0.6616, Generator Loss: 1.7267 D(x): 0.7762, D(G(z)): 0.2671 Epoch: [14/20], Batch Num: [16/600] Discriminator Loss: 0.6074, Generator Loss: 2.0603 D(x): 0.8248, D(G(z)): 0.2568 Epoch: [14/20], Batch Num: [17/600] Discriminator Loss: 0.5931, Generator Loss: 1.9612 D(x): 0.7748, D(G(z)): 0.1976 Epoch: [14/20], Batch Num: [18/600] Discriminator Loss: 0.6679, Generator Loss: 2.0442 D(x): 0.7303, D(G(z)): 0.1918 Epoch: [14/20], Batch Num: [19/600] Discriminator Loss: 0.6508, Generator Loss: 1.8076 D(x): 0.7600, D(G(z)): 0.1989 Epoch: [14/20], Batch Num: [20/600] Discriminator Loss: 0.5725, Generator Loss: 1.6203 D(x): 0.8358, D(G(z)): 0.2339 Epoch: [14/20], Batch Num: [21/600] Discriminator Loss: 0.6249, Generator Loss: 1.8199 D(x): 0.8258, D(G(z)): 0.2654 Epoch: [14/20], Batch Num: [22/600] Discriminator Loss: 0.5772, Generator Loss: 2.0060 D(x): 0.8252, D(G(z)): 0.2452 Epoch: [14/20], Batch Num: [23/600] Discriminator Loss: 0.6358, Generator Loss: 2.0827 D(x): 0.8067, D(G(z)): 0.2297 Epoch: [14/20], Batch Num: [24/600] Discriminator Loss: 0.5178, Generator Loss: 2.4682 D(x): 0.8382, D(G(z)): 0.2168 Epoch: [14/20], Batch Num: [25/600] Discriminator Loss: 0.5974, Generator Loss: 2.2827 D(x): 0.7567, D(G(z)): 0.1590 Epoch: [14/20], Batch Num: [26/600] Discriminator Loss: 0.7034, Generator Loss: 2.0599 D(x): 0.7388, D(G(z)): 0.1980 Epoch: [14/20], Batch Num: [27/600] Discriminator Loss: 0.6225, Generator Loss: 1.9666 D(x): 0.7976, D(G(z)): 0.2423 Epoch: [14/20], Batch Num: [28/600] Discriminator Loss: 0.6139, Generator Loss: 1.7834 D(x): 0.8160, D(G(z)): 0.2425 Epoch: [14/20], Batch Num: [29/600] Discriminator Loss: 0.7546, Generator Loss: 1.6963 D(x): 0.7750, D(G(z)): 0.2613 Epoch: [14/20], Batch Num: [30/600] Discriminator Loss: 0.7693, Generator Loss: 1.8507 D(x): 0.7939, D(G(z)): 0.2729 Epoch: [14/20], Batch Num: [31/600] Discriminator Loss: 0.7214, Generator Loss: 2.1207 D(x): 0.7797, D(G(z)): 0.2690 Epoch: [14/20], Batch Num: [32/600] Discriminator Loss: 0.7624, Generator Loss: 2.0920 D(x): 0.7407, D(G(z)): 0.2109 Epoch: [14/20], Batch Num: [33/600] Discriminator Loss: 0.6466, Generator Loss: 1.8141 D(x): 0.7605, D(G(z)): 0.1674 Epoch: [14/20], Batch Num: 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D(x): 0.6691, D(G(z)): 0.2593 Epoch: [14/20], Batch Num: [43/600] Discriminator Loss: 1.1735, Generator Loss: 1.9822 D(x): 0.6047, D(G(z)): 0.2546 Epoch: [14/20], Batch Num: [44/600] Discriminator Loss: 0.9656, Generator Loss: 1.5584 D(x): 0.6776, D(G(z)): 0.2494 Epoch: [14/20], Batch Num: [45/600] Discriminator Loss: 0.9731, Generator Loss: 1.4985 D(x): 0.7610, D(G(z)): 0.3177 Epoch: [14/20], Batch Num: [46/600] Discriminator Loss: 1.0182, Generator Loss: 1.5337 D(x): 0.7750, D(G(z)): 0.3800 Epoch: [14/20], Batch Num: [47/600] Discriminator Loss: 0.8306, Generator Loss: 1.9155 D(x): 0.7568, D(G(z)): 0.2901 Epoch: [14/20], Batch Num: [48/600] Discriminator Loss: 0.9474, Generator Loss: 1.9031 D(x): 0.6755, D(G(z)): 0.2743 Epoch: [14/20], Batch Num: [49/600] Discriminator Loss: 0.9355, Generator Loss: 1.9871 D(x): 0.6766, D(G(z)): 0.2766 Epoch: [14/20], Batch Num: [50/600] Discriminator Loss: 1.0086, Generator Loss: 1.8388 D(x): 0.6508, D(G(z)): 0.2424 Epoch: [14/20], Batch Num: 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D(x): 0.7009, D(G(z)): 0.3079 Epoch: [14/20], Batch Num: [60/600] Discriminator Loss: 0.9265, Generator Loss: 1.3558 D(x): 0.6980, D(G(z)): 0.2962 Epoch: [14/20], Batch Num: [61/600] Discriminator Loss: 0.9370, Generator Loss: 1.4764 D(x): 0.7218, D(G(z)): 0.3321 Epoch: [14/20], Batch Num: [62/600] Discriminator Loss: 1.0051, Generator Loss: 1.5483 D(x): 0.6805, D(G(z)): 0.3308 Epoch: [14/20], Batch Num: [63/600] Discriminator Loss: 0.8945, Generator Loss: 1.6651 D(x): 0.7256, D(G(z)): 0.2989 Epoch: [14/20], Batch Num: [64/600] Discriminator Loss: 0.8257, Generator Loss: 1.6095 D(x): 0.7177, D(G(z)): 0.2731 Epoch: [14/20], Batch Num: [65/600] Discriminator Loss: 0.8555, Generator Loss: 1.6446 D(x): 0.6892, D(G(z)): 0.2506 Epoch: [14/20], Batch Num: [66/600] Discriminator Loss: 0.8086, Generator Loss: 1.6378 D(x): 0.7378, D(G(z)): 0.2759 Epoch: [14/20], Batch Num: [67/600] Discriminator Loss: 0.8272, Generator Loss: 1.5630 D(x): 0.7539, D(G(z)): 0.3031 Epoch: [14/20], Batch Num: 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D(x): 0.7615, D(G(z)): 0.2388 Epoch: [14/20], Batch Num: [77/600] Discriminator Loss: 0.6169, Generator Loss: 1.9422 D(x): 0.7481, D(G(z)): 0.1835 Epoch: [14/20], Batch Num: [78/600] Discriminator Loss: 0.7089, Generator Loss: 2.0775 D(x): 0.7900, D(G(z)): 0.2534 Epoch: [14/20], Batch Num: [79/600] Discriminator Loss: 0.6720, Generator Loss: 1.7955 D(x): 0.7690, D(G(z)): 0.2254 Epoch: [14/20], Batch Num: [80/600] Discriminator Loss: 0.7183, Generator Loss: 1.9626 D(x): 0.7832, D(G(z)): 0.2797 Epoch: [14/20], Batch Num: [81/600] Discriminator Loss: 0.7020, Generator Loss: 1.9251 D(x): 0.7719, D(G(z)): 0.2561 Epoch: [14/20], Batch Num: [82/600] Discriminator Loss: 0.7524, Generator Loss: 2.1538 D(x): 0.7541, D(G(z)): 0.2592 Epoch: [14/20], Batch Num: [83/600] Discriminator Loss: 0.7490, Generator Loss: 1.9347 D(x): 0.7556, D(G(z)): 0.2501 Epoch: [14/20], Batch Num: [84/600] Discriminator Loss: 0.7179, Generator Loss: 2.0325 D(x): 0.7219, D(G(z)): 0.2083 Epoch: [14/20], Batch Num: [85/600] Discriminator Loss: 0.5632, Generator Loss: 2.0585 D(x): 0.7944, D(G(z)): 0.1957 Epoch: [14/20], Batch Num: [86/600] Discriminator Loss: 0.6437, Generator Loss: 1.7030 D(x): 0.7530, D(G(z)): 0.1990 Epoch: [14/20], Batch Num: [87/600] Discriminator Loss: 0.8145, Generator Loss: 1.9597 D(x): 0.8215, D(G(z)): 0.2893 Epoch: [14/20], Batch Num: [88/600] Discriminator Loss: 0.7265, Generator Loss: 2.1355 D(x): 0.8143, D(G(z)): 0.2590 Epoch: [14/20], Batch Num: [89/600] Discriminator Loss: 0.7380, Generator Loss: 2.3880 D(x): 0.7560, D(G(z)): 0.2426 Epoch: [14/20], Batch Num: [90/600] Discriminator Loss: 0.7134, Generator Loss: 2.3496 D(x): 0.7205, D(G(z)): 0.1870 Epoch: [14/20], Batch Num: [91/600] Discriminator Loss: 0.6932, Generator Loss: 2.1713 D(x): 0.7536, D(G(z)): 0.1878 Epoch: [14/20], Batch Num: [92/600] Discriminator Loss: 0.7607, Generator Loss: 1.7455 D(x): 0.7433, D(G(z)): 0.2131 Epoch: [14/20], Batch Num: [93/600] Discriminator Loss: 0.8067, Generator Loss: 1.9878 D(x): 0.8046, D(G(z)): 0.3032 Epoch: [14/20], Batch Num: [94/600] Discriminator Loss: 0.8966, Generator Loss: 1.9465 D(x): 0.7623, D(G(z)): 0.3036 Epoch: [14/20], Batch Num: [95/600] Discriminator Loss: 0.7750, Generator Loss: 2.0821 D(x): 0.7353, D(G(z)): 0.2483 Epoch: [14/20], Batch Num: [96/600] Discriminator Loss: 0.8726, Generator Loss: 1.9705 D(x): 0.7163, D(G(z)): 0.2307 Epoch: [14/20], Batch Num: [97/600] Discriminator Loss: 0.8708, Generator Loss: 2.0448 D(x): 0.7183, D(G(z)): 0.2307 Epoch: [14/20], Batch Num: [98/600] Discriminator Loss: 1.0486, Generator Loss: 1.7552 D(x): 0.6580, D(G(z)): 0.2609 Epoch: [14/20], Batch Num: [99/600] Discriminator Loss: 0.9617, Generator Loss: 1.6223 D(x): 0.7463, D(G(z)): 0.3085 Epoch: 14, Batch Num: [100/600]
Epoch: [14/20], Batch Num: [100/600] Discriminator Loss: 0.9329, Generator Loss: 1.7882 D(x): 0.7722, D(G(z)): 0.3403 Epoch: [14/20], Batch Num: [101/600] Discriminator Loss: 0.9069, Generator Loss: 2.0320 D(x): 0.7415, D(G(z)): 0.2974 Epoch: [14/20], Batch Num: [102/600] Discriminator Loss: 0.9692, Generator Loss: 2.2689 D(x): 0.6736, D(G(z)): 0.2560 Epoch: [14/20], Batch Num: [103/600] Discriminator Loss: 1.2378, Generator Loss: 1.8782 D(x): 0.5664, D(G(z)): 0.1903 Epoch: [14/20], Batch Num: [104/600] Discriminator Loss: 0.9198, Generator Loss: 1.5121 D(x): 0.7134, D(G(z)): 0.2744 Epoch: [14/20], Batch Num: [105/600] Discriminator Loss: 0.9366, Generator Loss: 1.2634 D(x): 0.7356, D(G(z)): 0.3349 Epoch: [14/20], Batch Num: [106/600] Discriminator Loss: 0.9412, Generator Loss: 1.5707 D(x): 0.7996, D(G(z)): 0.3712 Epoch: [14/20], Batch Num: [107/600] Discriminator Loss: 0.9206, Generator Loss: 1.7849 D(x): 0.7604, D(G(z)): 0.3107 Epoch: [14/20], Batch Num: [108/600] Discriminator Loss: 1.1840, Generator Loss: 2.1226 D(x): 0.6788, D(G(z)): 0.3228 Epoch: [14/20], Batch Num: [109/600] Discriminator Loss: 1.0717, Generator Loss: 1.9884 D(x): 0.6083, D(G(z)): 0.2344 Epoch: [14/20], Batch Num: [110/600] Discriminator Loss: 0.8143, Generator Loss: 1.9123 D(x): 0.6643, D(G(z)): 0.2039 Epoch: [14/20], Batch Num: [111/600] Discriminator Loss: 1.1839, Generator Loss: 1.5274 D(x): 0.6230, D(G(z)): 0.2667 Epoch: [14/20], Batch Num: [112/600] Discriminator Loss: 0.8922, Generator Loss: 1.1188 D(x): 0.7185, D(G(z)): 0.3106 Epoch: [14/20], Batch Num: [113/600] Discriminator Loss: 0.9274, Generator Loss: 1.1744 D(x): 0.7730, D(G(z)): 0.3707 Epoch: [14/20], Batch Num: [114/600] Discriminator Loss: 1.2584, Generator Loss: 1.4151 D(x): 0.7412, D(G(z)): 0.4571 Epoch: [14/20], Batch Num: [115/600] Discriminator Loss: 0.9437, Generator Loss: 1.7066 D(x): 0.7316, D(G(z)): 0.3288 Epoch: [14/20], Batch Num: [116/600] Discriminator Loss: 0.6553, Generator Loss: 2.0075 D(x): 0.7788, D(G(z)): 0.2383 Epoch: [14/20], Batch Num: [117/600] Discriminator Loss: 1.0083, Generator Loss: 2.0046 D(x): 0.5760, D(G(z)): 0.1929 Epoch: [14/20], Batch Num: [118/600] Discriminator Loss: 0.8296, Generator Loss: 1.7820 D(x): 0.6662, D(G(z)): 0.2149 Epoch: [14/20], Batch Num: [119/600] Discriminator Loss: 0.8128, Generator Loss: 1.5431 D(x): 0.6648, D(G(z)): 0.2149 Epoch: [14/20], Batch Num: [120/600] Discriminator Loss: 0.6920, Generator Loss: 1.3208 D(x): 0.8029, D(G(z)): 0.2995 Epoch: [14/20], Batch Num: [121/600] Discriminator Loss: 0.7942, Generator Loss: 1.3298 D(x): 0.8092, D(G(z)): 0.3472 Epoch: [14/20], Batch Num: [122/600] Discriminator Loss: 0.7711, Generator Loss: 1.5342 D(x): 0.8324, D(G(z)): 0.3551 Epoch: [14/20], Batch Num: [123/600] Discriminator Loss: 0.6691, Generator Loss: 1.9273 D(x): 0.8198, D(G(z)): 0.2946 Epoch: [14/20], Batch Num: [124/600] Discriminator Loss: 0.7329, Generator Loss: 2.1281 D(x): 0.7474, D(G(z)): 0.2401 Epoch: [14/20], Batch Num: [125/600] Discriminator Loss: 0.7345, Generator Loss: 2.1877 D(x): 0.6775, D(G(z)): 0.1667 Epoch: [14/20], Batch Num: [126/600] Discriminator Loss: 0.7199, Generator Loss: 2.1490 D(x): 0.7171, D(G(z)): 0.1973 Epoch: [14/20], Batch Num: [127/600] Discriminator Loss: 0.6029, Generator Loss: 1.8908 D(x): 0.7728, D(G(z)): 0.1915 Epoch: [14/20], Batch Num: [128/600] Discriminator Loss: 0.5333, Generator Loss: 1.9126 D(x): 0.7940, D(G(z)): 0.1876 Epoch: [14/20], Batch Num: [129/600] Discriminator Loss: 0.6164, Generator Loss: 1.8944 D(x): 0.8197, D(G(z)): 0.2373 Epoch: [14/20], Batch Num: [130/600] Discriminator Loss: 0.6308, Generator Loss: 1.7470 D(x): 0.7885, D(G(z)): 0.2356 Epoch: [14/20], Batch Num: [131/600] Discriminator Loss: 0.7004, Generator Loss: 1.9554 D(x): 0.8054, D(G(z)): 0.2682 Epoch: [14/20], Batch Num: [132/600] Discriminator Loss: 0.5141, Generator Loss: 1.9130 D(x): 0.8279, D(G(z)): 0.2040 Epoch: [14/20], Batch Num: [133/600] Discriminator Loss: 0.6959, Generator Loss: 2.1025 D(x): 0.7710, D(G(z)): 0.2217 Epoch: [14/20], Batch Num: [134/600] Discriminator Loss: 0.5341, Generator Loss: 2.0921 D(x): 0.8065, D(G(z)): 0.1945 Epoch: [14/20], Batch Num: [135/600] Discriminator Loss: 0.6514, Generator Loss: 2.0808 D(x): 0.7490, D(G(z)): 0.1654 Epoch: [14/20], Batch Num: [136/600] Discriminator Loss: 0.5612, Generator Loss: 2.1272 D(x): 0.8422, D(G(z)): 0.2093 Epoch: [14/20], Batch Num: [137/600] Discriminator Loss: 0.5334, Generator Loss: 2.1339 D(x): 0.8000, D(G(z)): 0.1700 Epoch: [14/20], Batch Num: [138/600] Discriminator Loss: 0.4829, Generator Loss: 2.1669 D(x): 0.8310, D(G(z)): 0.1776 Epoch: [14/20], Batch Num: [139/600] Discriminator Loss: 0.6004, Generator Loss: 2.0256 D(x): 0.8504, D(G(z)): 0.2364 Epoch: [14/20], Batch Num: [140/600] Discriminator Loss: 0.4942, Generator Loss: 2.1901 D(x): 0.8250, D(G(z)): 0.1875 Epoch: [14/20], Batch Num: [141/600] Discriminator Loss: 0.5686, Generator Loss: 2.2886 D(x): 0.8200, D(G(z)): 0.2080 Epoch: [14/20], Batch Num: [142/600] Discriminator Loss: 0.5291, Generator Loss: 2.0767 D(x): 0.8184, D(G(z)): 0.1800 Epoch: [14/20], Batch Num: [143/600] Discriminator Loss: 0.4600, Generator Loss: 2.3703 D(x): 0.8587, D(G(z)): 0.1873 Epoch: [14/20], Batch Num: [144/600] Discriminator Loss: 0.5854, Generator Loss: 2.0118 D(x): 0.8255, D(G(z)): 0.2110 Epoch: [14/20], Batch Num: [145/600] Discriminator Loss: 0.6180, Generator Loss: 2.6600 D(x): 0.8292, D(G(z)): 0.2186 Epoch: [14/20], Batch Num: [146/600] Discriminator Loss: 0.6578, Generator Loss: 2.6651 D(x): 0.8178, D(G(z)): 0.2068 Epoch: [14/20], Batch Num: [147/600] Discriminator Loss: 0.6260, Generator Loss: 2.8452 D(x): 0.7711, D(G(z)): 0.1788 Epoch: [14/20], Batch Num: [148/600] Discriminator Loss: 0.5341, Generator Loss: 2.4352 D(x): 0.7814, D(G(z)): 0.1364 Epoch: [14/20], Batch Num: [149/600] Discriminator Loss: 0.6375, Generator Loss: 2.1264 D(x): 0.7987, D(G(z)): 0.1956 Epoch: [14/20], Batch Num: [150/600] Discriminator Loss: 0.7096, Generator Loss: 1.9147 D(x): 0.8180, D(G(z)): 0.2517 Epoch: [14/20], Batch Num: [151/600] Discriminator Loss: 0.7372, Generator Loss: 2.0467 D(x): 0.8357, D(G(z)): 0.2760 Epoch: [14/20], Batch Num: [152/600] Discriminator Loss: 0.7290, Generator Loss: 2.5335 D(x): 0.7968, D(G(z)): 0.2612 Epoch: [14/20], Batch Num: [153/600] Discriminator Loss: 0.7954, Generator Loss: 2.4703 D(x): 0.7723, D(G(z)): 0.2015 Epoch: [14/20], Batch Num: [154/600] Discriminator Loss: 0.7300, Generator Loss: 2.5090 D(x): 0.7331, D(G(z)): 0.1794 Epoch: [14/20], Batch Num: [155/600] Discriminator Loss: 0.8123, Generator Loss: 2.2828 D(x): 0.6958, D(G(z)): 0.1831 Epoch: [14/20], Batch Num: [156/600] Discriminator Loss: 0.8411, Generator Loss: 1.7217 D(x): 0.7665, D(G(z)): 0.2654 Epoch: [14/20], Batch Num: [157/600] Discriminator Loss: 0.7960, Generator Loss: 1.7900 D(x): 0.8251, D(G(z)): 0.3052 Epoch: [14/20], Batch Num: [158/600] Discriminator Loss: 1.0135, Generator Loss: 2.0363 D(x): 0.7521, D(G(z)): 0.3390 Epoch: [14/20], Batch Num: [159/600] Discriminator Loss: 0.8288, Generator Loss: 2.2772 D(x): 0.7729, D(G(z)): 0.2572 Epoch: [14/20], Batch Num: [160/600] Discriminator Loss: 1.0595, Generator Loss: 2.2106 D(x): 0.6264, D(G(z)): 0.1957 Epoch: [14/20], Batch Num: [161/600] Discriminator Loss: 0.9372, Generator Loss: 1.7691 D(x): 0.6469, D(G(z)): 0.1965 Epoch: [14/20], Batch Num: [162/600] Discriminator Loss: 0.9820, Generator Loss: 1.3362 D(x): 0.6971, D(G(z)): 0.2815 Epoch: [14/20], Batch Num: [163/600] Discriminator Loss: 1.1169, Generator Loss: 1.2020 D(x): 0.7542, D(G(z)): 0.3880 Epoch: [14/20], Batch Num: [164/600] Discriminator Loss: 1.2132, Generator Loss: 1.7739 D(x): 0.7602, D(G(z)): 0.4263 Epoch: [14/20], Batch Num: [165/600] Discriminator Loss: 1.1441, Generator Loss: 1.9785 D(x): 0.7526, D(G(z)): 0.3808 Epoch: [14/20], Batch Num: [166/600] Discriminator Loss: 1.0023, Generator Loss: 2.1361 D(x): 0.6036, D(G(z)): 0.2050 Epoch: [14/20], Batch Num: [167/600] Discriminator Loss: 1.1513, Generator Loss: 2.0292 D(x): 0.6030, D(G(z)): 0.2288 Epoch: [14/20], Batch Num: [168/600] Discriminator Loss: 0.9999, Generator Loss: 1.7355 D(x): 0.6724, D(G(z)): 0.2663 Epoch: [14/20], Batch Num: [169/600] Discriminator Loss: 1.1285, Generator Loss: 1.5816 D(x): 0.6707, D(G(z)): 0.3034 Epoch: [14/20], Batch Num: [170/600] Discriminator Loss: 1.0790, Generator Loss: 1.3692 D(x): 0.7353, D(G(z)): 0.4061 Epoch: [14/20], Batch Num: [171/600] Discriminator Loss: 0.9004, Generator Loss: 1.4657 D(x): 0.7507, D(G(z)): 0.3333 Epoch: [14/20], Batch Num: [172/600] Discriminator Loss: 1.0790, Generator Loss: 1.4105 D(x): 0.6991, D(G(z)): 0.3606 Epoch: [14/20], Batch Num: [173/600] Discriminator Loss: 1.1503, Generator Loss: 1.8001 D(x): 0.6713, D(G(z)): 0.3481 Epoch: [14/20], Batch Num: [174/600] Discriminator Loss: 1.0093, Generator Loss: 1.5883 D(x): 0.6728, D(G(z)): 0.3117 Epoch: [14/20], Batch Num: [175/600] Discriminator Loss: 1.0732, Generator Loss: 1.5010 D(x): 0.6580, D(G(z)): 0.3239 Epoch: [14/20], Batch Num: [176/600] Discriminator Loss: 1.0148, Generator Loss: 1.5492 D(x): 0.6587, D(G(z)): 0.3214 Epoch: [14/20], Batch Num: [177/600] Discriminator Loss: 1.0289, Generator Loss: 1.4541 D(x): 0.6430, D(G(z)): 0.2948 Epoch: [14/20], Batch Num: [178/600] Discriminator Loss: 0.9553, Generator Loss: 1.3039 D(x): 0.6835, D(G(z)): 0.3138 Epoch: [14/20], Batch Num: [179/600] Discriminator Loss: 1.0479, Generator Loss: 1.4954 D(x): 0.6928, D(G(z)): 0.3527 Epoch: [14/20], Batch Num: [180/600] Discriminator Loss: 1.0402, Generator Loss: 1.4502 D(x): 0.6781, D(G(z)): 0.3542 Epoch: [14/20], Batch Num: [181/600] Discriminator Loss: 0.8130, Generator Loss: 1.6061 D(x): 0.7441, D(G(z)): 0.2974 Epoch: [14/20], Batch Num: [182/600] Discriminator Loss: 0.9001, Generator Loss: 1.6762 D(x): 0.7562, D(G(z)): 0.3526 Epoch: [14/20], Batch Num: [183/600] Discriminator Loss: 0.8640, Generator Loss: 1.7906 D(x): 0.6602, D(G(z)): 0.2428 Epoch: [14/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [14/20], Batch Num: [200/600] Discriminator Loss: 0.7217, Generator Loss: 1.9534 D(x): 0.7766, D(G(z)): 0.2823 Epoch: [14/20], Batch Num: [201/600] Discriminator Loss: 0.7277, Generator Loss: 2.0834 D(x): 0.7354, D(G(z)): 0.2380 Epoch: [14/20], Batch Num: [202/600] Discriminator Loss: 0.6655, Generator Loss: 1.9150 D(x): 0.7466, D(G(z)): 0.2179 Epoch: [14/20], Batch Num: [203/600] Discriminator Loss: 0.6839, Generator Loss: 1.9558 D(x): 0.7349, D(G(z)): 0.2255 Epoch: [14/20], Batch Num: [204/600] Discriminator Loss: 0.7285, Generator Loss: 1.8962 D(x): 0.7418, D(G(z)): 0.2386 Epoch: [14/20], Batch Num: [205/600] Discriminator Loss: 0.7290, Generator Loss: 1.7641 D(x): 0.7441, D(G(z)): 0.2437 Epoch: [14/20], Batch Num: [206/600] Discriminator Loss: 0.7172, Generator Loss: 1.7775 D(x): 0.7825, D(G(z)): 0.2736 Epoch: [14/20], Batch Num: [207/600] Discriminator Loss: 0.7396, Generator Loss: 1.7781 D(x): 0.7652, D(G(z)): 0.2744 Epoch: [14/20], Batch Num: [208/600] Discriminator Loss: 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0.3377 Epoch: [14/20], Batch Num: [217/600] Discriminator Loss: 0.9737, Generator Loss: 2.2766 D(x): 0.6712, D(G(z)): 0.2472 Epoch: [14/20], Batch Num: [218/600] Discriminator Loss: 1.0208, Generator Loss: 2.0330 D(x): 0.6487, D(G(z)): 0.2256 Epoch: [14/20], Batch Num: [219/600] Discriminator Loss: 0.9717, Generator Loss: 1.6206 D(x): 0.6162, D(G(z)): 0.1930 Epoch: [14/20], Batch Num: [220/600] Discriminator Loss: 0.9516, Generator Loss: 1.3668 D(x): 0.7405, D(G(z)): 0.3148 Epoch: [14/20], Batch Num: [221/600] Discriminator Loss: 1.0589, Generator Loss: 1.4399 D(x): 0.7762, D(G(z)): 0.4330 Epoch: [14/20], Batch Num: [222/600] Discriminator Loss: 0.9498, Generator Loss: 1.6308 D(x): 0.7346, D(G(z)): 0.3502 Epoch: [14/20], Batch Num: [223/600] Discriminator Loss: 0.9277, Generator Loss: 1.7457 D(x): 0.7236, D(G(z)): 0.3168 Epoch: [14/20], Batch Num: [224/600] Discriminator Loss: 1.2317, Generator Loss: 1.8189 D(x): 0.6091, D(G(z)): 0.2953 Epoch: [14/20], Batch Num: [225/600] Discriminator Loss: 1.1020, Generator Loss: 1.5640 D(x): 0.6113, D(G(z)): 0.2675 Epoch: [14/20], Batch Num: [226/600] Discriminator Loss: 1.1864, Generator Loss: 1.3920 D(x): 0.6400, D(G(z)): 0.3348 Epoch: [14/20], Batch Num: [227/600] Discriminator Loss: 1.1319, Generator Loss: 1.2463 D(x): 0.6808, D(G(z)): 0.3445 Epoch: [14/20], Batch Num: [228/600] Discriminator Loss: 1.1051, Generator Loss: 1.3207 D(x): 0.7537, D(G(z)): 0.4420 Epoch: [14/20], Batch Num: [229/600] Discriminator Loss: 1.0997, Generator Loss: 1.3005 D(x): 0.7003, D(G(z)): 0.4053 Epoch: [14/20], Batch Num: [230/600] Discriminator Loss: 1.0484, Generator Loss: 1.4254 D(x): 0.6721, D(G(z)): 0.3274 Epoch: [14/20], Batch Num: [231/600] Discriminator Loss: 0.9193, Generator Loss: 1.5907 D(x): 0.6280, D(G(z)): 0.2351 Epoch: [14/20], Batch Num: [232/600] Discriminator Loss: 0.9580, Generator Loss: 1.5500 D(x): 0.6311, D(G(z)): 0.2402 Epoch: [14/20], Batch Num: [233/600] Discriminator Loss: 0.7920, Generator Loss: 1.3765 D(x): 0.7066, D(G(z)): 0.2642 Epoch: [14/20], Batch Num: [234/600] Discriminator Loss: 0.8383, Generator Loss: 1.3150 D(x): 0.7312, D(G(z)): 0.3190 Epoch: [14/20], Batch Num: [235/600] Discriminator Loss: 0.8868, Generator Loss: 1.3270 D(x): 0.7476, D(G(z)): 0.3499 Epoch: [14/20], Batch Num: [236/600] Discriminator Loss: 1.1154, Generator Loss: 1.4225 D(x): 0.6963, D(G(z)): 0.4091 Epoch: [14/20], Batch Num: [237/600] Discriminator Loss: 0.8460, Generator Loss: 1.4627 D(x): 0.7560, D(G(z)): 0.3469 Epoch: [14/20], Batch Num: [238/600] Discriminator Loss: 0.9760, Generator Loss: 1.5979 D(x): 0.6651, D(G(z)): 0.3097 Epoch: [14/20], Batch Num: [239/600] Discriminator Loss: 0.8396, Generator Loss: 1.7035 D(x): 0.6614, D(G(z)): 0.2418 Epoch: [14/20], Batch Num: [240/600] Discriminator Loss: 0.8219, Generator Loss: 1.5162 D(x): 0.6778, D(G(z)): 0.2380 Epoch: [14/20], Batch Num: [241/600] Discriminator Loss: 0.7203, Generator Loss: 1.7267 D(x): 0.7276, D(G(z)): 0.2490 Epoch: [14/20], Batch Num: [242/600] Discriminator Loss: 0.6877, Generator Loss: 1.4873 D(x): 0.7921, D(G(z)): 0.2971 Epoch: [14/20], Batch Num: [243/600] Discriminator Loss: 0.6097, Generator Loss: 1.4523 D(x): 0.7802, D(G(z)): 0.2439 Epoch: [14/20], Batch Num: [244/600] Discriminator Loss: 0.7061, Generator Loss: 1.5150 D(x): 0.7910, D(G(z)): 0.2840 Epoch: [14/20], Batch Num: [245/600] Discriminator Loss: 0.6153, Generator Loss: 1.7112 D(x): 0.8257, D(G(z)): 0.2747 Epoch: [14/20], Batch Num: [246/600] Discriminator Loss: 0.5480, Generator Loss: 1.9165 D(x): 0.8169, D(G(z)): 0.2218 Epoch: [14/20], Batch Num: [247/600] Discriminator Loss: 0.5129, Generator Loss: 2.2595 D(x): 0.8096, D(G(z)): 0.1945 Epoch: [14/20], Batch Num: [248/600] Discriminator Loss: 0.6320, Generator Loss: 2.2563 D(x): 0.7750, D(G(z)): 0.2035 Epoch: [14/20], Batch Num: [249/600] Discriminator Loss: 0.5969, Generator Loss: 2.2833 D(x): 0.7415, D(G(z)): 0.1411 Epoch: [14/20], Batch Num: [250/600] Discriminator Loss: 0.4556, Generator Loss: 1.9482 D(x): 0.8429, D(G(z)): 0.1810 Epoch: [14/20], Batch Num: [251/600] Discriminator Loss: 0.5302, Generator Loss: 2.0565 D(x): 0.8592, D(G(z)): 0.2129 Epoch: [14/20], Batch Num: [252/600] Discriminator Loss: 0.4448, Generator Loss: 2.0643 D(x): 0.8731, D(G(z)): 0.2041 Epoch: [14/20], Batch Num: [253/600] Discriminator Loss: 0.4493, Generator Loss: 2.3635 D(x): 0.8590, D(G(z)): 0.1906 Epoch: [14/20], Batch Num: [254/600] Discriminator Loss: 0.5778, Generator Loss: 2.5146 D(x): 0.8239, D(G(z)): 0.2128 Epoch: [14/20], Batch Num: [255/600] Discriminator Loss: 0.4668, Generator Loss: 2.2990 D(x): 0.8258, D(G(z)): 0.1622 Epoch: [14/20], Batch Num: [256/600] Discriminator Loss: 0.5203, Generator Loss: 2.4652 D(x): 0.7946, D(G(z)): 0.1480 Epoch: [14/20], Batch Num: [257/600] Discriminator Loss: 0.3892, Generator Loss: 2.4219 D(x): 0.8469, D(G(z)): 0.1439 Epoch: [14/20], Batch Num: [258/600] Discriminator Loss: 0.4395, Generator Loss: 2.0825 D(x): 0.8699, D(G(z)): 0.1942 Epoch: [14/20], Batch Num: [259/600] Discriminator Loss: 0.5930, Generator Loss: 2.1497 D(x): 0.8229, D(G(z)): 0.2247 Epoch: [14/20], Batch Num: [260/600] Discriminator Loss: 0.4361, Generator Loss: 2.3611 D(x): 0.8669, D(G(z)): 0.1807 Epoch: [14/20], Batch Num: [261/600] Discriminator Loss: 0.4850, Generator Loss: 2.5074 D(x): 0.8645, D(G(z)): 0.1965 Epoch: [14/20], Batch Num: [262/600] Discriminator Loss: 0.5678, Generator Loss: 2.5584 D(x): 0.8315, D(G(z)): 0.1826 Epoch: [14/20], Batch Num: [263/600] Discriminator Loss: 0.5606, Generator Loss: 2.9621 D(x): 0.8211, D(G(z)): 0.1591 Epoch: [14/20], Batch Num: [264/600] Discriminator Loss: 0.6522, Generator Loss: 2.7311 D(x): 0.7628, D(G(z)): 0.1363 Epoch: [14/20], Batch Num: [265/600] Discriminator Loss: 0.5235, Generator Loss: 2.5464 D(x): 0.8395, D(G(z)): 0.1889 Epoch: [14/20], Batch Num: [266/600] Discriminator Loss: 0.5802, Generator Loss: 2.1961 D(x): 0.8285, D(G(z)): 0.2060 Epoch: [14/20], Batch Num: [267/600] Discriminator Loss: 0.6698, Generator Loss: 2.5688 D(x): 0.8012, D(G(z)): 0.2187 Epoch: [14/20], Batch Num: [268/600] Discriminator Loss: 0.7816, Generator Loss: 2.0028 D(x): 0.7461, D(G(z)): 0.2196 Epoch: [14/20], Batch Num: [269/600] Discriminator Loss: 0.7039, Generator Loss: 2.3720 D(x): 0.8011, D(G(z)): 0.2517 Epoch: [14/20], Batch Num: [270/600] Discriminator Loss: 0.8493, Generator Loss: 2.0733 D(x): 0.7487, D(G(z)): 0.2223 Epoch: [14/20], Batch Num: [271/600] Discriminator Loss: 1.0464, Generator Loss: 1.6615 D(x): 0.6577, D(G(z)): 0.2203 Epoch: [14/20], Batch Num: [272/600] Discriminator Loss: 1.0659, Generator Loss: 1.8422 D(x): 0.8100, D(G(z)): 0.3855 Epoch: [14/20], Batch Num: [273/600] Discriminator Loss: 1.0649, Generator Loss: 2.1662 D(x): 0.7171, D(G(z)): 0.3169 Epoch: [14/20], Batch Num: [274/600] Discriminator Loss: 1.2286, Generator Loss: 2.2830 D(x): 0.6349, D(G(z)): 0.2769 Epoch: [14/20], Batch Num: [275/600] Discriminator Loss: 1.0098, Generator Loss: 1.8853 D(x): 0.6552, D(G(z)): 0.2077 Epoch: [14/20], Batch Num: [276/600] Discriminator Loss: 0.8834, Generator Loss: 1.4427 D(x): 0.7404, D(G(z)): 0.2920 Epoch: [14/20], Batch Num: [277/600] Discriminator Loss: 1.0124, Generator Loss: 1.5501 D(x): 0.7150, D(G(z)): 0.3261 Epoch: [14/20], Batch Num: [278/600] Discriminator Loss: 0.9807, Generator Loss: 1.7076 D(x): 0.7719, D(G(z)): 0.3490 Epoch: [14/20], Batch Num: [279/600] Discriminator Loss: 0.9975, Generator Loss: 1.6901 D(x): 0.6799, D(G(z)): 0.2829 Epoch: [14/20], Batch Num: [280/600] Discriminator Loss: 1.1087, Generator Loss: 1.8946 D(x): 0.6774, D(G(z)): 0.2932 Epoch: [14/20], Batch Num: [281/600] Discriminator Loss: 0.9289, Generator Loss: 1.7922 D(x): 0.6882, D(G(z)): 0.2501 Epoch: [14/20], Batch Num: [282/600] Discriminator Loss: 0.8068, Generator Loss: 1.6537 D(x): 0.7346, D(G(z)): 0.2404 Epoch: [14/20], Batch Num: [283/600] Discriminator Loss: 1.0440, Generator Loss: 1.7995 D(x): 0.6949, D(G(z)): 0.2841 Epoch: [14/20], Batch Num: [284/600] Discriminator Loss: 0.7627, Generator Loss: 1.7195 D(x): 0.7595, D(G(z)): 0.2414 Epoch: [14/20], Batch Num: [285/600] Discriminator Loss: 0.9018, Generator Loss: 1.6616 D(x): 0.7281, D(G(z)): 0.2691 Epoch: [14/20], Batch Num: [286/600] Discriminator Loss: 0.7849, Generator Loss: 1.8916 D(x): 0.7897, D(G(z)): 0.3052 Epoch: [14/20], Batch Num: [287/600] Discriminator Loss: 0.8662, Generator Loss: 1.9720 D(x): 0.7054, D(G(z)): 0.2493 Epoch: [14/20], Batch Num: [288/600] Discriminator Loss: 0.7766, Generator Loss: 1.9190 D(x): 0.7544, D(G(z)): 0.2574 Epoch: [14/20], Batch Num: [289/600] Discriminator Loss: 0.7894, Generator Loss: 1.9442 D(x): 0.7323, D(G(z)): 0.2454 Epoch: [14/20], Batch Num: [290/600] Discriminator Loss: 0.8774, Generator Loss: 1.8116 D(x): 0.6652, D(G(z)): 0.1905 Epoch: [14/20], Batch Num: [291/600] Discriminator Loss: 0.7481, Generator Loss: 1.6547 D(x): 0.7865, D(G(z)): 0.2812 Epoch: [14/20], Batch Num: [292/600] Discriminator Loss: 0.8738, Generator Loss: 1.7302 D(x): 0.7900, D(G(z)): 0.3247 Epoch: [14/20], Batch Num: [293/600] Discriminator Loss: 0.6733, Generator Loss: 2.0847 D(x): 0.8492, D(G(z)): 0.3099 Epoch: [14/20], Batch Num: [294/600] Discriminator Loss: 0.6669, Generator Loss: 2.0813 D(x): 0.7704, D(G(z)): 0.2101 Epoch: [14/20], Batch Num: [295/600] Discriminator Loss: 0.7321, Generator Loss: 2.1898 D(x): 0.7453, D(G(z)): 0.1977 Epoch: [14/20], Batch Num: [296/600] Discriminator Loss: 0.6673, Generator Loss: 2.1227 D(x): 0.7479, D(G(z)): 0.1822 Epoch: [14/20], Batch Num: [297/600] Discriminator Loss: 0.6078, Generator Loss: 1.8223 D(x): 0.7935, D(G(z)): 0.1855 Epoch: [14/20], Batch Num: [298/600] Discriminator Loss: 0.6205, Generator Loss: 2.0846 D(x): 0.7835, D(G(z)): 0.2018 Epoch: [14/20], Batch Num: [299/600] Discriminator Loss: 0.7143, Generator Loss: 1.8956 D(x): 0.7956, D(G(z)): 0.2843 Epoch: 14, Batch Num: [300/600]
Epoch: [14/20], Batch Num: [300/600] Discriminator Loss: 0.5741, Generator Loss: 1.9782 D(x): 0.8171, D(G(z)): 0.2224 Epoch: [14/20], Batch Num: [301/600] Discriminator Loss: 0.5960, Generator Loss: 2.0287 D(x): 0.8335, D(G(z)): 0.2384 Epoch: [14/20], Batch Num: [302/600] Discriminator Loss: 0.5724, Generator Loss: 1.9927 D(x): 0.8150, D(G(z)): 0.2157 Epoch: [14/20], Batch Num: [303/600] Discriminator Loss: 0.5965, Generator Loss: 2.1248 D(x): 0.8007, D(G(z)): 0.1855 Epoch: [14/20], Batch Num: [304/600] Discriminator Loss: 0.5371, Generator Loss: 2.1580 D(x): 0.8061, D(G(z)): 0.1790 Epoch: [14/20], Batch Num: [305/600] Discriminator Loss: 0.4994, Generator Loss: 1.9741 D(x): 0.8343, D(G(z)): 0.1917 Epoch: [14/20], Batch Num: [306/600] Discriminator Loss: 0.4000, Generator Loss: 1.9789 D(x): 0.8531, D(G(z)): 0.1646 Epoch: [14/20], Batch Num: [307/600] Discriminator Loss: 0.6381, Generator Loss: 2.0311 D(x): 0.8164, D(G(z)): 0.2266 Epoch: [14/20], Batch Num: [308/600] Discriminator Loss: 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0.2166 Epoch: [14/20], Batch Num: [317/600] Discriminator Loss: 0.8146, Generator Loss: 2.3087 D(x): 0.6571, D(G(z)): 0.1432 Epoch: [14/20], Batch Num: [318/600] Discriminator Loss: 0.9431, Generator Loss: 1.9189 D(x): 0.7031, D(G(z)): 0.2505 Epoch: [14/20], Batch Num: [319/600] Discriminator Loss: 0.8028, Generator Loss: 1.6010 D(x): 0.7899, D(G(z)): 0.2747 Epoch: [14/20], Batch Num: [320/600] Discriminator Loss: 0.7164, Generator Loss: 1.9000 D(x): 0.8080, D(G(z)): 0.2819 Epoch: [14/20], Batch Num: [321/600] Discriminator Loss: 0.8062, Generator Loss: 1.9049 D(x): 0.8277, D(G(z)): 0.3118 Epoch: [14/20], Batch Num: [322/600] Discriminator Loss: 0.9051, Generator Loss: 2.0036 D(x): 0.7210, D(G(z)): 0.2278 Epoch: [14/20], Batch Num: [323/600] Discriminator Loss: 0.9328, Generator Loss: 1.8494 D(x): 0.6626, D(G(z)): 0.1990 Epoch: [14/20], Batch Num: [324/600] Discriminator Loss: 0.8180, Generator Loss: 1.7479 D(x): 0.7530, D(G(z)): 0.2696 Epoch: [14/20], Batch Num: [325/600] Discriminator Loss: 1.0378, Generator Loss: 1.5977 D(x): 0.6671, D(G(z)): 0.2817 Epoch: [14/20], Batch Num: [326/600] Discriminator Loss: 0.8376, Generator Loss: 1.4376 D(x): 0.7577, D(G(z)): 0.2724 Epoch: [14/20], Batch Num: [327/600] Discriminator Loss: 1.0563, Generator Loss: 1.7192 D(x): 0.7451, D(G(z)): 0.3692 Epoch: [14/20], Batch Num: [328/600] Discriminator Loss: 0.9964, Generator Loss: 1.9225 D(x): 0.7171, D(G(z)): 0.3044 Epoch: [14/20], Batch Num: [329/600] Discriminator Loss: 0.9421, Generator Loss: 1.9544 D(x): 0.6892, D(G(z)): 0.2560 Epoch: [14/20], Batch Num: [330/600] Discriminator Loss: 0.9185, Generator Loss: 1.8712 D(x): 0.6914, D(G(z)): 0.2278 Epoch: [14/20], Batch Num: [331/600] Discriminator Loss: 1.0144, Generator Loss: 1.5283 D(x): 0.6321, D(G(z)): 0.2441 Epoch: [14/20], Batch Num: [332/600] Discriminator Loss: 1.0175, Generator Loss: 1.3862 D(x): 0.7003, D(G(z)): 0.2937 Epoch: [14/20], Batch Num: [333/600] Discriminator Loss: 0.9222, Generator Loss: 1.3439 D(x): 0.7749, D(G(z)): 0.3286 Epoch: [14/20], Batch Num: [334/600] Discriminator Loss: 1.0537, Generator Loss: 1.5836 D(x): 0.7807, D(G(z)): 0.3730 Epoch: [14/20], Batch Num: [335/600] Discriminator Loss: 0.9015, Generator Loss: 1.7036 D(x): 0.7323, D(G(z)): 0.2917 Epoch: [14/20], Batch Num: [336/600] Discriminator Loss: 0.8095, Generator Loss: 1.7107 D(x): 0.7584, D(G(z)): 0.2714 Epoch: [14/20], Batch Num: [337/600] Discriminator Loss: 0.8860, Generator Loss: 1.8665 D(x): 0.6506, D(G(z)): 0.1949 Epoch: [14/20], Batch Num: [338/600] Discriminator Loss: 0.8218, Generator Loss: 1.8317 D(x): 0.7254, D(G(z)): 0.2484 Epoch: [14/20], Batch Num: [339/600] Discriminator Loss: 0.8617, Generator Loss: 1.8049 D(x): 0.7262, D(G(z)): 0.2841 Epoch: [14/20], Batch Num: [340/600] Discriminator Loss: 0.8896, Generator Loss: 1.8525 D(x): 0.7406, D(G(z)): 0.2935 Epoch: [14/20], Batch Num: [341/600] Discriminator Loss: 0.7368, Generator Loss: 1.6844 D(x): 0.7460, D(G(z)): 0.2294 Epoch: [14/20], Batch Num: 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1.8253 D(x): 0.7328, D(G(z)): 0.1903 Epoch: [14/20], Batch Num: [351/600] Discriminator Loss: 0.5502, Generator Loss: 1.8715 D(x): 0.8070, D(G(z)): 0.2148 Epoch: [14/20], Batch Num: [352/600] Discriminator Loss: 0.5709, Generator Loss: 1.6921 D(x): 0.8358, D(G(z)): 0.2317 Epoch: [14/20], Batch Num: [353/600] Discriminator Loss: 0.6639, Generator Loss: 1.7324 D(x): 0.8072, D(G(z)): 0.2510 Epoch: [14/20], Batch Num: [354/600] Discriminator Loss: 0.5976, Generator Loss: 1.8282 D(x): 0.8156, D(G(z)): 0.2378 Epoch: [14/20], Batch Num: [355/600] Discriminator Loss: 0.6748, Generator Loss: 1.7561 D(x): 0.7560, D(G(z)): 0.2358 Epoch: [14/20], Batch Num: [356/600] Discriminator Loss: 0.6628, Generator Loss: 1.7760 D(x): 0.8198, D(G(z)): 0.2668 Epoch: [14/20], Batch Num: [357/600] Discriminator Loss: 0.5684, Generator Loss: 2.0267 D(x): 0.8121, D(G(z)): 0.2205 Epoch: [14/20], Batch Num: [358/600] Discriminator Loss: 0.5665, Generator Loss: 1.9096 D(x): 0.8173, D(G(z)): 0.2031 Epoch: [14/20], Batch Num: [359/600] Discriminator Loss: 0.6231, Generator Loss: 2.1184 D(x): 0.7826, D(G(z)): 0.2165 Epoch: [14/20], Batch Num: [360/600] Discriminator Loss: 0.7612, Generator Loss: 1.9474 D(x): 0.7316, D(G(z)): 0.2271 Epoch: [14/20], Batch Num: [361/600] Discriminator Loss: 0.7701, Generator Loss: 2.1973 D(x): 0.7504, D(G(z)): 0.2386 Epoch: [14/20], Batch Num: [362/600] Discriminator Loss: 0.6119, Generator Loss: 2.0725 D(x): 0.8052, D(G(z)): 0.2099 Epoch: [14/20], Batch Num: [363/600] Discriminator Loss: 0.8389, Generator Loss: 1.7912 D(x): 0.7265, D(G(z)): 0.2394 Epoch: [14/20], Batch Num: [364/600] Discriminator Loss: 0.6373, Generator Loss: 1.7454 D(x): 0.8157, D(G(z)): 0.2569 Epoch: [14/20], Batch Num: [365/600] Discriminator Loss: 0.6666, Generator Loss: 1.7541 D(x): 0.8306, D(G(z)): 0.2593 Epoch: [14/20], Batch Num: [366/600] Discriminator Loss: 0.8325, Generator Loss: 2.1734 D(x): 0.7999, D(G(z)): 0.3115 Epoch: [14/20], Batch Num: [367/600] Discriminator Loss: 0.7067, Generator Loss: 2.4289 D(x): 0.7659, D(G(z)): 0.2258 Epoch: [14/20], Batch Num: [368/600] Discriminator Loss: 0.7153, Generator Loss: 2.2200 D(x): 0.7116, D(G(z)): 0.1762 Epoch: [14/20], Batch Num: [369/600] Discriminator Loss: 0.7329, Generator Loss: 2.0594 D(x): 0.7289, D(G(z)): 0.2116 Epoch: [14/20], Batch Num: [370/600] Discriminator Loss: 0.6674, Generator Loss: 1.6604 D(x): 0.7828, D(G(z)): 0.2369 Epoch: [14/20], Batch Num: [371/600] Discriminator Loss: 0.7970, Generator Loss: 1.6458 D(x): 0.8035, D(G(z)): 0.3308 Epoch: [14/20], Batch Num: [372/600] Discriminator Loss: 0.6886, Generator Loss: 1.7727 D(x): 0.8169, D(G(z)): 0.2875 Epoch: [14/20], Batch Num: [373/600] Discriminator Loss: 0.8101, Generator Loss: 2.1045 D(x): 0.7747, D(G(z)): 0.2835 Epoch: [14/20], Batch Num: [374/600] Discriminator Loss: 0.8582, Generator Loss: 2.1479 D(x): 0.6953, D(G(z)): 0.2240 Epoch: [14/20], Batch Num: [375/600] Discriminator Loss: 0.8474, Generator Loss: 1.9941 D(x): 0.7001, D(G(z)): 0.2162 Epoch: [14/20], Batch Num: [376/600] Discriminator Loss: 0.8117, Generator Loss: 1.6445 D(x): 0.7241, D(G(z)): 0.2495 Epoch: [14/20], Batch Num: [377/600] Discriminator Loss: 0.8714, Generator Loss: 1.3878 D(x): 0.7894, D(G(z)): 0.3341 Epoch: [14/20], Batch Num: [378/600] Discriminator Loss: 0.8400, Generator Loss: 1.5739 D(x): 0.7543, D(G(z)): 0.3110 Epoch: [14/20], Batch Num: [379/600] Discriminator Loss: 1.0145, Generator Loss: 1.5474 D(x): 0.7317, D(G(z)): 0.3396 Epoch: [14/20], Batch Num: [380/600] Discriminator Loss: 0.7721, Generator Loss: 1.8069 D(x): 0.7889, D(G(z)): 0.3115 Epoch: [14/20], Batch Num: [381/600] Discriminator Loss: 0.7913, Generator Loss: 2.1006 D(x): 0.7591, D(G(z)): 0.2665 Epoch: [14/20], Batch Num: [382/600] Discriminator Loss: 0.8855, Generator Loss: 2.0357 D(x): 0.6542, D(G(z)): 0.2237 Epoch: [14/20], Batch Num: [383/600] Discriminator Loss: 0.8938, Generator Loss: 1.7094 D(x): 0.6632, D(G(z)): 0.2165 Epoch: [14/20], Batch Num: [384/600] Discriminator Loss: 0.7375, Generator Loss: 1.7226 D(x): 0.7734, D(G(z)): 0.2549 Epoch: [14/20], Batch Num: [385/600] Discriminator Loss: 0.7925, Generator Loss: 1.7272 D(x): 0.7834, D(G(z)): 0.3076 Epoch: [14/20], Batch Num: [386/600] Discriminator Loss: 0.7449, Generator Loss: 1.8074 D(x): 0.8076, D(G(z)): 0.2996 Epoch: [14/20], Batch Num: [387/600] Discriminator Loss: 0.5928, Generator Loss: 1.8534 D(x): 0.8107, D(G(z)): 0.2475 Epoch: [14/20], Batch Num: [388/600] Discriminator Loss: 0.7900, Generator Loss: 2.1919 D(x): 0.7362, D(G(z)): 0.2475 Epoch: [14/20], Batch Num: [389/600] Discriminator Loss: 0.7306, Generator Loss: 1.9010 D(x): 0.6929, D(G(z)): 0.1754 Epoch: [14/20], Batch Num: [390/600] Discriminator Loss: 0.6571, Generator Loss: 1.8405 D(x): 0.7734, D(G(z)): 0.2176 Epoch: [14/20], Batch Num: [391/600] Discriminator Loss: 0.6679, Generator Loss: 1.9233 D(x): 0.7739, D(G(z)): 0.2394 Epoch: [14/20], Batch Num: [392/600] Discriminator Loss: 0.6903, Generator Loss: 1.5957 D(x): 0.7799, D(G(z)): 0.2424 Epoch: [14/20], Batch Num: [393/600] Discriminator Loss: 0.5818, Generator Loss: 1.7542 D(x): 0.8301, D(G(z)): 0.2398 Epoch: [14/20], Batch Num: [394/600] Discriminator Loss: 0.6224, Generator Loss: 1.8636 D(x): 0.8063, D(G(z)): 0.2499 Epoch: [14/20], Batch Num: [395/600] Discriminator Loss: 0.6451, Generator Loss: 1.9890 D(x): 0.7886, D(G(z)): 0.2276 Epoch: [14/20], Batch Num: [396/600] Discriminator Loss: 0.7404, Generator Loss: 1.9419 D(x): 0.7879, D(G(z)): 0.2611 Epoch: [14/20], Batch Num: [397/600] Discriminator Loss: 0.6491, Generator Loss: 2.2911 D(x): 0.8059, D(G(z)): 0.2338 Epoch: [14/20], Batch Num: [398/600] Discriminator Loss: 0.6373, Generator Loss: 2.4305 D(x): 0.8142, D(G(z)): 0.2382 Epoch: [14/20], Batch Num: [399/600] Discriminator Loss: 0.5324, Generator Loss: 2.5230 D(x): 0.8013, D(G(z)): 0.1602 Epoch: 14, Batch Num: [400/600]
Epoch: [14/20], Batch Num: [400/600] Discriminator Loss: 0.7623, Generator Loss: 2.3437 D(x): 0.6891, D(G(z)): 0.1690 Epoch: [14/20], Batch Num: [401/600] Discriminator Loss: 0.7487, Generator Loss: 1.7988 D(x): 0.7266, D(G(z)): 0.1968 Epoch: [14/20], Batch Num: [402/600] Discriminator Loss: 0.6654, Generator Loss: 1.7618 D(x): 0.8016, D(G(z)): 0.2543 Epoch: [14/20], Batch Num: [403/600] Discriminator Loss: 0.7854, Generator Loss: 2.0390 D(x): 0.8337, D(G(z)): 0.3198 Epoch: [14/20], Batch Num: [404/600] Discriminator Loss: 0.6746, Generator Loss: 1.8902 D(x): 0.8316, D(G(z)): 0.2747 Epoch: [14/20], Batch Num: [405/600] Discriminator Loss: 0.7351, Generator Loss: 2.5328 D(x): 0.7784, D(G(z)): 0.2244 Epoch: [14/20], Batch Num: [406/600] Discriminator Loss: 0.9009, Generator Loss: 2.4721 D(x): 0.6788, D(G(z)): 0.1797 Epoch: [14/20], Batch Num: [407/600] Discriminator Loss: 0.6200, Generator Loss: 2.2199 D(x): 0.7672, D(G(z)): 0.1609 Epoch: [14/20], Batch Num: [408/600] Discriminator Loss: 0.6935, Generator Loss: 1.9229 D(x): 0.7680, D(G(z)): 0.2228 Epoch: [14/20], Batch Num: [409/600] Discriminator Loss: 0.7894, Generator Loss: 1.8800 D(x): 0.7713, D(G(z)): 0.2588 Epoch: [14/20], Batch Num: [410/600] Discriminator Loss: 0.7032, Generator Loss: 2.1401 D(x): 0.8226, D(G(z)): 0.2822 Epoch: [14/20], Batch Num: [411/600] Discriminator Loss: 0.8200, Generator Loss: 2.1390 D(x): 0.7574, D(G(z)): 0.2663 Epoch: [14/20], Batch Num: [412/600] Discriminator Loss: 0.7902, Generator Loss: 2.1621 D(x): 0.7070, D(G(z)): 0.2105 Epoch: [14/20], Batch Num: [413/600] Discriminator Loss: 0.8247, Generator Loss: 1.7630 D(x): 0.7022, D(G(z)): 0.2197 Epoch: [14/20], Batch Num: [414/600] Discriminator Loss: 0.9554, Generator Loss: 1.4863 D(x): 0.7408, D(G(z)): 0.3070 Epoch: [14/20], Batch Num: [415/600] Discriminator Loss: 0.9230, Generator Loss: 1.7608 D(x): 0.7938, D(G(z)): 0.3537 Epoch: [14/20], Batch Num: [416/600] Discriminator Loss: 0.8133, Generator Loss: 2.0381 D(x): 0.7456, D(G(z)): 0.2658 Epoch: [14/20], Batch Num: [417/600] Discriminator Loss: 0.8808, Generator Loss: 1.8889 D(x): 0.6820, D(G(z)): 0.2223 Epoch: [14/20], Batch Num: [418/600] Discriminator Loss: 0.9333, Generator Loss: 1.6637 D(x): 0.6961, D(G(z)): 0.2702 Epoch: [14/20], Batch Num: [419/600] Discriminator Loss: 0.8445, Generator Loss: 1.6770 D(x): 0.7447, D(G(z)): 0.2767 Epoch: [14/20], Batch Num: [420/600] Discriminator Loss: 0.9273, Generator Loss: 1.6243 D(x): 0.7743, D(G(z)): 0.3421 Epoch: [14/20], Batch Num: [421/600] Discriminator Loss: 0.7815, Generator Loss: 1.7691 D(x): 0.7393, D(G(z)): 0.2720 Epoch: [14/20], Batch Num: [422/600] Discriminator Loss: 0.8232, Generator Loss: 1.8492 D(x): 0.7231, D(G(z)): 0.2665 Epoch: [14/20], Batch Num: [423/600] Discriminator Loss: 1.0204, Generator Loss: 1.6175 D(x): 0.6619, D(G(z)): 0.2646 Epoch: [14/20], Batch Num: [424/600] Discriminator Loss: 1.0522, Generator Loss: 1.4667 D(x): 0.7042, D(G(z)): 0.3270 Epoch: [14/20], Batch Num: [425/600] Discriminator Loss: 1.0182, Generator Loss: 1.8063 D(x): 0.7556, D(G(z)): 0.3652 Epoch: [14/20], Batch Num: [426/600] Discriminator Loss: 0.8533, Generator Loss: 2.1847 D(x): 0.7031, D(G(z)): 0.2505 Epoch: [14/20], Batch Num: [427/600] Discriminator Loss: 0.7827, Generator Loss: 2.0883 D(x): 0.7353, D(G(z)): 0.2312 Epoch: [14/20], Batch Num: [428/600] Discriminator Loss: 0.8435, Generator Loss: 1.8584 D(x): 0.7077, D(G(z)): 0.2270 Epoch: [14/20], Batch Num: [429/600] Discriminator Loss: 0.7209, Generator Loss: 1.6759 D(x): 0.7541, D(G(z)): 0.2132 Epoch: [14/20], Batch Num: [430/600] Discriminator Loss: 0.7618, Generator Loss: 1.6203 D(x): 0.7886, D(G(z)): 0.2610 Epoch: [14/20], Batch Num: [431/600] Discriminator Loss: 0.7803, Generator Loss: 1.7332 D(x): 0.7815, D(G(z)): 0.2766 Epoch: [14/20], Batch Num: [432/600] Discriminator Loss: 0.6317, Generator Loss: 1.8111 D(x): 0.8332, D(G(z)): 0.2660 Epoch: [14/20], Batch Num: [433/600] Discriminator Loss: 0.7254, Generator Loss: 2.2083 D(x): 0.7502, D(G(z)): 0.2241 Epoch: [14/20], Batch Num: [434/600] Discriminator Loss: 0.6346, Generator Loss: 2.0845 D(x): 0.7622, D(G(z)): 0.1990 Epoch: [14/20], Batch Num: [435/600] Discriminator Loss: 0.7526, Generator Loss: 2.3791 D(x): 0.7381, D(G(z)): 0.2034 Epoch: [14/20], Batch Num: [436/600] Discriminator Loss: 0.7494, Generator Loss: 1.9733 D(x): 0.7385, D(G(z)): 0.1992 Epoch: [14/20], Batch Num: [437/600] Discriminator Loss: 0.7280, Generator Loss: 2.0827 D(x): 0.7734, D(G(z)): 0.2332 Epoch: [14/20], Batch Num: [438/600] Discriminator Loss: 0.5634, Generator Loss: 2.1340 D(x): 0.8438, D(G(z)): 0.2354 Epoch: [14/20], Batch Num: [439/600] Discriminator Loss: 0.6561, Generator Loss: 2.1385 D(x): 0.7824, D(G(z)): 0.2035 Epoch: [14/20], Batch Num: [440/600] Discriminator Loss: 0.6577, Generator Loss: 2.2249 D(x): 0.8310, D(G(z)): 0.2572 Epoch: [14/20], Batch Num: [441/600] Discriminator Loss: 0.7168, Generator Loss: 2.3472 D(x): 0.7603, D(G(z)): 0.1904 Epoch: [14/20], Batch Num: 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2.2888 D(x): 0.7710, D(G(z)): 0.2090 Epoch: [14/20], Batch Num: [451/600] Discriminator Loss: 0.6702, Generator Loss: 2.3303 D(x): 0.7774, D(G(z)): 0.1856 Epoch: [14/20], Batch Num: [452/600] Discriminator Loss: 0.7474, Generator Loss: 2.0522 D(x): 0.7203, D(G(z)): 0.1708 Epoch: [14/20], Batch Num: [453/600] Discriminator Loss: 0.7162, Generator Loss: 1.8803 D(x): 0.8199, D(G(z)): 0.2663 Epoch: [14/20], Batch Num: [454/600] Discriminator Loss: 0.8537, Generator Loss: 1.9333 D(x): 0.7506, D(G(z)): 0.2654 Epoch: [14/20], Batch Num: [455/600] Discriminator Loss: 0.7703, Generator Loss: 1.9363 D(x): 0.8041, D(G(z)): 0.2847 Epoch: [14/20], Batch Num: [456/600] Discriminator Loss: 0.7591, Generator Loss: 2.0719 D(x): 0.7382, D(G(z)): 0.2323 Epoch: [14/20], Batch Num: [457/600] Discriminator Loss: 0.9329, Generator Loss: 1.8536 D(x): 0.6885, D(G(z)): 0.2646 Epoch: [14/20], Batch Num: [458/600] Discriminator Loss: 0.8721, Generator Loss: 1.7963 D(x): 0.7442, D(G(z)): 0.2748 Epoch: [14/20], 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Generator Loss: 1.8992 D(x): 0.7213, D(G(z)): 0.2913 Epoch: [14/20], Batch Num: [468/600] Discriminator Loss: 1.0291, Generator Loss: 1.6986 D(x): 0.6156, D(G(z)): 0.2186 Epoch: [14/20], Batch Num: [469/600] Discriminator Loss: 1.0272, Generator Loss: 1.4502 D(x): 0.6415, D(G(z)): 0.2629 Epoch: [14/20], Batch Num: [470/600] Discriminator Loss: 0.7684, Generator Loss: 1.2354 D(x): 0.7244, D(G(z)): 0.2553 Epoch: [14/20], Batch Num: [471/600] Discriminator Loss: 1.0067, Generator Loss: 1.2823 D(x): 0.7454, D(G(z)): 0.3579 Epoch: [14/20], Batch Num: [472/600] Discriminator Loss: 0.8977, Generator Loss: 1.3234 D(x): 0.7916, D(G(z)): 0.3746 Epoch: [14/20], Batch Num: [473/600] Discriminator Loss: 0.7472, Generator Loss: 1.6373 D(x): 0.7992, D(G(z)): 0.3153 Epoch: [14/20], Batch Num: [474/600] Discriminator Loss: 0.8381, Generator Loss: 1.9791 D(x): 0.7331, D(G(z)): 0.2833 Epoch: [14/20], Batch Num: [475/600] Discriminator Loss: 0.7835, Generator Loss: 1.9628 D(x): 0.7080, D(G(z)): 0.2298 Epoch: [14/20], Batch Num: [476/600] Discriminator Loss: 0.7295, Generator Loss: 2.0326 D(x): 0.7222, D(G(z)): 0.2050 Epoch: [14/20], Batch Num: [477/600] Discriminator Loss: 0.9177, Generator Loss: 1.8196 D(x): 0.6491, D(G(z)): 0.1834 Epoch: [14/20], Batch Num: [478/600] Discriminator Loss: 0.6914, Generator Loss: 1.4757 D(x): 0.7550, D(G(z)): 0.2246 Epoch: [14/20], Batch Num: [479/600] Discriminator Loss: 0.6351, Generator Loss: 1.3467 D(x): 0.8224, D(G(z)): 0.2791 Epoch: [14/20], Batch Num: [480/600] Discriminator Loss: 0.7647, Generator Loss: 1.4387 D(x): 0.8363, D(G(z)): 0.3171 Epoch: [14/20], Batch Num: [481/600] Discriminator Loss: 0.7155, Generator Loss: 1.7418 D(x): 0.8387, D(G(z)): 0.3001 Epoch: [14/20], Batch Num: [482/600] Discriminator Loss: 0.5741, Generator Loss: 1.9530 D(x): 0.8295, D(G(z)): 0.2401 Epoch: [14/20], Batch Num: [483/600] Discriminator Loss: 0.6189, Generator Loss: 2.2043 D(x): 0.7662, D(G(z)): 0.1898 Epoch: [14/20], Batch Num: [484/600] Discriminator Loss: 0.6650, Generator Loss: 2.0520 D(x): 0.7506, D(G(z)): 0.1952 Epoch: [14/20], Batch Num: [485/600] Discriminator Loss: 0.6246, Generator Loss: 2.1178 D(x): 0.7509, D(G(z)): 0.1893 Epoch: [14/20], Batch Num: [486/600] Discriminator Loss: 0.6296, Generator Loss: 1.9929 D(x): 0.7814, D(G(z)): 0.1804 Epoch: [14/20], Batch Num: [487/600] Discriminator Loss: 0.5966, Generator Loss: 1.9395 D(x): 0.7779, D(G(z)): 0.2045 Epoch: [14/20], Batch Num: [488/600] Discriminator Loss: 0.5183, Generator Loss: 1.5480 D(x): 0.8395, D(G(z)): 0.2247 Epoch: [14/20], Batch Num: [489/600] Discriminator Loss: 0.6163, Generator Loss: 1.7930 D(x): 0.8414, D(G(z)): 0.2793 Epoch: [14/20], Batch Num: [490/600] Discriminator Loss: 0.5109, Generator Loss: 2.0188 D(x): 0.8930, D(G(z)): 0.2570 Epoch: [14/20], Batch Num: [491/600] Discriminator Loss: 0.4313, Generator Loss: 2.5026 D(x): 0.8636, D(G(z)): 0.1867 Epoch: [14/20], Batch Num: [492/600] Discriminator Loss: 0.5479, Generator Loss: 2.3584 D(x): 0.7697, D(G(z)): 0.1471 Epoch: [14/20], Batch Num: [493/600] Discriminator Loss: 0.6336, Generator Loss: 2.7237 D(x): 0.7526, D(G(z)): 0.1459 Epoch: [14/20], Batch Num: [494/600] Discriminator Loss: 0.6225, Generator Loss: 2.1376 D(x): 0.7677, D(G(z)): 0.1677 Epoch: [14/20], Batch Num: [495/600] Discriminator Loss: 0.5104, Generator Loss: 1.7948 D(x): 0.8219, D(G(z)): 0.1891 Epoch: [14/20], Batch Num: [496/600] Discriminator Loss: 0.5313, Generator Loss: 1.7949 D(x): 0.8598, D(G(z)): 0.2338 Epoch: [14/20], Batch Num: [497/600] Discriminator Loss: 0.5867, Generator Loss: 1.8511 D(x): 0.8445, D(G(z)): 0.2327 Epoch: [14/20], Batch Num: [498/600] Discriminator Loss: 0.7299, Generator Loss: 2.2131 D(x): 0.8519, D(G(z)): 0.2857 Epoch: [14/20], Batch Num: [499/600] Discriminator Loss: 0.6776, Generator Loss: 2.5319 D(x): 0.7938, D(G(z)): 0.2292 Epoch: 14, Batch Num: [500/600]
Epoch: [14/20], Batch Num: [500/600] Discriminator Loss: 0.7024, Generator Loss: 2.5820 D(x): 0.7280, D(G(z)): 0.1643 Epoch: [14/20], Batch Num: [501/600] Discriminator Loss: 0.7898, Generator Loss: 2.2614 D(x): 0.6881, D(G(z)): 0.1413 Epoch: [14/20], Batch Num: [502/600] Discriminator Loss: 0.8141, Generator Loss: 1.8886 D(x): 0.7283, D(G(z)): 0.2079 Epoch: [14/20], Batch Num: [503/600] Discriminator Loss: 0.6798, Generator Loss: 1.5480 D(x): 0.8260, D(G(z)): 0.2770 Epoch: [14/20], Batch Num: [504/600] Discriminator Loss: 0.6465, Generator Loss: 1.7093 D(x): 0.8633, D(G(z)): 0.2746 Epoch: [14/20], Batch Num: [505/600] Discriminator Loss: 0.8984, Generator Loss: 2.0313 D(x): 0.8144, D(G(z)): 0.3194 Epoch: [14/20], Batch Num: [506/600] Discriminator Loss: 0.6704, Generator Loss: 2.2454 D(x): 0.7838, D(G(z)): 0.2209 Epoch: [14/20], Batch Num: [507/600] Discriminator Loss: 0.8256, Generator Loss: 2.1673 D(x): 0.7045, D(G(z)): 0.1897 Epoch: [14/20], Batch Num: [508/600] Discriminator Loss: 0.8783, Generator Loss: 2.3782 D(x): 0.7448, D(G(z)): 0.2709 Epoch: [14/20], Batch Num: [509/600] Discriminator Loss: 0.9570, Generator Loss: 1.9532 D(x): 0.6646, D(G(z)): 0.1989 Epoch: [14/20], Batch Num: [510/600] Discriminator Loss: 0.7784, Generator Loss: 1.6724 D(x): 0.7469, D(G(z)): 0.2228 Epoch: [14/20], Batch Num: [511/600] Discriminator Loss: 0.8898, Generator Loss: 1.6850 D(x): 0.7250, D(G(z)): 0.2664 Epoch: [14/20], Batch Num: [512/600] Discriminator Loss: 0.8615, Generator Loss: 1.4788 D(x): 0.7815, D(G(z)): 0.3312 Epoch: [14/20], Batch Num: [513/600] Discriminator Loss: 0.9784, Generator Loss: 1.8125 D(x): 0.7802, D(G(z)): 0.3708 Epoch: [14/20], Batch Num: [514/600] Discriminator Loss: 0.9791, Generator Loss: 1.6723 D(x): 0.7206, D(G(z)): 0.2866 Epoch: [14/20], Batch Num: [515/600] Discriminator Loss: 0.9351, Generator Loss: 1.8519 D(x): 0.6974, D(G(z)): 0.2601 Epoch: [14/20], Batch Num: [516/600] Discriminator Loss: 1.0165, Generator Loss: 1.6235 D(x): 0.6219, D(G(z)): 0.2327 Epoch: [14/20], Batch Num: [517/600] Discriminator Loss: 0.9175, Generator Loss: 1.4917 D(x): 0.7143, D(G(z)): 0.2908 Epoch: [14/20], Batch Num: [518/600] Discriminator Loss: 0.8638, Generator Loss: 1.4967 D(x): 0.7761, D(G(z)): 0.3319 Epoch: [14/20], Batch Num: [519/600] Discriminator Loss: 1.0879, Generator Loss: 1.6528 D(x): 0.7287, D(G(z)): 0.3641 Epoch: [14/20], Batch Num: [520/600] Discriminator Loss: 0.9667, Generator Loss: 1.7498 D(x): 0.7276, D(G(z)): 0.2914 Epoch: [14/20], Batch Num: [521/600] Discriminator Loss: 0.7976, Generator Loss: 2.0194 D(x): 0.6983, D(G(z)): 0.2262 Epoch: [14/20], Batch Num: [522/600] Discriminator Loss: 0.8447, Generator Loss: 2.0583 D(x): 0.6857, D(G(z)): 0.2323 Epoch: [14/20], Batch Num: [523/600] Discriminator Loss: 0.8397, Generator Loss: 1.6733 D(x): 0.6888, D(G(z)): 0.2358 Epoch: [14/20], Batch Num: [524/600] Discriminator Loss: 0.6572, Generator Loss: 1.5862 D(x): 0.7918, D(G(z)): 0.2331 Epoch: [14/20], Batch Num: [525/600] Discriminator Loss: 0.8250, Generator Loss: 1.6346 D(x): 0.7998, D(G(z)): 0.3377 Epoch: [14/20], Batch Num: [526/600] Discriminator Loss: 0.7214, Generator Loss: 1.6372 D(x): 0.7963, D(G(z)): 0.2868 Epoch: [14/20], Batch Num: [527/600] Discriminator Loss: 0.7885, Generator Loss: 2.0339 D(x): 0.7235, D(G(z)): 0.2655 Epoch: [14/20], Batch Num: [528/600] Discriminator Loss: 0.8320, Generator Loss: 2.0272 D(x): 0.7437, D(G(z)): 0.2759 Epoch: [14/20], Batch Num: [529/600] Discriminator Loss: 0.8646, Generator Loss: 2.1767 D(x): 0.6737, D(G(z)): 0.2287 Epoch: [14/20], Batch Num: [530/600] Discriminator Loss: 0.7641, Generator Loss: 2.0050 D(x): 0.7723, D(G(z)): 0.2758 Epoch: [14/20], Batch Num: [531/600] Discriminator Loss: 0.7130, Generator Loss: 2.0831 D(x): 0.7555, D(G(z)): 0.2192 Epoch: [14/20], Batch Num: [532/600] Discriminator Loss: 0.6672, Generator Loss: 2.0682 D(x): 0.7722, D(G(z)): 0.2255 Epoch: [14/20], Batch Num: [533/600] Discriminator Loss: 0.6671, Generator Loss: 1.9861 D(x): 0.7519, D(G(z)): 0.1994 Epoch: [14/20], Batch Num: [534/600] Discriminator Loss: 0.7265, Generator Loss: 1.7579 D(x): 0.7699, D(G(z)): 0.2402 Epoch: [14/20], Batch Num: [535/600] Discriminator Loss: 0.5229, Generator Loss: 1.7541 D(x): 0.8542, D(G(z)): 0.2224 Epoch: [14/20], Batch Num: [536/600] Discriminator Loss: 0.6806, Generator Loss: 2.0380 D(x): 0.8037, D(G(z)): 0.2485 Epoch: [14/20], Batch Num: [537/600] Discriminator Loss: 0.6406, Generator Loss: 2.2128 D(x): 0.7992, D(G(z)): 0.2206 Epoch: [14/20], Batch Num: [538/600] Discriminator Loss: 0.6083, Generator Loss: 2.5698 D(x): 0.7962, D(G(z)): 0.2072 Epoch: [14/20], Batch Num: [539/600] Discriminator Loss: 0.6990, Generator Loss: 2.2426 D(x): 0.7207, D(G(z)): 0.1597 Epoch: [14/20], Batch Num: [540/600] Discriminator Loss: 0.6583, Generator Loss: 2.2061 D(x): 0.7774, D(G(z)): 0.2093 Epoch: [14/20], Batch Num: [541/600] Discriminator Loss: 0.6989, Generator Loss: 2.1521 D(x): 0.7935, D(G(z)): 0.2438 Epoch: [14/20], Batch Num: [542/600] Discriminator Loss: 0.6741, Generator Loss: 2.1119 D(x): 0.7855, D(G(z)): 0.2357 Epoch: [14/20], Batch Num: [543/600] Discriminator Loss: 0.5811, Generator Loss: 2.4366 D(x): 0.8629, D(G(z)): 0.2343 Epoch: [14/20], Batch Num: [544/600] Discriminator Loss: 0.6348, Generator Loss: 2.6229 D(x): 0.8267, D(G(z)): 0.2190 Epoch: [14/20], Batch Num: [545/600] Discriminator Loss: 0.6467, Generator Loss: 2.5722 D(x): 0.7436, D(G(z)): 0.1361 Epoch: [14/20], Batch Num: [546/600] Discriminator Loss: 0.6595, Generator Loss: 2.1828 D(x): 0.7769, D(G(z)): 0.1685 Epoch: [14/20], Batch Num: [547/600] Discriminator Loss: 0.6065, Generator Loss: 2.0194 D(x): 0.8197, D(G(z)): 0.2178 Epoch: [14/20], Batch Num: [548/600] Discriminator Loss: 0.7977, Generator Loss: 2.0404 D(x): 0.7192, D(G(z)): 0.2006 Epoch: [14/20], Batch Num: [549/600] Discriminator Loss: 0.6860, Generator Loss: 1.7191 D(x): 0.8106, D(G(z)): 0.2324 Epoch: [14/20], Batch Num: [550/600] Discriminator Loss: 0.9346, Generator Loss: 1.8450 D(x): 0.7862, D(G(z)): 0.3272 Epoch: [14/20], Batch Num: [551/600] Discriminator Loss: 0.8096, Generator Loss: 2.4403 D(x): 0.8493, D(G(z)): 0.3237 Epoch: [14/20], Batch Num: [552/600] Discriminator Loss: 0.9401, Generator Loss: 2.5407 D(x): 0.6744, D(G(z)): 0.2059 Epoch: [14/20], Batch Num: [553/600] Discriminator Loss: 0.9610, Generator Loss: 2.1439 D(x): 0.6793, D(G(z)): 0.2221 Epoch: [14/20], Batch Num: [554/600] Discriminator Loss: 0.9958, Generator Loss: 1.7180 D(x): 0.6930, D(G(z)): 0.2620 Epoch: [14/20], Batch Num: [555/600] Discriminator Loss: 1.0624, Generator Loss: 1.5450 D(x): 0.7024, D(G(z)): 0.3166 Epoch: [14/20], Batch Num: [556/600] Discriminator Loss: 1.0546, Generator Loss: 1.7071 D(x): 0.7511, D(G(z)): 0.3339 Epoch: [14/20], Batch Num: [557/600] Discriminator Loss: 1.1153, Generator Loss: 1.8059 D(x): 0.7169, D(G(z)): 0.3154 Epoch: [14/20], Batch Num: [558/600] Discriminator Loss: 1.0780, Generator Loss: 1.5646 D(x): 0.6875, D(G(z)): 0.3174 Epoch: [14/20], Batch Num: [559/600] Discriminator Loss: 1.0813, Generator Loss: 1.9121 D(x): 0.6781, D(G(z)): 0.3125 Epoch: [14/20], Batch Num: [560/600] Discriminator Loss: 1.1498, Generator Loss: 1.8711 D(x): 0.6016, D(G(z)): 0.2524 Epoch: [14/20], Batch Num: [561/600] Discriminator Loss: 1.1017, Generator Loss: 1.4739 D(x): 0.6721, D(G(z)): 0.3345 Epoch: [14/20], Batch Num: [562/600] Discriminator Loss: 1.1265, Generator Loss: 1.7143 D(x): 0.7171, D(G(z)): 0.3586 Epoch: [14/20], Batch Num: [563/600] Discriminator Loss: 1.3219, Generator Loss: 1.6186 D(x): 0.6714, D(G(z)): 0.3774 Epoch: [14/20], Batch Num: [564/600] Discriminator Loss: 1.1468, Generator Loss: 1.6680 D(x): 0.6464, D(G(z)): 0.3215 Epoch: [14/20], Batch Num: [565/600] Discriminator Loss: 0.9862, Generator Loss: 1.8074 D(x): 0.6588, D(G(z)): 0.2532 Epoch: [14/20], Batch Num: [566/600] Discriminator Loss: 1.1366, Generator Loss: 1.5630 D(x): 0.6142, D(G(z)): 0.2918 Epoch: [14/20], Batch Num: [567/600] Discriminator Loss: 0.9257, Generator Loss: 1.4107 D(x): 0.6664, D(G(z)): 0.2742 Epoch: [14/20], Batch Num: [568/600] Discriminator Loss: 0.9823, Generator Loss: 1.3692 D(x): 0.6800, D(G(z)): 0.3002 Epoch: [14/20], Batch Num: [569/600] Discriminator Loss: 0.8227, Generator Loss: 1.2630 D(x): 0.7713, D(G(z)): 0.3390 Epoch: [14/20], Batch Num: [570/600] Discriminator Loss: 0.9760, Generator Loss: 1.2960 D(x): 0.7068, D(G(z)): 0.3487 Epoch: [14/20], Batch Num: [571/600] Discriminator Loss: 0.8469, Generator Loss: 1.4473 D(x): 0.7640, D(G(z)): 0.3404 Epoch: [14/20], Batch Num: [572/600] Discriminator Loss: 0.7679, Generator Loss: 1.6403 D(x): 0.7623, D(G(z)): 0.3101 Epoch: [14/20], Batch Num: [573/600] Discriminator Loss: 0.8054, Generator Loss: 1.7409 D(x): 0.7292, D(G(z)): 0.2767 Epoch: [14/20], Batch Num: [574/600] Discriminator Loss: 0.7728, Generator Loss: 1.8880 D(x): 0.6630, D(G(z)): 0.2004 Epoch: [14/20], Batch Num: [575/600] Discriminator Loss: 0.7415, Generator Loss: 1.8964 D(x): 0.7043, D(G(z)): 0.2050 Epoch: [14/20], Batch Num: [576/600] Discriminator Loss: 0.7452, Generator Loss: 1.6864 D(x): 0.7087, D(G(z)): 0.2297 Epoch: [14/20], Batch Num: [577/600] Discriminator Loss: 0.6414, Generator Loss: 1.4357 D(x): 0.8147, D(G(z)): 0.2718 Epoch: [14/20], Batch Num: [578/600] Discriminator Loss: 0.6294, Generator Loss: 1.4372 D(x): 0.7919, D(G(z)): 0.2608 Epoch: [14/20], Batch Num: [579/600] Discriminator Loss: 0.5257, Generator Loss: 1.6788 D(x): 0.8405, D(G(z)): 0.2377 Epoch: [14/20], Batch Num: [580/600] Discriminator Loss: 0.6329, Generator Loss: 1.7163 D(x): 0.7978, D(G(z)): 0.2602 Epoch: [14/20], Batch Num: [581/600] Discriminator Loss: 0.6379, Generator Loss: 1.7577 D(x): 0.7857, D(G(z)): 0.2336 Epoch: [14/20], Batch Num: [582/600] Discriminator Loss: 0.5864, Generator Loss: 1.9107 D(x): 0.8244, D(G(z)): 0.2443 Epoch: [14/20], Batch Num: [583/600] Discriminator Loss: 0.5486, Generator Loss: 2.2453 D(x): 0.8395, D(G(z)): 0.2192 Epoch: [14/20], Batch Num: [584/600] Discriminator Loss: 0.6546, Generator Loss: 2.4684 D(x): 0.7435, D(G(z)): 0.1524 Epoch: [14/20], Batch Num: [585/600] Discriminator Loss: 0.5702, Generator Loss: 2.2021 D(x): 0.7511, D(G(z)): 0.1451 Epoch: [14/20], Batch Num: [586/600] Discriminator Loss: 0.5902, Generator Loss: 2.0028 D(x): 0.7679, D(G(z)): 0.1818 Epoch: [14/20], Batch Num: [587/600] Discriminator Loss: 0.5369, Generator Loss: 1.9798 D(x): 0.8612, D(G(z)): 0.2363 Epoch: [14/20], Batch Num: [588/600] Discriminator Loss: 0.4698, Generator Loss: 1.7134 D(x): 0.8799, D(G(z)): 0.2230 Epoch: [14/20], Batch Num: [589/600] Discriminator Loss: 0.5502, Generator Loss: 2.2550 D(x): 0.8712, D(G(z)): 0.2606 Epoch: [14/20], Batch Num: [590/600] Discriminator Loss: 0.5731, Generator Loss: 2.2387 D(x): 0.8311, D(G(z)): 0.2214 Epoch: [14/20], Batch Num: [591/600] Discriminator Loss: 0.5399, Generator Loss: 2.3218 D(x): 0.8179, D(G(z)): 0.1732 Epoch: [14/20], Batch Num: [592/600] Discriminator Loss: 0.6515, Generator Loss: 2.2942 D(x): 0.7180, D(G(z)): 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Epoch: [15/20], Batch Num: [0/600] Discriminator Loss: 0.9004, Generator Loss: 1.7915 D(x): 0.7237, D(G(z)): 0.2685 Epoch: [15/20], Batch Num: [1/600] Discriminator Loss: 0.9596, Generator Loss: 1.6654 D(x): 0.7328, D(G(z)): 0.3016 Epoch: [15/20], Batch Num: [2/600] Discriminator Loss: 0.8489, Generator Loss: 1.5395 D(x): 0.7982, D(G(z)): 0.3215 Epoch: [15/20], Batch Num: [3/600] Discriminator Loss: 0.9896, Generator Loss: 1.6905 D(x): 0.7258, D(G(z)): 0.3224 Epoch: [15/20], Batch Num: [4/600] Discriminator Loss: 1.0421, Generator Loss: 1.7298 D(x): 0.7064, D(G(z)): 0.3273 Epoch: [15/20], Batch Num: [5/600] Discriminator Loss: 0.9477, Generator Loss: 1.7205 D(x): 0.6799, D(G(z)): 0.2593 Epoch: [15/20], Batch Num: [6/600] Discriminator Loss: 1.0149, Generator Loss: 1.6661 D(x): 0.7387, D(G(z)): 0.3311 Epoch: [15/20], Batch Num: [7/600] Discriminator Loss: 0.8976, Generator Loss: 1.6479 D(x): 0.6967, D(G(z)): 0.2692 Epoch: [15/20], Batch Num: [8/600] Discriminator Loss: 1.1361, Generator 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D(x): 0.7895, D(G(z)): 0.2352 Epoch: [15/20], Batch Num: [26/600] Discriminator Loss: 0.7193, Generator Loss: 1.8223 D(x): 0.7934, D(G(z)): 0.2740 Epoch: [15/20], Batch Num: [27/600] Discriminator Loss: 0.6173, Generator Loss: 1.8755 D(x): 0.7656, D(G(z)): 0.1964 Epoch: [15/20], Batch Num: [28/600] Discriminator Loss: 0.5931, Generator Loss: 2.0412 D(x): 0.8488, D(G(z)): 0.2496 Epoch: [15/20], Batch Num: [29/600] Discriminator Loss: 0.5409, Generator Loss: 2.0848 D(x): 0.8372, D(G(z)): 0.2131 Epoch: [15/20], Batch Num: [30/600] Discriminator Loss: 0.6173, Generator Loss: 2.2060 D(x): 0.8054, D(G(z)): 0.2134 Epoch: [15/20], Batch Num: [31/600] Discriminator Loss: 0.6211, Generator Loss: 2.2067 D(x): 0.7339, D(G(z)): 0.1634 Epoch: [15/20], Batch Num: [32/600] Discriminator Loss: 0.4580, Generator Loss: 2.1079 D(x): 0.8104, D(G(z)): 0.1591 Epoch: [15/20], Batch Num: [33/600] Discriminator Loss: 0.5993, Generator Loss: 2.0564 D(x): 0.8171, D(G(z)): 0.2205 Epoch: [15/20], Batch Num: 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D(x): 0.7839, D(G(z)): 0.2667 Epoch: [15/20], Batch Num: [43/600] Discriminator Loss: 0.9890, Generator Loss: 1.7233 D(x): 0.7795, D(G(z)): 0.3293 Epoch: [15/20], Batch Num: [44/600] Discriminator Loss: 0.7436, Generator Loss: 1.8476 D(x): 0.7376, D(G(z)): 0.2253 Epoch: [15/20], Batch Num: [45/600] Discriminator Loss: 0.6068, Generator Loss: 2.1244 D(x): 0.8298, D(G(z)): 0.2391 Epoch: [15/20], Batch Num: [46/600] Discriminator Loss: 0.8445, Generator Loss: 2.2077 D(x): 0.7294, D(G(z)): 0.2361 Epoch: [15/20], Batch Num: [47/600] Discriminator Loss: 0.8683, Generator Loss: 1.8806 D(x): 0.6877, D(G(z)): 0.2043 Epoch: [15/20], Batch Num: [48/600] Discriminator Loss: 0.9871, Generator Loss: 1.5344 D(x): 0.6990, D(G(z)): 0.2717 Epoch: [15/20], Batch Num: [49/600] Discriminator Loss: 0.8955, Generator Loss: 1.6119 D(x): 0.7477, D(G(z)): 0.2743 Epoch: [15/20], Batch Num: [50/600] Discriminator Loss: 1.0137, Generator Loss: 1.7630 D(x): 0.8013, D(G(z)): 0.3932 Epoch: [15/20], Batch Num: [51/600] Discriminator Loss: 0.9083, Generator Loss: 1.9946 D(x): 0.6867, D(G(z)): 0.2498 Epoch: [15/20], Batch Num: [52/600] Discriminator Loss: 1.0207, Generator Loss: 2.2012 D(x): 0.6918, D(G(z)): 0.2601 Epoch: [15/20], Batch Num: [53/600] Discriminator Loss: 0.8747, Generator Loss: 1.9297 D(x): 0.6975, D(G(z)): 0.2415 Epoch: [15/20], Batch Num: [54/600] Discriminator Loss: 0.9765, Generator Loss: 1.7891 D(x): 0.6930, D(G(z)): 0.2511 Epoch: [15/20], Batch Num: [55/600] Discriminator Loss: 0.8883, Generator Loss: 1.6153 D(x): 0.7215, D(G(z)): 0.2534 Epoch: [15/20], Batch Num: [56/600] Discriminator Loss: 1.1058, Generator Loss: 1.5241 D(x): 0.6976, D(G(z)): 0.3260 Epoch: [15/20], Batch Num: [57/600] Discriminator Loss: 1.0896, Generator Loss: 1.6038 D(x): 0.7064, D(G(z)): 0.3619 Epoch: [15/20], Batch Num: [58/600] Discriminator Loss: 0.9044, Generator Loss: 1.9556 D(x): 0.7437, D(G(z)): 0.2850 Epoch: [15/20], Batch Num: [59/600] Discriminator Loss: 0.9085, Generator Loss: 1.7743 D(x): 0.7228, D(G(z)): 0.2620 Epoch: [15/20], Batch Num: [60/600] Discriminator Loss: 0.8779, Generator Loss: 1.7978 D(x): 0.7028, D(G(z)): 0.2183 Epoch: [15/20], Batch Num: [61/600] Discriminator Loss: 0.8613, Generator Loss: 1.6660 D(x): 0.6855, D(G(z)): 0.2119 Epoch: [15/20], Batch Num: [62/600] Discriminator Loss: 0.7746, Generator Loss: 1.5732 D(x): 0.7683, D(G(z)): 0.2478 Epoch: [15/20], Batch Num: [63/600] Discriminator Loss: 0.8867, Generator Loss: 1.3910 D(x): 0.7487, D(G(z)): 0.3131 Epoch: [15/20], Batch Num: [64/600] Discriminator Loss: 0.7518, Generator Loss: 1.5872 D(x): 0.7524, D(G(z)): 0.2777 Epoch: [15/20], Batch Num: [65/600] Discriminator Loss: 0.7664, Generator Loss: 1.5707 D(x): 0.8102, D(G(z)): 0.3232 Epoch: [15/20], Batch Num: [66/600] Discriminator Loss: 0.7077, Generator Loss: 1.9567 D(x): 0.7651, D(G(z)): 0.2537 Epoch: [15/20], Batch Num: [67/600] Discriminator Loss: 0.8356, Generator Loss: 1.9812 D(x): 0.6908, D(G(z)): 0.2151 Epoch: [15/20], Batch Num: [68/600] Discriminator Loss: 0.6744, Generator Loss: 2.1911 D(x): 0.7510, D(G(z)): 0.2047 Epoch: [15/20], Batch Num: [69/600] Discriminator Loss: 0.7238, Generator Loss: 1.9592 D(x): 0.7189, D(G(z)): 0.2138 Epoch: [15/20], Batch Num: [70/600] Discriminator Loss: 0.6295, Generator Loss: 1.8372 D(x): 0.7649, D(G(z)): 0.1928 Epoch: [15/20], Batch Num: [71/600] Discriminator Loss: 0.6775, Generator Loss: 1.8042 D(x): 0.8152, D(G(z)): 0.2876 Epoch: [15/20], Batch Num: [72/600] Discriminator Loss: 0.6768, Generator Loss: 1.7058 D(x): 0.8031, D(G(z)): 0.2634 Epoch: [15/20], Batch Num: [73/600] Discriminator Loss: 0.5566, Generator Loss: 1.8820 D(x): 0.8299, D(G(z)): 0.2271 Epoch: [15/20], Batch Num: [74/600] Discriminator Loss: 0.6594, Generator Loss: 1.9572 D(x): 0.8180, D(G(z)): 0.2619 Epoch: [15/20], Batch Num: [75/600] Discriminator Loss: 0.7080, Generator Loss: 2.0565 D(x): 0.7314, D(G(z)): 0.1912 Epoch: [15/20], Batch Num: [76/600] Discriminator Loss: 0.6225, Generator Loss: 2.2087 D(x): 0.7650, D(G(z)): 0.1862 Epoch: [15/20], Batch Num: [77/600] Discriminator Loss: 0.6133, Generator Loss: 2.1658 D(x): 0.8095, D(G(z)): 0.1987 Epoch: [15/20], Batch Num: [78/600] Discriminator Loss: 0.6791, Generator Loss: 1.8923 D(x): 0.7661, D(G(z)): 0.1984 Epoch: [15/20], Batch Num: [79/600] Discriminator Loss: 0.6295, Generator Loss: 1.8349 D(x): 0.8183, D(G(z)): 0.2424 Epoch: [15/20], Batch Num: [80/600] Discriminator Loss: 0.6396, Generator Loss: 2.0714 D(x): 0.8194, D(G(z)): 0.2388 Epoch: [15/20], Batch Num: [81/600] Discriminator Loss: 0.6951, Generator Loss: 2.0304 D(x): 0.7951, D(G(z)): 0.2504 Epoch: [15/20], Batch Num: [82/600] Discriminator Loss: 0.7133, Generator Loss: 1.8313 D(x): 0.8122, D(G(z)): 0.2605 Epoch: [15/20], Batch Num: [83/600] Discriminator Loss: 0.6536, Generator Loss: 2.1136 D(x): 0.8019, D(G(z)): 0.2354 Epoch: [15/20], Batch Num: [84/600] Discriminator Loss: 0.5119, Generator Loss: 2.0559 D(x): 0.8110, D(G(z)): 0.1695 Epoch: [15/20], Batch Num: [85/600] Discriminator Loss: 0.8013, Generator Loss: 2.1306 D(x): 0.7386, D(G(z)): 0.2277 Epoch: [15/20], Batch Num: [86/600] Discriminator Loss: 0.6932, Generator Loss: 1.9784 D(x): 0.8046, D(G(z)): 0.2652 Epoch: [15/20], Batch Num: [87/600] Discriminator Loss: 0.9233, Generator Loss: 2.0645 D(x): 0.7209, D(G(z)): 0.2717 Epoch: [15/20], Batch Num: [88/600] Discriminator Loss: 0.7486, Generator Loss: 2.0501 D(x): 0.7766, D(G(z)): 0.2467 Epoch: [15/20], Batch Num: [89/600] Discriminator Loss: 0.8676, Generator Loss: 1.5924 D(x): 0.7402, D(G(z)): 0.2667 Epoch: [15/20], Batch Num: [90/600] Discriminator Loss: 0.8762, Generator Loss: 1.6898 D(x): 0.7637, D(G(z)): 0.3141 Epoch: [15/20], Batch Num: [91/600] Discriminator Loss: 0.7011, Generator Loss: 1.7593 D(x): 0.8000, D(G(z)): 0.2802 Epoch: [15/20], Batch Num: [92/600] Discriminator Loss: 0.7336, Generator Loss: 2.1757 D(x): 0.8107, D(G(z)): 0.2823 Epoch: [15/20], Batch Num: [93/600] Discriminator Loss: 0.8073, Generator Loss: 1.9478 D(x): 0.7113, D(G(z)): 0.2223 Epoch: [15/20], Batch Num: [94/600] Discriminator Loss: 0.8458, Generator Loss: 1.9220 D(x): 0.7072, D(G(z)): 0.2097 Epoch: [15/20], Batch Num: [95/600] Discriminator Loss: 1.0321, Generator Loss: 1.4849 D(x): 0.6634, D(G(z)): 0.2831 Epoch: [15/20], Batch Num: [96/600] Discriminator Loss: 0.8258, Generator Loss: 1.4991 D(x): 0.8066, D(G(z)): 0.3459 Epoch: [15/20], Batch Num: [97/600] Discriminator Loss: 0.9995, Generator Loss: 1.8535 D(x): 0.7686, D(G(z)): 0.3566 Epoch: [15/20], Batch Num: [98/600] Discriminator Loss: 1.0252, Generator Loss: 1.9203 D(x): 0.6710, D(G(z)): 0.2821 Epoch: [15/20], Batch Num: [99/600] Discriminator Loss: 1.0293, Generator Loss: 1.9547 D(x): 0.6130, D(G(z)): 0.2149 Epoch: 15, Batch Num: [100/600]
Epoch: [15/20], Batch Num: [100/600] Discriminator Loss: 1.1865, Generator Loss: 1.5860 D(x): 0.5892, D(G(z)): 0.2823 Epoch: [15/20], Batch Num: [101/600] Discriminator Loss: 0.9909, Generator Loss: 1.2298 D(x): 0.7276, D(G(z)): 0.3301 Epoch: [15/20], Batch Num: [102/600] Discriminator Loss: 0.9993, Generator Loss: 1.2544 D(x): 0.7654, D(G(z)): 0.3909 Epoch: [15/20], Batch Num: [103/600] Discriminator Loss: 1.0242, Generator Loss: 1.5271 D(x): 0.7968, D(G(z)): 0.4252 Epoch: [15/20], Batch Num: [104/600] Discriminator Loss: 1.0179, Generator Loss: 1.9455 D(x): 0.6899, D(G(z)): 0.3297 Epoch: [15/20], Batch Num: [105/600] Discriminator Loss: 0.9921, Generator Loss: 1.9520 D(x): 0.6440, D(G(z)): 0.2470 Epoch: [15/20], Batch Num: [106/600] Discriminator Loss: 0.9486, Generator Loss: 1.8620 D(x): 0.6514, D(G(z)): 0.2498 Epoch: [15/20], Batch Num: [107/600] Discriminator Loss: 1.0425, Generator Loss: 1.6063 D(x): 0.6104, D(G(z)): 0.2603 Epoch: [15/20], Batch Num: [108/600] Discriminator Loss: 0.9601, Generator Loss: 1.4522 D(x): 0.6748, D(G(z)): 0.2686 Epoch: [15/20], Batch Num: [109/600] Discriminator Loss: 0.9620, Generator Loss: 1.3562 D(x): 0.7246, D(G(z)): 0.3508 Epoch: [15/20], Batch Num: [110/600] Discriminator Loss: 0.9046, Generator Loss: 1.3244 D(x): 0.7358, D(G(z)): 0.3277 Epoch: [15/20], Batch Num: [111/600] Discriminator Loss: 0.8772, Generator Loss: 1.4172 D(x): 0.7798, D(G(z)): 0.3763 Epoch: [15/20], Batch Num: [112/600] Discriminator Loss: 0.9169, Generator Loss: 1.5193 D(x): 0.7189, D(G(z)): 0.3164 Epoch: [15/20], Batch Num: [113/600] Discriminator Loss: 0.7704, Generator Loss: 1.7119 D(x): 0.7052, D(G(z)): 0.2387 Epoch: [15/20], Batch Num: [114/600] Discriminator Loss: 0.8824, Generator Loss: 1.8086 D(x): 0.6842, D(G(z)): 0.2597 Epoch: [15/20], Batch Num: [115/600] Discriminator Loss: 0.8993, Generator Loss: 1.6191 D(x): 0.6716, D(G(z)): 0.2506 Epoch: [15/20], Batch Num: [116/600] Discriminator Loss: 0.7562, Generator Loss: 1.5150 D(x): 0.7317, D(G(z)): 0.2615 Epoch: [15/20], Batch Num: [117/600] Discriminator Loss: 0.8254, Generator Loss: 1.5731 D(x): 0.7532, D(G(z)): 0.3147 Epoch: [15/20], Batch Num: [118/600] Discriminator Loss: 0.7126, Generator Loss: 1.5691 D(x): 0.7770, D(G(z)): 0.2746 Epoch: [15/20], Batch Num: [119/600] Discriminator Loss: 0.7405, Generator Loss: 1.6302 D(x): 0.7466, D(G(z)): 0.2577 Epoch: [15/20], Batch Num: [120/600] Discriminator Loss: 0.6555, Generator Loss: 1.9648 D(x): 0.8137, D(G(z)): 0.2843 Epoch: [15/20], Batch Num: [121/600] Discriminator Loss: 0.7395, Generator Loss: 1.7944 D(x): 0.7302, D(G(z)): 0.2380 Epoch: [15/20], Batch Num: [122/600] Discriminator Loss: 0.6803, Generator Loss: 1.9206 D(x): 0.7055, D(G(z)): 0.1775 Epoch: [15/20], Batch Num: [123/600] Discriminator Loss: 0.6471, Generator Loss: 1.7883 D(x): 0.7625, D(G(z)): 0.2022 Epoch: [15/20], Batch Num: [124/600] Discriminator Loss: 0.7121, Generator Loss: 1.6564 D(x): 0.7665, D(G(z)): 0.2632 Epoch: [15/20], Batch Num: [125/600] Discriminator Loss: 0.6039, Generator Loss: 1.6631 D(x): 0.8103, D(G(z)): 0.2500 Epoch: [15/20], Batch Num: [126/600] Discriminator Loss: 0.6599, Generator Loss: 1.8234 D(x): 0.7954, D(G(z)): 0.2467 Epoch: [15/20], Batch Num: [127/600] Discriminator Loss: 0.7351, Generator Loss: 2.0999 D(x): 0.8020, D(G(z)): 0.2699 Epoch: [15/20], Batch Num: [128/600] Discriminator Loss: 0.4164, Generator Loss: 2.0893 D(x): 0.8471, D(G(z)): 0.1621 Epoch: [15/20], Batch Num: [129/600] Discriminator Loss: 0.6385, Generator Loss: 2.3049 D(x): 0.7791, D(G(z)): 0.2150 Epoch: [15/20], Batch Num: [130/600] Discriminator Loss: 0.5807, Generator Loss: 2.0614 D(x): 0.7471, D(G(z)): 0.1634 Epoch: [15/20], Batch Num: [131/600] Discriminator Loss: 0.7281, Generator Loss: 1.9905 D(x): 0.7451, D(G(z)): 0.1997 Epoch: [15/20], Batch Num: [132/600] Discriminator Loss: 0.6377, Generator Loss: 2.1628 D(x): 0.8168, D(G(z)): 0.2460 Epoch: [15/20], Batch Num: [133/600] Discriminator Loss: 0.7048, Generator Loss: 2.1112 D(x): 0.8018, D(G(z)): 0.2708 Epoch: [15/20], Batch Num: [134/600] Discriminator Loss: 0.6729, Generator Loss: 2.2504 D(x): 0.7853, D(G(z)): 0.2299 Epoch: [15/20], Batch Num: [135/600] Discriminator Loss: 0.6410, Generator Loss: 2.3990 D(x): 0.8174, D(G(z)): 0.2422 Epoch: [15/20], Batch Num: [136/600] Discriminator Loss: 0.7409, Generator Loss: 2.4693 D(x): 0.7187, D(G(z)): 0.1888 Epoch: [15/20], Batch Num: [137/600] Discriminator Loss: 0.6841, Generator Loss: 1.9935 D(x): 0.7535, D(G(z)): 0.1779 Epoch: [15/20], Batch Num: [138/600] Discriminator Loss: 0.9044, Generator Loss: 1.8371 D(x): 0.7279, D(G(z)): 0.2526 Epoch: [15/20], Batch Num: [139/600] Discriminator Loss: 0.9614, Generator Loss: 1.7436 D(x): 0.7670, D(G(z)): 0.3293 Epoch: [15/20], Batch Num: [140/600] Discriminator Loss: 0.8173, Generator Loss: 1.8670 D(x): 0.8009, D(G(z)): 0.3251 Epoch: [15/20], Batch Num: [141/600] Discriminator Loss: 0.9644, Generator Loss: 2.2782 D(x): 0.7254, D(G(z)): 0.2741 Epoch: [15/20], Batch Num: [142/600] Discriminator Loss: 1.0051, Generator Loss: 2.2507 D(x): 0.6511, D(G(z)): 0.2090 Epoch: [15/20], Batch Num: [143/600] Discriminator Loss: 1.0967, Generator Loss: 1.7033 D(x): 0.6377, D(G(z)): 0.2368 Epoch: [15/20], Batch Num: [144/600] Discriminator Loss: 0.9761, Generator Loss: 1.5570 D(x): 0.7493, D(G(z)): 0.3311 Epoch: [15/20], Batch Num: [145/600] Discriminator Loss: 0.9352, Generator Loss: 1.5277 D(x): 0.7938, D(G(z)): 0.3443 Epoch: [15/20], Batch Num: [146/600] Discriminator Loss: 0.9671, Generator Loss: 1.6548 D(x): 0.7257, D(G(z)): 0.3250 Epoch: [15/20], Batch Num: [147/600] Discriminator Loss: 1.1645, Generator Loss: 1.8241 D(x): 0.6926, D(G(z)): 0.3364 Epoch: [15/20], Batch Num: [148/600] Discriminator Loss: 0.9612, Generator Loss: 1.6866 D(x): 0.6469, D(G(z)): 0.2435 Epoch: [15/20], Batch Num: [149/600] Discriminator Loss: 1.1037, Generator Loss: 1.3946 D(x): 0.6134, D(G(z)): 0.2703 Epoch: [15/20], Batch Num: [150/600] Discriminator Loss: 0.9212, Generator Loss: 1.3376 D(x): 0.7149, D(G(z)): 0.3334 Epoch: [15/20], Batch Num: [151/600] Discriminator Loss: 0.9617, Generator Loss: 1.1481 D(x): 0.7054, D(G(z)): 0.3288 Epoch: [15/20], Batch Num: [152/600] Discriminator Loss: 0.9975, Generator Loss: 1.4624 D(x): 0.7577, D(G(z)): 0.3776 Epoch: [15/20], Batch Num: [153/600] Discriminator Loss: 0.9145, Generator Loss: 1.7160 D(x): 0.7194, D(G(z)): 0.3081 Epoch: [15/20], Batch Num: [154/600] Discriminator Loss: 0.9690, Generator Loss: 1.7118 D(x): 0.6590, D(G(z)): 0.2708 Epoch: [15/20], Batch Num: [155/600] Discriminator Loss: 0.9870, Generator Loss: 1.8657 D(x): 0.6761, D(G(z)): 0.3113 Epoch: [15/20], Batch Num: [156/600] Discriminator Loss: 0.9621, Generator Loss: 1.6711 D(x): 0.6281, D(G(z)): 0.2081 Epoch: [15/20], Batch Num: [157/600] Discriminator Loss: 0.7687, Generator Loss: 1.3983 D(x): 0.7053, D(G(z)): 0.2301 Epoch: [15/20], Batch Num: [158/600] Discriminator Loss: 0.8088, Generator Loss: 1.3553 D(x): 0.7510, D(G(z)): 0.2880 Epoch: [15/20], Batch Num: [159/600] Discriminator Loss: 0.9379, Generator Loss: 1.3719 D(x): 0.7310, D(G(z)): 0.3390 Epoch: [15/20], Batch Num: [160/600] Discriminator Loss: 0.8544, Generator Loss: 1.4777 D(x): 0.8162, D(G(z)): 0.3672 Epoch: [15/20], Batch Num: [161/600] Discriminator Loss: 0.7284, Generator Loss: 1.6918 D(x): 0.7815, D(G(z)): 0.3051 Epoch: [15/20], Batch Num: [162/600] Discriminator Loss: 0.6855, Generator Loss: 1.7906 D(x): 0.7643, D(G(z)): 0.2601 Epoch: [15/20], Batch Num: [163/600] Discriminator Loss: 0.8880, Generator Loss: 1.9464 D(x): 0.6436, D(G(z)): 0.1956 Epoch: [15/20], Batch Num: [164/600] Discriminator Loss: 0.6911, Generator Loss: 1.9104 D(x): 0.7341, D(G(z)): 0.2098 Epoch: [15/20], Batch Num: [165/600] Discriminator Loss: 0.6901, Generator Loss: 1.6454 D(x): 0.7382, D(G(z)): 0.2210 Epoch: [15/20], Batch Num: [166/600] Discriminator Loss: 0.7165, Generator Loss: 1.5623 D(x): 0.7512, D(G(z)): 0.2412 Epoch: [15/20], Batch Num: [167/600] Discriminator Loss: 0.7027, Generator Loss: 1.4598 D(x): 0.7907, D(G(z)): 0.2751 Epoch: [15/20], Batch Num: [168/600] Discriminator Loss: 0.5556, Generator Loss: 1.8096 D(x): 0.8690, D(G(z)): 0.2746 Epoch: [15/20], Batch Num: [169/600] Discriminator Loss: 0.7800, Generator Loss: 1.8703 D(x): 0.8032, D(G(z)): 0.3111 Epoch: [15/20], Batch Num: [170/600] Discriminator Loss: 0.6601, Generator Loss: 2.0573 D(x): 0.7819, D(G(z)): 0.2457 Epoch: [15/20], Batch Num: [171/600] Discriminator Loss: 0.5721, Generator Loss: 2.1974 D(x): 0.7867, D(G(z)): 0.1890 Epoch: [15/20], Batch Num: [172/600] Discriminator Loss: 0.6663, Generator Loss: 2.1260 D(x): 0.7165, D(G(z)): 0.1753 Epoch: [15/20], Batch Num: [173/600] Discriminator Loss: 0.6297, Generator Loss: 2.1972 D(x): 0.7621, D(G(z)): 0.1894 Epoch: [15/20], Batch Num: [174/600] Discriminator Loss: 0.6672, Generator Loss: 2.1174 D(x): 0.7602, D(G(z)): 0.2038 Epoch: [15/20], Batch Num: [175/600] Discriminator Loss: 0.6216, Generator Loss: 1.7270 D(x): 0.7997, D(G(z)): 0.2295 Epoch: [15/20], Batch Num: [176/600] Discriminator Loss: 0.7028, Generator Loss: 1.8642 D(x): 0.8264, D(G(z)): 0.2721 Epoch: [15/20], Batch Num: [177/600] Discriminator Loss: 0.5580, Generator Loss: 1.8959 D(x): 0.8343, D(G(z)): 0.2305 Epoch: [15/20], Batch Num: [178/600] Discriminator Loss: 0.7027, Generator Loss: 2.1197 D(x): 0.7708, D(G(z)): 0.2253 Epoch: [15/20], Batch Num: [179/600] Discriminator Loss: 0.6146, Generator Loss: 2.2554 D(x): 0.7870, D(G(z)): 0.1862 Epoch: [15/20], Batch Num: [180/600] Discriminator Loss: 0.6682, Generator Loss: 2.0310 D(x): 0.7819, D(G(z)): 0.2056 Epoch: [15/20], Batch Num: [181/600] Discriminator Loss: 0.6808, Generator Loss: 2.0066 D(x): 0.7595, D(G(z)): 0.1951 Epoch: [15/20], Batch Num: [182/600] Discriminator Loss: 0.7797, Generator Loss: 1.9755 D(x): 0.7687, D(G(z)): 0.2565 Epoch: [15/20], Batch Num: [183/600] Discriminator Loss: 0.8477, Generator Loss: 2.0323 D(x): 0.7831, D(G(z)): 0.2843 Epoch: [15/20], Batch Num: [184/600] Discriminator Loss: 0.6754, Generator Loss: 2.2255 D(x): 0.7854, D(G(z)): 0.2424 Epoch: [15/20], Batch Num: [185/600] Discriminator Loss: 0.6503, Generator Loss: 2.1243 D(x): 0.7867, D(G(z)): 0.2096 Epoch: [15/20], Batch Num: [186/600] Discriminator Loss: 0.7419, Generator Loss: 1.9803 D(x): 0.7577, D(G(z)): 0.2110 Epoch: [15/20], Batch Num: [187/600] Discriminator Loss: 0.9472, Generator Loss: 1.7631 D(x): 0.7181, D(G(z)): 0.2763 Epoch: [15/20], Batch Num: [188/600] Discriminator Loss: 0.8780, Generator Loss: 1.5063 D(x): 0.7416, D(G(z)): 0.2580 Epoch: [15/20], Batch Num: [189/600] Discriminator Loss: 0.9752, Generator Loss: 1.4989 D(x): 0.7074, D(G(z)): 0.2821 Epoch: [15/20], Batch Num: [190/600] Discriminator Loss: 0.7858, Generator Loss: 1.6895 D(x): 0.7955, D(G(z)): 0.3076 Epoch: [15/20], Batch Num: [191/600] Discriminator Loss: 1.1580, Generator Loss: 1.7815 D(x): 0.6693, D(G(z)): 0.3231 Epoch: [15/20], Batch Num: [192/600] Discriminator Loss: 0.9292, Generator Loss: 1.7716 D(x): 0.7145, D(G(z)): 0.2759 Epoch: [15/20], Batch Num: [193/600] Discriminator Loss: 1.0777, Generator Loss: 1.7885 D(x): 0.6559, D(G(z)): 0.2724 Epoch: [15/20], Batch Num: [194/600] Discriminator Loss: 0.9894, Generator Loss: 1.4489 D(x): 0.6859, D(G(z)): 0.2902 Epoch: [15/20], Batch Num: [195/600] Discriminator Loss: 1.0682, Generator Loss: 1.3117 D(x): 0.6678, D(G(z)): 0.3184 Epoch: [15/20], Batch Num: [196/600] Discriminator Loss: 1.0472, Generator Loss: 1.2762 D(x): 0.7082, D(G(z)): 0.3492 Epoch: [15/20], Batch Num: [197/600] Discriminator Loss: 0.9030, Generator Loss: 1.4257 D(x): 0.7378, D(G(z)): 0.3479 Epoch: [15/20], Batch Num: [198/600] Discriminator Loss: 1.0792, Generator Loss: 1.4572 D(x): 0.6719, D(G(z)): 0.3544 Epoch: [15/20], Batch Num: [199/600] Discriminator Loss: 1.0978, Generator Loss: 1.5625 D(x): 0.6973, D(G(z)): 0.3461 Epoch: 15, Batch Num: [200/600]
Epoch: [15/20], Batch Num: [200/600] Discriminator Loss: 0.9210, Generator Loss: 1.7866 D(x): 0.6593, D(G(z)): 0.2619 Epoch: [15/20], Batch Num: [201/600] Discriminator Loss: 0.9937, Generator Loss: 1.5098 D(x): 0.6417, D(G(z)): 0.2721 Epoch: [15/20], Batch Num: [202/600] Discriminator Loss: 0.9475, Generator Loss: 1.4887 D(x): 0.6668, D(G(z)): 0.2871 Epoch: [15/20], Batch Num: [203/600] Discriminator Loss: 0.8352, Generator Loss: 1.3432 D(x): 0.7060, D(G(z)): 0.2764 Epoch: [15/20], Batch Num: [204/600] Discriminator Loss: 0.9786, Generator Loss: 1.4667 D(x): 0.7904, D(G(z)): 0.3876 Epoch: [15/20], Batch Num: [205/600] Discriminator Loss: 0.8481, Generator Loss: 1.6542 D(x): 0.7844, D(G(z)): 0.3470 Epoch: [15/20], Batch Num: [206/600] Discriminator Loss: 0.7046, Generator Loss: 1.8696 D(x): 0.7777, D(G(z)): 0.2725 Epoch: [15/20], Batch Num: [207/600] Discriminator Loss: 0.8470, Generator Loss: 1.9701 D(x): 0.6617, D(G(z)): 0.2091 Epoch: [15/20], Batch Num: [208/600] Discriminator Loss: 0.7288, Generator Loss: 1.9531 D(x): 0.7178, D(G(z)): 0.2097 Epoch: [15/20], Batch Num: [209/600] Discriminator Loss: 0.8113, Generator Loss: 1.7121 D(x): 0.7038, D(G(z)): 0.2308 Epoch: [15/20], Batch Num: [210/600] Discriminator Loss: 0.8530, Generator Loss: 1.7072 D(x): 0.6930, D(G(z)): 0.2645 Epoch: [15/20], Batch Num: [211/600] Discriminator Loss: 0.7991, Generator Loss: 1.5805 D(x): 0.7236, D(G(z)): 0.2741 Epoch: [15/20], Batch Num: [212/600] Discriminator Loss: 0.7520, Generator Loss: 1.5367 D(x): 0.7650, D(G(z)): 0.2709 Epoch: [15/20], Batch Num: [213/600] Discriminator Loss: 0.8266, Generator Loss: 1.5765 D(x): 0.7661, D(G(z)): 0.3260 Epoch: [15/20], Batch Num: [214/600] Discriminator Loss: 0.7724, Generator Loss: 1.8354 D(x): 0.7961, D(G(z)): 0.3304 Epoch: [15/20], Batch Num: [215/600] Discriminator Loss: 0.6544, Generator Loss: 2.1445 D(x): 0.7950, D(G(z)): 0.2723 Epoch: [15/20], Batch Num: [216/600] Discriminator Loss: 0.8079, Generator Loss: 1.9112 D(x): 0.7031, D(G(z)): 0.2232 Epoch: [15/20], Batch Num: [217/600] Discriminator Loss: 0.7976, Generator Loss: 2.0802 D(x): 0.7004, D(G(z)): 0.2067 Epoch: [15/20], Batch Num: [218/600] Discriminator Loss: 0.8496, Generator Loss: 1.8504 D(x): 0.6657, D(G(z)): 0.2146 Epoch: [15/20], Batch Num: [219/600] Discriminator Loss: 0.7731, Generator Loss: 1.4411 D(x): 0.7362, D(G(z)): 0.2500 Epoch: [15/20], Batch Num: [220/600] Discriminator Loss: 0.7509, Generator Loss: 1.5640 D(x): 0.7910, D(G(z)): 0.3090 Epoch: [15/20], Batch Num: [221/600] Discriminator Loss: 0.8732, Generator Loss: 1.7715 D(x): 0.7819, D(G(z)): 0.3211 Epoch: [15/20], Batch Num: [222/600] Discriminator Loss: 0.8226, Generator Loss: 2.0492 D(x): 0.7590, D(G(z)): 0.2875 Epoch: [15/20], Batch Num: [223/600] Discriminator Loss: 1.0329, Generator Loss: 1.7896 D(x): 0.6653, D(G(z)): 0.2881 Epoch: [15/20], Batch Num: [224/600] Discriminator Loss: 0.8657, Generator Loss: 2.0939 D(x): 0.6725, D(G(z)): 0.2165 Epoch: [15/20], Batch Num: [225/600] Discriminator Loss: 1.0613, Generator Loss: 1.7318 D(x): 0.6540, D(G(z)): 0.3004 Epoch: [15/20], Batch Num: [226/600] Discriminator Loss: 0.9928, Generator Loss: 1.5783 D(x): 0.6641, D(G(z)): 0.2842 Epoch: [15/20], Batch Num: [227/600] Discriminator Loss: 0.9421, Generator Loss: 1.3431 D(x): 0.7223, D(G(z)): 0.3266 Epoch: [15/20], Batch Num: [228/600] Discriminator Loss: 0.9135, Generator Loss: 1.4221 D(x): 0.7451, D(G(z)): 0.3179 Epoch: [15/20], Batch Num: [229/600] Discriminator Loss: 0.9313, Generator Loss: 1.7831 D(x): 0.7415, D(G(z)): 0.3432 Epoch: [15/20], Batch Num: [230/600] Discriminator Loss: 1.2019, Generator Loss: 2.0871 D(x): 0.6464, D(G(z)): 0.3467 Epoch: [15/20], Batch Num: [231/600] Discriminator Loss: 1.1688, Generator Loss: 1.8448 D(x): 0.6177, D(G(z)): 0.2900 Epoch: [15/20], Batch Num: [232/600] Discriminator Loss: 1.0781, Generator Loss: 1.4553 D(x): 0.6265, D(G(z)): 0.2237 Epoch: [15/20], Batch Num: [233/600] Discriminator Loss: 1.0051, Generator Loss: 1.3184 D(x): 0.6839, D(G(z)): 0.3300 Epoch: [15/20], Batch Num: [234/600] Discriminator Loss: 1.1063, Generator Loss: 1.0648 D(x): 0.7070, D(G(z)): 0.3893 Epoch: [15/20], Batch Num: [235/600] Discriminator Loss: 1.2039, Generator Loss: 1.4295 D(x): 0.7412, D(G(z)): 0.4517 Epoch: [15/20], Batch Num: [236/600] Discriminator Loss: 1.1763, Generator Loss: 1.4806 D(x): 0.6323, D(G(z)): 0.3539 Epoch: [15/20], Batch Num: [237/600] Discriminator Loss: 1.0675, Generator Loss: 1.6916 D(x): 0.6359, D(G(z)): 0.2888 Epoch: [15/20], Batch Num: [238/600] Discriminator Loss: 1.0385, Generator Loss: 1.4774 D(x): 0.6274, D(G(z)): 0.2640 Epoch: [15/20], Batch Num: [239/600] Discriminator Loss: 1.0286, Generator Loss: 1.3837 D(x): 0.6334, D(G(z)): 0.2950 Epoch: [15/20], Batch Num: [240/600] Discriminator Loss: 1.0150, Generator Loss: 1.2582 D(x): 0.6270, D(G(z)): 0.3034 Epoch: [15/20], Batch Num: [241/600] Discriminator Loss: 1.0009, Generator Loss: 1.0864 D(x): 0.7077, D(G(z)): 0.3588 Epoch: [15/20], Batch Num: 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1.4515 D(x): 0.7882, D(G(z)): 0.3081 Epoch: [15/20], Batch Num: [251/600] Discriminator Loss: 0.7629, Generator Loss: 1.7166 D(x): 0.7534, D(G(z)): 0.3108 Epoch: [15/20], Batch Num: [252/600] Discriminator Loss: 0.7447, Generator Loss: 1.6332 D(x): 0.7526, D(G(z)): 0.2774 Epoch: [15/20], Batch Num: [253/600] Discriminator Loss: 0.6323, Generator Loss: 1.8927 D(x): 0.7951, D(G(z)): 0.2595 Epoch: [15/20], Batch Num: [254/600] Discriminator Loss: 0.6856, Generator Loss: 1.8965 D(x): 0.7694, D(G(z)): 0.2429 Epoch: [15/20], Batch Num: [255/600] Discriminator Loss: 0.7099, Generator Loss: 1.7882 D(x): 0.7456, D(G(z)): 0.2279 Epoch: [15/20], Batch Num: [256/600] Discriminator Loss: 0.6502, Generator Loss: 1.9563 D(x): 0.7462, D(G(z)): 0.2029 Epoch: [15/20], Batch Num: [257/600] Discriminator Loss: 0.6967, Generator Loss: 2.0556 D(x): 0.7513, D(G(z)): 0.2188 Epoch: [15/20], Batch Num: [258/600] Discriminator Loss: 0.5942, Generator Loss: 1.7659 D(x): 0.7752, D(G(z)): 0.2108 Epoch: [15/20], Batch Num: [259/600] Discriminator Loss: 0.5822, Generator Loss: 1.9522 D(x): 0.8294, D(G(z)): 0.2396 Epoch: [15/20], Batch Num: [260/600] Discriminator Loss: 0.5545, Generator Loss: 1.9385 D(x): 0.8737, D(G(z)): 0.2595 Epoch: [15/20], Batch Num: [261/600] Discriminator Loss: 0.5373, Generator Loss: 2.3546 D(x): 0.8451, D(G(z)): 0.2266 Epoch: [15/20], Batch Num: [262/600] Discriminator Loss: 0.4778, Generator Loss: 2.4547 D(x): 0.8169, D(G(z)): 0.1592 Epoch: [15/20], Batch Num: [263/600] Discriminator Loss: 0.5975, Generator Loss: 2.5254 D(x): 0.7523, D(G(z)): 0.1545 Epoch: [15/20], Batch Num: [264/600] Discriminator Loss: 0.4961, Generator Loss: 2.4535 D(x): 0.7891, D(G(z)): 0.1389 Epoch: [15/20], Batch Num: [265/600] Discriminator Loss: 0.6038, Generator Loss: 2.3363 D(x): 0.7915, D(G(z)): 0.1688 Epoch: [15/20], Batch Num: [266/600] Discriminator Loss: 0.6520, Generator Loss: 1.8290 D(x): 0.7969, D(G(z)): 0.2023 Epoch: [15/20], Batch Num: [267/600] Discriminator Loss: 0.5152, Generator Loss: 2.3087 D(x): 0.8943, D(G(z)): 0.2539 Epoch: [15/20], Batch Num: [268/600] Discriminator Loss: 0.6596, Generator Loss: 2.6565 D(x): 0.8397, D(G(z)): 0.2343 Epoch: [15/20], Batch Num: [269/600] Discriminator Loss: 0.5911, Generator Loss: 2.8943 D(x): 0.8120, D(G(z)): 0.2040 Epoch: [15/20], Batch Num: [270/600] Discriminator Loss: 0.6476, Generator Loss: 2.7717 D(x): 0.7478, D(G(z)): 0.1508 Epoch: [15/20], Batch Num: [271/600] Discriminator Loss: 0.7162, Generator Loss: 2.4728 D(x): 0.7103, D(G(z)): 0.1333 Epoch: [15/20], Batch Num: [272/600] Discriminator Loss: 0.6210, Generator Loss: 2.2290 D(x): 0.7776, D(G(z)): 0.1885 Epoch: [15/20], Batch Num: [273/600] Discriminator Loss: 0.5721, Generator Loss: 2.0225 D(x): 0.8405, D(G(z)): 0.2183 Epoch: [15/20], Batch Num: [274/600] Discriminator Loss: 0.6833, Generator Loss: 2.0229 D(x): 0.8807, D(G(z)): 0.3007 Epoch: [15/20], Batch Num: [275/600] Discriminator Loss: 0.7257, Generator Loss: 2.7634 D(x): 0.8538, D(G(z)): 0.2802 Epoch: [15/20], Batch Num: [276/600] Discriminator Loss: 0.7686, Generator Loss: 2.6403 D(x): 0.7458, D(G(z)): 0.1791 Epoch: [15/20], Batch Num: [277/600] Discriminator Loss: 0.8400, Generator Loss: 2.7408 D(x): 0.7156, D(G(z)): 0.1945 Epoch: [15/20], Batch Num: [278/600] Discriminator Loss: 0.8660, Generator Loss: 2.3831 D(x): 0.7167, D(G(z)): 0.1860 Epoch: [15/20], Batch Num: [279/600] Discriminator Loss: 0.7343, Generator Loss: 1.6009 D(x): 0.7028, D(G(z)): 0.1458 Epoch: [15/20], Batch Num: [280/600] Discriminator Loss: 0.9119, Generator Loss: 1.4186 D(x): 0.7399, D(G(z)): 0.2813 Epoch: [15/20], Batch Num: [281/600] Discriminator Loss: 0.9820, Generator Loss: 1.7398 D(x): 0.8427, D(G(z)): 0.4085 Epoch: [15/20], Batch Num: [282/600] Discriminator Loss: 0.9335, Generator Loss: 2.0351 D(x): 0.7981, D(G(z)): 0.3590 Epoch: [15/20], Batch Num: [283/600] Discriminator Loss: 0.9605, Generator Loss: 2.3786 D(x): 0.6618, D(G(z)): 0.2012 Epoch: [15/20], Batch Num: [284/600] Discriminator Loss: 1.2510, Generator Loss: 2.2362 D(x): 0.5567, D(G(z)): 0.1906 Epoch: [15/20], Batch Num: [285/600] Discriminator Loss: 1.0655, Generator Loss: 1.7731 D(x): 0.6543, D(G(z)): 0.2488 Epoch: [15/20], Batch Num: [286/600] Discriminator Loss: 0.8954, Generator Loss: 1.3846 D(x): 0.7361, D(G(z)): 0.2881 Epoch: [15/20], Batch Num: [287/600] Discriminator Loss: 1.1111, Generator Loss: 1.6121 D(x): 0.7488, D(G(z)): 0.4040 Epoch: [15/20], Batch Num: [288/600] Discriminator Loss: 1.1189, Generator Loss: 1.7407 D(x): 0.7474, D(G(z)): 0.3725 Epoch: [15/20], Batch Num: [289/600] Discriminator Loss: 1.0119, Generator Loss: 1.6039 D(x): 0.6618, D(G(z)): 0.2867 Epoch: [15/20], Batch Num: [290/600] Discriminator Loss: 1.1308, Generator Loss: 1.9367 D(x): 0.6494, D(G(z)): 0.3084 Epoch: [15/20], Batch Num: [291/600] Discriminator Loss: 0.9788, Generator Loss: 1.8396 D(x): 0.6460, D(G(z)): 0.2600 Epoch: [15/20], Batch Num: [292/600] Discriminator Loss: 1.2374, Generator Loss: 1.3805 D(x): 0.5817, D(G(z)): 0.3006 Epoch: [15/20], Batch Num: [293/600] Discriminator Loss: 1.2229, Generator Loss: 1.2476 D(x): 0.6548, D(G(z)): 0.3412 Epoch: [15/20], Batch Num: [294/600] Discriminator Loss: 1.0491, Generator Loss: 1.0849 D(x): 0.7117, D(G(z)): 0.3499 Epoch: [15/20], Batch Num: [295/600] Discriminator Loss: 1.1014, Generator Loss: 1.2546 D(x): 0.7575, D(G(z)): 0.4298 Epoch: [15/20], Batch Num: [296/600] Discriminator Loss: 1.1671, Generator Loss: 1.2996 D(x): 0.6823, D(G(z)): 0.3809 Epoch: [15/20], Batch Num: [297/600] Discriminator Loss: 0.9395, Generator Loss: 1.5209 D(x): 0.6801, D(G(z)): 0.3005 Epoch: [15/20], Batch Num: [298/600] Discriminator Loss: 1.0673, Generator Loss: 1.5694 D(x): 0.6343, D(G(z)): 0.3078 Epoch: [15/20], Batch Num: [299/600] Discriminator Loss: 1.1092, Generator Loss: 1.5302 D(x): 0.6066, D(G(z)): 0.3050 Epoch: 15, Batch Num: [300/600]
Epoch: [15/20], Batch Num: [300/600] Discriminator Loss: 1.0951, Generator Loss: 1.4532 D(x): 0.6351, D(G(z)): 0.2924 Epoch: [15/20], Batch Num: [301/600] Discriminator Loss: 0.9565, Generator Loss: 1.4771 D(x): 0.6964, D(G(z)): 0.3250 Epoch: [15/20], Batch Num: [302/600] Discriminator Loss: 0.9988, Generator Loss: 1.4316 D(x): 0.6965, D(G(z)): 0.3316 Epoch: [15/20], Batch Num: [303/600] Discriminator Loss: 1.0086, Generator Loss: 1.4014 D(x): 0.7018, D(G(z)): 0.3672 Epoch: [15/20], Batch Num: [304/600] Discriminator Loss: 1.0202, Generator Loss: 1.2887 D(x): 0.7064, D(G(z)): 0.3605 Epoch: [15/20], Batch Num: [305/600] Discriminator Loss: 0.8416, Generator Loss: 1.4316 D(x): 0.7087, D(G(z)): 0.3069 Epoch: [15/20], Batch Num: [306/600] Discriminator Loss: 0.8408, Generator Loss: 1.3909 D(x): 0.7380, D(G(z)): 0.3100 Epoch: [15/20], Batch Num: [307/600] Discriminator Loss: 0.8305, Generator Loss: 1.4143 D(x): 0.7370, D(G(z)): 0.3137 Epoch: [15/20], Batch Num: [308/600] Discriminator Loss: 0.7343, Generator Loss: 1.4731 D(x): 0.7571, D(G(z)): 0.2817 Epoch: [15/20], Batch Num: [309/600] Discriminator Loss: 0.8554, Generator Loss: 1.4734 D(x): 0.7053, D(G(z)): 0.3062 Epoch: [15/20], Batch Num: [310/600] Discriminator Loss: 0.7762, Generator Loss: 1.5495 D(x): 0.7214, D(G(z)): 0.2756 Epoch: [15/20], Batch Num: [311/600] Discriminator Loss: 0.7762, Generator Loss: 1.4312 D(x): 0.7190, D(G(z)): 0.2732 Epoch: [15/20], Batch Num: [312/600] Discriminator Loss: 0.7042, Generator Loss: 1.6035 D(x): 0.7412, D(G(z)): 0.2471 Epoch: [15/20], Batch Num: [313/600] Discriminator Loss: 0.8036, Generator Loss: 1.4714 D(x): 0.7387, D(G(z)): 0.2987 Epoch: [15/20], Batch Num: [314/600] Discriminator Loss: 0.8038, Generator Loss: 1.3909 D(x): 0.7651, D(G(z)): 0.3265 Epoch: [15/20], Batch Num: [315/600] Discriminator Loss: 0.6557, Generator Loss: 1.5232 D(x): 0.8010, D(G(z)): 0.2814 Epoch: [15/20], Batch Num: [316/600] Discriminator Loss: 0.7906, Generator Loss: 1.4416 D(x): 0.7547, D(G(z)): 0.2913 Epoch: [15/20], Batch Num: [317/600] Discriminator Loss: 0.7380, Generator Loss: 1.6443 D(x): 0.7725, D(G(z)): 0.3033 Epoch: [15/20], Batch Num: [318/600] Discriminator Loss: 0.7047, Generator Loss: 1.5917 D(x): 0.7605, D(G(z)): 0.2720 Epoch: [15/20], Batch Num: [319/600] Discriminator Loss: 0.6269, Generator Loss: 1.7683 D(x): 0.7628, D(G(z)): 0.2284 Epoch: [15/20], Batch Num: [320/600] Discriminator Loss: 0.7646, Generator Loss: 1.5679 D(x): 0.6972, D(G(z)): 0.2447 Epoch: [15/20], Batch Num: [321/600] Discriminator Loss: 0.6934, Generator Loss: 1.7643 D(x): 0.7777, D(G(z)): 0.2544 Epoch: [15/20], Batch Num: [322/600] Discriminator Loss: 0.7625, Generator Loss: 1.4691 D(x): 0.7623, D(G(z)): 0.2876 Epoch: [15/20], Batch Num: [323/600] Discriminator Loss: 0.7303, Generator Loss: 1.5008 D(x): 0.7989, D(G(z)): 0.3060 Epoch: [15/20], Batch Num: [324/600] Discriminator Loss: 0.7813, Generator Loss: 1.7724 D(x): 0.7990, D(G(z)): 0.3190 Epoch: [15/20], Batch Num: [325/600] Discriminator Loss: 0.7892, Generator Loss: 1.8582 D(x): 0.7808, D(G(z)): 0.3118 Epoch: [15/20], Batch Num: [326/600] Discriminator Loss: 0.7203, Generator Loss: 2.0320 D(x): 0.7718, D(G(z)): 0.2631 Epoch: [15/20], Batch Num: [327/600] Discriminator Loss: 0.7635, Generator Loss: 2.1571 D(x): 0.7121, D(G(z)): 0.2203 Epoch: [15/20], Batch Num: [328/600] Discriminator Loss: 0.5806, Generator Loss: 1.9752 D(x): 0.7668, D(G(z)): 0.1743 Epoch: [15/20], Batch Num: [329/600] Discriminator Loss: 0.6564, Generator Loss: 1.9609 D(x): 0.7666, D(G(z)): 0.2177 Epoch: [15/20], Batch Num: [330/600] Discriminator Loss: 0.7132, Generator Loss: 1.7421 D(x): 0.7185, D(G(z)): 0.2119 Epoch: [15/20], Batch Num: [331/600] Discriminator Loss: 0.7898, Generator Loss: 1.5082 D(x): 0.7733, D(G(z)): 0.2974 Epoch: [15/20], Batch Num: [332/600] Discriminator Loss: 0.6688, Generator Loss: 1.5924 D(x): 0.7945, D(G(z)): 0.2610 Epoch: [15/20], Batch Num: [333/600] Discriminator Loss: 0.7307, Generator Loss: 1.9667 D(x): 0.8116, D(G(z)): 0.3219 Epoch: [15/20], Batch Num: [334/600] Discriminator Loss: 1.0030, Generator Loss: 2.1211 D(x): 0.7514, D(G(z)): 0.3176 Epoch: [15/20], Batch Num: [335/600] Discriminator Loss: 0.8095, Generator Loss: 2.2621 D(x): 0.7231, D(G(z)): 0.2391 Epoch: [15/20], Batch Num: [336/600] Discriminator Loss: 1.1030, Generator Loss: 1.9874 D(x): 0.5773, D(G(z)): 0.2163 Epoch: [15/20], Batch Num: [337/600] Discriminator Loss: 0.8088, Generator Loss: 1.8236 D(x): 0.6856, D(G(z)): 0.2237 Epoch: [15/20], Batch Num: [338/600] Discriminator Loss: 1.0968, Generator Loss: 1.4926 D(x): 0.7694, D(G(z)): 0.3797 Epoch: [15/20], Batch Num: [339/600] Discriminator Loss: 1.0370, Generator Loss: 1.6664 D(x): 0.7388, D(G(z)): 0.3628 Epoch: [15/20], Batch Num: [340/600] Discriminator Loss: 0.9094, Generator Loss: 2.0307 D(x): 0.7694, D(G(z)): 0.3373 Epoch: [15/20], Batch Num: [341/600] Discriminator Loss: 1.0347, Generator Loss: 2.1393 D(x): 0.7091, D(G(z)): 0.2903 Epoch: [15/20], Batch Num: [342/600] Discriminator Loss: 0.9010, Generator Loss: 2.0902 D(x): 0.6289, D(G(z)): 0.1763 Epoch: [15/20], Batch Num: [343/600] Discriminator Loss: 0.9915, Generator Loss: 1.7846 D(x): 0.6390, D(G(z)): 0.2370 Epoch: [15/20], Batch Num: [344/600] Discriminator Loss: 1.0853, Generator Loss: 1.3616 D(x): 0.6236, D(G(z)): 0.2581 Epoch: [15/20], Batch Num: [345/600] Discriminator Loss: 0.9943, Generator Loss: 1.2146 D(x): 0.7605, D(G(z)): 0.3655 Epoch: [15/20], Batch Num: [346/600] Discriminator Loss: 0.9966, Generator Loss: 1.3065 D(x): 0.8273, D(G(z)): 0.4252 Epoch: [15/20], Batch Num: [347/600] Discriminator Loss: 1.0635, Generator Loss: 1.6418 D(x): 0.7590, D(G(z)): 0.3796 Epoch: [15/20], Batch Num: [348/600] Discriminator Loss: 0.8744, Generator Loss: 1.9097 D(x): 0.6929, D(G(z)): 0.2433 Epoch: [15/20], Batch Num: [349/600] Discriminator Loss: 1.1123, Generator Loss: 1.8364 D(x): 0.5722, D(G(z)): 0.2328 Epoch: [15/20], Batch Num: [350/600] Discriminator Loss: 1.0763, Generator Loss: 1.5722 D(x): 0.6046, D(G(z)): 0.2617 Epoch: [15/20], Batch Num: [351/600] Discriminator Loss: 0.9981, Generator Loss: 1.3716 D(x): 0.6470, D(G(z)): 0.2949 Epoch: [15/20], Batch Num: [352/600] Discriminator Loss: 1.1068, Generator Loss: 1.2420 D(x): 0.6884, D(G(z)): 0.3507 Epoch: [15/20], Batch Num: [353/600] Discriminator Loss: 0.9591, Generator Loss: 1.3338 D(x): 0.7538, D(G(z)): 0.3741 Epoch: [15/20], Batch Num: [354/600] Discriminator Loss: 0.8779, Generator Loss: 1.3092 D(x): 0.7430, D(G(z)): 0.3441 Epoch: [15/20], Batch Num: [355/600] Discriminator Loss: 0.8663, Generator Loss: 1.4163 D(x): 0.7102, D(G(z)): 0.2981 Epoch: [15/20], Batch Num: [356/600] Discriminator Loss: 0.9040, Generator Loss: 1.5413 D(x): 0.7140, D(G(z)): 0.3132 Epoch: [15/20], Batch Num: [357/600] Discriminator Loss: 0.8734, Generator Loss: 1.5049 D(x): 0.7081, D(G(z)): 0.3092 Epoch: [15/20], Batch Num: [358/600] Discriminator Loss: 0.9562, Generator Loss: 1.5718 D(x): 0.6524, D(G(z)): 0.2950 Epoch: [15/20], Batch Num: [359/600] Discriminator Loss: 0.9090, Generator Loss: 1.4509 D(x): 0.6938, D(G(z)): 0.2919 Epoch: [15/20], Batch Num: [360/600] Discriminator Loss: 1.0053, Generator Loss: 1.4607 D(x): 0.6459, D(G(z)): 0.3036 Epoch: [15/20], Batch Num: [361/600] Discriminator Loss: 0.9296, Generator Loss: 1.3673 D(x): 0.6549, D(G(z)): 0.2624 Epoch: [15/20], Batch Num: [362/600] Discriminator Loss: 0.7601, Generator Loss: 1.3866 D(x): 0.7525, D(G(z)): 0.2944 Epoch: [15/20], Batch Num: [363/600] Discriminator Loss: 0.9097, Generator Loss: 1.4042 D(x): 0.7130, D(G(z)): 0.3325 Epoch: [15/20], Batch Num: [364/600] Discriminator Loss: 0.9167, Generator Loss: 1.4171 D(x): 0.7087, D(G(z)): 0.3337 Epoch: [15/20], Batch Num: [365/600] Discriminator Loss: 0.7271, Generator Loss: 1.4522 D(x): 0.7793, D(G(z)): 0.3101 Epoch: [15/20], Batch Num: [366/600] Discriminator Loss: 0.7294, Generator Loss: 1.8015 D(x): 0.7489, D(G(z)): 0.2656 Epoch: [15/20], Batch Num: [367/600] Discriminator Loss: 0.8736, Generator Loss: 1.7809 D(x): 0.6591, D(G(z)): 0.2497 Epoch: [15/20], Batch Num: [368/600] Discriminator Loss: 0.7437, Generator Loss: 1.5567 D(x): 0.7104, D(G(z)): 0.2327 Epoch: [15/20], Batch Num: [369/600] Discriminator Loss: 0.7267, Generator Loss: 1.6529 D(x): 0.7707, D(G(z)): 0.2873 Epoch: [15/20], Batch Num: [370/600] Discriminator Loss: 0.7716, Generator Loss: 1.6828 D(x): 0.7218, D(G(z)): 0.2652 Epoch: [15/20], Batch Num: [371/600] Discriminator Loss: 0.8181, Generator Loss: 1.5135 D(x): 0.7426, D(G(z)): 0.2983 Epoch: [15/20], Batch Num: [372/600] Discriminator Loss: 0.5864, Generator Loss: 1.6161 D(x): 0.8259, D(G(z)): 0.2677 Epoch: [15/20], Batch Num: [373/600] Discriminator Loss: 0.6734, Generator Loss: 1.9255 D(x): 0.7900, D(G(z)): 0.2493 Epoch: [15/20], Batch Num: [374/600] Discriminator Loss: 0.7669, Generator Loss: 2.2580 D(x): 0.7663, D(G(z)): 0.2775 Epoch: [15/20], Batch Num: [375/600] Discriminator Loss: 0.6924, Generator Loss: 2.0716 D(x): 0.7007, D(G(z)): 0.1846 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Epoch: [15/20], Batch Num: [400/600] Discriminator Loss: 1.0236, Generator Loss: 1.6671 D(x): 0.7187, D(G(z)): 0.3200 Epoch: [15/20], Batch Num: [401/600] Discriminator Loss: 1.0208, Generator Loss: 1.3930 D(x): 0.6648, D(G(z)): 0.2816 Epoch: [15/20], Batch Num: [402/600] Discriminator Loss: 1.1876, Generator Loss: 1.4406 D(x): 0.6830, D(G(z)): 0.3843 Epoch: [15/20], Batch Num: [403/600] Discriminator Loss: 1.2642, Generator Loss: 1.3840 D(x): 0.6229, D(G(z)): 0.3650 Epoch: [15/20], Batch Num: [404/600] Discriminator Loss: 1.1212, Generator Loss: 1.2861 D(x): 0.6694, D(G(z)): 0.3482 Epoch: [15/20], Batch Num: [405/600] Discriminator Loss: 1.0714, Generator Loss: 1.5512 D(x): 0.6787, D(G(z)): 0.3429 Epoch: [15/20], Batch Num: [406/600] Discriminator Loss: 1.0537, Generator Loss: 1.6829 D(x): 0.6562, D(G(z)): 0.2916 Epoch: [15/20], Batch Num: [407/600] Discriminator Loss: 1.1323, Generator Loss: 1.5983 D(x): 0.5907, D(G(z)): 0.2921 Epoch: [15/20], Batch Num: [408/600] Discriminator Loss: 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Discriminator Loss: 0.5330, Generator Loss: 2.2823 D(x): 0.7927, D(G(z)): 0.1735 Epoch: [15/20], Batch Num: [426/600] Discriminator Loss: 0.4301, Generator Loss: 2.0937 D(x): 0.8269, D(G(z)): 0.1349 Epoch: [15/20], Batch Num: [427/600] Discriminator Loss: 0.5602, Generator Loss: 2.1533 D(x): 0.8236, D(G(z)): 0.2227 Epoch: [15/20], Batch Num: [428/600] Discriminator Loss: 0.5542, Generator Loss: 1.9375 D(x): 0.8689, D(G(z)): 0.2340 Epoch: [15/20], Batch Num: [429/600] Discriminator Loss: 0.5210, Generator Loss: 2.1688 D(x): 0.8648, D(G(z)): 0.2324 Epoch: [15/20], Batch Num: [430/600] Discriminator Loss: 0.4180, Generator Loss: 2.3245 D(x): 0.8699, D(G(z)): 0.1851 Epoch: [15/20], Batch Num: [431/600] Discriminator Loss: 0.4484, Generator Loss: 2.4053 D(x): 0.8765, D(G(z)): 0.1734 Epoch: [15/20], Batch Num: [432/600] Discriminator Loss: 0.4536, Generator Loss: 2.8455 D(x): 0.8464, D(G(z)): 0.1663 Epoch: [15/20], Batch Num: [433/600] Discriminator Loss: 0.4619, Generator Loss: 2.8553 D(x): 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Generator Loss: 1.7913 D(x): 0.7643, D(G(z)): 0.2308 Epoch: [15/20], Batch Num: [468/600] Discriminator Loss: 0.7003, Generator Loss: 1.8103 D(x): 0.7787, D(G(z)): 0.2434 Epoch: [15/20], Batch Num: [469/600] Discriminator Loss: 0.6728, Generator Loss: 1.9566 D(x): 0.7692, D(G(z)): 0.2477 Epoch: [15/20], Batch Num: [470/600] Discriminator Loss: 0.6119, Generator Loss: 1.9262 D(x): 0.8095, D(G(z)): 0.2476 Epoch: [15/20], Batch Num: [471/600] Discriminator Loss: 0.5754, Generator Loss: 1.8936 D(x): 0.8310, D(G(z)): 0.2458 Epoch: [15/20], Batch Num: [472/600] Discriminator Loss: 0.6885, Generator Loss: 2.1969 D(x): 0.8097, D(G(z)): 0.2716 Epoch: [15/20], Batch Num: [473/600] Discriminator Loss: 0.7267, Generator Loss: 2.5107 D(x): 0.7432, D(G(z)): 0.2333 Epoch: [15/20], Batch Num: [474/600] Discriminator Loss: 0.6070, Generator Loss: 2.6331 D(x): 0.7583, D(G(z)): 0.1649 Epoch: [15/20], Batch Num: [475/600] Discriminator Loss: 0.6900, Generator Loss: 2.3240 D(x): 0.7497, D(G(z)): 0.1946 Epoch: [15/20], Batch Num: [476/600] Discriminator Loss: 0.7065, Generator Loss: 2.0922 D(x): 0.7197, D(G(z)): 0.1733 Epoch: [15/20], Batch Num: [477/600] Discriminator Loss: 0.7378, Generator Loss: 1.6941 D(x): 0.7693, D(G(z)): 0.2572 Epoch: [15/20], Batch Num: [478/600] Discriminator Loss: 0.7287, Generator Loss: 1.7758 D(x): 0.8063, D(G(z)): 0.2623 Epoch: [15/20], Batch Num: [479/600] Discriminator Loss: 0.7617, Generator Loss: 1.8694 D(x): 0.8185, D(G(z)): 0.2868 Epoch: [15/20], Batch Num: [480/600] Discriminator Loss: 0.8460, Generator Loss: 2.0710 D(x): 0.7400, D(G(z)): 0.2607 Epoch: [15/20], Batch Num: [481/600] Discriminator Loss: 0.7714, Generator Loss: 2.1160 D(x): 0.7311, D(G(z)): 0.2048 Epoch: [15/20], Batch Num: [482/600] Discriminator Loss: 0.6215, Generator Loss: 2.0448 D(x): 0.7746, D(G(z)): 0.2157 Epoch: [15/20], Batch Num: [483/600] Discriminator Loss: 0.9088, Generator Loss: 1.9164 D(x): 0.7125, D(G(z)): 0.2622 Epoch: [15/20], Batch Num: [484/600] Discriminator Loss: 0.7498, Generator Loss: 1.6501 D(x): 0.7540, D(G(z)): 0.2571 Epoch: [15/20], Batch Num: [485/600] Discriminator Loss: 0.7840, Generator Loss: 1.7833 D(x): 0.7688, D(G(z)): 0.2598 Epoch: [15/20], Batch Num: [486/600] Discriminator Loss: 0.9449, Generator Loss: 1.7361 D(x): 0.7054, D(G(z)): 0.2792 Epoch: [15/20], Batch Num: [487/600] Discriminator Loss: 0.9879, Generator Loss: 1.9127 D(x): 0.7335, D(G(z)): 0.3246 Epoch: [15/20], Batch Num: [488/600] Discriminator Loss: 1.0290, Generator Loss: 1.8074 D(x): 0.6761, D(G(z)): 0.2621 Epoch: [15/20], Batch Num: [489/600] Discriminator Loss: 0.9156, Generator Loss: 1.7835 D(x): 0.7224, D(G(z)): 0.2749 Epoch: [15/20], Batch Num: [490/600] Discriminator Loss: 0.9211, Generator Loss: 1.7998 D(x): 0.7265, D(G(z)): 0.2614 Epoch: [15/20], Batch Num: [491/600] Discriminator Loss: 0.9657, Generator Loss: 1.5052 D(x): 0.6891, D(G(z)): 0.2802 Epoch: [15/20], Batch Num: [492/600] Discriminator Loss: 0.9185, Generator Loss: 1.6293 D(x): 0.7384, D(G(z)): 0.3264 Epoch: [15/20], Batch Num: [493/600] Discriminator Loss: 1.1418, Generator Loss: 1.5283 D(x): 0.6650, D(G(z)): 0.3288 Epoch: [15/20], Batch Num: [494/600] Discriminator Loss: 1.0549, Generator Loss: 1.4948 D(x): 0.6607, D(G(z)): 0.2936 Epoch: [15/20], Batch Num: [495/600] Discriminator Loss: 1.0900, Generator Loss: 1.5174 D(x): 0.6874, D(G(z)): 0.3527 Epoch: [15/20], Batch Num: [496/600] Discriminator Loss: 1.0891, Generator Loss: 1.5134 D(x): 0.6883, D(G(z)): 0.3392 Epoch: [15/20], Batch Num: [497/600] Discriminator Loss: 0.9255, Generator Loss: 1.5428 D(x): 0.7315, D(G(z)): 0.3170 Epoch: [15/20], Batch Num: [498/600] Discriminator Loss: 1.1326, Generator Loss: 1.5696 D(x): 0.6627, D(G(z)): 0.3568 Epoch: [15/20], Batch Num: [499/600] Discriminator Loss: 1.0284, Generator Loss: 1.5757 D(x): 0.6471, D(G(z)): 0.2760 Epoch: 15, Batch Num: [500/600]
Epoch: [15/20], Batch Num: [500/600] Discriminator Loss: 1.0734, Generator Loss: 1.4677 D(x): 0.6642, D(G(z)): 0.3260 Epoch: [15/20], Batch Num: [501/600] Discriminator Loss: 1.2118, Generator Loss: 1.5425 D(x): 0.5877, D(G(z)): 0.3257 Epoch: [15/20], Batch Num: [502/600] Discriminator Loss: 1.1192, Generator Loss: 1.3554 D(x): 0.5988, D(G(z)): 0.2940 Epoch: [15/20], Batch Num: [503/600] Discriminator Loss: 1.1406, Generator Loss: 1.3245 D(x): 0.6730, D(G(z)): 0.3690 Epoch: [15/20], Batch Num: [504/600] Discriminator Loss: 1.1435, Generator Loss: 1.2960 D(x): 0.7284, D(G(z)): 0.4526 Epoch: [15/20], Batch Num: [505/600] Discriminator Loss: 0.9028, Generator Loss: 1.4149 D(x): 0.7310, D(G(z)): 0.3327 Epoch: [15/20], Batch Num: [506/600] Discriminator Loss: 0.9461, Generator Loss: 1.6353 D(x): 0.6764, D(G(z)): 0.3050 Epoch: [15/20], Batch Num: [507/600] Discriminator Loss: 0.9675, Generator Loss: 1.6724 D(x): 0.6448, D(G(z)): 0.2601 Epoch: [15/20], Batch Num: [508/600] Discriminator Loss: 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0.2546 Epoch: [15/20], Batch Num: [517/600] Discriminator Loss: 0.8118, Generator Loss: 1.8484 D(x): 0.6690, D(G(z)): 0.2073 Epoch: [15/20], Batch Num: [518/600] Discriminator Loss: 0.8228, Generator Loss: 1.8344 D(x): 0.6731, D(G(z)): 0.2096 Epoch: [15/20], Batch Num: [519/600] Discriminator Loss: 0.8519, Generator Loss: 1.5539 D(x): 0.6683, D(G(z)): 0.2274 Epoch: [15/20], Batch Num: [520/600] Discriminator Loss: 0.8040, Generator Loss: 1.4840 D(x): 0.7383, D(G(z)): 0.2965 Epoch: [15/20], Batch Num: [521/600] Discriminator Loss: 0.8484, Generator Loss: 1.3448 D(x): 0.7507, D(G(z)): 0.3235 Epoch: [15/20], Batch Num: [522/600] Discriminator Loss: 0.8247, Generator Loss: 1.3876 D(x): 0.7804, D(G(z)): 0.3352 Epoch: [15/20], Batch Num: [523/600] Discriminator Loss: 0.6595, Generator Loss: 1.6667 D(x): 0.8159, D(G(z)): 0.2933 Epoch: [15/20], Batch Num: [524/600] Discriminator Loss: 0.7451, Generator Loss: 1.7196 D(x): 0.7810, D(G(z)): 0.2768 Epoch: [15/20], Batch Num: [525/600] Discriminator Loss: 0.8681, Generator Loss: 2.0663 D(x): 0.6967, D(G(z)): 0.2723 Epoch: [15/20], Batch Num: [526/600] Discriminator Loss: 0.7108, Generator Loss: 1.9447 D(x): 0.7033, D(G(z)): 0.1955 Epoch: [15/20], Batch Num: [527/600] Discriminator Loss: 0.7750, Generator Loss: 1.9436 D(x): 0.7026, D(G(z)): 0.2337 Epoch: [15/20], Batch Num: [528/600] Discriminator Loss: 0.6236, Generator Loss: 1.7981 D(x): 0.7765, D(G(z)): 0.2289 Epoch: [15/20], Batch Num: [529/600] Discriminator Loss: 0.6380, Generator Loss: 1.8183 D(x): 0.7946, D(G(z)): 0.2501 Epoch: [15/20], Batch Num: [530/600] Discriminator Loss: 0.8288, Generator Loss: 1.6657 D(x): 0.7575, D(G(z)): 0.2947 Epoch: [15/20], Batch Num: [531/600] Discriminator Loss: 0.6406, Generator Loss: 1.8013 D(x): 0.7816, D(G(z)): 0.2338 Epoch: [15/20], Batch Num: [532/600] Discriminator Loss: 0.6256, Generator Loss: 1.7395 D(x): 0.8062, D(G(z)): 0.2349 Epoch: [15/20], Batch Num: [533/600] Discriminator Loss: 0.8480, Generator Loss: 1.9768 D(x): 0.6916, D(G(z)): 0.2361 Epoch: [15/20], Batch Num: [534/600] Discriminator Loss: 0.6584, Generator Loss: 1.8322 D(x): 0.7823, D(G(z)): 0.2382 Epoch: [15/20], Batch Num: [535/600] Discriminator Loss: 0.8054, Generator Loss: 1.9998 D(x): 0.7651, D(G(z)): 0.3002 Epoch: [15/20], Batch Num: [536/600] Discriminator Loss: 0.7509, Generator Loss: 2.0646 D(x): 0.7665, D(G(z)): 0.2457 Epoch: [15/20], Batch Num: [537/600] Discriminator Loss: 0.7478, Generator Loss: 1.9291 D(x): 0.7259, D(G(z)): 0.2135 Epoch: [15/20], Batch Num: [538/600] Discriminator Loss: 0.7783, Generator Loss: 1.8410 D(x): 0.7479, D(G(z)): 0.2351 Epoch: [15/20], Batch Num: [539/600] Discriminator Loss: 0.9175, Generator Loss: 1.6170 D(x): 0.7242, D(G(z)): 0.2724 Epoch: [15/20], Batch Num: [540/600] Discriminator Loss: 0.8664, Generator Loss: 1.7671 D(x): 0.7529, D(G(z)): 0.3007 Epoch: [15/20], Batch Num: [541/600] Discriminator Loss: 0.9283, Generator Loss: 1.7874 D(x): 0.7193, D(G(z)): 0.2806 Epoch: [15/20], Batch Num: 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1.7043 D(x): 0.6566, D(G(z)): 0.3048 Epoch: [15/20], Batch Num: [551/600] Discriminator Loss: 1.2544, Generator Loss: 1.3070 D(x): 0.6042, D(G(z)): 0.3316 Epoch: [15/20], Batch Num: [552/600] Discriminator Loss: 0.9697, Generator Loss: 1.3684 D(x): 0.7529, D(G(z)): 0.3442 Epoch: [15/20], Batch Num: [553/600] Discriminator Loss: 0.9702, Generator Loss: 1.6374 D(x): 0.7551, D(G(z)): 0.3946 Epoch: [15/20], Batch Num: [554/600] Discriminator Loss: 0.9690, Generator Loss: 1.8710 D(x): 0.6753, D(G(z)): 0.2915 Epoch: [15/20], Batch Num: [555/600] Discriminator Loss: 1.0436, Generator Loss: 1.7412 D(x): 0.6438, D(G(z)): 0.3044 Epoch: [15/20], Batch Num: [556/600] Discriminator Loss: 0.9687, Generator Loss: 1.4567 D(x): 0.6843, D(G(z)): 0.3068 Epoch: [15/20], Batch Num: [557/600] Discriminator Loss: 1.0422, Generator Loss: 1.5472 D(x): 0.6226, D(G(z)): 0.2818 Epoch: [15/20], Batch Num: [558/600] Discriminator Loss: 0.9837, Generator Loss: 1.4180 D(x): 0.6993, D(G(z)): 0.3332 Epoch: [15/20], Batch Num: [559/600] Discriminator Loss: 0.8091, Generator Loss: 1.4677 D(x): 0.7408, D(G(z)): 0.3057 Epoch: [15/20], Batch Num: [560/600] Discriminator Loss: 0.8726, Generator Loss: 1.5884 D(x): 0.7391, D(G(z)): 0.3267 Epoch: [15/20], Batch Num: [561/600] Discriminator Loss: 0.8067, Generator Loss: 1.8695 D(x): 0.7359, D(G(z)): 0.3016 Epoch: [15/20], Batch Num: [562/600] Discriminator Loss: 0.6904, Generator Loss: 1.6938 D(x): 0.7376, D(G(z)): 0.2361 Epoch: [15/20], Batch Num: [563/600] Discriminator Loss: 0.8158, Generator Loss: 1.7298 D(x): 0.7427, D(G(z)): 0.2950 Epoch: [15/20], Batch Num: [564/600] Discriminator Loss: 0.7543, Generator Loss: 1.7489 D(x): 0.7113, D(G(z)): 0.2215 Epoch: [15/20], Batch Num: [565/600] Discriminator Loss: 0.6664, Generator Loss: 1.8573 D(x): 0.7619, D(G(z)): 0.2368 Epoch: [15/20], Batch Num: [566/600] Discriminator Loss: 0.5914, Generator Loss: 1.9491 D(x): 0.7976, D(G(z)): 0.2190 Epoch: [15/20], Batch Num: [567/600] Discriminator Loss: 0.7631, Generator Loss: 1.8666 D(x): 0.7339, D(G(z)): 0.2517 Epoch: [15/20], Batch Num: [568/600] Discriminator Loss: 0.5801, Generator Loss: 2.0095 D(x): 0.7846, D(G(z)): 0.2006 Epoch: [15/20], Batch Num: [569/600] Discriminator Loss: 0.6941, Generator Loss: 1.9060 D(x): 0.7630, D(G(z)): 0.2547 Epoch: [15/20], Batch Num: [570/600] Discriminator Loss: 0.5263, Generator Loss: 2.0226 D(x): 0.8175, D(G(z)): 0.2142 Epoch: [15/20], Batch Num: [571/600] Discriminator Loss: 0.5911, Generator Loss: 2.1240 D(x): 0.8163, D(G(z)): 0.2296 Epoch: [15/20], Batch Num: [572/600] Discriminator Loss: 0.5955, Generator Loss: 2.3257 D(x): 0.7975, D(G(z)): 0.2159 Epoch: [15/20], Batch Num: [573/600] Discriminator Loss: 0.5738, Generator Loss: 2.4085 D(x): 0.7826, D(G(z)): 0.1746 Epoch: [15/20], Batch Num: [574/600] Discriminator Loss: 0.4987, Generator Loss: 2.3601 D(x): 0.8229, D(G(z)): 0.1891 Epoch: [15/20], Batch Num: [575/600] Discriminator Loss: 0.5759, Generator Loss: 2.3876 D(x): 0.7897, D(G(z)): 0.1823 Epoch: [15/20], Batch Num: [576/600] Discriminator Loss: 0.5546, Generator Loss: 2.4063 D(x): 0.8215, D(G(z)): 0.1868 Epoch: [15/20], Batch Num: [577/600] Discriminator Loss: 0.5058, Generator Loss: 2.3080 D(x): 0.8391, D(G(z)): 0.2137 Epoch: [15/20], Batch Num: [578/600] Discriminator Loss: 0.5020, Generator Loss: 2.4492 D(x): 0.8137, D(G(z)): 0.1748 Epoch: [15/20], Batch Num: [579/600] Discriminator Loss: 0.5862, Generator Loss: 2.4417 D(x): 0.8094, D(G(z)): 0.2019 Epoch: [15/20], Batch Num: [580/600] Discriminator Loss: 0.5340, Generator Loss: 2.6853 D(x): 0.8643, D(G(z)): 0.1891 Epoch: [15/20], Batch Num: [581/600] Discriminator Loss: 0.5376, Generator Loss: 2.7773 D(x): 0.7978, D(G(z)): 0.1614 Epoch: [15/20], Batch Num: [582/600] Discriminator Loss: 0.6313, Generator Loss: 2.6468 D(x): 0.7756, D(G(z)): 0.1791 Epoch: [15/20], Batch Num: [583/600] Discriminator Loss: 0.6307, Generator Loss: 2.4112 D(x): 0.7695, D(G(z)): 0.1939 Epoch: [15/20], Batch Num: [584/600] Discriminator Loss: 0.6278, Generator Loss: 1.9664 D(x): 0.8097, D(G(z)): 0.2117 Epoch: [15/20], Batch Num: [585/600] Discriminator Loss: 0.7226, Generator Loss: 2.1087 D(x): 0.8172, D(G(z)): 0.2536 Epoch: [15/20], Batch Num: [586/600] Discriminator Loss: 0.7930, Generator Loss: 2.2525 D(x): 0.7639, D(G(z)): 0.2476 Epoch: [15/20], Batch Num: [587/600] Discriminator Loss: 0.7426, Generator Loss: 2.2693 D(x): 0.7709, D(G(z)): 0.2084 Epoch: [15/20], Batch Num: [588/600] Discriminator Loss: 0.8189, Generator Loss: 2.3661 D(x): 0.7461, D(G(z)): 0.2139 Epoch: [15/20], Batch Num: [589/600] Discriminator Loss: 0.9192, Generator Loss: 1.9702 D(x): 0.7246, D(G(z)): 0.2462 Epoch: [15/20], Batch Num: [590/600] Discriminator Loss: 0.8527, Generator Loss: 1.8840 D(x): 0.7263, D(G(z)): 0.2386 Epoch: [15/20], Batch Num: [591/600] Discriminator Loss: 1.1613, Generator Loss: 1.5961 D(x): 0.7328, D(G(z)): 0.3760 Epoch: [15/20], Batch Num: [592/600] Discriminator Loss: 1.2954, Generator Loss: 2.1453 D(x): 0.7037, D(G(z)): 0.3868 Epoch: [15/20], Batch Num: [593/600] Discriminator Loss: 1.2850, Generator Loss: 1.7994 D(x): 0.6301, D(G(z)): 0.2945 Epoch: [15/20], Batch Num: [594/600] Discriminator Loss: 1.1263, Generator Loss: 1.5836 D(x): 0.6527, D(G(z)): 0.2657 Epoch: [15/20], Batch Num: [595/600] Discriminator Loss: 1.1519, Generator Loss: 1.2904 D(x): 0.6537, D(G(z)): 0.3159 Epoch: [15/20], Batch Num: [596/600] Discriminator Loss: 1.2844, Generator Loss: 1.2757 D(x): 0.7043, D(G(z)): 0.3722 Epoch: [15/20], Batch Num: [597/600] Discriminator Loss: 1.4917, Generator Loss: 1.3184 D(x): 0.6355, D(G(z)): 0.4322 Epoch: [15/20], Batch Num: [598/600] Discriminator Loss: 1.3096, Generator Loss: 1.4058 D(x): 0.6626, D(G(z)): 0.3973 Epoch: [15/20], Batch Num: [599/600] Discriminator Loss: 1.2052, Generator Loss: 1.4323 D(x): 0.6903, D(G(z)): 0.3988 Epoch: 16, Batch Num: [0/600]
Epoch: [16/20], Batch Num: [0/600] Discriminator Loss: 1.0507, Generator Loss: 1.6809 D(x): 0.6374, D(G(z)): 0.2923 Epoch: [16/20], Batch Num: [1/600] Discriminator Loss: 1.2109, Generator Loss: 1.6784 D(x): 0.5746, D(G(z)): 0.2949 Epoch: [16/20], Batch Num: [2/600] Discriminator Loss: 1.2743, Generator Loss: 1.6692 D(x): 0.6092, D(G(z)): 0.3640 Epoch: [16/20], Batch Num: [3/600] Discriminator Loss: 1.2621, Generator Loss: 1.2603 D(x): 0.6679, D(G(z)): 0.4101 Epoch: [16/20], Batch Num: [4/600] Discriminator Loss: 1.0997, Generator Loss: 1.3030 D(x): 0.6715, D(G(z)): 0.3521 Epoch: [16/20], Batch Num: [5/600] Discriminator Loss: 1.1297, Generator Loss: 1.3889 D(x): 0.6825, D(G(z)): 0.4079 Epoch: [16/20], Batch Num: [6/600] Discriminator Loss: 1.0092, Generator Loss: 1.4833 D(x): 0.7151, D(G(z)): 0.3646 Epoch: [16/20], Batch Num: [7/600] Discriminator Loss: 1.0845, Generator Loss: 1.7108 D(x): 0.6246, D(G(z)): 0.3187 Epoch: [16/20], Batch Num: [8/600] Discriminator Loss: 0.8313, Generator Loss: 1.7378 D(x): 0.6940, D(G(z)): 0.2631 Epoch: [16/20], Batch Num: [9/600] Discriminator Loss: 0.8619, Generator Loss: 1.5494 D(x): 0.6986, D(G(z)): 0.2889 Epoch: [16/20], Batch Num: [10/600] Discriminator Loss: 0.9533, Generator Loss: 1.5829 D(x): 0.6347, D(G(z)): 0.2640 Epoch: [16/20], Batch Num: [11/600] Discriminator Loss: 0.9028, Generator Loss: 1.3392 D(x): 0.6564, D(G(z)): 0.2807 Epoch: [16/20], Batch Num: [12/600] Discriminator Loss: 0.7802, Generator Loss: 1.3085 D(x): 0.7351, D(G(z)): 0.2843 Epoch: [16/20], Batch Num: [13/600] Discriminator Loss: 0.8093, Generator Loss: 1.5068 D(x): 0.7789, D(G(z)): 0.3470 Epoch: [16/20], Batch Num: [14/600] Discriminator Loss: 0.6819, Generator Loss: 1.5550 D(x): 0.7749, D(G(z)): 0.2877 Epoch: [16/20], Batch Num: [15/600] Discriminator Loss: 0.7292, Generator Loss: 1.6486 D(x): 0.7577, D(G(z)): 0.2728 Epoch: [16/20], Batch Num: [16/600] Discriminator Loss: 0.7433, Generator Loss: 1.8061 D(x): 0.7299, D(G(z)): 0.2645 Epoch: [16/20], Batch Num: [17/600] Discriminator Loss: 0.8036, Generator Loss: 1.7578 D(x): 0.7248, D(G(z)): 0.2727 Epoch: [16/20], Batch Num: [18/600] Discriminator Loss: 0.5708, Generator Loss: 1.9400 D(x): 0.7965, D(G(z)): 0.2072 Epoch: [16/20], Batch Num: [19/600] Discriminator Loss: 0.6119, Generator Loss: 1.9151 D(x): 0.7523, D(G(z)): 0.2003 Epoch: [16/20], Batch Num: [20/600] Discriminator Loss: 0.6247, Generator Loss: 2.0327 D(x): 0.7597, D(G(z)): 0.2162 Epoch: [16/20], Batch Num: [21/600] Discriminator Loss: 0.6685, Generator Loss: 1.6837 D(x): 0.7495, D(G(z)): 0.2172 Epoch: [16/20], Batch Num: [22/600] Discriminator Loss: 0.5623, Generator Loss: 1.8353 D(x): 0.8147, D(G(z)): 0.2337 Epoch: [16/20], Batch Num: [23/600] Discriminator Loss: 0.5479, Generator Loss: 1.8482 D(x): 0.8168, D(G(z)): 0.2337 Epoch: [16/20], Batch Num: [24/600] Discriminator Loss: 0.6461, Generator Loss: 1.8011 D(x): 0.8144, D(G(z)): 0.2717 Epoch: [16/20], Batch Num: [25/600] Discriminator Loss: 0.5139, Generator Loss: 2.0347 D(x): 0.8282, D(G(z)): 0.2186 Epoch: [16/20], Batch Num: [26/600] Discriminator Loss: 0.6583, Generator Loss: 2.0840 D(x): 0.7868, D(G(z)): 0.2263 Epoch: [16/20], Batch Num: [27/600] Discriminator Loss: 0.7522, Generator Loss: 2.0403 D(x): 0.7728, D(G(z)): 0.2397 Epoch: [16/20], Batch Num: [28/600] Discriminator Loss: 0.6464, Generator Loss: 2.1861 D(x): 0.7911, D(G(z)): 0.2178 Epoch: [16/20], Batch Num: [29/600] Discriminator Loss: 0.7212, Generator Loss: 2.2017 D(x): 0.7288, D(G(z)): 0.2204 Epoch: [16/20], Batch Num: [30/600] Discriminator Loss: 0.6577, Generator Loss: 2.0883 D(x): 0.7867, D(G(z)): 0.2188 Epoch: [16/20], Batch Num: [31/600] Discriminator Loss: 0.7820, Generator Loss: 1.9089 D(x): 0.7811, D(G(z)): 0.2729 Epoch: [16/20], Batch Num: [32/600] Discriminator Loss: 0.8817, Generator Loss: 1.9609 D(x): 0.7114, D(G(z)): 0.2402 Epoch: [16/20], Batch Num: [33/600] Discriminator Loss: 0.8913, Generator Loss: 1.7937 D(x): 0.7662, D(G(z)): 0.3068 Epoch: [16/20], Batch Num: [34/600] Discriminator Loss: 0.8234, Generator Loss: 2.0633 D(x): 0.7744, D(G(z)): 0.2778 Epoch: [16/20], Batch Num: [35/600] Discriminator Loss: 0.8617, Generator Loss: 1.8632 D(x): 0.7194, D(G(z)): 0.2529 Epoch: [16/20], Batch Num: [36/600] Discriminator Loss: 0.9474, Generator Loss: 1.7413 D(x): 0.7166, D(G(z)): 0.2887 Epoch: [16/20], Batch Num: [37/600] Discriminator Loss: 1.0135, Generator Loss: 1.8502 D(x): 0.7109, D(G(z)): 0.3232 Epoch: [16/20], Batch Num: [38/600] Discriminator Loss: 1.0944, Generator Loss: 1.4281 D(x): 0.6685, D(G(z)): 0.2866 Epoch: [16/20], Batch Num: [39/600] Discriminator Loss: 1.2761, Generator Loss: 1.5269 D(x): 0.6817, D(G(z)): 0.3505 Epoch: [16/20], Batch Num: [40/600] Discriminator Loss: 1.1980, Generator Loss: 1.4286 D(x): 0.6660, D(G(z)): 0.3679 Epoch: [16/20], Batch Num: [41/600] Discriminator Loss: 1.1828, Generator Loss: 1.4861 D(x): 0.6344, D(G(z)): 0.3400 Epoch: [16/20], Batch Num: [42/600] Discriminator Loss: 1.1569, Generator Loss: 1.3476 D(x): 0.6018, D(G(z)): 0.2851 Epoch: [16/20], Batch Num: [43/600] Discriminator Loss: 1.4764, Generator Loss: 1.1570 D(x): 0.5721, D(G(z)): 0.3831 Epoch: [16/20], Batch Num: [44/600] Discriminator Loss: 1.2624, Generator Loss: 1.1676 D(x): 0.7105, D(G(z)): 0.4365 Epoch: [16/20], Batch Num: [45/600] Discriminator Loss: 1.2633, Generator Loss: 1.3461 D(x): 0.6657, D(G(z)): 0.4055 Epoch: [16/20], Batch Num: [46/600] Discriminator Loss: 1.2269, Generator Loss: 1.4425 D(x): 0.6647, D(G(z)): 0.3602 Epoch: [16/20], Batch Num: [47/600] Discriminator Loss: 1.1057, Generator Loss: 1.3641 D(x): 0.5936, D(G(z)): 0.2719 Epoch: [16/20], Batch Num: [48/600] Discriminator Loss: 1.3882, Generator Loss: 1.3668 D(x): 0.5202, D(G(z)): 0.3339 Epoch: [16/20], Batch Num: [49/600] Discriminator Loss: 1.0612, Generator Loss: 1.1361 D(x): 0.6563, D(G(z)): 0.3459 Epoch: [16/20], Batch Num: [50/600] Discriminator Loss: 1.0907, Generator Loss: 1.1232 D(x): 0.6880, D(G(z)): 0.3780 Epoch: [16/20], Batch Num: 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D(x): 0.7177, D(G(z)): 0.3344 Epoch: [16/20], Batch Num: [60/600] Discriminator Loss: 0.7493, Generator Loss: 1.1845 D(x): 0.7580, D(G(z)): 0.3146 Epoch: [16/20], Batch Num: [61/600] Discriminator Loss: 0.7726, Generator Loss: 1.2647 D(x): 0.7453, D(G(z)): 0.3217 Epoch: [16/20], Batch Num: [62/600] Discriminator Loss: 0.8266, Generator Loss: 1.4155 D(x): 0.7443, D(G(z)): 0.3254 Epoch: [16/20], Batch Num: [63/600] Discriminator Loss: 0.7571, Generator Loss: 1.4885 D(x): 0.7328, D(G(z)): 0.2956 Epoch: [16/20], Batch Num: [64/600] Discriminator Loss: 0.7251, Generator Loss: 1.5318 D(x): 0.7325, D(G(z)): 0.2738 Epoch: [16/20], Batch Num: [65/600] Discriminator Loss: 0.6951, Generator Loss: 1.6655 D(x): 0.7425, D(G(z)): 0.2524 Epoch: [16/20], Batch Num: [66/600] Discriminator Loss: 0.6994, Generator Loss: 1.6369 D(x): 0.7305, D(G(z)): 0.2598 Epoch: [16/20], Batch Num: [67/600] Discriminator Loss: 0.7429, Generator Loss: 1.5781 D(x): 0.7295, D(G(z)): 0.2707 Epoch: [16/20], Batch Num: 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D(x): 0.7553, D(G(z)): 0.2256 Epoch: [16/20], Batch Num: [77/600] Discriminator Loss: 0.6167, Generator Loss: 1.9398 D(x): 0.8012, D(G(z)): 0.2406 Epoch: [16/20], Batch Num: [78/600] Discriminator Loss: 0.5547, Generator Loss: 1.9669 D(x): 0.7930, D(G(z)): 0.1968 Epoch: [16/20], Batch Num: [79/600] Discriminator Loss: 0.5430, Generator Loss: 2.1695 D(x): 0.8249, D(G(z)): 0.2214 Epoch: [16/20], Batch Num: [80/600] Discriminator Loss: 0.4010, Generator Loss: 2.1173 D(x): 0.8631, D(G(z)): 0.1780 Epoch: [16/20], Batch Num: [81/600] Discriminator Loss: 0.5304, Generator Loss: 2.1721 D(x): 0.8025, D(G(z)): 0.1754 Epoch: [16/20], Batch Num: [82/600] Discriminator Loss: 0.6037, Generator Loss: 1.9293 D(x): 0.8256, D(G(z)): 0.2075 Epoch: [16/20], Batch Num: [83/600] Discriminator Loss: 0.6463, Generator Loss: 2.0656 D(x): 0.8081, D(G(z)): 0.2430 Epoch: [16/20], Batch Num: [84/600] Discriminator Loss: 0.5582, Generator Loss: 1.9967 D(x): 0.8414, D(G(z)): 0.2258 Epoch: [16/20], Batch Num: [85/600] Discriminator Loss: 0.6348, Generator Loss: 2.4293 D(x): 0.8286, D(G(z)): 0.2490 Epoch: [16/20], Batch Num: [86/600] Discriminator Loss: 0.6396, Generator Loss: 2.4116 D(x): 0.7760, D(G(z)): 0.1822 Epoch: [16/20], Batch Num: [87/600] Discriminator Loss: 0.8157, Generator Loss: 1.8929 D(x): 0.7129, D(G(z)): 0.1954 Epoch: [16/20], Batch Num: [88/600] Discriminator Loss: 0.8155, Generator Loss: 1.8405 D(x): 0.7904, D(G(z)): 0.2832 Epoch: [16/20], Batch Num: [89/600] Discriminator Loss: 0.9328, Generator Loss: 1.8527 D(x): 0.7196, D(G(z)): 0.2726 Epoch: [16/20], Batch Num: [90/600] Discriminator Loss: 0.8109, Generator Loss: 1.8043 D(x): 0.7918, D(G(z)): 0.2914 Epoch: [16/20], Batch Num: [91/600] Discriminator Loss: 0.9135, Generator Loss: 2.0333 D(x): 0.7685, D(G(z)): 0.3264 Epoch: [16/20], Batch Num: [92/600] Discriminator Loss: 0.8670, Generator Loss: 2.1956 D(x): 0.7286, D(G(z)): 0.2579 Epoch: [16/20], Batch Num: [93/600] Discriminator Loss: 0.9094, Generator Loss: 1.7951 D(x): 0.7117, D(G(z)): 0.2290 Epoch: [16/20], Batch Num: [94/600] Discriminator Loss: 1.1808, Generator Loss: 1.6174 D(x): 0.6556, D(G(z)): 0.2810 Epoch: [16/20], Batch Num: [95/600] Discriminator Loss: 0.9059, Generator Loss: 1.6927 D(x): 0.7416, D(G(z)): 0.3063 Epoch: [16/20], Batch Num: [96/600] Discriminator Loss: 1.0190, Generator Loss: 1.4222 D(x): 0.7073, D(G(z)): 0.3327 Epoch: [16/20], Batch Num: [97/600] Discriminator Loss: 1.0933, Generator Loss: 1.5483 D(x): 0.6957, D(G(z)): 0.3226 Epoch: [16/20], Batch Num: [98/600] Discriminator Loss: 0.9079, Generator Loss: 1.5069 D(x): 0.7097, D(G(z)): 0.2980 Epoch: [16/20], Batch Num: [99/600] Discriminator Loss: 1.1363, Generator Loss: 1.6321 D(x): 0.6407, D(G(z)): 0.2900 Epoch: 16, Batch Num: [100/600]
Epoch: [16/20], Batch Num: [100/600] Discriminator Loss: 1.0777, Generator Loss: 1.4753 D(x): 0.6853, D(G(z)): 0.3330 Epoch: [16/20], Batch Num: [101/600] Discriminator Loss: 1.0227, Generator Loss: 1.5338 D(x): 0.7095, D(G(z)): 0.3362 Epoch: [16/20], Batch Num: [102/600] Discriminator Loss: 0.9935, Generator Loss: 1.5409 D(x): 0.7359, D(G(z)): 0.3420 Epoch: [16/20], Batch Num: [103/600] Discriminator Loss: 0.8951, Generator Loss: 1.5535 D(x): 0.7334, D(G(z)): 0.3085 Epoch: [16/20], Batch Num: [104/600] Discriminator Loss: 1.0182, Generator Loss: 1.7004 D(x): 0.6418, D(G(z)): 0.2682 Epoch: [16/20], Batch Num: [105/600] Discriminator Loss: 0.7990, Generator Loss: 1.7446 D(x): 0.7562, D(G(z)): 0.2962 Epoch: [16/20], Batch Num: [106/600] Discriminator Loss: 0.8438, Generator Loss: 1.5888 D(x): 0.6730, D(G(z)): 0.2338 Epoch: [16/20], Batch Num: [107/600] Discriminator Loss: 0.8152, Generator Loss: 1.6851 D(x): 0.7364, D(G(z)): 0.2805 Epoch: [16/20], Batch Num: [108/600] Discriminator Loss: 0.8684, Generator Loss: 1.5561 D(x): 0.7157, D(G(z)): 0.2816 Epoch: [16/20], Batch Num: [109/600] Discriminator Loss: 0.8080, Generator Loss: 1.6323 D(x): 0.7216, D(G(z)): 0.2506 Epoch: [16/20], Batch Num: [110/600] Discriminator Loss: 0.9011, Generator Loss: 1.7034 D(x): 0.7812, D(G(z)): 0.3249 Epoch: [16/20], Batch Num: [111/600] Discriminator Loss: 0.7958, Generator Loss: 1.7191 D(x): 0.7513, D(G(z)): 0.2783 Epoch: [16/20], Batch Num: [112/600] Discriminator Loss: 0.6252, Generator Loss: 2.0501 D(x): 0.7801, D(G(z)): 0.2265 Epoch: [16/20], Batch Num: [113/600] Discriminator Loss: 0.8312, Generator Loss: 2.0612 D(x): 0.6939, D(G(z)): 0.2136 Epoch: [16/20], Batch Num: [114/600] Discriminator Loss: 0.7927, Generator Loss: 1.9428 D(x): 0.7288, D(G(z)): 0.2196 Epoch: [16/20], Batch Num: [115/600] Discriminator Loss: 0.5225, Generator Loss: 1.5862 D(x): 0.8135, D(G(z)): 0.1884 Epoch: [16/20], Batch Num: [116/600] Discriminator Loss: 0.5423, Generator Loss: 1.7338 D(x): 0.8292, D(G(z)): 0.2290 Epoch: [16/20], Batch Num: [117/600] Discriminator Loss: 0.6951, Generator Loss: 1.6533 D(x): 0.8139, D(G(z)): 0.2584 Epoch: [16/20], Batch Num: [118/600] Discriminator Loss: 0.6054, Generator Loss: 1.9307 D(x): 0.8501, D(G(z)): 0.2775 Epoch: [16/20], Batch Num: [119/600] Discriminator Loss: 0.7165, Generator Loss: 2.1044 D(x): 0.8022, D(G(z)): 0.2546 Epoch: [16/20], Batch Num: [120/600] Discriminator Loss: 0.6937, Generator Loss: 2.1089 D(x): 0.7732, D(G(z)): 0.2038 Epoch: [16/20], Batch Num: [121/600] Discriminator Loss: 0.6548, Generator Loss: 2.1794 D(x): 0.7599, D(G(z)): 0.1814 Epoch: [16/20], Batch Num: [122/600] Discriminator Loss: 0.5802, Generator Loss: 2.0546 D(x): 0.8059, D(G(z)): 0.1880 Epoch: [16/20], Batch Num: [123/600] Discriminator Loss: 0.6323, Generator Loss: 1.7993 D(x): 0.7787, D(G(z)): 0.1939 Epoch: [16/20], Batch Num: [124/600] Discriminator Loss: 0.5725, Generator Loss: 1.7817 D(x): 0.7948, D(G(z)): 0.1877 Epoch: [16/20], Batch Num: [125/600] Discriminator Loss: 0.5429, Generator Loss: 1.7321 D(x): 0.8785, D(G(z)): 0.2648 Epoch: [16/20], Batch Num: [126/600] Discriminator Loss: 0.7750, Generator Loss: 1.9515 D(x): 0.8365, D(G(z)): 0.2987 Epoch: [16/20], Batch Num: [127/600] Discriminator Loss: 0.5544, Generator Loss: 2.2080 D(x): 0.8252, D(G(z)): 0.2023 Epoch: [16/20], Batch Num: [128/600] Discriminator Loss: 0.5547, Generator Loss: 2.3012 D(x): 0.7895, D(G(z)): 0.1661 Epoch: [16/20], Batch Num: [129/600] Discriminator Loss: 0.6514, Generator Loss: 2.4423 D(x): 0.7559, D(G(z)): 0.1719 Epoch: [16/20], Batch Num: [130/600] Discriminator Loss: 0.8832, Generator Loss: 2.1407 D(x): 0.7143, D(G(z)): 0.2072 Epoch: [16/20], Batch Num: [131/600] Discriminator Loss: 0.7938, Generator Loss: 2.1140 D(x): 0.7557, D(G(z)): 0.2377 Epoch: [16/20], Batch Num: [132/600] Discriminator Loss: 0.9931, Generator Loss: 1.8591 D(x): 0.7397, D(G(z)): 0.2800 Epoch: [16/20], Batch Num: [133/600] Discriminator Loss: 0.7898, Generator Loss: 2.0334 D(x): 0.7912, D(G(z)): 0.2741 Epoch: [16/20], Batch Num: [134/600] Discriminator Loss: 0.7109, Generator Loss: 1.9903 D(x): 0.7654, D(G(z)): 0.2234 Epoch: [16/20], Batch Num: [135/600] Discriminator Loss: 0.8386, Generator Loss: 1.7510 D(x): 0.7735, D(G(z)): 0.2815 Epoch: [16/20], Batch Num: [136/600] Discriminator Loss: 0.9005, Generator Loss: 1.8729 D(x): 0.7652, D(G(z)): 0.3069 Epoch: [16/20], Batch Num: [137/600] Discriminator Loss: 0.7334, Generator Loss: 2.0340 D(x): 0.7756, D(G(z)): 0.2552 Epoch: [16/20], Batch Num: [138/600] Discriminator Loss: 1.0934, Generator Loss: 2.1255 D(x): 0.7109, D(G(z)): 0.2687 Epoch: [16/20], Batch Num: [139/600] Discriminator Loss: 1.1202, Generator Loss: 2.0707 D(x): 0.6308, D(G(z)): 0.2638 Epoch: [16/20], Batch Num: [140/600] Discriminator Loss: 0.9895, Generator Loss: 1.6911 D(x): 0.6840, D(G(z)): 0.2613 Epoch: [16/20], Batch Num: [141/600] Discriminator Loss: 1.2515, Generator Loss: 1.6921 D(x): 0.6766, D(G(z)): 0.3422 Epoch: [16/20], Batch Num: [142/600] Discriminator Loss: 1.1331, Generator Loss: 1.6980 D(x): 0.7708, D(G(z)): 0.3819 Epoch: [16/20], Batch Num: [143/600] Discriminator Loss: 0.9545, Generator Loss: 1.6290 D(x): 0.7043, D(G(z)): 0.2630 Epoch: [16/20], Batch Num: [144/600] Discriminator Loss: 0.9185, Generator Loss: 1.6819 D(x): 0.6803, D(G(z)): 0.2445 Epoch: [16/20], Batch Num: [145/600] Discriminator Loss: 1.0493, Generator Loss: 1.5903 D(x): 0.6612, D(G(z)): 0.2835 Epoch: [16/20], Batch Num: [146/600] Discriminator Loss: 0.8448, Generator Loss: 1.5218 D(x): 0.7418, D(G(z)): 0.2921 Epoch: [16/20], Batch Num: [147/600] Discriminator Loss: 0.8267, Generator Loss: 1.5832 D(x): 0.7261, D(G(z)): 0.2735 Epoch: [16/20], Batch Num: [148/600] Discriminator Loss: 1.0199, Generator Loss: 1.5478 D(x): 0.7046, D(G(z)): 0.3289 Epoch: [16/20], Batch Num: [149/600] Discriminator Loss: 1.0820, Generator Loss: 1.4895 D(x): 0.7020, D(G(z)): 0.3500 Epoch: [16/20], Batch Num: [150/600] Discriminator Loss: 1.0488, Generator Loss: 1.5087 D(x): 0.6717, D(G(z)): 0.3138 Epoch: [16/20], Batch Num: [151/600] Discriminator Loss: 0.9848, Generator Loss: 1.6101 D(x): 0.6547, D(G(z)): 0.2846 Epoch: [16/20], Batch Num: [152/600] Discriminator Loss: 1.0155, Generator Loss: 1.5921 D(x): 0.7206, D(G(z)): 0.3430 Epoch: [16/20], Batch Num: [153/600] Discriminator Loss: 1.0433, Generator Loss: 1.7200 D(x): 0.6173, D(G(z)): 0.2652 Epoch: [16/20], Batch Num: [154/600] Discriminator Loss: 0.9473, Generator Loss: 1.5316 D(x): 0.6477, D(G(z)): 0.2420 Epoch: [16/20], Batch Num: [155/600] Discriminator Loss: 0.9480, Generator Loss: 1.4312 D(x): 0.7136, D(G(z)): 0.3338 Epoch: [16/20], Batch Num: [156/600] Discriminator Loss: 1.0516, Generator Loss: 1.5246 D(x): 0.7757, D(G(z)): 0.3838 Epoch: [16/20], Batch Num: [157/600] Discriminator Loss: 0.8448, Generator Loss: 1.4875 D(x): 0.7534, D(G(z)): 0.3150 Epoch: [16/20], Batch Num: [158/600] Discriminator Loss: 0.8926, Generator Loss: 1.6704 D(x): 0.7115, D(G(z)): 0.2889 Epoch: [16/20], Batch Num: [159/600] Discriminator Loss: 0.9489, Generator Loss: 1.7845 D(x): 0.6349, D(G(z)): 0.2468 Epoch: [16/20], Batch Num: [160/600] Discriminator Loss: 0.9110, Generator Loss: 1.6578 D(x): 0.7003, D(G(z)): 0.2995 Epoch: [16/20], Batch Num: [161/600] Discriminator Loss: 0.8926, Generator Loss: 1.6757 D(x): 0.6799, D(G(z)): 0.2541 Epoch: [16/20], Batch Num: [162/600] Discriminator Loss: 0.8562, Generator Loss: 1.7542 D(x): 0.6905, D(G(z)): 0.2877 Epoch: [16/20], Batch Num: [163/600] Discriminator Loss: 0.8845, Generator Loss: 1.5758 D(x): 0.6850, D(G(z)): 0.2752 Epoch: [16/20], Batch Num: [164/600] Discriminator Loss: 0.8562, Generator Loss: 1.3434 D(x): 0.7137, D(G(z)): 0.3078 Epoch: [16/20], Batch Num: [165/600] Discriminator Loss: 0.8990, Generator Loss: 1.5473 D(x): 0.7605, D(G(z)): 0.3515 Epoch: [16/20], Batch Num: [166/600] Discriminator Loss: 0.7737, Generator Loss: 1.6068 D(x): 0.7557, D(G(z)): 0.2997 Epoch: [16/20], Batch Num: [167/600] Discriminator Loss: 0.8875, Generator Loss: 1.7130 D(x): 0.6841, D(G(z)): 0.2792 Epoch: [16/20], Batch Num: [168/600] Discriminator Loss: 0.8310, Generator Loss: 1.6657 D(x): 0.7381, D(G(z)): 0.3004 Epoch: [16/20], Batch Num: [169/600] Discriminator Loss: 0.6881, Generator Loss: 1.5065 D(x): 0.7439, D(G(z)): 0.2461 Epoch: [16/20], Batch Num: [170/600] Discriminator Loss: 0.8074, Generator Loss: 1.5453 D(x): 0.7290, D(G(z)): 0.2637 Epoch: [16/20], Batch Num: [171/600] Discriminator Loss: 0.8948, Generator Loss: 1.5049 D(x): 0.6723, D(G(z)): 0.2777 Epoch: [16/20], Batch Num: [172/600] Discriminator Loss: 0.7850, Generator Loss: 1.6431 D(x): 0.7418, D(G(z)): 0.2842 Epoch: [16/20], Batch Num: [173/600] Discriminator Loss: 0.8449, Generator Loss: 1.4640 D(x): 0.7568, D(G(z)): 0.3292 Epoch: [16/20], Batch Num: [174/600] Discriminator Loss: 0.7704, Generator Loss: 1.4861 D(x): 0.7728, D(G(z)): 0.3064 Epoch: [16/20], Batch Num: [175/600] Discriminator Loss: 0.8318, Generator Loss: 1.4970 D(x): 0.7193, D(G(z)): 0.2989 Epoch: [16/20], Batch Num: [176/600] Discriminator Loss: 0.7728, Generator Loss: 1.8052 D(x): 0.7261, D(G(z)): 0.2596 Epoch: [16/20], Batch Num: [177/600] Discriminator Loss: 0.7863, Generator Loss: 1.6466 D(x): 0.7278, D(G(z)): 0.2653 Epoch: [16/20], Batch Num: [178/600] Discriminator Loss: 0.9013, Generator Loss: 1.7412 D(x): 0.6864, D(G(z)): 0.2725 Epoch: [16/20], Batch Num: [179/600] Discriminator Loss: 0.9020, Generator Loss: 1.7330 D(x): 0.6761, D(G(z)): 0.2451 Epoch: [16/20], Batch Num: [180/600] Discriminator Loss: 0.7788, Generator Loss: 1.5159 D(x): 0.7343, D(G(z)): 0.2650 Epoch: [16/20], Batch Num: [181/600] Discriminator Loss: 0.7298, Generator Loss: 1.5402 D(x): 0.7713, D(G(z)): 0.2800 Epoch: [16/20], Batch Num: [182/600] Discriminator Loss: 0.7180, Generator Loss: 1.5123 D(x): 0.7552, D(G(z)): 0.2613 Epoch: [16/20], Batch Num: [183/600] Discriminator Loss: 0.7793, Generator Loss: 1.4559 D(x): 0.7704, D(G(z)): 0.3078 Epoch: [16/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [16/20], Batch Num: [200/600] Discriminator Loss: 0.6083, Generator Loss: 1.8126 D(x): 0.8103, D(G(z)): 0.2367 Epoch: [16/20], Batch Num: [201/600] Discriminator Loss: 0.7572, Generator Loss: 1.8006 D(x): 0.7577, D(G(z)): 0.2422 Epoch: [16/20], Batch Num: [202/600] Discriminator Loss: 0.7366, Generator Loss: 2.0569 D(x): 0.7855, D(G(z)): 0.2666 Epoch: [16/20], Batch Num: [203/600] Discriminator Loss: 0.8372, Generator Loss: 1.9481 D(x): 0.7282, D(G(z)): 0.2275 Epoch: [16/20], Batch Num: [204/600] Discriminator Loss: 0.8354, Generator Loss: 1.8869 D(x): 0.7250, D(G(z)): 0.2309 Epoch: [16/20], Batch Num: [205/600] Discriminator Loss: 0.7471, Generator Loss: 1.8677 D(x): 0.7495, D(G(z)): 0.2447 Epoch: [16/20], Batch Num: [206/600] Discriminator Loss: 0.6798, Generator Loss: 1.7732 D(x): 0.7566, D(G(z)): 0.2183 Epoch: [16/20], Batch Num: [207/600] Discriminator Loss: 0.7275, Generator Loss: 1.7398 D(x): 0.7837, D(G(z)): 0.2685 Epoch: [16/20], Batch Num: [208/600] Discriminator Loss: 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Discriminator Loss: 0.7822, Generator Loss: 2.0300 D(x): 0.7025, D(G(z)): 0.1812 Epoch: [16/20], Batch Num: [226/600] Discriminator Loss: 1.1468, Generator Loss: 1.3921 D(x): 0.6203, D(G(z)): 0.2601 Epoch: [16/20], Batch Num: [227/600] Discriminator Loss: 0.9122, Generator Loss: 1.0676 D(x): 0.7498, D(G(z)): 0.2932 Epoch: [16/20], Batch Num: [228/600] Discriminator Loss: 0.9744, Generator Loss: 1.3250 D(x): 0.8131, D(G(z)): 0.3949 Epoch: [16/20], Batch Num: [229/600] Discriminator Loss: 1.0237, Generator Loss: 1.6113 D(x): 0.7714, D(G(z)): 0.3680 Epoch: [16/20], Batch Num: [230/600] Discriminator Loss: 1.0388, Generator Loss: 1.7946 D(x): 0.6729, D(G(z)): 0.2913 Epoch: [16/20], Batch Num: [231/600] Discriminator Loss: 1.0956, Generator Loss: 2.1405 D(x): 0.6225, D(G(z)): 0.2368 Epoch: [16/20], Batch Num: [232/600] Discriminator Loss: 1.3029, Generator Loss: 1.5769 D(x): 0.5451, D(G(z)): 0.2544 Epoch: [16/20], Batch Num: [233/600] Discriminator Loss: 1.1097, Generator Loss: 1.4047 D(x): 0.6729, D(G(z)): 0.3272 Epoch: [16/20], Batch Num: [234/600] Discriminator Loss: 1.1869, Generator Loss: 1.4438 D(x): 0.6811, D(G(z)): 0.3696 Epoch: [16/20], Batch Num: [235/600] Discriminator Loss: 0.9572, Generator Loss: 1.2915 D(x): 0.7180, D(G(z)): 0.3254 Epoch: [16/20], Batch Num: [236/600] Discriminator Loss: 1.1202, Generator Loss: 1.1828 D(x): 0.6495, D(G(z)): 0.3386 Epoch: [16/20], Batch Num: [237/600] Discriminator Loss: 1.1238, Generator Loss: 1.4356 D(x): 0.7102, D(G(z)): 0.3819 Epoch: [16/20], Batch Num: [238/600] Discriminator Loss: 1.0054, Generator Loss: 1.6572 D(x): 0.6934, D(G(z)): 0.3188 Epoch: [16/20], Batch Num: [239/600] Discriminator Loss: 1.3100, Generator Loss: 1.8190 D(x): 0.5845, D(G(z)): 0.3265 Epoch: [16/20], Batch Num: [240/600] Discriminator Loss: 1.1115, Generator Loss: 1.5334 D(x): 0.5973, D(G(z)): 0.2788 Epoch: [16/20], Batch Num: [241/600] Discriminator Loss: 1.1498, Generator Loss: 1.3343 D(x): 0.6372, D(G(z)): 0.3161 Epoch: [16/20], Batch Num: 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1.4041 D(x): 0.6575, D(G(z)): 0.3520 Epoch: [16/20], Batch Num: [251/600] Discriminator Loss: 0.9444, Generator Loss: 1.2944 D(x): 0.7080, D(G(z)): 0.3325 Epoch: [16/20], Batch Num: [252/600] Discriminator Loss: 0.9621, Generator Loss: 1.3831 D(x): 0.7419, D(G(z)): 0.3812 Epoch: [16/20], Batch Num: [253/600] Discriminator Loss: 1.0161, Generator Loss: 1.3837 D(x): 0.7042, D(G(z)): 0.3685 Epoch: [16/20], Batch Num: [254/600] Discriminator Loss: 0.8788, Generator Loss: 1.4637 D(x): 0.7154, D(G(z)): 0.3135 Epoch: [16/20], Batch Num: [255/600] Discriminator Loss: 1.0094, Generator Loss: 1.6062 D(x): 0.6163, D(G(z)): 0.2877 Epoch: [16/20], Batch Num: [256/600] Discriminator Loss: 0.9161, Generator Loss: 1.4488 D(x): 0.6757, D(G(z)): 0.3125 Epoch: [16/20], Batch Num: [257/600] Discriminator Loss: 0.9971, Generator Loss: 1.5764 D(x): 0.6576, D(G(z)): 0.3273 Epoch: [16/20], Batch Num: [258/600] Discriminator Loss: 0.8110, Generator Loss: 1.6001 D(x): 0.6990, D(G(z)): 0.2855 Epoch: [16/20], Batch Num: [259/600] Discriminator Loss: 0.9804, Generator Loss: 1.4491 D(x): 0.6307, D(G(z)): 0.2874 Epoch: [16/20], Batch Num: [260/600] Discriminator Loss: 0.8188, Generator Loss: 1.1825 D(x): 0.6879, D(G(z)): 0.2752 Epoch: [16/20], Batch Num: [261/600] Discriminator Loss: 0.8957, Generator Loss: 1.2464 D(x): 0.7147, D(G(z)): 0.3186 Epoch: [16/20], Batch Num: [262/600] Discriminator Loss: 0.8979, Generator Loss: 1.3810 D(x): 0.7440, D(G(z)): 0.3702 Epoch: [16/20], Batch Num: [263/600] Discriminator Loss: 0.9654, Generator Loss: 1.4287 D(x): 0.7565, D(G(z)): 0.4003 Epoch: [16/20], Batch Num: [264/600] Discriminator Loss: 0.9050, Generator Loss: 1.6019 D(x): 0.7257, D(G(z)): 0.3405 Epoch: [16/20], Batch Num: [265/600] Discriminator Loss: 0.9291, Generator Loss: 1.8208 D(x): 0.6813, D(G(z)): 0.3068 Epoch: [16/20], Batch Num: [266/600] Discriminator Loss: 0.8842, Generator Loss: 1.7478 D(x): 0.6315, D(G(z)): 0.2335 Epoch: [16/20], Batch Num: [267/600] Discriminator Loss: 0.8600, Generator Loss: 1.7359 D(x): 0.6693, D(G(z)): 0.2485 Epoch: [16/20], Batch Num: [268/600] Discriminator Loss: 0.8279, Generator Loss: 1.6899 D(x): 0.7192, D(G(z)): 0.2912 Epoch: [16/20], Batch Num: [269/600] Discriminator Loss: 0.8331, Generator Loss: 1.5702 D(x): 0.7294, D(G(z)): 0.3003 Epoch: [16/20], Batch Num: [270/600] Discriminator Loss: 0.7940, Generator Loss: 1.4695 D(x): 0.7168, D(G(z)): 0.2618 Epoch: [16/20], Batch Num: [271/600] Discriminator Loss: 0.8208, Generator Loss: 1.5858 D(x): 0.7374, D(G(z)): 0.3019 Epoch: [16/20], Batch Num: [272/600] Discriminator Loss: 0.8945, Generator Loss: 1.4711 D(x): 0.7250, D(G(z)): 0.3317 Epoch: [16/20], Batch Num: [273/600] Discriminator Loss: 0.9762, Generator Loss: 1.5585 D(x): 0.7215, D(G(z)): 0.3634 Epoch: [16/20], Batch Num: [274/600] Discriminator Loss: 0.9781, Generator Loss: 1.7139 D(x): 0.6769, D(G(z)): 0.3284 Epoch: [16/20], Batch Num: [275/600] Discriminator Loss: 1.0402, Generator Loss: 1.5901 D(x): 0.6151, D(G(z)): 0.2929 Epoch: [16/20], Batch Num: [276/600] Discriminator Loss: 0.9863, Generator Loss: 1.4423 D(x): 0.6589, D(G(z)): 0.2951 Epoch: [16/20], Batch Num: [277/600] Discriminator Loss: 0.9194, Generator Loss: 1.4117 D(x): 0.6912, D(G(z)): 0.3066 Epoch: [16/20], Batch Num: [278/600] Discriminator Loss: 0.9187, Generator Loss: 1.4100 D(x): 0.6731, D(G(z)): 0.2933 Epoch: [16/20], Batch Num: [279/600] Discriminator Loss: 0.9345, Generator Loss: 1.4968 D(x): 0.7296, D(G(z)): 0.3612 Epoch: [16/20], Batch Num: [280/600] Discriminator Loss: 0.9151, Generator Loss: 1.4724 D(x): 0.7549, D(G(z)): 0.3645 Epoch: [16/20], Batch Num: [281/600] Discriminator Loss: 1.0622, Generator Loss: 1.5586 D(x): 0.6253, D(G(z)): 0.3029 Epoch: [16/20], Batch Num: [282/600] Discriminator Loss: 0.9711, Generator Loss: 1.7076 D(x): 0.6779, D(G(z)): 0.3170 Epoch: [16/20], Batch Num: [283/600] Discriminator Loss: 1.1511, Generator Loss: 1.5223 D(x): 0.6360, D(G(z)): 0.3440 Epoch: [16/20], Batch Num: [284/600] Discriminator Loss: 1.0310, Generator Loss: 1.4986 D(x): 0.6893, D(G(z)): 0.3333 Epoch: [16/20], Batch Num: [285/600] Discriminator Loss: 1.0131, Generator Loss: 1.5831 D(x): 0.6570, D(G(z)): 0.3076 Epoch: [16/20], Batch Num: [286/600] Discriminator Loss: 1.1123, Generator Loss: 1.3736 D(x): 0.6576, D(G(z)): 0.3498 Epoch: [16/20], Batch Num: [287/600] Discriminator Loss: 1.0388, Generator Loss: 1.3299 D(x): 0.7212, D(G(z)): 0.3679 Epoch: [16/20], Batch Num: [288/600] Discriminator Loss: 1.0481, Generator Loss: 1.5864 D(x): 0.6968, D(G(z)): 0.3420 Epoch: [16/20], Batch Num: [289/600] Discriminator Loss: 1.0618, Generator Loss: 1.6371 D(x): 0.6668, D(G(z)): 0.3552 Epoch: [16/20], Batch Num: [290/600] Discriminator Loss: 1.1115, Generator Loss: 1.4981 D(x): 0.6010, D(G(z)): 0.2765 Epoch: [16/20], Batch Num: [291/600] Discriminator Loss: 0.9648, Generator Loss: 1.4405 D(x): 0.6542, D(G(z)): 0.2796 Epoch: [16/20], Batch Num: [292/600] Discriminator Loss: 1.0394, Generator Loss: 1.3412 D(x): 0.6857, D(G(z)): 0.3239 Epoch: [16/20], Batch Num: [293/600] Discriminator Loss: 1.1115, Generator Loss: 1.2875 D(x): 0.7005, D(G(z)): 0.3822 Epoch: [16/20], Batch Num: [294/600] Discriminator Loss: 0.9572, Generator Loss: 1.2331 D(x): 0.7014, D(G(z)): 0.3309 Epoch: [16/20], Batch Num: [295/600] Discriminator Loss: 0.9109, Generator Loss: 1.5765 D(x): 0.7091, D(G(z)): 0.3219 Epoch: [16/20], Batch Num: [296/600] Discriminator Loss: 1.0054, Generator Loss: 1.5116 D(x): 0.6402, D(G(z)): 0.2834 Epoch: [16/20], Batch Num: [297/600] Discriminator Loss: 0.9886, Generator Loss: 1.5795 D(x): 0.6667, D(G(z)): 0.3259 Epoch: [16/20], Batch Num: [298/600] Discriminator Loss: 0.9559, Generator Loss: 1.3891 D(x): 0.6349, D(G(z)): 0.2888 Epoch: [16/20], Batch Num: [299/600] Discriminator Loss: 0.9801, Generator Loss: 1.4472 D(x): 0.6966, D(G(z)): 0.3398 Epoch: 16, Batch Num: [300/600]
Epoch: [16/20], Batch Num: [300/600] Discriminator Loss: 0.9694, Generator Loss: 1.3654 D(x): 0.6914, D(G(z)): 0.3452 Epoch: [16/20], Batch Num: [301/600] Discriminator Loss: 0.9932, Generator Loss: 1.3653 D(x): 0.7221, D(G(z)): 0.3778 Epoch: [16/20], Batch Num: [302/600] Discriminator Loss: 0.8716, Generator Loss: 1.5891 D(x): 0.7314, D(G(z)): 0.3187 Epoch: [16/20], Batch Num: [303/600] Discriminator Loss: 0.7825, Generator Loss: 1.5344 D(x): 0.7306, D(G(z)): 0.2641 Epoch: [16/20], Batch Num: [304/600] Discriminator Loss: 0.9524, Generator Loss: 1.7560 D(x): 0.6641, D(G(z)): 0.2801 Epoch: [16/20], Batch Num: [305/600] Discriminator Loss: 0.8944, Generator Loss: 1.5207 D(x): 0.6588, D(G(z)): 0.2513 Epoch: [16/20], Batch Num: [306/600] Discriminator Loss: 0.9347, Generator Loss: 1.5207 D(x): 0.7103, D(G(z)): 0.3120 Epoch: [16/20], Batch Num: [307/600] Discriminator Loss: 0.9236, Generator Loss: 1.4030 D(x): 0.6943, D(G(z)): 0.2910 Epoch: [16/20], Batch Num: [308/600] Discriminator Loss: 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0.3113 Epoch: [16/20], Batch Num: [317/600] Discriminator Loss: 0.9775, Generator Loss: 1.7217 D(x): 0.6961, D(G(z)): 0.3166 Epoch: [16/20], Batch Num: [318/600] Discriminator Loss: 0.9274, Generator Loss: 1.7191 D(x): 0.6815, D(G(z)): 0.2881 Epoch: [16/20], Batch Num: [319/600] Discriminator Loss: 0.7758, Generator Loss: 1.7355 D(x): 0.7180, D(G(z)): 0.2480 Epoch: [16/20], Batch Num: [320/600] Discriminator Loss: 0.8942, Generator Loss: 1.6388 D(x): 0.7029, D(G(z)): 0.2896 Epoch: [16/20], Batch Num: [321/600] Discriminator Loss: 0.8785, Generator Loss: 1.6380 D(x): 0.7343, D(G(z)): 0.3205 Epoch: [16/20], Batch Num: [322/600] Discriminator Loss: 0.8296, Generator Loss: 1.5176 D(x): 0.7282, D(G(z)): 0.2957 Epoch: [16/20], Batch Num: [323/600] Discriminator Loss: 0.9074, Generator Loss: 1.5404 D(x): 0.7188, D(G(z)): 0.3195 Epoch: [16/20], Batch Num: [324/600] Discriminator Loss: 0.9366, Generator Loss: 1.5314 D(x): 0.7257, D(G(z)): 0.3134 Epoch: [16/20], Batch Num: [325/600] Discriminator Loss: 0.8105, Generator Loss: 1.7005 D(x): 0.7536, D(G(z)): 0.3081 Epoch: [16/20], Batch Num: [326/600] Discriminator Loss: 0.9580, Generator Loss: 1.6456 D(x): 0.7079, D(G(z)): 0.3204 Epoch: [16/20], Batch Num: [327/600] Discriminator Loss: 0.8659, Generator Loss: 1.7774 D(x): 0.7173, D(G(z)): 0.2853 Epoch: [16/20], Batch Num: [328/600] Discriminator Loss: 0.9871, Generator Loss: 1.5752 D(x): 0.6385, D(G(z)): 0.2531 Epoch: [16/20], Batch Num: [329/600] Discriminator Loss: 0.9584, Generator Loss: 1.6901 D(x): 0.6773, D(G(z)): 0.2797 Epoch: [16/20], Batch Num: [330/600] Discriminator Loss: 0.8149, Generator Loss: 1.5003 D(x): 0.7356, D(G(z)): 0.2904 Epoch: [16/20], Batch Num: [331/600] Discriminator Loss: 0.9247, Generator Loss: 1.4075 D(x): 0.7556, D(G(z)): 0.3448 Epoch: [16/20], Batch Num: [332/600] Discriminator Loss: 0.9338, Generator Loss: 1.6412 D(x): 0.7644, D(G(z)): 0.3613 Epoch: [16/20], Batch Num: [333/600] Discriminator Loss: 1.0213, Generator Loss: 1.5567 D(x): 0.6691, D(G(z)): 0.3252 Epoch: [16/20], Batch Num: [334/600] Discriminator Loss: 0.9962, Generator Loss: 1.7997 D(x): 0.6464, D(G(z)): 0.2786 Epoch: [16/20], Batch Num: [335/600] Discriminator Loss: 0.9895, Generator Loss: 1.5129 D(x): 0.6310, D(G(z)): 0.2671 Epoch: [16/20], Batch Num: [336/600] Discriminator Loss: 1.0586, Generator Loss: 1.3984 D(x): 0.6512, D(G(z)): 0.3331 Epoch: [16/20], Batch Num: [337/600] Discriminator Loss: 0.9590, Generator Loss: 1.3655 D(x): 0.7070, D(G(z)): 0.3345 Epoch: [16/20], Batch Num: [338/600] Discriminator Loss: 0.9967, Generator Loss: 1.4699 D(x): 0.7601, D(G(z)): 0.3765 Epoch: [16/20], Batch Num: [339/600] Discriminator Loss: 0.9022, Generator Loss: 1.4252 D(x): 0.6979, D(G(z)): 0.3112 Epoch: [16/20], Batch Num: [340/600] Discriminator Loss: 0.9223, Generator Loss: 1.5293 D(x): 0.7128, D(G(z)): 0.3042 Epoch: [16/20], Batch Num: [341/600] Discriminator Loss: 0.9189, Generator Loss: 1.4947 D(x): 0.7193, D(G(z)): 0.3278 Epoch: [16/20], Batch Num: 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1.5276 D(x): 0.7426, D(G(z)): 0.2833 Epoch: [16/20], Batch Num: [351/600] Discriminator Loss: 0.9821, Generator Loss: 1.5191 D(x): 0.7231, D(G(z)): 0.3488 Epoch: [16/20], Batch Num: [352/600] Discriminator Loss: 1.0280, Generator Loss: 1.4659 D(x): 0.6940, D(G(z)): 0.3424 Epoch: [16/20], Batch Num: [353/600] Discriminator Loss: 0.9113, Generator Loss: 1.5076 D(x): 0.6702, D(G(z)): 0.2666 Epoch: [16/20], Batch Num: [354/600] Discriminator Loss: 0.9206, Generator Loss: 1.5720 D(x): 0.6870, D(G(z)): 0.2633 Epoch: [16/20], Batch Num: [355/600] Discriminator Loss: 0.7919, Generator Loss: 1.5609 D(x): 0.7463, D(G(z)): 0.2807 Epoch: [16/20], Batch Num: [356/600] Discriminator Loss: 0.9023, Generator Loss: 1.5320 D(x): 0.7389, D(G(z)): 0.3367 Epoch: [16/20], Batch Num: [357/600] Discriminator Loss: 0.9826, Generator Loss: 1.5419 D(x): 0.6623, D(G(z)): 0.2873 Epoch: [16/20], Batch Num: [358/600] Discriminator Loss: 0.8910, Generator Loss: 1.6062 D(x): 0.7380, D(G(z)): 0.3055 Epoch: [16/20], Batch Num: [359/600] Discriminator Loss: 0.9754, Generator Loss: 1.7304 D(x): 0.7012, D(G(z)): 0.3132 Epoch: [16/20], Batch Num: [360/600] Discriminator Loss: 0.9250, Generator Loss: 1.6862 D(x): 0.7009, D(G(z)): 0.3068 Epoch: [16/20], Batch Num: [361/600] Discriminator Loss: 0.9069, Generator Loss: 1.8151 D(x): 0.6610, D(G(z)): 0.2484 Epoch: [16/20], Batch Num: [362/600] Discriminator Loss: 0.9418, Generator Loss: 1.6053 D(x): 0.6885, D(G(z)): 0.2930 Epoch: [16/20], Batch Num: [363/600] Discriminator Loss: 0.9132, Generator Loss: 1.6481 D(x): 0.6893, D(G(z)): 0.2971 Epoch: [16/20], Batch Num: [364/600] Discriminator Loss: 0.8039, Generator Loss: 1.6069 D(x): 0.7337, D(G(z)): 0.2772 Epoch: [16/20], Batch Num: [365/600] Discriminator Loss: 0.8354, Generator Loss: 1.2674 D(x): 0.7387, D(G(z)): 0.3104 Epoch: [16/20], Batch Num: [366/600] Discriminator Loss: 1.0425, Generator Loss: 1.5525 D(x): 0.6657, D(G(z)): 0.3356 Epoch: [16/20], Batch Num: [367/600] Discriminator Loss: 0.9751, Generator Loss: 1.5016 D(x): 0.6954, D(G(z)): 0.3390 Epoch: [16/20], Batch Num: [368/600] Discriminator Loss: 1.0263, Generator Loss: 1.4902 D(x): 0.6911, D(G(z)): 0.3518 Epoch: [16/20], Batch Num: [369/600] Discriminator Loss: 1.0804, Generator Loss: 1.6069 D(x): 0.6543, D(G(z)): 0.3106 Epoch: [16/20], Batch Num: [370/600] Discriminator Loss: 0.8588, Generator Loss: 1.4680 D(x): 0.6576, D(G(z)): 0.2452 Epoch: [16/20], Batch Num: [371/600] Discriminator Loss: 1.0881, Generator Loss: 1.3740 D(x): 0.6064, D(G(z)): 0.2903 Epoch: [16/20], Batch Num: [372/600] Discriminator Loss: 1.0822, Generator Loss: 1.1117 D(x): 0.6463, D(G(z)): 0.3374 Epoch: [16/20], Batch Num: [373/600] Discriminator Loss: 1.0062, Generator Loss: 1.1499 D(x): 0.7295, D(G(z)): 0.3631 Epoch: [16/20], Batch Num: [374/600] Discriminator Loss: 1.1294, Generator Loss: 1.1778 D(x): 0.7056, D(G(z)): 0.4119 Epoch: [16/20], Batch Num: [375/600] Discriminator Loss: 0.9181, Generator Loss: 1.3495 D(x): 0.7506, D(G(z)): 0.3303 Epoch: [16/20], Batch Num: [376/600] Discriminator Loss: 1.0675, Generator Loss: 1.5348 D(x): 0.6545, D(G(z)): 0.3505 Epoch: [16/20], Batch Num: [377/600] Discriminator Loss: 1.0149, Generator Loss: 1.5204 D(x): 0.6272, D(G(z)): 0.2728 Epoch: [16/20], Batch Num: [378/600] Discriminator Loss: 1.0899, Generator Loss: 1.5202 D(x): 0.6023, D(G(z)): 0.2910 Epoch: [16/20], Batch Num: [379/600] Discriminator Loss: 1.1489, Generator Loss: 1.2128 D(x): 0.5615, D(G(z)): 0.2831 Epoch: [16/20], Batch Num: [380/600] Discriminator Loss: 0.9747, Generator Loss: 1.0970 D(x): 0.7052, D(G(z)): 0.3406 Epoch: [16/20], Batch Num: [381/600] Discriminator Loss: 1.0193, Generator Loss: 1.3237 D(x): 0.7317, D(G(z)): 0.4041 Epoch: [16/20], Batch Num: [382/600] Discriminator Loss: 1.1101, Generator Loss: 1.3477 D(x): 0.7282, D(G(z)): 0.4391 Epoch: [16/20], Batch Num: [383/600] Discriminator Loss: 0.8821, Generator Loss: 1.7810 D(x): 0.6890, D(G(z)): 0.3076 Epoch: [16/20], Batch Num: [384/600] Discriminator Loss: 0.8895, Generator Loss: 1.6834 D(x): 0.6299, D(G(z)): 0.2365 Epoch: [16/20], Batch Num: [385/600] Discriminator Loss: 0.9280, Generator Loss: 1.4902 D(x): 0.6007, D(G(z)): 0.2309 Epoch: [16/20], Batch Num: [386/600] Discriminator Loss: 0.8663, Generator Loss: 1.5449 D(x): 0.6858, D(G(z)): 0.2642 Epoch: [16/20], Batch Num: [387/600] Discriminator Loss: 0.9467, Generator Loss: 1.3653 D(x): 0.6881, D(G(z)): 0.3170 Epoch: [16/20], Batch Num: [388/600] Discriminator Loss: 0.9011, Generator Loss: 1.4253 D(x): 0.7373, D(G(z)): 0.3415 Epoch: [16/20], Batch Num: [389/600] Discriminator Loss: 0.8544, Generator Loss: 1.5788 D(x): 0.7387, D(G(z)): 0.3237 Epoch: [16/20], Batch Num: [390/600] Discriminator Loss: 0.8225, Generator Loss: 1.5577 D(x): 0.7271, D(G(z)): 0.2848 Epoch: [16/20], Batch Num: [391/600] Discriminator Loss: 0.9323, Generator Loss: 1.9349 D(x): 0.6562, D(G(z)): 0.2854 Epoch: [16/20], Batch Num: [392/600] Discriminator Loss: 0.8403, Generator Loss: 1.6618 D(x): 0.6958, D(G(z)): 0.2645 Epoch: [16/20], Batch Num: [393/600] Discriminator Loss: 0.6615, Generator Loss: 1.7195 D(x): 0.7524, D(G(z)): 0.2454 Epoch: [16/20], Batch Num: [394/600] Discriminator Loss: 0.7559, Generator Loss: 1.7191 D(x): 0.7458, D(G(z)): 0.2737 Epoch: [16/20], Batch Num: [395/600] Discriminator Loss: 0.7011, Generator Loss: 1.8767 D(x): 0.7998, D(G(z)): 0.3001 Epoch: [16/20], Batch Num: [396/600] Discriminator Loss: 0.6735, Generator Loss: 2.0024 D(x): 0.7687, D(G(z)): 0.2390 Epoch: [16/20], Batch Num: [397/600] Discriminator Loss: 0.7484, Generator Loss: 1.9877 D(x): 0.7090, D(G(z)): 0.2085 Epoch: [16/20], Batch Num: [398/600] Discriminator Loss: 0.8658, Generator Loss: 1.9201 D(x): 0.7230, D(G(z)): 0.2836 Epoch: [16/20], Batch Num: [399/600] Discriminator Loss: 0.9036, Generator Loss: 2.0249 D(x): 0.7541, D(G(z)): 0.2867 Epoch: 16, Batch Num: [400/600]
Epoch: [16/20], Batch Num: [400/600] Discriminator Loss: 0.8622, Generator Loss: 1.9714 D(x): 0.7326, D(G(z)): 0.2837 Epoch: [16/20], Batch Num: [401/600] Discriminator Loss: 0.8968, Generator Loss: 1.9050 D(x): 0.6725, D(G(z)): 0.2445 Epoch: [16/20], Batch Num: [402/600] Discriminator Loss: 0.9468, Generator Loss: 1.7280 D(x): 0.7336, D(G(z)): 0.2976 Epoch: [16/20], Batch Num: [403/600] Discriminator Loss: 0.7590, Generator Loss: 1.7747 D(x): 0.7663, D(G(z)): 0.2737 Epoch: [16/20], Batch Num: [404/600] Discriminator Loss: 0.8033, Generator Loss: 1.8617 D(x): 0.8051, D(G(z)): 0.3152 Epoch: [16/20], Batch Num: [405/600] Discriminator Loss: 0.9210, Generator Loss: 1.8886 D(x): 0.6899, D(G(z)): 0.2769 Epoch: [16/20], Batch Num: [406/600] Discriminator Loss: 0.8665, Generator Loss: 2.0087 D(x): 0.7198, D(G(z)): 0.2590 Epoch: [16/20], Batch Num: [407/600] Discriminator Loss: 0.8877, Generator Loss: 1.7112 D(x): 0.6885, D(G(z)): 0.2604 Epoch: [16/20], Batch Num: [408/600] Discriminator Loss: 0.9033, Generator Loss: 1.8186 D(x): 0.7275, D(G(z)): 0.3084 Epoch: [16/20], Batch Num: [409/600] Discriminator Loss: 1.1499, Generator Loss: 1.7110 D(x): 0.6914, D(G(z)): 0.3697 Epoch: [16/20], Batch Num: [410/600] Discriminator Loss: 1.0892, Generator Loss: 1.5981 D(x): 0.6674, D(G(z)): 0.3148 Epoch: [16/20], Batch Num: [411/600] Discriminator Loss: 1.1510, Generator Loss: 1.3633 D(x): 0.6260, D(G(z)): 0.3031 Epoch: [16/20], Batch Num: [412/600] Discriminator Loss: 1.0523, Generator Loss: 1.2803 D(x): 0.7197, D(G(z)): 0.3696 Epoch: [16/20], Batch Num: [413/600] Discriminator Loss: 1.1733, Generator Loss: 1.2792 D(x): 0.6698, D(G(z)): 0.3701 Epoch: [16/20], Batch Num: [414/600] Discriminator Loss: 1.1939, Generator Loss: 1.3834 D(x): 0.6525, D(G(z)): 0.3837 Epoch: [16/20], Batch Num: [415/600] Discriminator Loss: 1.0104, Generator Loss: 1.3057 D(x): 0.6739, D(G(z)): 0.3245 Epoch: [16/20], Batch Num: [416/600] Discriminator Loss: 0.9983, Generator Loss: 1.2642 D(x): 0.6788, D(G(z)): 0.3421 Epoch: [16/20], Batch Num: [417/600] Discriminator Loss: 1.2549, Generator Loss: 1.2734 D(x): 0.5984, D(G(z)): 0.3729 Epoch: [16/20], Batch Num: [418/600] Discriminator Loss: 1.1786, Generator Loss: 1.1837 D(x): 0.6577, D(G(z)): 0.4045 Epoch: [16/20], Batch Num: [419/600] Discriminator Loss: 1.1674, Generator Loss: 1.1962 D(x): 0.6416, D(G(z)): 0.3790 Epoch: [16/20], Batch Num: [420/600] Discriminator Loss: 1.2405, Generator Loss: 1.3297 D(x): 0.6321, D(G(z)): 0.4236 Epoch: [16/20], Batch Num: [421/600] Discriminator Loss: 1.0942, Generator Loss: 1.2003 D(x): 0.6164, D(G(z)): 0.3389 Epoch: [16/20], Batch Num: [422/600] Discriminator Loss: 0.9624, Generator Loss: 1.2976 D(x): 0.6701, D(G(z)): 0.3459 Epoch: [16/20], Batch Num: [423/600] Discriminator Loss: 0.9801, Generator Loss: 1.2518 D(x): 0.6695, D(G(z)): 0.3456 Epoch: [16/20], Batch Num: [424/600] Discriminator Loss: 0.9250, Generator Loss: 1.2063 D(x): 0.6846, D(G(z)): 0.3422 Epoch: [16/20], Batch Num: [425/600] Discriminator Loss: 1.0568, Generator Loss: 1.1728 D(x): 0.6138, D(G(z)): 0.3291 Epoch: [16/20], Batch Num: [426/600] Discriminator Loss: 0.9747, Generator Loss: 1.1599 D(x): 0.6662, D(G(z)): 0.3429 Epoch: [16/20], Batch Num: [427/600] Discriminator Loss: 0.8359, Generator Loss: 1.1875 D(x): 0.7439, D(G(z)): 0.3447 Epoch: [16/20], Batch Num: [428/600] Discriminator Loss: 1.0605, Generator Loss: 1.2193 D(x): 0.6628, D(G(z)): 0.3864 Epoch: [16/20], Batch Num: [429/600] Discriminator Loss: 0.9033, Generator Loss: 1.2672 D(x): 0.7124, D(G(z)): 0.3417 Epoch: [16/20], Batch Num: [430/600] Discriminator Loss: 0.8428, Generator Loss: 1.3631 D(x): 0.7138, D(G(z)): 0.3287 Epoch: [16/20], Batch Num: [431/600] Discriminator Loss: 0.8643, Generator Loss: 1.4583 D(x): 0.6757, D(G(z)): 0.2857 Epoch: [16/20], Batch Num: [432/600] Discriminator Loss: 0.8584, Generator Loss: 1.3532 D(x): 0.6615, D(G(z)): 0.2685 Epoch: [16/20], Batch Num: [433/600] Discriminator Loss: 0.8784, Generator Loss: 1.1968 D(x): 0.6942, D(G(z)): 0.3050 Epoch: [16/20], Batch Num: [434/600] Discriminator Loss: 0.8769, Generator Loss: 1.3052 D(x): 0.7420, D(G(z)): 0.3414 Epoch: [16/20], Batch Num: [435/600] Discriminator Loss: 0.9270, Generator Loss: 1.3171 D(x): 0.7356, D(G(z)): 0.3648 Epoch: [16/20], Batch Num: [436/600] Discriminator Loss: 0.7545, Generator Loss: 1.4861 D(x): 0.7813, D(G(z)): 0.3095 Epoch: [16/20], Batch Num: [437/600] Discriminator Loss: 0.8024, Generator Loss: 1.6076 D(x): 0.7397, D(G(z)): 0.2884 Epoch: [16/20], Batch Num: [438/600] Discriminator Loss: 0.7108, Generator Loss: 1.7515 D(x): 0.7098, D(G(z)): 0.2205 Epoch: [16/20], Batch Num: [439/600] Discriminator Loss: 0.7351, Generator Loss: 1.7902 D(x): 0.7113, D(G(z)): 0.2357 Epoch: [16/20], Batch Num: [440/600] Discriminator Loss: 0.6473, Generator Loss: 1.6937 D(x): 0.7699, D(G(z)): 0.2340 Epoch: [16/20], Batch Num: [441/600] Discriminator Loss: 0.6314, Generator Loss: 1.4873 D(x): 0.7443, D(G(z)): 0.2151 Epoch: [16/20], Batch Num: 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2.0758 D(x): 0.7567, D(G(z)): 0.2675 Epoch: [16/20], Batch Num: [451/600] Discriminator Loss: 0.7784, Generator Loss: 2.1386 D(x): 0.7236, D(G(z)): 0.2193 Epoch: [16/20], Batch Num: [452/600] Discriminator Loss: 0.7657, Generator Loss: 2.0996 D(x): 0.7367, D(G(z)): 0.2311 Epoch: [16/20], Batch Num: [453/600] Discriminator Loss: 0.9335, Generator Loss: 1.8055 D(x): 0.7350, D(G(z)): 0.2840 Epoch: [16/20], Batch Num: [454/600] Discriminator Loss: 0.9737, Generator Loss: 1.7382 D(x): 0.6553, D(G(z)): 0.2769 Epoch: [16/20], Batch Num: [455/600] Discriminator Loss: 0.9054, Generator Loss: 1.5340 D(x): 0.7379, D(G(z)): 0.2878 Epoch: [16/20], Batch Num: [456/600] Discriminator Loss: 0.8684, Generator Loss: 1.6696 D(x): 0.8137, D(G(z)): 0.3390 Epoch: [16/20], Batch Num: [457/600] Discriminator Loss: 0.9623, Generator Loss: 1.8689 D(x): 0.7468, D(G(z)): 0.3011 Epoch: [16/20], Batch Num: [458/600] Discriminator Loss: 0.9305, Generator Loss: 2.0214 D(x): 0.6573, D(G(z)): 0.2109 Epoch: [16/20], Batch Num: [459/600] Discriminator Loss: 1.1356, Generator Loss: 1.4975 D(x): 0.6019, D(G(z)): 0.2397 Epoch: [16/20], Batch Num: [460/600] Discriminator Loss: 1.0595, Generator Loss: 1.2769 D(x): 0.6776, D(G(z)): 0.2941 Epoch: [16/20], Batch Num: [461/600] Discriminator Loss: 1.2541, Generator Loss: 1.3912 D(x): 0.7607, D(G(z)): 0.4386 Epoch: [16/20], Batch Num: [462/600] Discriminator Loss: 1.2237, Generator Loss: 1.6832 D(x): 0.7216, D(G(z)): 0.4139 Epoch: [16/20], Batch Num: [463/600] Discriminator Loss: 1.2158, Generator Loss: 1.7720 D(x): 0.6098, D(G(z)): 0.3304 Epoch: [16/20], Batch Num: [464/600] Discriminator Loss: 1.2993, Generator Loss: 1.8805 D(x): 0.5293, D(G(z)): 0.2765 Epoch: [16/20], Batch Num: [465/600] Discriminator Loss: 1.2057, Generator Loss: 1.4292 D(x): 0.5481, D(G(z)): 0.2488 Epoch: [16/20], Batch Num: [466/600] Discriminator Loss: 1.5350, Generator Loss: 1.0275 D(x): 0.5013, D(G(z)): 0.3460 Epoch: [16/20], Batch Num: [467/600] Discriminator Loss: 1.1654, Generator Loss: 0.8363 D(x): 0.7428, D(G(z)): 0.4357 Epoch: [16/20], Batch Num: [468/600] Discriminator Loss: 1.2138, Generator Loss: 1.1564 D(x): 0.7660, D(G(z)): 0.4764 Epoch: [16/20], Batch Num: [469/600] Discriminator Loss: 1.1751, Generator Loss: 1.2784 D(x): 0.6819, D(G(z)): 0.4072 Epoch: [16/20], Batch Num: [470/600] Discriminator Loss: 1.1687, Generator Loss: 1.4600 D(x): 0.6116, D(G(z)): 0.3531 Epoch: [16/20], Batch Num: [471/600] Discriminator Loss: 1.2404, Generator Loss: 1.4289 D(x): 0.5106, D(G(z)): 0.2731 Epoch: [16/20], Batch Num: [472/600] Discriminator Loss: 1.1556, Generator Loss: 1.5139 D(x): 0.5733, D(G(z)): 0.3019 Epoch: [16/20], Batch Num: [473/600] Discriminator Loss: 1.1665, Generator Loss: 1.2222 D(x): 0.5800, D(G(z)): 0.3194 Epoch: [16/20], Batch Num: [474/600] Discriminator Loss: 1.0796, Generator Loss: 1.1116 D(x): 0.6519, D(G(z)): 0.3878 Epoch: [16/20], Batch Num: [475/600] Discriminator Loss: 1.1522, Generator Loss: 0.9845 D(x): 0.6690, D(G(z)): 0.4153 Epoch: [16/20], Batch Num: [476/600] Discriminator Loss: 1.1259, Generator Loss: 1.0280 D(x): 0.6852, D(G(z)): 0.4339 Epoch: [16/20], Batch Num: [477/600] Discriminator Loss: 0.9987, Generator Loss: 1.1599 D(x): 0.7192, D(G(z)): 0.4103 Epoch: [16/20], Batch Num: [478/600] Discriminator Loss: 1.0512, Generator Loss: 1.3047 D(x): 0.6613, D(G(z)): 0.3795 Epoch: [16/20], Batch Num: [479/600] Discriminator Loss: 0.9824, Generator Loss: 1.3643 D(x): 0.6618, D(G(z)): 0.3438 Epoch: [16/20], Batch Num: [480/600] Discriminator Loss: 0.9701, Generator Loss: 1.4109 D(x): 0.6181, D(G(z)): 0.2903 Epoch: [16/20], Batch Num: [481/600] Discriminator Loss: 0.9127, Generator Loss: 1.4628 D(x): 0.6486, D(G(z)): 0.2969 Epoch: [16/20], Batch Num: [482/600] Discriminator Loss: 0.9704, Generator Loss: 1.5046 D(x): 0.6134, D(G(z)): 0.2846 Epoch: [16/20], Batch Num: [483/600] Discriminator Loss: 0.8085, Generator Loss: 1.1951 D(x): 0.7057, D(G(z)): 0.2915 Epoch: [16/20], Batch Num: [484/600] Discriminator Loss: 0.9847, Generator Loss: 1.0946 D(x): 0.6447, D(G(z)): 0.3344 Epoch: [16/20], Batch Num: [485/600] Discriminator Loss: 0.8600, Generator Loss: 1.1233 D(x): 0.7127, D(G(z)): 0.3380 Epoch: [16/20], Batch Num: [486/600] Discriminator Loss: 0.9107, Generator Loss: 1.2303 D(x): 0.7413, D(G(z)): 0.3779 Epoch: [16/20], Batch Num: [487/600] Discriminator Loss: 0.8117, Generator Loss: 1.3126 D(x): 0.7704, D(G(z)): 0.3664 Epoch: [16/20], Batch Num: [488/600] Discriminator Loss: 0.7832, Generator Loss: 1.3443 D(x): 0.7641, D(G(z)): 0.3300 Epoch: [16/20], Batch Num: [489/600] Discriminator Loss: 0.8486, Generator Loss: 1.4783 D(x): 0.7221, D(G(z)): 0.3202 Epoch: [16/20], Batch Num: [490/600] Discriminator Loss: 0.8954, Generator Loss: 1.6905 D(x): 0.6881, D(G(z)): 0.3079 Epoch: [16/20], Batch Num: [491/600] Discriminator Loss: 0.7513, Generator Loss: 1.6799 D(x): 0.6859, D(G(z)): 0.2284 Epoch: [16/20], Batch Num: [492/600] Discriminator Loss: 0.8262, Generator Loss: 1.7236 D(x): 0.6619, D(G(z)): 0.2379 Epoch: [16/20], Batch Num: [493/600] Discriminator Loss: 0.7204, Generator Loss: 1.6312 D(x): 0.7032, D(G(z)): 0.2281 Epoch: [16/20], Batch Num: [494/600] Discriminator Loss: 0.6707, Generator Loss: 1.6135 D(x): 0.7403, D(G(z)): 0.2464 Epoch: [16/20], Batch Num: [495/600] Discriminator Loss: 0.7151, Generator Loss: 1.3409 D(x): 0.7848, D(G(z)): 0.2955 Epoch: [16/20], Batch Num: [496/600] Discriminator Loss: 0.6555, Generator Loss: 1.5908 D(x): 0.8268, D(G(z)): 0.3217 Epoch: [16/20], Batch Num: [497/600] Discriminator Loss: 0.7470, Generator Loss: 1.7348 D(x): 0.7550, D(G(z)): 0.2813 Epoch: [16/20], Batch Num: [498/600] Discriminator Loss: 0.6768, Generator Loss: 1.8484 D(x): 0.7593, D(G(z)): 0.2566 Epoch: [16/20], Batch Num: [499/600] Discriminator Loss: 0.7014, Generator Loss: 1.8929 D(x): 0.7020, D(G(z)): 0.1989 Epoch: 16, Batch Num: [500/600]
Epoch: [16/20], Batch Num: [500/600] Discriminator Loss: 0.7141, Generator Loss: 1.8101 D(x): 0.7369, D(G(z)): 0.2360 Epoch: [16/20], Batch Num: [501/600] Discriminator Loss: 0.7102, Generator Loss: 1.6239 D(x): 0.7824, D(G(z)): 0.2798 Epoch: [16/20], Batch Num: [502/600] Discriminator Loss: 0.6898, Generator Loss: 1.6010 D(x): 0.7900, D(G(z)): 0.2811 Epoch: [16/20], Batch Num: [503/600] Discriminator Loss: 0.6217, Generator Loss: 1.8197 D(x): 0.8120, D(G(z)): 0.2570 Epoch: [16/20], Batch Num: [504/600] Discriminator Loss: 0.7623, Generator Loss: 1.9222 D(x): 0.7288, D(G(z)): 0.2438 Epoch: [16/20], Batch Num: [505/600] Discriminator Loss: 0.7945, Generator Loss: 2.1119 D(x): 0.7229, D(G(z)): 0.2227 Epoch: [16/20], Batch Num: [506/600] Discriminator Loss: 0.6632, Generator Loss: 1.8866 D(x): 0.7531, D(G(z)): 0.2209 Epoch: [16/20], Batch Num: [507/600] Discriminator Loss: 0.7751, Generator Loss: 1.6069 D(x): 0.7455, D(G(z)): 0.2758 Epoch: [16/20], Batch Num: [508/600] Discriminator Loss: 0.7785, Generator Loss: 1.8718 D(x): 0.8184, D(G(z)): 0.3234 Epoch: [16/20], Batch Num: [509/600] Discriminator Loss: 0.8156, Generator Loss: 1.8726 D(x): 0.7208, D(G(z)): 0.2642 Epoch: [16/20], Batch Num: [510/600] Discriminator Loss: 0.8788, Generator Loss: 1.7802 D(x): 0.7072, D(G(z)): 0.2613 Epoch: [16/20], Batch Num: [511/600] Discriminator Loss: 0.9449, Generator Loss: 1.6781 D(x): 0.7188, D(G(z)): 0.2782 Epoch: [16/20], Batch Num: [512/600] Discriminator Loss: 1.0308, Generator Loss: 1.6089 D(x): 0.6664, D(G(z)): 0.2907 Epoch: [16/20], Batch Num: [513/600] Discriminator Loss: 0.9937, Generator Loss: 1.6270 D(x): 0.7679, D(G(z)): 0.3516 Epoch: [16/20], Batch Num: [514/600] Discriminator Loss: 1.0111, Generator Loss: 1.7689 D(x): 0.7209, D(G(z)): 0.3323 Epoch: [16/20], Batch Num: [515/600] Discriminator Loss: 0.9577, Generator Loss: 1.6420 D(x): 0.6862, D(G(z)): 0.2619 Epoch: [16/20], Batch Num: [516/600] Discriminator Loss: 0.9098, Generator Loss: 1.5919 D(x): 0.7028, D(G(z)): 0.2862 Epoch: [16/20], Batch Num: [517/600] Discriminator Loss: 1.0707, Generator Loss: 1.3586 D(x): 0.6831, D(G(z)): 0.3236 Epoch: [16/20], Batch Num: [518/600] Discriminator Loss: 1.0540, Generator Loss: 1.5316 D(x): 0.7046, D(G(z)): 0.3736 Epoch: [16/20], Batch Num: [519/600] Discriminator Loss: 1.0281, Generator Loss: 1.4300 D(x): 0.7020, D(G(z)): 0.3303 Epoch: [16/20], Batch Num: [520/600] Discriminator Loss: 1.0190, Generator Loss: 1.6449 D(x): 0.7042, D(G(z)): 0.3408 Epoch: [16/20], Batch Num: [521/600] Discriminator Loss: 1.1805, Generator Loss: 1.5524 D(x): 0.6412, D(G(z)): 0.3536 Epoch: [16/20], Batch Num: [522/600] Discriminator Loss: 0.9701, Generator Loss: 1.3340 D(x): 0.6527, D(G(z)): 0.2859 Epoch: [16/20], Batch Num: [523/600] Discriminator Loss: 0.8978, Generator Loss: 1.2912 D(x): 0.6820, D(G(z)): 0.2856 Epoch: [16/20], Batch Num: [524/600] Discriminator Loss: 1.0374, Generator Loss: 1.2742 D(x): 0.6875, D(G(z)): 0.3410 Epoch: [16/20], Batch Num: [525/600] Discriminator Loss: 0.9472, Generator Loss: 1.1120 D(x): 0.7397, D(G(z)): 0.3653 Epoch: [16/20], Batch Num: [526/600] Discriminator Loss: 0.9840, Generator Loss: 1.2956 D(x): 0.7941, D(G(z)): 0.4076 Epoch: [16/20], Batch Num: [527/600] Discriminator Loss: 0.8898, Generator Loss: 1.5195 D(x): 0.7608, D(G(z)): 0.3479 Epoch: [16/20], Batch Num: [528/600] Discriminator Loss: 0.9088, Generator Loss: 1.8890 D(x): 0.6683, D(G(z)): 0.2637 Epoch: [16/20], Batch Num: [529/600] Discriminator Loss: 0.8371, Generator Loss: 1.7691 D(x): 0.6486, D(G(z)): 0.2174 Epoch: [16/20], Batch Num: [530/600] Discriminator Loss: 0.8419, Generator Loss: 1.5588 D(x): 0.6511, D(G(z)): 0.2283 Epoch: [16/20], Batch Num: [531/600] Discriminator Loss: 0.6985, Generator Loss: 1.3762 D(x): 0.7537, D(G(z)): 0.2594 Epoch: [16/20], Batch Num: [532/600] Discriminator Loss: 0.7971, Generator Loss: 1.2848 D(x): 0.7806, D(G(z)): 0.3255 Epoch: [16/20], Batch Num: [533/600] Discriminator Loss: 0.7937, Generator Loss: 1.4283 D(x): 0.8197, D(G(z)): 0.3500 Epoch: [16/20], Batch Num: [534/600] Discriminator Loss: 0.6431, Generator Loss: 1.5902 D(x): 0.8363, D(G(z)): 0.3042 Epoch: [16/20], Batch Num: [535/600] Discriminator Loss: 0.7544, Generator Loss: 1.8323 D(x): 0.7311, D(G(z)): 0.2643 Epoch: [16/20], Batch Num: [536/600] Discriminator Loss: 0.6449, Generator Loss: 1.9462 D(x): 0.7761, D(G(z)): 0.2299 Epoch: [16/20], Batch Num: [537/600] Discriminator Loss: 0.6696, Generator Loss: 2.0565 D(x): 0.7402, D(G(z)): 0.2096 Epoch: [16/20], Batch Num: [538/600] Discriminator Loss: 0.6934, Generator Loss: 1.8309 D(x): 0.7462, D(G(z)): 0.2240 Epoch: [16/20], Batch Num: [539/600] Discriminator Loss: 0.8240, Generator Loss: 1.9177 D(x): 0.6933, D(G(z)): 0.2441 Epoch: [16/20], Batch Num: [540/600] Discriminator Loss: 0.6874, Generator Loss: 1.7839 D(x): 0.7944, D(G(z)): 0.2548 Epoch: [16/20], Batch Num: [541/600] Discriminator Loss: 0.6383, Generator Loss: 1.7701 D(x): 0.8032, D(G(z)): 0.2499 Epoch: [16/20], Batch Num: [542/600] Discriminator Loss: 0.5088, Generator Loss: 1.8257 D(x): 0.8704, D(G(z)): 0.2520 Epoch: [16/20], Batch Num: [543/600] Discriminator Loss: 0.6399, Generator Loss: 1.8900 D(x): 0.7886, D(G(z)): 0.2454 Epoch: [16/20], Batch Num: [544/600] Discriminator Loss: 0.7453, Generator Loss: 1.9642 D(x): 0.7377, D(G(z)): 0.2386 Epoch: [16/20], Batch Num: [545/600] Discriminator Loss: 0.7465, Generator Loss: 1.7960 D(x): 0.7268, D(G(z)): 0.2327 Epoch: [16/20], Batch Num: [546/600] Discriminator Loss: 0.6353, Generator Loss: 1.8624 D(x): 0.7753, D(G(z)): 0.2239 Epoch: [16/20], Batch Num: [547/600] Discriminator Loss: 0.6443, Generator Loss: 1.6726 D(x): 0.7907, D(G(z)): 0.2499 Epoch: [16/20], Batch Num: [548/600] Discriminator Loss: 0.7183, Generator Loss: 1.8991 D(x): 0.8143, D(G(z)): 0.2751 Epoch: [16/20], Batch Num: [549/600] Discriminator Loss: 0.7862, Generator Loss: 2.0035 D(x): 0.7837, D(G(z)): 0.2714 Epoch: [16/20], Batch Num: [550/600] Discriminator Loss: 0.7608, Generator Loss: 1.9325 D(x): 0.7603, D(G(z)): 0.2618 Epoch: [16/20], Batch Num: [551/600] Discriminator Loss: 0.5900, Generator Loss: 1.9666 D(x): 0.8013, D(G(z)): 0.1980 Epoch: [16/20], Batch Num: [552/600] Discriminator Loss: 0.7119, Generator Loss: 1.8919 D(x): 0.7508, D(G(z)): 0.2300 Epoch: [16/20], Batch Num: [553/600] Discriminator Loss: 0.8527, Generator Loss: 1.8402 D(x): 0.7222, D(G(z)): 0.2505 Epoch: [16/20], Batch Num: [554/600] Discriminator Loss: 0.6277, Generator Loss: 1.6952 D(x): 0.8181, D(G(z)): 0.2507 Epoch: [16/20], Batch Num: [555/600] Discriminator Loss: 0.7657, Generator Loss: 1.8314 D(x): 0.7630, D(G(z)): 0.2642 Epoch: [16/20], Batch Num: [556/600] Discriminator Loss: 0.8181, Generator Loss: 2.0469 D(x): 0.7736, D(G(z)): 0.2880 Epoch: [16/20], Batch Num: [557/600] Discriminator Loss: 0.7740, Generator Loss: 2.1681 D(x): 0.7631, D(G(z)): 0.2560 Epoch: [16/20], Batch Num: [558/600] Discriminator Loss: 0.8753, Generator Loss: 2.1538 D(x): 0.7356, D(G(z)): 0.3002 Epoch: [16/20], Batch Num: [559/600] Discriminator Loss: 0.8496, Generator Loss: 2.0470 D(x): 0.6758, D(G(z)): 0.1813 Epoch: [16/20], Batch Num: [560/600] Discriminator Loss: 0.8840, Generator Loss: 1.6314 D(x): 0.6819, D(G(z)): 0.2472 Epoch: [16/20], Batch Num: [561/600] Discriminator Loss: 0.9244, Generator Loss: 1.4053 D(x): 0.7079, D(G(z)): 0.2992 Epoch: [16/20], Batch Num: [562/600] Discriminator Loss: 0.9756, Generator Loss: 1.4314 D(x): 0.7875, D(G(z)): 0.3802 Epoch: [16/20], Batch Num: [563/600] Discriminator Loss: 1.1432, Generator Loss: 1.5648 D(x): 0.6573, D(G(z)): 0.3512 Epoch: [16/20], Batch Num: [564/600] Discriminator Loss: 1.0401, Generator Loss: 1.5782 D(x): 0.6901, D(G(z)): 0.3233 Epoch: [16/20], Batch Num: [565/600] Discriminator Loss: 1.2673, Generator Loss: 1.5747 D(x): 0.6043, D(G(z)): 0.3066 Epoch: [16/20], Batch Num: [566/600] Discriminator Loss: 1.1383, Generator Loss: 1.5542 D(x): 0.6123, D(G(z)): 0.3013 Epoch: [16/20], Batch Num: [567/600] Discriminator Loss: 1.1795, Generator Loss: 1.3679 D(x): 0.6159, D(G(z)): 0.3218 Epoch: [16/20], Batch Num: [568/600] Discriminator Loss: 1.2708, Generator Loss: 1.4061 D(x): 0.6721, D(G(z)): 0.4082 Epoch: [16/20], Batch Num: [569/600] Discriminator Loss: 1.2079, Generator Loss: 1.3149 D(x): 0.6797, D(G(z)): 0.3722 Epoch: [16/20], Batch Num: [570/600] Discriminator Loss: 1.1879, Generator Loss: 1.6485 D(x): 0.6376, D(G(z)): 0.3737 Epoch: [16/20], Batch Num: [571/600] Discriminator Loss: 1.1150, Generator Loss: 1.5299 D(x): 0.6128, D(G(z)): 0.2834 Epoch: [16/20], Batch Num: [572/600] Discriminator Loss: 1.1702, Generator Loss: 1.2578 D(x): 0.5846, D(G(z)): 0.2954 Epoch: [16/20], Batch Num: [573/600] Discriminator Loss: 1.2327, Generator Loss: 1.2357 D(x): 0.7114, D(G(z)): 0.4200 Epoch: [16/20], Batch Num: [574/600] Discriminator Loss: 1.0658, Generator Loss: 1.3012 D(x): 0.6529, D(G(z)): 0.3270 Epoch: [16/20], Batch Num: [575/600] Discriminator Loss: 0.9658, Generator Loss: 1.4775 D(x): 0.6835, D(G(z)): 0.3169 Epoch: [16/20], Batch Num: [576/600] Discriminator Loss: 0.9096, Generator Loss: 1.4582 D(x): 0.6588, D(G(z)): 0.2740 Epoch: [16/20], Batch Num: [577/600] Discriminator Loss: 1.0903, Generator Loss: 1.2903 D(x): 0.6263, D(G(z)): 0.3118 Epoch: [16/20], Batch Num: [578/600] Discriminator Loss: 1.0831, Generator Loss: 1.4265 D(x): 0.6937, D(G(z)): 0.3815 Epoch: [16/20], Batch Num: [579/600] Discriminator Loss: 1.0286, Generator Loss: 1.4511 D(x): 0.6706, D(G(z)): 0.3386 Epoch: [16/20], Batch Num: [580/600] Discriminator Loss: 0.9805, Generator Loss: 1.4650 D(x): 0.6693, D(G(z)): 0.3141 Epoch: [16/20], Batch Num: [581/600] Discriminator Loss: 1.0224, Generator Loss: 1.5431 D(x): 0.6645, D(G(z)): 0.3289 Epoch: [16/20], Batch Num: [582/600] Discriminator Loss: 0.8976, Generator Loss: 1.5345 D(x): 0.6760, D(G(z)): 0.2947 Epoch: [16/20], Batch Num: [583/600] Discriminator Loss: 0.7657, Generator Loss: 1.5121 D(x): 0.7248, D(G(z)): 0.2694 Epoch: [16/20], Batch Num: [584/600] Discriminator Loss: 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Epoch: [17/20], Batch Num: [0/600] Discriminator Loss: 0.8118, Generator Loss: 2.0130 D(x): 0.7652, D(G(z)): 0.2699 Epoch: [17/20], Batch Num: [1/600] Discriminator Loss: 0.6515, Generator Loss: 1.7629 D(x): 0.7973, D(G(z)): 0.2337 Epoch: [17/20], Batch Num: [2/600] Discriminator Loss: 0.9041, Generator Loss: 2.1099 D(x): 0.7796, D(G(z)): 0.3246 Epoch: [17/20], Batch Num: [3/600] Discriminator Loss: 0.8653, Generator Loss: 2.5065 D(x): 0.7541, D(G(z)): 0.2816 Epoch: [17/20], Batch Num: [4/600] Discriminator Loss: 0.8554, Generator Loss: 2.3642 D(x): 0.7030, D(G(z)): 0.1969 Epoch: [17/20], Batch Num: [5/600] Discriminator Loss: 1.0802, Generator Loss: 1.8642 D(x): 0.6202, D(G(z)): 0.1891 Epoch: [17/20], Batch Num: [6/600] Discriminator Loss: 0.9499, Generator Loss: 1.6421 D(x): 0.7365, D(G(z)): 0.2950 Epoch: [17/20], Batch Num: [7/600] Discriminator Loss: 1.0419, Generator Loss: 1.6413 D(x): 0.7831, D(G(z)): 0.3874 Epoch: [17/20], Batch Num: [8/600] Discriminator Loss: 1.0496, Generator 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Num: [17/600] Discriminator Loss: 1.1402, Generator Loss: 1.1992 D(x): 0.6658, D(G(z)): 0.3735 Epoch: [17/20], Batch Num: [18/600] Discriminator Loss: 1.2477, Generator Loss: 1.2177 D(x): 0.6431, D(G(z)): 0.4077 Epoch: [17/20], Batch Num: [19/600] Discriminator Loss: 1.1313, Generator Loss: 1.2275 D(x): 0.6453, D(G(z)): 0.3560 Epoch: [17/20], Batch Num: [20/600] Discriminator Loss: 1.0545, Generator Loss: 1.1948 D(x): 0.6431, D(G(z)): 0.3271 Epoch: [17/20], Batch Num: [21/600] Discriminator Loss: 1.0541, Generator Loss: 1.3958 D(x): 0.6408, D(G(z)): 0.3361 Epoch: [17/20], Batch Num: [22/600] Discriminator Loss: 1.0841, Generator Loss: 1.2586 D(x): 0.6406, D(G(z)): 0.3560 Epoch: [17/20], Batch Num: [23/600] Discriminator Loss: 1.0486, Generator Loss: 1.3410 D(x): 0.6216, D(G(z)): 0.3201 Epoch: [17/20], Batch Num: [24/600] Discriminator Loss: 0.8843, Generator Loss: 1.2764 D(x): 0.6924, D(G(z)): 0.3106 Epoch: [17/20], Batch Num: [25/600] Discriminator Loss: 0.9740, Generator Loss: 1.2144 D(x): 0.7099, D(G(z)): 0.3639 Epoch: [17/20], Batch Num: [26/600] Discriminator Loss: 0.9916, Generator Loss: 1.4049 D(x): 0.6810, D(G(z)): 0.3493 Epoch: [17/20], Batch Num: [27/600] Discriminator Loss: 0.7927, Generator Loss: 1.3039 D(x): 0.7349, D(G(z)): 0.3048 Epoch: [17/20], Batch Num: [28/600] Discriminator Loss: 0.7252, Generator Loss: 1.3959 D(x): 0.7326, D(G(z)): 0.2773 Epoch: [17/20], Batch Num: [29/600] Discriminator Loss: 0.7547, Generator Loss: 1.3811 D(x): 0.7176, D(G(z)): 0.2681 Epoch: [17/20], Batch Num: [30/600] Discriminator Loss: 0.8658, Generator Loss: 1.4425 D(x): 0.6991, D(G(z)): 0.2970 Epoch: [17/20], Batch Num: [31/600] Discriminator Loss: 0.8018, Generator Loss: 1.5065 D(x): 0.7386, D(G(z)): 0.2955 Epoch: [17/20], Batch Num: [32/600] Discriminator Loss: 0.7667, Generator Loss: 1.6160 D(x): 0.7708, D(G(z)): 0.3147 Epoch: [17/20], Batch Num: [33/600] Discriminator Loss: 0.7992, Generator Loss: 1.6465 D(x): 0.7290, D(G(z)): 0.2784 Epoch: [17/20], Batch Num: 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D(x): 0.8251, D(G(z)): 0.2589 Epoch: [17/20], Batch Num: [43/600] Discriminator Loss: 0.6954, Generator Loss: 2.1176 D(x): 0.7583, D(G(z)): 0.2265 Epoch: [17/20], Batch Num: [44/600] Discriminator Loss: 0.7209, Generator Loss: 2.0662 D(x): 0.7509, D(G(z)): 0.2161 Epoch: [17/20], Batch Num: [45/600] Discriminator Loss: 0.6748, Generator Loss: 2.1097 D(x): 0.7591, D(G(z)): 0.2046 Epoch: [17/20], Batch Num: [46/600] Discriminator Loss: 0.6166, Generator Loss: 1.9753 D(x): 0.7659, D(G(z)): 0.1785 Epoch: [17/20], Batch Num: [47/600] Discriminator Loss: 0.6956, Generator Loss: 1.7435 D(x): 0.7636, D(G(z)): 0.2430 Epoch: [17/20], Batch Num: [48/600] Discriminator Loss: 0.6634, Generator Loss: 1.6663 D(x): 0.7772, D(G(z)): 0.2437 Epoch: [17/20], Batch Num: [49/600] Discriminator Loss: 0.8207, Generator Loss: 1.6756 D(x): 0.7737, D(G(z)): 0.3001 Epoch: [17/20], Batch Num: [50/600] Discriminator Loss: 0.7837, Generator Loss: 1.7473 D(x): 0.8078, D(G(z)): 0.3065 Epoch: [17/20], Batch Num: [51/600] Discriminator Loss: 0.7502, Generator Loss: 2.0284 D(x): 0.7789, D(G(z)): 0.2639 Epoch: [17/20], Batch Num: [52/600] Discriminator Loss: 0.8726, Generator Loss: 2.0315 D(x): 0.6920, D(G(z)): 0.2546 Epoch: [17/20], Batch Num: [53/600] Discriminator Loss: 0.7568, Generator Loss: 1.8595 D(x): 0.7352, D(G(z)): 0.2193 Epoch: [17/20], Batch Num: [54/600] Discriminator Loss: 0.8333, Generator Loss: 1.7825 D(x): 0.7232, D(G(z)): 0.2534 Epoch: [17/20], Batch Num: [55/600] Discriminator Loss: 0.9423, Generator Loss: 1.6138 D(x): 0.6906, D(G(z)): 0.2608 Epoch: [17/20], Batch Num: [56/600] Discriminator Loss: 0.9031, Generator Loss: 1.3963 D(x): 0.7532, D(G(z)): 0.3004 Epoch: [17/20], Batch Num: [57/600] Discriminator Loss: 1.0273, Generator Loss: 1.4374 D(x): 0.7468, D(G(z)): 0.3676 Epoch: [17/20], Batch Num: [58/600] Discriminator Loss: 1.0867, Generator Loss: 1.6330 D(x): 0.6985, D(G(z)): 0.3138 Epoch: [17/20], Batch Num: [59/600] Discriminator Loss: 0.9898, Generator Loss: 1.6477 D(x): 0.6751, D(G(z)): 0.2742 Epoch: [17/20], Batch Num: [60/600] Discriminator Loss: 1.1032, Generator Loss: 1.6118 D(x): 0.6340, D(G(z)): 0.2989 Epoch: [17/20], Batch Num: [61/600] Discriminator Loss: 1.0331, Generator Loss: 1.3661 D(x): 0.6984, D(G(z)): 0.3428 Epoch: [17/20], Batch Num: [62/600] Discriminator Loss: 1.0300, Generator Loss: 1.5395 D(x): 0.7220, D(G(z)): 0.3488 Epoch: [17/20], Batch Num: [63/600] Discriminator Loss: 1.1840, Generator Loss: 1.4245 D(x): 0.6793, D(G(z)): 0.3832 Epoch: [17/20], Batch Num: [64/600] Discriminator Loss: 1.1456, Generator Loss: 1.6904 D(x): 0.6656, D(G(z)): 0.3563 Epoch: [17/20], Batch Num: [65/600] Discriminator Loss: 1.2212, Generator Loss: 1.7714 D(x): 0.6465, D(G(z)): 0.3662 Epoch: [17/20], Batch Num: [66/600] Discriminator Loss: 1.2230, Generator Loss: 1.5274 D(x): 0.5924, D(G(z)): 0.2792 Epoch: [17/20], Batch Num: [67/600] Discriminator Loss: 1.3046, Generator Loss: 1.5778 D(x): 0.5859, D(G(z)): 0.3270 Epoch: [17/20], Batch Num: [68/600] Discriminator Loss: 1.1466, Generator Loss: 1.2494 D(x): 0.6302, D(G(z)): 0.3504 Epoch: [17/20], Batch Num: [69/600] Discriminator Loss: 1.0890, Generator Loss: 1.3594 D(x): 0.7056, D(G(z)): 0.3628 Epoch: [17/20], Batch Num: [70/600] Discriminator Loss: 1.1880, Generator Loss: 1.2389 D(x): 0.7001, D(G(z)): 0.4216 Epoch: [17/20], Batch Num: [71/600] Discriminator Loss: 0.9929, Generator Loss: 1.6021 D(x): 0.7199, D(G(z)): 0.3731 Epoch: [17/20], Batch Num: [72/600] Discriminator Loss: 0.9845, Generator Loss: 1.5344 D(x): 0.6534, D(G(z)): 0.2936 Epoch: [17/20], Batch Num: [73/600] Discriminator Loss: 1.1268, Generator Loss: 1.4490 D(x): 0.6046, D(G(z)): 0.3207 Epoch: [17/20], Batch Num: [74/600] Discriminator Loss: 1.0599, Generator Loss: 1.4983 D(x): 0.6485, D(G(z)): 0.3202 Epoch: [17/20], Batch Num: [75/600] Discriminator Loss: 0.9644, Generator Loss: 1.3592 D(x): 0.6411, D(G(z)): 0.3005 Epoch: [17/20], Batch Num: [76/600] Discriminator Loss: 0.9538, Generator Loss: 1.3520 D(x): 0.6762, D(G(z)): 0.3274 Epoch: [17/20], Batch Num: [77/600] Discriminator Loss: 0.9434, Generator Loss: 1.1912 D(x): 0.7324, D(G(z)): 0.3696 Epoch: [17/20], Batch Num: [78/600] Discriminator Loss: 0.9208, Generator Loss: 1.2252 D(x): 0.7364, D(G(z)): 0.3618 Epoch: [17/20], Batch Num: [79/600] Discriminator Loss: 0.9066, Generator Loss: 1.3830 D(x): 0.7480, D(G(z)): 0.3527 Epoch: [17/20], Batch Num: [80/600] Discriminator Loss: 0.9542, Generator Loss: 1.4589 D(x): 0.6802, D(G(z)): 0.3065 Epoch: [17/20], Batch Num: [81/600] Discriminator Loss: 0.9267, Generator Loss: 1.5285 D(x): 0.6824, D(G(z)): 0.3067 Epoch: [17/20], Batch Num: [82/600] Discriminator Loss: 0.9420, Generator Loss: 1.6198 D(x): 0.6741, D(G(z)): 0.2930 Epoch: [17/20], Batch Num: [83/600] Discriminator Loss: 0.8867, Generator Loss: 1.4939 D(x): 0.6247, D(G(z)): 0.2495 Epoch: [17/20], Batch Num: [84/600] Discriminator Loss: 0.8483, Generator Loss: 1.4171 D(x): 0.6844, D(G(z)): 0.2881 Epoch: [17/20], Batch Num: [85/600] Discriminator Loss: 0.7361, Generator Loss: 1.3507 D(x): 0.7360, D(G(z)): 0.2747 Epoch: [17/20], Batch Num: [86/600] Discriminator Loss: 0.8654, Generator Loss: 1.3286 D(x): 0.7320, D(G(z)): 0.3258 Epoch: [17/20], Batch Num: [87/600] Discriminator Loss: 0.9109, Generator Loss: 1.5056 D(x): 0.8078, D(G(z)): 0.4253 Epoch: [17/20], Batch Num: [88/600] Discriminator Loss: 0.8277, Generator Loss: 1.6500 D(x): 0.7384, D(G(z)): 0.3161 Epoch: [17/20], Batch Num: [89/600] Discriminator Loss: 0.8445, Generator Loss: 1.7353 D(x): 0.6845, D(G(z)): 0.2736 Epoch: [17/20], Batch Num: [90/600] Discriminator Loss: 0.8424, Generator Loss: 1.8094 D(x): 0.6696, D(G(z)): 0.2488 Epoch: [17/20], Batch Num: [91/600] Discriminator Loss: 0.7940, Generator Loss: 1.6800 D(x): 0.6591, D(G(z)): 0.2164 Epoch: [17/20], Batch Num: [92/600] Discriminator Loss: 0.7833, Generator Loss: 1.5328 D(x): 0.6876, D(G(z)): 0.2446 Epoch: [17/20], Batch Num: [93/600] Discriminator Loss: 0.8937, Generator Loss: 1.2799 D(x): 0.7066, D(G(z)): 0.3117 Epoch: [17/20], Batch Num: [94/600] Discriminator Loss: 0.8451, Generator Loss: 1.2643 D(x): 0.7994, D(G(z)): 0.3616 Epoch: [17/20], Batch Num: [95/600] Discriminator Loss: 0.7878, Generator Loss: 1.3844 D(x): 0.8076, D(G(z)): 0.3603 Epoch: [17/20], Batch Num: [96/600] Discriminator Loss: 0.8666, Generator Loss: 1.7203 D(x): 0.7590, D(G(z)): 0.3560 Epoch: [17/20], Batch Num: [97/600] Discriminator Loss: 0.6958, Generator Loss: 1.9837 D(x): 0.7283, D(G(z)): 0.2252 Epoch: [17/20], Batch Num: [98/600] Discriminator Loss: 0.9046, Generator Loss: 2.0210 D(x): 0.6452, D(G(z)): 0.2385 Epoch: [17/20], Batch Num: [99/600] Discriminator Loss: 0.8179, Generator Loss: 1.8230 D(x): 0.6650, D(G(z)): 0.1902 Epoch: 17, Batch Num: [100/600]
Epoch: [17/20], Batch Num: [100/600] Discriminator Loss: 0.7337, Generator Loss: 1.6181 D(x): 0.7396, D(G(z)): 0.2514 Epoch: [17/20], Batch Num: [101/600] Discriminator Loss: 0.6826, Generator Loss: 1.5064 D(x): 0.7826, D(G(z)): 0.2686 Epoch: [17/20], Batch Num: [102/600] Discriminator Loss: 0.7056, Generator Loss: 1.5264 D(x): 0.8142, D(G(z)): 0.3006 Epoch: [17/20], Batch Num: [103/600] Discriminator Loss: 0.7866, Generator Loss: 1.9484 D(x): 0.7876, D(G(z)): 0.3221 Epoch: [17/20], Batch Num: [104/600] Discriminator Loss: 0.9080, Generator Loss: 2.0540 D(x): 0.6984, D(G(z)): 0.2832 Epoch: [17/20], Batch Num: [105/600] Discriminator Loss: 0.8630, Generator Loss: 1.8472 D(x): 0.6674, D(G(z)): 0.2170 Epoch: [17/20], Batch Num: [106/600] Discriminator Loss: 0.8756, Generator Loss: 1.6732 D(x): 0.6730, D(G(z)): 0.2333 Epoch: [17/20], Batch Num: [107/600] Discriminator Loss: 1.0662, Generator Loss: 1.4910 D(x): 0.6983, D(G(z)): 0.3212 Epoch: [17/20], Batch Num: [108/600] Discriminator Loss: 0.9956, Generator Loss: 1.4174 D(x): 0.7659, D(G(z)): 0.3860 Epoch: [17/20], Batch Num: [109/600] Discriminator Loss: 1.0247, Generator Loss: 1.5369 D(x): 0.6887, D(G(z)): 0.3185 Epoch: [17/20], Batch Num: [110/600] Discriminator Loss: 0.8823, Generator Loss: 1.6576 D(x): 0.6888, D(G(z)): 0.2942 Epoch: [17/20], Batch Num: [111/600] Discriminator Loss: 1.0834, Generator Loss: 1.6233 D(x): 0.6546, D(G(z)): 0.3227 Epoch: [17/20], Batch Num: [112/600] Discriminator Loss: 1.1614, Generator Loss: 1.3822 D(x): 0.6451, D(G(z)): 0.3202 Epoch: [17/20], Batch Num: [113/600] Discriminator Loss: 0.9764, Generator Loss: 1.3518 D(x): 0.6786, D(G(z)): 0.3106 Epoch: [17/20], Batch Num: [114/600] Discriminator Loss: 1.0197, Generator Loss: 1.4181 D(x): 0.6869, D(G(z)): 0.3596 Epoch: [17/20], Batch Num: [115/600] Discriminator Loss: 1.0113, Generator Loss: 1.3279 D(x): 0.6589, D(G(z)): 0.3205 Epoch: [17/20], Batch Num: [116/600] Discriminator Loss: 1.0169, Generator Loss: 1.2928 D(x): 0.6659, D(G(z)): 0.3334 Epoch: [17/20], Batch Num: [117/600] Discriminator Loss: 1.2353, Generator Loss: 1.2805 D(x): 0.6407, D(G(z)): 0.3955 Epoch: [17/20], Batch Num: [118/600] Discriminator Loss: 1.1542, Generator Loss: 1.4737 D(x): 0.6682, D(G(z)): 0.3835 Epoch: [17/20], Batch Num: [119/600] Discriminator Loss: 1.0832, Generator Loss: 1.4460 D(x): 0.6776, D(G(z)): 0.3439 Epoch: [17/20], Batch Num: [120/600] Discriminator Loss: 0.8970, Generator Loss: 1.4508 D(x): 0.6701, D(G(z)): 0.2852 Epoch: [17/20], Batch Num: [121/600] Discriminator Loss: 0.9534, Generator Loss: 1.4182 D(x): 0.6342, D(G(z)): 0.2745 Epoch: [17/20], Batch Num: [122/600] Discriminator Loss: 0.9854, Generator Loss: 1.3363 D(x): 0.6619, D(G(z)): 0.3219 Epoch: [17/20], Batch Num: [123/600] Discriminator Loss: 1.0037, Generator Loss: 1.1944 D(x): 0.6859, D(G(z)): 0.3570 Epoch: [17/20], Batch Num: [124/600] Discriminator Loss: 0.8765, Generator Loss: 1.1589 D(x): 0.7497, D(G(z)): 0.3450 Epoch: [17/20], Batch Num: [125/600] Discriminator Loss: 0.9872, Generator Loss: 1.3821 D(x): 0.6963, D(G(z)): 0.3516 Epoch: [17/20], Batch Num: [126/600] Discriminator Loss: 0.8790, Generator Loss: 1.5475 D(x): 0.7316, D(G(z)): 0.3310 Epoch: [17/20], Batch Num: [127/600] Discriminator Loss: 0.8559, Generator Loss: 1.5484 D(x): 0.6708, D(G(z)): 0.2650 Epoch: [17/20], Batch Num: [128/600] Discriminator Loss: 0.7801, Generator Loss: 1.3711 D(x): 0.7140, D(G(z)): 0.2752 Epoch: [17/20], Batch Num: [129/600] Discriminator Loss: 0.8155, Generator Loss: 1.4454 D(x): 0.7245, D(G(z)): 0.2904 Epoch: [17/20], Batch Num: [130/600] Discriminator Loss: 0.8244, Generator Loss: 1.3995 D(x): 0.7517, D(G(z)): 0.3156 Epoch: [17/20], Batch Num: [131/600] Discriminator Loss: 0.8254, Generator Loss: 1.4467 D(x): 0.7359, D(G(z)): 0.3156 Epoch: [17/20], Batch Num: [132/600] Discriminator Loss: 0.8485, Generator Loss: 1.4332 D(x): 0.7088, D(G(z)): 0.2959 Epoch: [17/20], Batch Num: [133/600] Discriminator Loss: 0.8213, Generator Loss: 1.5093 D(x): 0.7350, D(G(z)): 0.3017 Epoch: [17/20], Batch Num: [134/600] Discriminator Loss: 0.7672, Generator Loss: 1.6003 D(x): 0.7355, D(G(z)): 0.2706 Epoch: [17/20], Batch Num: [135/600] Discriminator Loss: 0.8453, Generator Loss: 1.6012 D(x): 0.7094, D(G(z)): 0.2879 Epoch: [17/20], Batch Num: [136/600] Discriminator Loss: 0.8871, Generator Loss: 1.4748 D(x): 0.7287, D(G(z)): 0.2966 Epoch: [17/20], Batch Num: [137/600] Discriminator Loss: 0.7913, Generator Loss: 1.4749 D(x): 0.7568, D(G(z)): 0.3013 Epoch: [17/20], Batch Num: [138/600] Discriminator Loss: 0.7518, Generator Loss: 1.6200 D(x): 0.7553, D(G(z)): 0.2845 Epoch: [17/20], Batch Num: [139/600] Discriminator Loss: 0.7842, Generator Loss: 1.5453 D(x): 0.7110, D(G(z)): 0.2590 Epoch: [17/20], Batch Num: [140/600] Discriminator Loss: 0.7466, Generator Loss: 1.5720 D(x): 0.7591, D(G(z)): 0.2908 Epoch: [17/20], Batch Num: [141/600] Discriminator Loss: 0.8759, Generator Loss: 1.6795 D(x): 0.7329, D(G(z)): 0.3138 Epoch: [17/20], Batch Num: [142/600] Discriminator Loss: 0.7932, Generator Loss: 1.6951 D(x): 0.7449, D(G(z)): 0.2654 Epoch: [17/20], Batch Num: [143/600] Discriminator Loss: 0.6225, Generator Loss: 1.8814 D(x): 0.7737, D(G(z)): 0.2165 Epoch: [17/20], Batch Num: [144/600] Discriminator Loss: 0.8700, Generator Loss: 1.6874 D(x): 0.7009, D(G(z)): 0.2365 Epoch: [17/20], Batch Num: [145/600] Discriminator Loss: 0.7242, Generator Loss: 1.6711 D(x): 0.7546, D(G(z)): 0.2508 Epoch: [17/20], Batch Num: [146/600] Discriminator Loss: 0.7493, Generator Loss: 1.3878 D(x): 0.7463, D(G(z)): 0.2538 Epoch: [17/20], Batch Num: [147/600] Discriminator Loss: 0.8696, Generator Loss: 1.5979 D(x): 0.7712, D(G(z)): 0.3382 Epoch: [17/20], Batch Num: [148/600] Discriminator Loss: 0.8409, Generator Loss: 1.6224 D(x): 0.7824, D(G(z)): 0.3249 Epoch: [17/20], Batch Num: [149/600] Discriminator Loss: 0.7113, Generator Loss: 1.9624 D(x): 0.7803, D(G(z)): 0.2552 Epoch: [17/20], Batch Num: [150/600] Discriminator Loss: 0.8085, Generator Loss: 1.7704 D(x): 0.6963, D(G(z)): 0.2281 Epoch: [17/20], Batch Num: [151/600] Discriminator Loss: 0.8381, Generator Loss: 1.6357 D(x): 0.7129, D(G(z)): 0.2630 Epoch: [17/20], Batch Num: [152/600] Discriminator Loss: 0.7451, Generator Loss: 1.5971 D(x): 0.7751, D(G(z)): 0.2786 Epoch: [17/20], Batch Num: [153/600] Discriminator Loss: 0.7914, Generator Loss: 1.5300 D(x): 0.7331, D(G(z)): 0.2569 Epoch: [17/20], Batch Num: [154/600] Discriminator Loss: 0.7750, Generator Loss: 1.5308 D(x): 0.7825, D(G(z)): 0.3152 Epoch: [17/20], Batch Num: [155/600] Discriminator Loss: 0.8225, Generator Loss: 1.6557 D(x): 0.7270, D(G(z)): 0.2735 Epoch: [17/20], Batch Num: [156/600] Discriminator Loss: 0.8897, Generator Loss: 1.7389 D(x): 0.7063, D(G(z)): 0.3009 Epoch: [17/20], Batch Num: [157/600] Discriminator Loss: 0.7284, Generator Loss: 1.4850 D(x): 0.7480, D(G(z)): 0.2453 Epoch: [17/20], Batch Num: [158/600] Discriminator Loss: 0.8101, Generator Loss: 1.6025 D(x): 0.7700, D(G(z)): 0.2931 Epoch: [17/20], Batch Num: [159/600] Discriminator Loss: 0.8980, Generator Loss: 1.6481 D(x): 0.7348, D(G(z)): 0.3127 Epoch: [17/20], Batch Num: [160/600] Discriminator Loss: 0.9352, Generator Loss: 1.8073 D(x): 0.6838, D(G(z)): 0.2680 Epoch: [17/20], Batch Num: [161/600] Discriminator Loss: 0.8310, Generator Loss: 1.6939 D(x): 0.7326, D(G(z)): 0.2737 Epoch: [17/20], Batch Num: [162/600] Discriminator Loss: 0.8711, Generator Loss: 1.5420 D(x): 0.7081, D(G(z)): 0.2774 Epoch: [17/20], Batch Num: [163/600] Discriminator Loss: 1.0054, Generator Loss: 1.7044 D(x): 0.6997, D(G(z)): 0.3014 Epoch: [17/20], Batch Num: [164/600] Discriminator Loss: 0.8799, Generator Loss: 1.6656 D(x): 0.7074, D(G(z)): 0.2709 Epoch: [17/20], Batch Num: [165/600] Discriminator Loss: 0.8369, Generator Loss: 1.7410 D(x): 0.7505, D(G(z)): 0.3001 Epoch: [17/20], Batch Num: [166/600] Discriminator Loss: 0.8783, Generator Loss: 1.6488 D(x): 0.7695, D(G(z)): 0.3139 Epoch: [17/20], Batch Num: [167/600] Discriminator Loss: 1.1093, Generator Loss: 1.9902 D(x): 0.7121, D(G(z)): 0.3474 Epoch: [17/20], Batch Num: [168/600] Discriminator Loss: 0.9935, Generator Loss: 1.9370 D(x): 0.6427, D(G(z)): 0.2198 Epoch: [17/20], Batch Num: [169/600] Discriminator Loss: 0.8241, Generator Loss: 1.5965 D(x): 0.6957, D(G(z)): 0.2142 Epoch: [17/20], Batch Num: [170/600] Discriminator Loss: 0.9939, Generator Loss: 1.4890 D(x): 0.6799, D(G(z)): 0.2660 Epoch: [17/20], Batch Num: [171/600] Discriminator Loss: 0.9480, Generator Loss: 1.1907 D(x): 0.7775, D(G(z)): 0.3638 Epoch: [17/20], Batch Num: [172/600] Discriminator Loss: 0.8924, Generator Loss: 1.6042 D(x): 0.7471, D(G(z)): 0.3281 Epoch: [17/20], Batch Num: [173/600] Discriminator Loss: 1.0405, Generator Loss: 1.4007 D(x): 0.7112, D(G(z)): 0.3534 Epoch: [17/20], Batch Num: [174/600] Discriminator Loss: 0.8412, Generator Loss: 1.8773 D(x): 0.7536, D(G(z)): 0.2998 Epoch: [17/20], Batch Num: [175/600] Discriminator Loss: 0.8268, Generator Loss: 1.8019 D(x): 0.6904, D(G(z)): 0.2239 Epoch: [17/20], Batch Num: [176/600] Discriminator Loss: 0.7979, Generator Loss: 1.5877 D(x): 0.7022, D(G(z)): 0.2415 Epoch: [17/20], Batch Num: [177/600] Discriminator Loss: 0.9979, Generator Loss: 1.3966 D(x): 0.6737, D(G(z)): 0.2926 Epoch: [17/20], Batch Num: [178/600] Discriminator Loss: 0.9109, Generator Loss: 1.4350 D(x): 0.7409, D(G(z)): 0.3209 Epoch: [17/20], Batch Num: [179/600] Discriminator Loss: 1.0188, Generator Loss: 1.5696 D(x): 0.7703, D(G(z)): 0.3814 Epoch: [17/20], Batch Num: [180/600] Discriminator Loss: 0.9665, Generator Loss: 1.5979 D(x): 0.7134, D(G(z)): 0.3233 Epoch: [17/20], Batch Num: [181/600] Discriminator Loss: 0.8340, Generator Loss: 1.7975 D(x): 0.6900, D(G(z)): 0.2605 Epoch: [17/20], Batch Num: [182/600] Discriminator Loss: 0.8854, Generator Loss: 1.6756 D(x): 0.6977, D(G(z)): 0.2737 Epoch: [17/20], Batch Num: [183/600] Discriminator Loss: 0.9651, Generator Loss: 1.5906 D(x): 0.7121, D(G(z)): 0.2905 Epoch: [17/20], Batch Num: [184/600] Discriminator Loss: 0.7976, Generator Loss: 1.7110 D(x): 0.7634, D(G(z)): 0.3138 Epoch: [17/20], Batch Num: [185/600] Discriminator Loss: 0.9408, Generator Loss: 1.6072 D(x): 0.6978, D(G(z)): 0.3115 Epoch: [17/20], Batch Num: [186/600] Discriminator Loss: 1.0249, Generator Loss: 1.6151 D(x): 0.6522, D(G(z)): 0.2902 Epoch: [17/20], Batch Num: [187/600] Discriminator Loss: 0.9789, Generator Loss: 1.4724 D(x): 0.6897, D(G(z)): 0.3077 Epoch: [17/20], Batch Num: [188/600] Discriminator Loss: 0.9420, Generator Loss: 1.7130 D(x): 0.7110, D(G(z)): 0.3177 Epoch: [17/20], Batch Num: [189/600] Discriminator Loss: 0.7981, Generator Loss: 1.8182 D(x): 0.7338, D(G(z)): 0.2920 Epoch: [17/20], Batch Num: [190/600] Discriminator Loss: 0.8611, Generator Loss: 1.5880 D(x): 0.7306, D(G(z)): 0.2894 Epoch: [17/20], Batch Num: [191/600] Discriminator Loss: 0.9849, Generator Loss: 1.6002 D(x): 0.6702, D(G(z)): 0.2884 Epoch: [17/20], Batch Num: [192/600] Discriminator Loss: 0.9153, Generator Loss: 1.4813 D(x): 0.6982, D(G(z)): 0.3001 Epoch: [17/20], Batch Num: [193/600] Discriminator Loss: 0.8447, Generator Loss: 1.7124 D(x): 0.7479, D(G(z)): 0.3010 Epoch: [17/20], Batch Num: [194/600] Discriminator Loss: 0.9207, Generator Loss: 1.7207 D(x): 0.6954, D(G(z)): 0.2698 Epoch: [17/20], Batch Num: [195/600] Discriminator Loss: 0.8788, Generator Loss: 1.7117 D(x): 0.7035, D(G(z)): 0.2886 Epoch: [17/20], Batch Num: [196/600] Discriminator Loss: 0.7606, Generator Loss: 1.6158 D(x): 0.7250, D(G(z)): 0.2630 Epoch: [17/20], Batch Num: [197/600] Discriminator Loss: 0.7469, Generator Loss: 1.6241 D(x): 0.7944, D(G(z)): 0.3159 Epoch: [17/20], Batch Num: [198/600] Discriminator Loss: 0.9114, Generator Loss: 1.7173 D(x): 0.7076, D(G(z)): 0.3090 Epoch: [17/20], Batch Num: [199/600] Discriminator Loss: 0.8637, Generator Loss: 1.6305 D(x): 0.7060, D(G(z)): 0.2748 Epoch: 17, Batch Num: [200/600]
Epoch: [17/20], Batch Num: [200/600] Discriminator Loss: 0.8560, Generator Loss: 1.6610 D(x): 0.6795, D(G(z)): 0.2540 Epoch: [17/20], Batch Num: [201/600] Discriminator Loss: 0.8203, Generator Loss: 1.8948 D(x): 0.7573, D(G(z)): 0.2961 Epoch: [17/20], Batch Num: [202/600] Discriminator Loss: 0.9333, Generator Loss: 1.7317 D(x): 0.7021, D(G(z)): 0.3095 Epoch: [17/20], Batch Num: [203/600] Discriminator Loss: 0.7331, Generator Loss: 1.8168 D(x): 0.7653, D(G(z)): 0.2738 Epoch: [17/20], Batch Num: [204/600] Discriminator Loss: 0.9020, Generator Loss: 1.9041 D(x): 0.6683, D(G(z)): 0.2402 Epoch: [17/20], Batch Num: [205/600] Discriminator Loss: 0.7160, Generator Loss: 1.7919 D(x): 0.7626, D(G(z)): 0.2499 Epoch: [17/20], Batch Num: [206/600] Discriminator Loss: 0.7911, Generator Loss: 1.6945 D(x): 0.7365, D(G(z)): 0.2588 Epoch: [17/20], Batch Num: [207/600] Discriminator Loss: 0.7946, Generator Loss: 1.7235 D(x): 0.7293, D(G(z)): 0.2509 Epoch: [17/20], Batch Num: [208/600] Discriminator Loss: 0.9002, Generator Loss: 1.8063 D(x): 0.7304, D(G(z)): 0.3147 Epoch: [17/20], Batch Num: [209/600] Discriminator Loss: 0.7643, Generator Loss: 1.8273 D(x): 0.7778, D(G(z)): 0.2939 Epoch: [17/20], Batch Num: [210/600] Discriminator Loss: 0.8514, Generator Loss: 1.9650 D(x): 0.7539, D(G(z)): 0.3024 Epoch: [17/20], Batch Num: [211/600] Discriminator Loss: 0.9152, Generator Loss: 2.1397 D(x): 0.7064, D(G(z)): 0.2668 Epoch: [17/20], Batch Num: [212/600] Discriminator Loss: 0.8287, Generator Loss: 2.1472 D(x): 0.6966, D(G(z)): 0.2235 Epoch: [17/20], Batch Num: [213/600] Discriminator Loss: 0.8087, Generator Loss: 1.9410 D(x): 0.6924, D(G(z)): 0.2285 Epoch: [17/20], Batch Num: [214/600] Discriminator Loss: 0.8647, Generator Loss: 1.6624 D(x): 0.6933, D(G(z)): 0.2480 Epoch: [17/20], Batch Num: [215/600] Discriminator Loss: 0.7907, Generator Loss: 1.4996 D(x): 0.7560, D(G(z)): 0.3165 Epoch: [17/20], Batch Num: [216/600] Discriminator Loss: 0.8972, Generator Loss: 1.6426 D(x): 0.8132, D(G(z)): 0.3575 Epoch: [17/20], Batch Num: [217/600] Discriminator Loss: 0.8289, Generator Loss: 1.8418 D(x): 0.7769, D(G(z)): 0.3084 Epoch: [17/20], Batch Num: [218/600] Discriminator Loss: 0.9084, Generator Loss: 2.1604 D(x): 0.6833, D(G(z)): 0.2696 Epoch: [17/20], Batch Num: [219/600] Discriminator Loss: 0.9612, Generator Loss: 1.9710 D(x): 0.6129, D(G(z)): 0.2132 Epoch: [17/20], Batch Num: [220/600] Discriminator Loss: 0.8017, Generator Loss: 1.6518 D(x): 0.6800, D(G(z)): 0.2125 Epoch: [17/20], Batch Num: [221/600] Discriminator Loss: 0.8230, Generator Loss: 1.4694 D(x): 0.7556, D(G(z)): 0.3097 Epoch: [17/20], Batch Num: [222/600] Discriminator Loss: 0.8098, Generator Loss: 1.4935 D(x): 0.8349, D(G(z)): 0.3699 Epoch: [17/20], Batch Num: [223/600] Discriminator Loss: 0.8399, Generator Loss: 2.0200 D(x): 0.7923, D(G(z)): 0.3509 Epoch: [17/20], Batch Num: [224/600] Discriminator Loss: 0.8106, Generator Loss: 2.1990 D(x): 0.6919, D(G(z)): 0.2187 Epoch: [17/20], Batch Num: [225/600] Discriminator Loss: 0.9051, Generator Loss: 2.0031 D(x): 0.6725, D(G(z)): 0.2475 Epoch: [17/20], Batch Num: [226/600] Discriminator Loss: 0.8554, Generator Loss: 1.8749 D(x): 0.6798, D(G(z)): 0.2425 Epoch: [17/20], Batch Num: [227/600] Discriminator Loss: 0.8393, Generator Loss: 1.4760 D(x): 0.6939, D(G(z)): 0.2340 Epoch: [17/20], Batch Num: [228/600] Discriminator Loss: 0.9501, Generator Loss: 1.4046 D(x): 0.7449, D(G(z)): 0.3384 Epoch: [17/20], Batch Num: [229/600] Discriminator Loss: 0.7970, Generator Loss: 1.5733 D(x): 0.8531, D(G(z)): 0.3851 Epoch: [17/20], Batch Num: [230/600] Discriminator Loss: 0.7619, Generator Loss: 2.0186 D(x): 0.7742, D(G(z)): 0.2976 Epoch: [17/20], Batch Num: [231/600] Discriminator Loss: 0.7567, Generator Loss: 2.0496 D(x): 0.7359, D(G(z)): 0.2437 Epoch: [17/20], Batch Num: [232/600] Discriminator Loss: 0.8556, Generator Loss: 1.8758 D(x): 0.7046, D(G(z)): 0.2169 Epoch: [17/20], Batch Num: [233/600] Discriminator Loss: 0.7705, Generator Loss: 1.8400 D(x): 0.7157, D(G(z)): 0.2100 Epoch: [17/20], Batch Num: [234/600] Discriminator Loss: 0.7962, Generator Loss: 1.5569 D(x): 0.7165, D(G(z)): 0.2475 Epoch: [17/20], Batch Num: [235/600] Discriminator Loss: 0.6886, Generator Loss: 1.4787 D(x): 0.8084, D(G(z)): 0.2932 Epoch: [17/20], Batch Num: [236/600] Discriminator Loss: 1.0511, Generator Loss: 1.6635 D(x): 0.7328, D(G(z)): 0.3532 Epoch: [17/20], Batch Num: [237/600] Discriminator Loss: 0.8522, Generator Loss: 1.8479 D(x): 0.7810, D(G(z)): 0.3398 Epoch: [17/20], Batch Num: [238/600] Discriminator Loss: 0.7581, Generator Loss: 2.0161 D(x): 0.7706, D(G(z)): 0.2690 Epoch: [17/20], Batch Num: [239/600] Discriminator Loss: 0.8799, Generator Loss: 2.1927 D(x): 0.6902, D(G(z)): 0.2320 Epoch: [17/20], Batch Num: [240/600] Discriminator Loss: 0.7996, Generator Loss: 1.9114 D(x): 0.6760, D(G(z)): 0.1606 Epoch: [17/20], Batch Num: [241/600] Discriminator Loss: 0.7507, Generator Loss: 1.7921 D(x): 0.7583, D(G(z)): 0.2450 Epoch: [17/20], Batch Num: 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1.4046 D(x): 0.8268, D(G(z)): 0.3444 Epoch: [17/20], Batch Num: [251/600] Discriminator Loss: 0.8216, Generator Loss: 1.6947 D(x): 0.8029, D(G(z)): 0.3333 Epoch: [17/20], Batch Num: [252/600] Discriminator Loss: 0.7822, Generator Loss: 2.1290 D(x): 0.7910, D(G(z)): 0.2934 Epoch: [17/20], Batch Num: [253/600] Discriminator Loss: 0.8154, Generator Loss: 2.1573 D(x): 0.7057, D(G(z)): 0.2150 Epoch: [17/20], Batch Num: [254/600] Discriminator Loss: 0.6997, Generator Loss: 2.1732 D(x): 0.7204, D(G(z)): 0.1550 Epoch: [17/20], Batch Num: [255/600] Discriminator Loss: 0.8879, Generator Loss: 1.8969 D(x): 0.6886, D(G(z)): 0.2336 Epoch: [17/20], Batch Num: [256/600] Discriminator Loss: 0.6793, Generator Loss: 1.5803 D(x): 0.7673, D(G(z)): 0.2309 Epoch: [17/20], Batch Num: [257/600] Discriminator Loss: 0.7894, Generator Loss: 1.4671 D(x): 0.7883, D(G(z)): 0.3096 Epoch: [17/20], Batch Num: [258/600] Discriminator Loss: 0.8238, Generator Loss: 1.6113 D(x): 0.7949, D(G(z)): 0.3181 Epoch: [17/20], Batch Num: [259/600] Discriminator Loss: 0.7086, Generator Loss: 1.8251 D(x): 0.8143, D(G(z)): 0.2985 Epoch: [17/20], Batch Num: [260/600] Discriminator Loss: 0.9010, Generator Loss: 2.1439 D(x): 0.7243, D(G(z)): 0.2554 Epoch: [17/20], Batch Num: [261/600] Discriminator Loss: 0.8353, Generator Loss: 2.0725 D(x): 0.6931, D(G(z)): 0.2252 Epoch: [17/20], Batch Num: [262/600] Discriminator Loss: 0.8248, Generator Loss: 1.8720 D(x): 0.6957, D(G(z)): 0.2134 Epoch: [17/20], Batch Num: [263/600] Discriminator Loss: 0.8326, Generator Loss: 1.8436 D(x): 0.7556, D(G(z)): 0.2607 Epoch: [17/20], Batch Num: [264/600] Discriminator Loss: 0.7661, Generator Loss: 1.8134 D(x): 0.7701, D(G(z)): 0.2612 Epoch: [17/20], Batch Num: [265/600] Discriminator Loss: 0.8040, Generator Loss: 1.8636 D(x): 0.7425, D(G(z)): 0.2741 Epoch: [17/20], Batch Num: [266/600] Discriminator Loss: 0.9007, Generator Loss: 1.8905 D(x): 0.7709, D(G(z)): 0.3101 Epoch: [17/20], Batch Num: [267/600] Discriminator Loss: 0.9573, Generator Loss: 1.9927 D(x): 0.7625, D(G(z)): 0.3179 Epoch: [17/20], Batch Num: [268/600] Discriminator Loss: 0.8053, Generator Loss: 1.8967 D(x): 0.7044, D(G(z)): 0.2057 Epoch: [17/20], Batch Num: [269/600] Discriminator Loss: 0.9865, Generator Loss: 2.0733 D(x): 0.6624, D(G(z)): 0.2533 Epoch: [17/20], Batch Num: [270/600] Discriminator Loss: 0.7359, Generator Loss: 1.7739 D(x): 0.7246, D(G(z)): 0.2187 Epoch: [17/20], Batch Num: [271/600] Discriminator Loss: 0.9455, Generator Loss: 1.5475 D(x): 0.7258, D(G(z)): 0.2982 Epoch: [17/20], Batch Num: [272/600] Discriminator Loss: 1.0272, Generator Loss: 1.6140 D(x): 0.7296, D(G(z)): 0.3535 Epoch: [17/20], Batch Num: [273/600] Discriminator Loss: 0.9259, Generator Loss: 1.6820 D(x): 0.7130, D(G(z)): 0.3083 Epoch: [17/20], Batch Num: [274/600] Discriminator Loss: 0.9406, Generator Loss: 1.6156 D(x): 0.7247, D(G(z)): 0.3137 Epoch: [17/20], Batch Num: [275/600] Discriminator Loss: 1.0890, Generator Loss: 2.0659 D(x): 0.7592, D(G(z)): 0.3771 Epoch: [17/20], Batch Num: [276/600] Discriminator Loss: 1.0081, Generator Loss: 2.0931 D(x): 0.6707, D(G(z)): 0.2925 Epoch: [17/20], Batch Num: [277/600] Discriminator Loss: 1.1580, Generator Loss: 2.0139 D(x): 0.5991, D(G(z)): 0.2563 Epoch: [17/20], Batch Num: [278/600] Discriminator Loss: 0.9447, Generator Loss: 1.8060 D(x): 0.6267, D(G(z)): 0.2165 Epoch: [17/20], Batch Num: [279/600] Discriminator Loss: 1.2655, Generator Loss: 1.5107 D(x): 0.6220, D(G(z)): 0.3364 Epoch: [17/20], Batch Num: [280/600] Discriminator Loss: 0.9383, Generator Loss: 1.5019 D(x): 0.7336, D(G(z)): 0.3291 Epoch: [17/20], Batch Num: [281/600] Discriminator Loss: 1.1235, Generator Loss: 1.4746 D(x): 0.6914, D(G(z)): 0.3495 Epoch: [17/20], Batch Num: [282/600] Discriminator Loss: 1.0253, Generator Loss: 1.4999 D(x): 0.6910, D(G(z)): 0.3124 Epoch: [17/20], Batch Num: [283/600] Discriminator Loss: 0.9598, Generator Loss: 1.2262 D(x): 0.7136, D(G(z)): 0.3087 Epoch: [17/20], Batch Num: [284/600] Discriminator Loss: 1.0681, Generator Loss: 1.5564 D(x): 0.6520, D(G(z)): 0.3189 Epoch: [17/20], Batch Num: [285/600] Discriminator Loss: 0.9921, Generator Loss: 1.3148 D(x): 0.6560, D(G(z)): 0.3188 Epoch: [17/20], Batch Num: [286/600] Discriminator Loss: 0.9826, Generator Loss: 1.4685 D(x): 0.6990, D(G(z)): 0.3190 Epoch: [17/20], Batch Num: [287/600] Discriminator Loss: 1.0190, Generator Loss: 1.4429 D(x): 0.6621, D(G(z)): 0.3094 Epoch: [17/20], Batch Num: [288/600] Discriminator Loss: 1.1314, Generator Loss: 1.3629 D(x): 0.6357, D(G(z)): 0.3127 Epoch: [17/20], Batch Num: [289/600] Discriminator Loss: 0.9879, Generator Loss: 1.4677 D(x): 0.6804, D(G(z)): 0.3121 Epoch: [17/20], Batch Num: [290/600] Discriminator Loss: 0.8983, Generator Loss: 1.5563 D(x): 0.7002, D(G(z)): 0.2903 Epoch: [17/20], Batch Num: [291/600] Discriminator Loss: 1.0047, Generator Loss: 1.5250 D(x): 0.6543, D(G(z)): 0.2960 Epoch: [17/20], Batch Num: [292/600] Discriminator Loss: 0.9891, Generator Loss: 1.4908 D(x): 0.6700, D(G(z)): 0.3260 Epoch: [17/20], Batch Num: [293/600] Discriminator Loss: 0.9874, Generator Loss: 1.4952 D(x): 0.6838, D(G(z)): 0.3260 Epoch: [17/20], Batch Num: [294/600] Discriminator Loss: 0.9518, Generator Loss: 1.4087 D(x): 0.6781, D(G(z)): 0.3311 Epoch: [17/20], Batch Num: [295/600] Discriminator Loss: 0.9961, Generator Loss: 1.6360 D(x): 0.6618, D(G(z)): 0.3071 Epoch: [17/20], Batch Num: [296/600] Discriminator Loss: 0.9727, Generator Loss: 1.8411 D(x): 0.6727, D(G(z)): 0.3218 Epoch: [17/20], Batch Num: [297/600] Discriminator Loss: 0.8706, Generator Loss: 1.7889 D(x): 0.6742, D(G(z)): 0.2671 Epoch: [17/20], Batch Num: [298/600] Discriminator Loss: 0.8598, Generator Loss: 1.6680 D(x): 0.6834, D(G(z)): 0.2695 Epoch: [17/20], Batch Num: [299/600] Discriminator Loss: 0.8059, Generator Loss: 1.7731 D(x): 0.6917, D(G(z)): 0.2507 Epoch: 17, Batch Num: [300/600]
Epoch: [17/20], Batch Num: [300/600] Discriminator Loss: 0.9017, Generator Loss: 1.6835 D(x): 0.7112, D(G(z)): 0.2964 Epoch: [17/20], Batch Num: [301/600] Discriminator Loss: 0.9306, Generator Loss: 1.6905 D(x): 0.6841, D(G(z)): 0.2995 Epoch: [17/20], Batch Num: [302/600] Discriminator Loss: 0.9094, Generator Loss: 1.8773 D(x): 0.7149, D(G(z)): 0.3189 Epoch: [17/20], Batch Num: [303/600] Discriminator Loss: 0.8795, Generator Loss: 1.7648 D(x): 0.6836, D(G(z)): 0.2565 Epoch: [17/20], Batch Num: [304/600] Discriminator Loss: 0.7620, Generator Loss: 1.9576 D(x): 0.7430, D(G(z)): 0.2740 Epoch: [17/20], Batch Num: [305/600] Discriminator Loss: 0.7079, Generator Loss: 1.7724 D(x): 0.7227, D(G(z)): 0.2363 Epoch: [17/20], Batch Num: [306/600] Discriminator Loss: 0.8529, Generator Loss: 1.7698 D(x): 0.6899, D(G(z)): 0.2589 Epoch: [17/20], Batch Num: [307/600] Discriminator Loss: 0.7541, Generator Loss: 1.7789 D(x): 0.7504, D(G(z)): 0.2701 Epoch: [17/20], Batch Num: [308/600] Discriminator Loss: 0.8816, Generator Loss: 1.6280 D(x): 0.6995, D(G(z)): 0.2846 Epoch: [17/20], Batch Num: [309/600] Discriminator Loss: 0.8001, Generator Loss: 1.8443 D(x): 0.7154, D(G(z)): 0.2612 Epoch: [17/20], Batch Num: [310/600] Discriminator Loss: 0.8839, Generator Loss: 1.5685 D(x): 0.7011, D(G(z)): 0.2774 Epoch: [17/20], Batch Num: [311/600] Discriminator Loss: 0.9243, Generator Loss: 1.7417 D(x): 0.7095, D(G(z)): 0.3215 Epoch: [17/20], Batch Num: [312/600] Discriminator Loss: 0.8262, Generator Loss: 1.9539 D(x): 0.7236, D(G(z)): 0.2834 Epoch: [17/20], Batch Num: [313/600] Discriminator Loss: 0.8752, Generator Loss: 1.7461 D(x): 0.7088, D(G(z)): 0.2853 Epoch: [17/20], Batch Num: [314/600] Discriminator Loss: 0.9995, Generator Loss: 1.8796 D(x): 0.6602, D(G(z)): 0.2964 Epoch: [17/20], Batch Num: [315/600] Discriminator Loss: 0.7318, Generator Loss: 1.7440 D(x): 0.7367, D(G(z)): 0.2561 Epoch: [17/20], Batch Num: [316/600] Discriminator Loss: 0.8533, Generator Loss: 1.5897 D(x): 0.7024, D(G(z)): 0.2737 Epoch: [17/20], Batch Num: [317/600] Discriminator Loss: 0.9545, Generator Loss: 1.5637 D(x): 0.7108, D(G(z)): 0.3248 Epoch: [17/20], Batch Num: [318/600] Discriminator Loss: 0.8399, Generator Loss: 1.6514 D(x): 0.7125, D(G(z)): 0.2515 Epoch: [17/20], Batch Num: [319/600] Discriminator Loss: 1.0288, Generator Loss: 1.7577 D(x): 0.6971, D(G(z)): 0.3273 Epoch: [17/20], Batch Num: [320/600] Discriminator Loss: 0.9917, Generator Loss: 1.4574 D(x): 0.6586, D(G(z)): 0.2856 Epoch: [17/20], Batch Num: [321/600] Discriminator Loss: 0.9064, Generator Loss: 1.5755 D(x): 0.7134, D(G(z)): 0.3191 Epoch: [17/20], Batch Num: [322/600] Discriminator Loss: 1.1012, Generator Loss: 1.3787 D(x): 0.6639, D(G(z)): 0.3241 Epoch: [17/20], Batch Num: [323/600] Discriminator Loss: 0.9765, Generator Loss: 1.3160 D(x): 0.6535, D(G(z)): 0.2890 Epoch: [17/20], Batch Num: [324/600] Discriminator Loss: 0.9360, Generator Loss: 1.4035 D(x): 0.7114, D(G(z)): 0.3538 Epoch: [17/20], Batch Num: [325/600] Discriminator Loss: 0.9553, Generator Loss: 1.5395 D(x): 0.7481, D(G(z)): 0.3555 Epoch: [17/20], Batch Num: [326/600] Discriminator Loss: 1.0487, Generator Loss: 1.5100 D(x): 0.6577, D(G(z)): 0.3142 Epoch: [17/20], Batch Num: [327/600] Discriminator Loss: 1.0909, Generator Loss: 1.3364 D(x): 0.6167, D(G(z)): 0.2852 Epoch: [17/20], Batch Num: [328/600] Discriminator Loss: 1.0107, Generator Loss: 1.3868 D(x): 0.6881, D(G(z)): 0.3431 Epoch: [17/20], Batch Num: [329/600] Discriminator Loss: 1.0992, Generator Loss: 1.4366 D(x): 0.6229, D(G(z)): 0.3199 Epoch: [17/20], Batch Num: [330/600] Discriminator Loss: 0.9556, Generator Loss: 1.4010 D(x): 0.6897, D(G(z)): 0.3388 Epoch: [17/20], Batch Num: [331/600] Discriminator Loss: 0.9831, Generator Loss: 1.3234 D(x): 0.6797, D(G(z)): 0.3405 Epoch: [17/20], Batch Num: [332/600] Discriminator Loss: 0.9798, Generator Loss: 1.2250 D(x): 0.6823, D(G(z)): 0.3493 Epoch: [17/20], Batch Num: [333/600] Discriminator Loss: 0.8760, Generator Loss: 1.2765 D(x): 0.7466, D(G(z)): 0.3489 Epoch: [17/20], Batch Num: [334/600] Discriminator Loss: 0.7734, Generator Loss: 1.4645 D(x): 0.7572, D(G(z)): 0.3176 Epoch: [17/20], Batch Num: [335/600] Discriminator Loss: 0.9315, Generator Loss: 1.4400 D(x): 0.6785, D(G(z)): 0.3269 Epoch: [17/20], Batch Num: [336/600] Discriminator Loss: 0.9254, Generator Loss: 1.4682 D(x): 0.6790, D(G(z)): 0.3133 Epoch: [17/20], Batch Num: [337/600] Discriminator Loss: 0.8161, Generator Loss: 1.5457 D(x): 0.7412, D(G(z)): 0.3118 Epoch: [17/20], Batch Num: [338/600] Discriminator Loss: 0.7422, Generator Loss: 1.4838 D(x): 0.7098, D(G(z)): 0.2560 Epoch: [17/20], Batch Num: [339/600] Discriminator Loss: 0.8210, Generator Loss: 1.2670 D(x): 0.6996, D(G(z)): 0.2847 Epoch: [17/20], Batch Num: [340/600] Discriminator Loss: 0.8933, Generator Loss: 1.3502 D(x): 0.7248, D(G(z)): 0.3500 Epoch: [17/20], Batch Num: [341/600] Discriminator Loss: 0.9082, Generator Loss: 1.3834 D(x): 0.7550, D(G(z)): 0.3569 Epoch: [17/20], Batch Num: [342/600] Discriminator Loss: 0.8493, Generator Loss: 1.4313 D(x): 0.7321, D(G(z)): 0.3142 Epoch: [17/20], Batch Num: [343/600] Discriminator Loss: 0.8664, Generator Loss: 1.6769 D(x): 0.7361, D(G(z)): 0.3394 Epoch: [17/20], Batch Num: [344/600] Discriminator Loss: 0.8186, Generator Loss: 1.6579 D(x): 0.7038, D(G(z)): 0.2480 Epoch: [17/20], Batch Num: [345/600] Discriminator Loss: 0.7659, Generator Loss: 1.6666 D(x): 0.7240, D(G(z)): 0.2618 Epoch: [17/20], Batch Num: [346/600] Discriminator Loss: 0.7634, Generator Loss: 1.5376 D(x): 0.7177, D(G(z)): 0.2462 Epoch: [17/20], Batch Num: [347/600] Discriminator Loss: 0.7253, Generator Loss: 1.6445 D(x): 0.7444, D(G(z)): 0.2605 Epoch: [17/20], Batch Num: [348/600] Discriminator Loss: 0.7398, Generator Loss: 1.2946 D(x): 0.7687, D(G(z)): 0.2990 Epoch: [17/20], Batch Num: [349/600] Discriminator Loss: 0.8578, Generator Loss: 1.5991 D(x): 0.7770, D(G(z)): 0.3466 Epoch: [17/20], Batch Num: [350/600] Discriminator Loss: 0.7897, Generator Loss: 1.6796 D(x): 0.7730, D(G(z)): 0.3221 Epoch: [17/20], Batch Num: [351/600] Discriminator Loss: 0.7644, Generator Loss: 1.5882 D(x): 0.7130, D(G(z)): 0.2564 Epoch: [17/20], Batch Num: [352/600] Discriminator Loss: 0.8946, Generator Loss: 1.7307 D(x): 0.7155, D(G(z)): 0.2920 Epoch: [17/20], Batch Num: [353/600] Discriminator Loss: 0.6682, Generator Loss: 1.7568 D(x): 0.7514, D(G(z)): 0.2120 Epoch: [17/20], Batch Num: [354/600] Discriminator Loss: 0.7846, Generator Loss: 1.6053 D(x): 0.7358, D(G(z)): 0.2638 Epoch: [17/20], Batch Num: [355/600] Discriminator Loss: 0.7083, Generator Loss: 1.4841 D(x): 0.7903, D(G(z)): 0.2741 Epoch: [17/20], Batch Num: [356/600] Discriminator Loss: 0.7137, Generator Loss: 1.7284 D(x): 0.8192, D(G(z)): 0.3061 Epoch: [17/20], Batch Num: [357/600] Discriminator Loss: 0.7511, Generator Loss: 1.5736 D(x): 0.7578, D(G(z)): 0.2642 Epoch: [17/20], Batch Num: [358/600] Discriminator Loss: 0.8951, Generator Loss: 1.6636 D(x): 0.7075, D(G(z)): 0.2579 Epoch: [17/20], Batch Num: [359/600] Discriminator Loss: 0.6536, Generator Loss: 1.8326 D(x): 0.7853, D(G(z)): 0.2441 Epoch: [17/20], Batch Num: [360/600] Discriminator Loss: 0.7337, Generator Loss: 1.7742 D(x): 0.8008, D(G(z)): 0.2676 Epoch: [17/20], Batch Num: [361/600] Discriminator Loss: 0.7468, Generator Loss: 1.8637 D(x): 0.7887, D(G(z)): 0.2819 Epoch: [17/20], Batch Num: [362/600] Discriminator Loss: 0.7334, Generator Loss: 1.8092 D(x): 0.7470, D(G(z)): 0.2320 Epoch: [17/20], Batch Num: [363/600] Discriminator Loss: 0.7956, Generator Loss: 1.9257 D(x): 0.7531, D(G(z)): 0.2478 Epoch: [17/20], Batch Num: [364/600] Discriminator Loss: 0.7412, Generator Loss: 1.8282 D(x): 0.7842, D(G(z)): 0.2847 Epoch: [17/20], Batch Num: [365/600] Discriminator Loss: 0.6679, Generator Loss: 1.8506 D(x): 0.7599, D(G(z)): 0.2315 Epoch: [17/20], Batch Num: [366/600] Discriminator Loss: 0.7112, Generator Loss: 1.6906 D(x): 0.7748, D(G(z)): 0.2524 Epoch: [17/20], Batch Num: [367/600] Discriminator Loss: 0.6345, Generator Loss: 1.9491 D(x): 0.7879, D(G(z)): 0.2366 Epoch: [17/20], Batch Num: [368/600] Discriminator Loss: 0.7728, Generator Loss: 1.8804 D(x): 0.7490, D(G(z)): 0.2471 Epoch: [17/20], Batch Num: [369/600] Discriminator Loss: 0.6657, Generator Loss: 1.6741 D(x): 0.7828, D(G(z)): 0.2417 Epoch: [17/20], Batch Num: [370/600] Discriminator Loss: 0.8165, Generator Loss: 1.9058 D(x): 0.7987, D(G(z)): 0.2862 Epoch: [17/20], Batch Num: [371/600] Discriminator Loss: 0.5877, Generator Loss: 1.8905 D(x): 0.8232, D(G(z)): 0.2314 Epoch: [17/20], Batch Num: [372/600] Discriminator Loss: 0.6845, Generator Loss: 2.0099 D(x): 0.7995, D(G(z)): 0.2503 Epoch: [17/20], Batch Num: [373/600] Discriminator Loss: 0.6874, Generator Loss: 2.1872 D(x): 0.7876, D(G(z)): 0.2425 Epoch: [17/20], Batch Num: [374/600] Discriminator Loss: 0.9022, Generator Loss: 1.9776 D(x): 0.6690, D(G(z)): 0.2089 Epoch: [17/20], Batch Num: [375/600] Discriminator Loss: 0.7370, Generator Loss: 1.8093 D(x): 0.7809, D(G(z)): 0.2240 Epoch: [17/20], Batch Num: [376/600] Discriminator Loss: 0.6461, Generator Loss: 1.5736 D(x): 0.8143, D(G(z)): 0.2402 Epoch: [17/20], Batch Num: [377/600] Discriminator Loss: 0.6767, Generator Loss: 1.8413 D(x): 0.8288, D(G(z)): 0.2713 Epoch: [17/20], Batch Num: [378/600] Discriminator Loss: 0.6575, Generator Loss: 2.1914 D(x): 0.8592, D(G(z)): 0.2856 Epoch: [17/20], Batch Num: [379/600] Discriminator Loss: 0.6661, Generator Loss: 2.0252 D(x): 0.7556, D(G(z)): 0.1935 Epoch: [17/20], Batch Num: [380/600] Discriminator Loss: 0.9229, Generator Loss: 1.7643 D(x): 0.6690, D(G(z)): 0.2124 Epoch: [17/20], Batch Num: [381/600] Discriminator Loss: 0.7206, Generator Loss: 1.7467 D(x): 0.7777, D(G(z)): 0.2430 Epoch: [17/20], Batch Num: [382/600] Discriminator Loss: 0.7307, Generator Loss: 1.4886 D(x): 0.8362, D(G(z)): 0.3125 Epoch: [17/20], Batch Num: [383/600] Discriminator Loss: 0.7638, Generator Loss: 1.8674 D(x): 0.8180, D(G(z)): 0.3210 Epoch: [17/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [17/20], Batch Num: [400/600] Discriminator Loss: 0.8536, Generator Loss: 1.8059 D(x): 0.6731, D(G(z)): 0.2289 Epoch: [17/20], Batch Num: [401/600] Discriminator Loss: 1.0162, Generator Loss: 1.5490 D(x): 0.6375, D(G(z)): 0.2640 Epoch: [17/20], Batch Num: [402/600] Discriminator Loss: 0.8065, Generator Loss: 1.6368 D(x): 0.7395, D(G(z)): 0.2807 Epoch: [17/20], Batch Num: [403/600] Discriminator Loss: 0.8106, Generator Loss: 1.6499 D(x): 0.7737, D(G(z)): 0.3004 Epoch: [17/20], Batch Num: [404/600] Discriminator Loss: 1.0147, Generator Loss: 1.8000 D(x): 0.7243, D(G(z)): 0.3264 Epoch: [17/20], Batch Num: [405/600] Discriminator Loss: 0.9143, Generator Loss: 1.9032 D(x): 0.7236, D(G(z)): 0.3022 Epoch: [17/20], Batch Num: [406/600] Discriminator Loss: 0.8633, Generator Loss: 2.0134 D(x): 0.6644, D(G(z)): 0.2472 Epoch: [17/20], Batch Num: [407/600] Discriminator Loss: 0.7223, Generator Loss: 1.9101 D(x): 0.7403, D(G(z)): 0.2296 Epoch: [17/20], Batch Num: [408/600] Discriminator Loss: 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Discriminator Loss: 0.9586, Generator Loss: 1.4674 D(x): 0.6384, D(G(z)): 0.2397 Epoch: [17/20], Batch Num: [426/600] Discriminator Loss: 1.0237, Generator Loss: 1.4164 D(x): 0.6961, D(G(z)): 0.3601 Epoch: [17/20], Batch Num: [427/600] Discriminator Loss: 1.1706, Generator Loss: 1.2224 D(x): 0.6596, D(G(z)): 0.3761 Epoch: [17/20], Batch Num: [428/600] Discriminator Loss: 1.0340, Generator Loss: 1.3718 D(x): 0.7292, D(G(z)): 0.3927 Epoch: [17/20], Batch Num: [429/600] Discriminator Loss: 0.9987, Generator Loss: 1.2997 D(x): 0.7136, D(G(z)): 0.3481 Epoch: [17/20], Batch Num: [430/600] Discriminator Loss: 0.9148, Generator Loss: 1.5695 D(x): 0.7048, D(G(z)): 0.3308 Epoch: [17/20], Batch Num: [431/600] Discriminator Loss: 0.9046, Generator Loss: 1.6711 D(x): 0.6965, D(G(z)): 0.3026 Epoch: [17/20], Batch Num: [432/600] Discriminator Loss: 0.8612, Generator Loss: 1.6147 D(x): 0.6709, D(G(z)): 0.2545 Epoch: [17/20], Batch Num: [433/600] Discriminator Loss: 0.9269, Generator Loss: 1.5911 D(x): 0.6463, D(G(z)): 0.2514 Epoch: [17/20], Batch Num: [434/600] Discriminator Loss: 0.9752, Generator Loss: 1.4550 D(x): 0.6415, D(G(z)): 0.2679 Epoch: [17/20], Batch Num: [435/600] Discriminator Loss: 1.0531, Generator Loss: 1.0245 D(x): 0.6713, D(G(z)): 0.3632 Epoch: [17/20], Batch Num: [436/600] Discriminator Loss: 1.0439, Generator Loss: 1.0832 D(x): 0.7468, D(G(z)): 0.4163 Epoch: [17/20], Batch Num: [437/600] Discriminator Loss: 1.0514, Generator Loss: 1.2322 D(x): 0.7402, D(G(z)): 0.4161 Epoch: [17/20], Batch Num: [438/600] Discriminator Loss: 0.9854, Generator Loss: 1.4845 D(x): 0.6924, D(G(z)): 0.3612 Epoch: [17/20], Batch Num: [439/600] Discriminator Loss: 0.9104, Generator Loss: 1.5661 D(x): 0.7137, D(G(z)): 0.3349 Epoch: [17/20], Batch Num: [440/600] Discriminator Loss: 0.9080, Generator Loss: 1.6874 D(x): 0.6609, D(G(z)): 0.2778 Epoch: [17/20], Batch Num: [441/600] Discriminator Loss: 0.9174, Generator Loss: 1.7114 D(x): 0.6584, D(G(z)): 0.2667 Epoch: [17/20], Batch Num: 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Generator Loss: 1.7921 D(x): 0.8178, D(G(z)): 0.3403 Epoch: [17/20], Batch Num: [468/600] Discriminator Loss: 0.8246, Generator Loss: 2.0363 D(x): 0.7572, D(G(z)): 0.2887 Epoch: [17/20], Batch Num: [469/600] Discriminator Loss: 0.7533, Generator Loss: 1.9467 D(x): 0.7257, D(G(z)): 0.2370 Epoch: [17/20], Batch Num: [470/600] Discriminator Loss: 1.0156, Generator Loss: 2.0850 D(x): 0.6288, D(G(z)): 0.2619 Epoch: [17/20], Batch Num: [471/600] Discriminator Loss: 0.7225, Generator Loss: 2.0240 D(x): 0.6957, D(G(z)): 0.1641 Epoch: [17/20], Batch Num: [472/600] Discriminator Loss: 0.8413, Generator Loss: 1.5871 D(x): 0.7221, D(G(z)): 0.2764 Epoch: [17/20], Batch Num: [473/600] Discriminator Loss: 1.0171, Generator Loss: 1.5289 D(x): 0.7285, D(G(z)): 0.3398 Epoch: [17/20], Batch Num: [474/600] Discriminator Loss: 0.9201, Generator Loss: 1.5537 D(x): 0.7805, D(G(z)): 0.3388 Epoch: [17/20], Batch Num: [475/600] Discriminator Loss: 0.8641, Generator Loss: 1.6797 D(x): 0.7808, D(G(z)): 0.3409 Epoch: [17/20], Batch Num: [476/600] Discriminator Loss: 0.8486, Generator Loss: 1.7916 D(x): 0.7226, D(G(z)): 0.2579 Epoch: [17/20], Batch Num: [477/600] Discriminator Loss: 0.9534, Generator Loss: 1.9081 D(x): 0.6990, D(G(z)): 0.2728 Epoch: [17/20], Batch Num: [478/600] Discriminator Loss: 0.9516, Generator Loss: 1.9814 D(x): 0.7051, D(G(z)): 0.2524 Epoch: [17/20], Batch Num: [479/600] Discriminator Loss: 0.9447, Generator Loss: 1.8760 D(x): 0.6388, D(G(z)): 0.2351 Epoch: [17/20], Batch Num: [480/600] Discriminator Loss: 0.9923, Generator Loss: 1.4017 D(x): 0.6751, D(G(z)): 0.2996 Epoch: [17/20], Batch Num: [481/600] Discriminator Loss: 0.9876, Generator Loss: 1.3968 D(x): 0.6792, D(G(z)): 0.2904 Epoch: [17/20], Batch Num: [482/600] Discriminator Loss: 1.0110, Generator Loss: 1.2836 D(x): 0.7011, D(G(z)): 0.3364 Epoch: [17/20], Batch Num: [483/600] Discriminator Loss: 1.2319, Generator Loss: 1.3379 D(x): 0.7055, D(G(z)): 0.4136 Epoch: [17/20], Batch Num: [484/600] Discriminator Loss: 1.1762, Generator Loss: 1.5270 D(x): 0.6805, D(G(z)): 0.3722 Epoch: [17/20], Batch Num: [485/600] Discriminator Loss: 0.9331, Generator Loss: 1.6116 D(x): 0.6857, D(G(z)): 0.2923 Epoch: [17/20], Batch Num: [486/600] Discriminator Loss: 0.9374, Generator Loss: 1.7868 D(x): 0.7002, D(G(z)): 0.2836 Epoch: [17/20], Batch Num: [487/600] Discriminator Loss: 0.9902, Generator Loss: 1.6172 D(x): 0.6434, D(G(z)): 0.2602 Epoch: [17/20], Batch Num: [488/600] Discriminator Loss: 0.8949, Generator Loss: 1.5050 D(x): 0.6635, D(G(z)): 0.2484 Epoch: [17/20], Batch Num: [489/600] Discriminator Loss: 0.8766, Generator Loss: 1.4078 D(x): 0.7098, D(G(z)): 0.2930 Epoch: [17/20], Batch Num: [490/600] Discriminator Loss: 0.8371, Generator Loss: 1.3369 D(x): 0.7243, D(G(z)): 0.3040 Epoch: [17/20], Batch Num: [491/600] Discriminator Loss: 0.9485, Generator Loss: 1.4575 D(x): 0.7542, D(G(z)): 0.3549 Epoch: [17/20], Batch Num: [492/600] Discriminator Loss: 0.9622, Generator Loss: 1.4172 D(x): 0.7166, D(G(z)): 0.3367 Epoch: [17/20], Batch Num: [493/600] Discriminator Loss: 0.8604, Generator Loss: 1.8113 D(x): 0.7681, D(G(z)): 0.3413 Epoch: [17/20], Batch Num: [494/600] Discriminator Loss: 0.8289, Generator Loss: 1.9419 D(x): 0.6782, D(G(z)): 0.2409 Epoch: [17/20], Batch Num: [495/600] Discriminator Loss: 0.7638, Generator Loss: 1.9778 D(x): 0.6806, D(G(z)): 0.2128 Epoch: [17/20], Batch Num: [496/600] Discriminator Loss: 0.7849, Generator Loss: 1.7264 D(x): 0.7217, D(G(z)): 0.2206 Epoch: [17/20], Batch Num: [497/600] Discriminator Loss: 0.7758, Generator Loss: 1.5626 D(x): 0.6995, D(G(z)): 0.2294 Epoch: [17/20], Batch Num: [498/600] Discriminator Loss: 0.7205, Generator Loss: 1.3618 D(x): 0.7404, D(G(z)): 0.2434 Epoch: [17/20], Batch Num: [499/600] Discriminator Loss: 0.9276, Generator Loss: 1.5755 D(x): 0.7623, D(G(z)): 0.3662 Epoch: 17, Batch Num: [500/600]
Epoch: [17/20], Batch Num: [500/600] Discriminator Loss: 0.8105, Generator Loss: 1.6720 D(x): 0.7804, D(G(z)): 0.3328 Epoch: [17/20], Batch Num: [501/600] Discriminator Loss: 0.6954, Generator Loss: 1.9987 D(x): 0.8021, D(G(z)): 0.2843 Epoch: [17/20], Batch Num: [502/600] Discriminator Loss: 0.7234, Generator Loss: 2.0454 D(x): 0.7146, D(G(z)): 0.2110 Epoch: [17/20], Batch Num: [503/600] Discriminator Loss: 0.8218, Generator Loss: 2.0531 D(x): 0.6707, D(G(z)): 0.2078 Epoch: [17/20], Batch Num: [504/600] Discriminator Loss: 0.7165, Generator Loss: 1.8588 D(x): 0.6816, D(G(z)): 0.1859 Epoch: [17/20], Batch Num: [505/600] Discriminator Loss: 0.6915, Generator Loss: 1.8664 D(x): 0.7472, D(G(z)): 0.2305 Epoch: [17/20], Batch Num: [506/600] Discriminator Loss: 0.7482, Generator Loss: 1.6461 D(x): 0.7581, D(G(z)): 0.2756 Epoch: [17/20], Batch Num: [507/600] Discriminator Loss: 0.7648, Generator Loss: 1.5405 D(x): 0.8023, D(G(z)): 0.2972 Epoch: [17/20], Batch Num: [508/600] Discriminator Loss: 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Discriminator Loss: 0.9923, Generator Loss: 1.0612 D(x): 0.7099, D(G(z)): 0.3353 Epoch: [17/20], Batch Num: [526/600] Discriminator Loss: 0.9266, Generator Loss: 1.1725 D(x): 0.7995, D(G(z)): 0.3897 Epoch: [17/20], Batch Num: [527/600] Discriminator Loss: 0.9665, Generator Loss: 1.4813 D(x): 0.8022, D(G(z)): 0.4252 Epoch: [17/20], Batch Num: [528/600] Discriminator Loss: 1.1012, Generator Loss: 1.5992 D(x): 0.6725, D(G(z)): 0.3302 Epoch: [17/20], Batch Num: [529/600] Discriminator Loss: 0.8911, Generator Loss: 1.7140 D(x): 0.6798, D(G(z)): 0.2795 Epoch: [17/20], Batch Num: [530/600] Discriminator Loss: 0.9738, Generator Loss: 1.7178 D(x): 0.6152, D(G(z)): 0.2172 Epoch: [17/20], Batch Num: [531/600] Discriminator Loss: 1.0409, Generator Loss: 1.4575 D(x): 0.5866, D(G(z)): 0.2487 Epoch: [17/20], Batch Num: [532/600] Discriminator Loss: 0.9477, Generator Loss: 1.1542 D(x): 0.6498, D(G(z)): 0.2841 Epoch: [17/20], Batch Num: [533/600] Discriminator Loss: 0.7684, Generator Loss: 1.0808 D(x): 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1.7848 D(x): 0.7610, D(G(z)): 0.2190 Epoch: [17/20], Batch Num: [551/600] Discriminator Loss: 0.7374, Generator Loss: 1.6297 D(x): 0.7939, D(G(z)): 0.2890 Epoch: [17/20], Batch Num: [552/600] Discriminator Loss: 0.8316, Generator Loss: 1.7166 D(x): 0.8304, D(G(z)): 0.3389 Epoch: [17/20], Batch Num: [553/600] Discriminator Loss: 0.6945, Generator Loss: 2.0005 D(x): 0.8207, D(G(z)): 0.2645 Epoch: [17/20], Batch Num: [554/600] Discriminator Loss: 0.5988, Generator Loss: 2.1298 D(x): 0.8111, D(G(z)): 0.2424 Epoch: [17/20], Batch Num: [555/600] Discriminator Loss: 0.8034, Generator Loss: 2.2481 D(x): 0.7131, D(G(z)): 0.2100 Epoch: [17/20], Batch Num: [556/600] Discriminator Loss: 0.7453, Generator Loss: 2.1072 D(x): 0.7355, D(G(z)): 0.2117 Epoch: [17/20], Batch Num: [557/600] Discriminator Loss: 0.6795, Generator Loss: 1.9689 D(x): 0.7621, D(G(z)): 0.1986 Epoch: [17/20], Batch Num: [558/600] Discriminator Loss: 0.7028, Generator Loss: 1.8405 D(x): 0.7851, D(G(z)): 0.2477 Epoch: [17/20], Batch Num: [559/600] Discriminator Loss: 0.8122, Generator Loss: 2.0468 D(x): 0.7105, D(G(z)): 0.2427 Epoch: [17/20], Batch Num: [560/600] Discriminator Loss: 0.8921, Generator Loss: 1.6599 D(x): 0.7554, D(G(z)): 0.3129 Epoch: [17/20], Batch Num: [561/600] Discriminator Loss: 0.9056, Generator Loss: 1.6868 D(x): 0.7565, D(G(z)): 0.3124 Epoch: [17/20], Batch Num: [562/600] Discriminator Loss: 0.8107, Generator Loss: 1.7072 D(x): 0.7600, D(G(z)): 0.2746 Epoch: [17/20], Batch Num: [563/600] Discriminator Loss: 0.9231, Generator Loss: 1.8349 D(x): 0.7323, D(G(z)): 0.3066 Epoch: [17/20], Batch Num: [564/600] Discriminator Loss: 0.8477, Generator Loss: 1.9917 D(x): 0.7126, D(G(z)): 0.2622 Epoch: [17/20], Batch Num: [565/600] Discriminator Loss: 0.8811, Generator Loss: 1.8363 D(x): 0.7031, D(G(z)): 0.2283 Epoch: [17/20], Batch Num: [566/600] Discriminator Loss: 0.8553, Generator Loss: 1.7376 D(x): 0.7088, D(G(z)): 0.2479 Epoch: [17/20], Batch Num: [567/600] Discriminator Loss: 0.9841, Generator Loss: 1.5086 D(x): 0.7500, D(G(z)): 0.3155 Epoch: [17/20], Batch Num: [568/600] Discriminator Loss: 0.9547, Generator Loss: 1.4537 D(x): 0.6704, D(G(z)): 0.2757 Epoch: [17/20], Batch Num: [569/600] Discriminator Loss: 0.8031, Generator Loss: 1.4615 D(x): 0.7879, D(G(z)): 0.3392 Epoch: [17/20], Batch Num: [570/600] Discriminator Loss: 1.1474, Generator Loss: 1.5162 D(x): 0.6789, D(G(z)): 0.3524 Epoch: [17/20], Batch Num: [571/600] Discriminator Loss: 1.0794, Generator Loss: 1.7353 D(x): 0.7118, D(G(z)): 0.3686 Epoch: [17/20], Batch Num: [572/600] Discriminator Loss: 1.0080, Generator Loss: 1.6622 D(x): 0.6408, D(G(z)): 0.2590 Epoch: [17/20], Batch Num: [573/600] Discriminator Loss: 1.0252, Generator Loss: 1.4948 D(x): 0.6465, D(G(z)): 0.2699 Epoch: [17/20], Batch Num: [574/600] Discriminator Loss: 1.0094, Generator Loss: 1.4727 D(x): 0.6927, D(G(z)): 0.2928 Epoch: [17/20], Batch Num: [575/600] Discriminator Loss: 1.0027, Generator Loss: 1.3192 D(x): 0.6409, D(G(z)): 0.2940 Epoch: [17/20], Batch Num: [576/600] Discriminator Loss: 0.7785, Generator Loss: 1.3032 D(x): 0.8171, D(G(z)): 0.3498 Epoch: [17/20], Batch Num: [577/600] Discriminator Loss: 1.1386, Generator Loss: 1.4329 D(x): 0.6903, D(G(z)): 0.3667 Epoch: [17/20], Batch Num: [578/600] Discriminator Loss: 0.9191, Generator Loss: 1.5025 D(x): 0.7468, D(G(z)): 0.3385 Epoch: [17/20], Batch Num: [579/600] Discriminator Loss: 1.0168, Generator Loss: 1.6745 D(x): 0.6722, D(G(z)): 0.3166 Epoch: [17/20], Batch Num: [580/600] Discriminator Loss: 0.8506, Generator Loss: 1.9007 D(x): 0.6767, D(G(z)): 0.2306 Epoch: [17/20], Batch Num: [581/600] Discriminator Loss: 0.8769, Generator Loss: 1.8211 D(x): 0.6453, D(G(z)): 0.2276 Epoch: [17/20], Batch Num: [582/600] Discriminator Loss: 0.7783, Generator Loss: 1.4873 D(x): 0.6870, D(G(z)): 0.2417 Epoch: [17/20], Batch Num: [583/600] Discriminator Loss: 0.7861, Generator Loss: 1.3333 D(x): 0.7138, D(G(z)): 0.2681 Epoch: [17/20], Batch Num: [584/600] Discriminator Loss: 0.9024, Generator Loss: 1.2477 D(x): 0.7433, D(G(z)): 0.3443 Epoch: [17/20], Batch Num: [585/600] Discriminator Loss: 0.7610, Generator Loss: 1.4746 D(x): 0.8101, D(G(z)): 0.3471 Epoch: [17/20], Batch Num: [586/600] Discriminator Loss: 0.7792, Generator Loss: 1.8242 D(x): 0.7522, D(G(z)): 0.2967 Epoch: [17/20], Batch Num: [587/600] Discriminator Loss: 0.7835, Generator Loss: 2.0134 D(x): 0.7533, D(G(z)): 0.3024 Epoch: [17/20], Batch Num: [588/600] Discriminator Loss: 0.7624, Generator Loss: 2.0769 D(x): 0.6988, D(G(z)): 0.2322 Epoch: [17/20], Batch Num: [589/600] Discriminator Loss: 0.8009, Generator Loss: 2.2336 D(x): 0.6622, D(G(z)): 0.1886 Epoch: [17/20], Batch Num: [590/600] Discriminator Loss: 0.7192, Generator Loss: 2.1547 D(x): 0.7203, D(G(z)): 0.2119 Epoch: [17/20], Batch Num: [591/600] Discriminator Loss: 0.5334, Generator Loss: 1.9715 D(x): 0.8080, D(G(z)): 0.2065 Epoch: [17/20], Batch Num: [592/600] Discriminator Loss: 0.5756, Generator Loss: 1.9217 D(x): 0.8240, D(G(z)): 0.2345 Epoch: [17/20], Batch Num: [593/600] Discriminator Loss: 0.6042, Generator Loss: 2.1405 D(x): 0.7801, D(G(z)): 0.2047 Epoch: [17/20], Batch Num: [594/600] Discriminator Loss: 0.6254, Generator Loss: 2.1464 D(x): 0.8221, D(G(z)): 0.2407 Epoch: [17/20], Batch Num: [595/600] Discriminator Loss: 0.5172, Generator Loss: 2.1769 D(x): 0.8164, D(G(z)): 0.1912 Epoch: [17/20], Batch Num: [596/600] Discriminator Loss: 0.4874, Generator Loss: 2.0646 D(x): 0.8454, D(G(z)): 0.2012 Epoch: [17/20], Batch Num: [597/600] Discriminator Loss: 0.5712, Generator Loss: 2.4152 D(x): 0.8017, D(G(z)): 0.1860 Epoch: [17/20], Batch Num: [598/600] Discriminator Loss: 0.6407, Generator Loss: 2.5058 D(x): 0.7747, D(G(z)): 0.1699 Epoch: [17/20], Batch Num: [599/600] Discriminator Loss: 0.6137, Generator Loss: 2.4721 D(x): 0.7558, D(G(z)): 0.1362 Epoch: 18, Batch Num: [0/600]
Epoch: [18/20], Batch Num: [0/600] Discriminator Loss: 0.6399, Generator Loss: 2.0375 D(x): 0.7959, D(G(z)): 0.2082 Epoch: [18/20], Batch Num: [1/600] Discriminator Loss: 0.5535, Generator Loss: 2.1049 D(x): 0.8344, D(G(z)): 0.1944 Epoch: [18/20], Batch Num: [2/600] Discriminator Loss: 0.7699, Generator Loss: 2.3058 D(x): 0.8004, D(G(z)): 0.2653 Epoch: [18/20], Batch Num: [3/600] Discriminator Loss: 0.7719, Generator Loss: 2.7096 D(x): 0.8138, D(G(z)): 0.2709 Epoch: [18/20], Batch Num: [4/600] Discriminator Loss: 0.7990, Generator Loss: 2.7461 D(x): 0.7123, D(G(z)): 0.1512 Epoch: [18/20], Batch Num: [5/600] Discriminator Loss: 0.8381, Generator Loss: 2.5747 D(x): 0.6939, D(G(z)): 0.1799 Epoch: [18/20], Batch Num: [6/600] Discriminator Loss: 0.7971, Generator Loss: 1.9757 D(x): 0.7282, D(G(z)): 0.2061 Epoch: [18/20], Batch Num: [7/600] Discriminator Loss: 0.9744, Generator Loss: 1.8371 D(x): 0.8175, D(G(z)): 0.3580 Epoch: [18/20], Batch Num: [8/600] Discriminator Loss: 0.9198, Generator Loss: 2.4928 D(x): 0.8140, D(G(z)): 0.3246 Epoch: [18/20], Batch Num: [9/600] Discriminator Loss: 1.0058, Generator Loss: 2.4809 D(x): 0.6450, D(G(z)): 0.1964 Epoch: [18/20], Batch Num: [10/600] Discriminator Loss: 0.9831, Generator Loss: 2.0286 D(x): 0.6542, D(G(z)): 0.2323 Epoch: [18/20], Batch Num: [11/600] Discriminator Loss: 0.9845, Generator Loss: 1.9907 D(x): 0.7408, D(G(z)): 0.3167 Epoch: [18/20], Batch Num: [12/600] Discriminator Loss: 1.0592, Generator Loss: 1.3071 D(x): 0.6618, D(G(z)): 0.2904 Epoch: [18/20], Batch Num: [13/600] Discriminator Loss: 1.1758, Generator Loss: 1.5963 D(x): 0.7135, D(G(z)): 0.3867 Epoch: [18/20], Batch Num: [14/600] Discriminator Loss: 1.2402, Generator Loss: 1.8140 D(x): 0.7279, D(G(z)): 0.4031 Epoch: [18/20], Batch Num: [15/600] Discriminator Loss: 1.4390, Generator Loss: 1.6335 D(x): 0.5405, D(G(z)): 0.3321 Epoch: [18/20], Batch Num: [16/600] Discriminator Loss: 1.3707, Generator Loss: 1.6032 D(x): 0.5970, D(G(z)): 0.3426 Epoch: [18/20], Batch Num: [17/600] Discriminator Loss: 1.2363, Generator Loss: 1.3871 D(x): 0.6672, D(G(z)): 0.3572 Epoch: [18/20], Batch Num: [18/600] Discriminator Loss: 1.2935, Generator Loss: 1.4305 D(x): 0.6328, D(G(z)): 0.3601 Epoch: [18/20], Batch Num: [19/600] Discriminator Loss: 1.3601, Generator Loss: 1.2787 D(x): 0.5909, D(G(z)): 0.3580 Epoch: [18/20], Batch Num: [20/600] Discriminator Loss: 1.3129, Generator Loss: 1.1084 D(x): 0.5770, D(G(z)): 0.3512 Epoch: [18/20], Batch Num: [21/600] Discriminator Loss: 1.3507, Generator Loss: 1.1226 D(x): 0.6123, D(G(z)): 0.3835 Epoch: [18/20], Batch Num: [22/600] Discriminator Loss: 1.4170, Generator Loss: 1.0368 D(x): 0.6198, D(G(z)): 0.4670 Epoch: [18/20], Batch Num: [23/600] Discriminator Loss: 1.3354, Generator Loss: 1.1172 D(x): 0.6234, D(G(z)): 0.4450 Epoch: [18/20], Batch Num: [24/600] Discriminator Loss: 1.2251, Generator Loss: 1.1894 D(x): 0.6191, D(G(z)): 0.3989 Epoch: [18/20], Batch Num: [25/600] Discriminator Loss: 1.1293, Generator Loss: 1.2790 D(x): 0.6317, D(G(z)): 0.3697 Epoch: [18/20], Batch Num: [26/600] Discriminator Loss: 1.1583, Generator Loss: 1.2148 D(x): 0.5742, D(G(z)): 0.3147 Epoch: [18/20], Batch Num: [27/600] Discriminator Loss: 1.1593, Generator Loss: 1.2632 D(x): 0.5500, D(G(z)): 0.3173 Epoch: [18/20], Batch Num: [28/600] Discriminator Loss: 1.1138, Generator Loss: 1.1716 D(x): 0.6029, D(G(z)): 0.3488 Epoch: [18/20], Batch Num: [29/600] Discriminator Loss: 0.9979, Generator Loss: 1.1508 D(x): 0.6363, D(G(z)): 0.3271 Epoch: [18/20], Batch Num: [30/600] Discriminator Loss: 0.9457, Generator Loss: 1.0430 D(x): 0.7206, D(G(z)): 0.3658 Epoch: [18/20], Batch Num: [31/600] Discriminator Loss: 0.9655, Generator Loss: 1.1077 D(x): 0.7419, D(G(z)): 0.4032 Epoch: [18/20], Batch Num: [32/600] Discriminator Loss: 0.9983, Generator Loss: 1.1787 D(x): 0.6845, D(G(z)): 0.3738 Epoch: [18/20], Batch Num: [33/600] Discriminator Loss: 1.0246, Generator Loss: 1.2509 D(x): 0.6948, D(G(z)): 0.3810 Epoch: [18/20], Batch Num: [34/600] Discriminator Loss: 0.8268, Generator Loss: 1.4090 D(x): 0.7300, D(G(z)): 0.3301 Epoch: [18/20], Batch Num: [35/600] Discriminator Loss: 0.8646, Generator Loss: 1.4515 D(x): 0.7090, D(G(z)): 0.3264 Epoch: [18/20], Batch Num: [36/600] Discriminator Loss: 0.7904, Generator Loss: 1.4574 D(x): 0.6944, D(G(z)): 0.2584 Epoch: [18/20], Batch Num: [37/600] Discriminator Loss: 0.7973, Generator Loss: 1.4710 D(x): 0.6812, D(G(z)): 0.2549 Epoch: [18/20], Batch Num: [38/600] Discriminator Loss: 0.7249, Generator Loss: 1.4811 D(x): 0.7158, D(G(z)): 0.2450 Epoch: [18/20], Batch Num: [39/600] Discriminator Loss: 0.8057, Generator Loss: 1.4654 D(x): 0.7210, D(G(z)): 0.2872 Epoch: [18/20], Batch Num: [40/600] Discriminator Loss: 0.6285, Generator Loss: 1.5668 D(x): 0.8150, D(G(z)): 0.2899 Epoch: [18/20], Batch Num: [41/600] Discriminator Loss: 0.7676, Generator Loss: 1.6550 D(x): 0.7671, D(G(z)): 0.3099 Epoch: [18/20], Batch Num: [42/600] Discriminator Loss: 0.6985, Generator Loss: 1.6467 D(x): 0.7940, D(G(z)): 0.2867 Epoch: [18/20], Batch Num: [43/600] Discriminator Loss: 0.7287, Generator Loss: 1.9257 D(x): 0.7657, D(G(z)): 0.2900 Epoch: [18/20], Batch Num: [44/600] Discriminator Loss: 0.6515, Generator Loss: 1.8602 D(x): 0.7829, D(G(z)): 0.2585 Epoch: [18/20], Batch Num: [45/600] Discriminator Loss: 0.6720, Generator Loss: 2.1536 D(x): 0.7484, D(G(z)): 0.2049 Epoch: [18/20], Batch Num: [46/600] Discriminator Loss: 0.5566, Generator Loss: 2.0992 D(x): 0.7593, D(G(z)): 0.1519 Epoch: [18/20], Batch Num: [47/600] Discriminator Loss: 0.5993, Generator Loss: 2.1727 D(x): 0.7513, D(G(z)): 0.1756 Epoch: [18/20], Batch Num: [48/600] Discriminator Loss: 0.6212, Generator Loss: 2.0334 D(x): 0.7841, D(G(z)): 0.2167 Epoch: [18/20], Batch Num: [49/600] Discriminator Loss: 0.5332, Generator Loss: 1.9410 D(x): 0.8034, D(G(z)): 0.1922 Epoch: [18/20], Batch Num: [50/600] Discriminator Loss: 0.6344, Generator Loss: 1.8501 D(x): 0.8716, D(G(z)): 0.2702 Epoch: [18/20], Batch Num: 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D(x): 0.7523, D(G(z)): 0.3117 Epoch: [18/20], Batch Num: [60/600] Discriminator Loss: 0.9525, Generator Loss: 1.7912 D(x): 0.7333, D(G(z)): 0.3021 Epoch: [18/20], Batch Num: [61/600] Discriminator Loss: 0.7828, Generator Loss: 1.9846 D(x): 0.7633, D(G(z)): 0.2700 Epoch: [18/20], Batch Num: [62/600] Discriminator Loss: 0.9876, Generator Loss: 1.7165 D(x): 0.6645, D(G(z)): 0.2545 Epoch: [18/20], Batch Num: [63/600] Discriminator Loss: 0.9641, Generator Loss: 1.8397 D(x): 0.7499, D(G(z)): 0.3155 Epoch: [18/20], Batch Num: [64/600] Discriminator Loss: 0.9497, Generator Loss: 1.8855 D(x): 0.6460, D(G(z)): 0.2301 Epoch: [18/20], Batch Num: [65/600] Discriminator Loss: 1.1124, Generator Loss: 1.5378 D(x): 0.6544, D(G(z)): 0.2885 Epoch: [18/20], Batch Num: [66/600] Discriminator Loss: 1.1624, Generator Loss: 1.4846 D(x): 0.7175, D(G(z)): 0.3741 Epoch: [18/20], Batch Num: [67/600] Discriminator Loss: 1.2326, Generator Loss: 1.6853 D(x): 0.6662, D(G(z)): 0.3693 Epoch: [18/20], Batch Num: 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D(x): 0.6328, D(G(z)): 0.3429 Epoch: [18/20], Batch Num: [77/600] Discriminator Loss: 1.1827, Generator Loss: 1.2206 D(x): 0.6238, D(G(z)): 0.3680 Epoch: [18/20], Batch Num: [78/600] Discriminator Loss: 1.1568, Generator Loss: 1.1628 D(x): 0.6108, D(G(z)): 0.3522 Epoch: [18/20], Batch Num: [79/600] Discriminator Loss: 1.2113, Generator Loss: 1.2003 D(x): 0.6527, D(G(z)): 0.4049 Epoch: [18/20], Batch Num: [80/600] Discriminator Loss: 0.9702, Generator Loss: 1.2421 D(x): 0.6852, D(G(z)): 0.3407 Epoch: [18/20], Batch Num: [81/600] Discriminator Loss: 0.9751, Generator Loss: 1.2685 D(x): 0.6652, D(G(z)): 0.3381 Epoch: [18/20], Batch Num: [82/600] Discriminator Loss: 0.9989, Generator Loss: 1.4914 D(x): 0.6295, D(G(z)): 0.3040 Epoch: [18/20], Batch Num: [83/600] Discriminator Loss: 0.9473, Generator Loss: 1.3335 D(x): 0.6618, D(G(z)): 0.3173 Epoch: [18/20], Batch Num: [84/600] Discriminator Loss: 0.9324, Generator Loss: 1.4349 D(x): 0.6843, D(G(z)): 0.3385 Epoch: [18/20], Batch Num: [85/600] Discriminator Loss: 0.9186, Generator Loss: 1.3945 D(x): 0.6688, D(G(z)): 0.3134 Epoch: [18/20], Batch Num: [86/600] Discriminator Loss: 0.8721, Generator Loss: 1.2444 D(x): 0.6781, D(G(z)): 0.2906 Epoch: [18/20], Batch Num: [87/600] Discriminator Loss: 0.8144, Generator Loss: 1.3334 D(x): 0.7230, D(G(z)): 0.3084 Epoch: [18/20], Batch Num: [88/600] Discriminator Loss: 0.8216, Generator Loss: 1.4215 D(x): 0.7569, D(G(z)): 0.3561 Epoch: [18/20], Batch Num: [89/600] Discriminator Loss: 0.7938, Generator Loss: 1.5829 D(x): 0.7477, D(G(z)): 0.3169 Epoch: [18/20], Batch Num: [90/600] Discriminator Loss: 0.8078, Generator Loss: 1.6377 D(x): 0.7187, D(G(z)): 0.2964 Epoch: [18/20], Batch Num: [91/600] Discriminator Loss: 0.7263, Generator Loss: 1.5848 D(x): 0.7264, D(G(z)): 0.2494 Epoch: [18/20], Batch Num: [92/600] Discriminator Loss: 0.7757, Generator Loss: 1.7734 D(x): 0.7324, D(G(z)): 0.2912 Epoch: [18/20], Batch Num: [93/600] Discriminator Loss: 0.7375, Generator Loss: 1.7972 D(x): 0.6880, D(G(z)): 0.2111 Epoch: [18/20], Batch Num: [94/600] Discriminator Loss: 0.5969, Generator Loss: 1.6700 D(x): 0.7668, D(G(z)): 0.2004 Epoch: [18/20], Batch Num: [95/600] Discriminator Loss: 0.5906, Generator Loss: 1.8452 D(x): 0.8032, D(G(z)): 0.2555 Epoch: [18/20], Batch Num: [96/600] Discriminator Loss: 0.6955, Generator Loss: 1.7727 D(x): 0.7554, D(G(z)): 0.2551 Epoch: [18/20], Batch Num: [97/600] Discriminator Loss: 0.6122, Generator Loss: 1.6591 D(x): 0.7955, D(G(z)): 0.2395 Epoch: [18/20], Batch Num: [98/600] Discriminator Loss: 0.6406, Generator Loss: 1.8094 D(x): 0.8024, D(G(z)): 0.2817 Epoch: [18/20], Batch Num: [99/600] Discriminator Loss: 0.5631, Generator Loss: 1.8769 D(x): 0.8116, D(G(z)): 0.2234 Epoch: 18, Batch Num: [100/600]
Epoch: [18/20], Batch Num: [100/600] Discriminator Loss: 0.6351, Generator Loss: 2.0929 D(x): 0.7788, D(G(z)): 0.2346 Epoch: [18/20], Batch Num: [101/600] Discriminator Loss: 0.6108, Generator Loss: 2.2042 D(x): 0.7921, D(G(z)): 0.2349 Epoch: [18/20], Batch Num: [102/600] Discriminator Loss: 0.7500, Generator Loss: 2.1964 D(x): 0.6898, D(G(z)): 0.1997 Epoch: [18/20], Batch Num: [103/600] Discriminator Loss: 0.5899, Generator Loss: 2.1303 D(x): 0.8024, D(G(z)): 0.2179 Epoch: [18/20], Batch Num: [104/600] Discriminator Loss: 0.5711, Generator Loss: 2.1713 D(x): 0.7995, D(G(z)): 0.1961 Epoch: [18/20], Batch Num: [105/600] Discriminator Loss: 0.6045, Generator Loss: 2.1272 D(x): 0.8357, D(G(z)): 0.2482 Epoch: [18/20], Batch Num: [106/600] Discriminator Loss: 0.6803, Generator Loss: 2.1625 D(x): 0.7730, D(G(z)): 0.2169 Epoch: [18/20], Batch Num: [107/600] Discriminator Loss: 0.5801, Generator Loss: 2.5777 D(x): 0.8023, D(G(z)): 0.2032 Epoch: [18/20], Batch Num: [108/600] Discriminator Loss: 0.5470, Generator Loss: 2.2146 D(x): 0.8028, D(G(z)): 0.1845 Epoch: [18/20], Batch Num: [109/600] Discriminator Loss: 0.7604, Generator Loss: 2.0523 D(x): 0.7527, D(G(z)): 0.2205 Epoch: [18/20], Batch Num: [110/600] Discriminator Loss: 0.6290, Generator Loss: 1.9340 D(x): 0.8073, D(G(z)): 0.2366 Epoch: [18/20], Batch Num: [111/600] Discriminator Loss: 0.7258, Generator Loss: 2.3180 D(x): 0.8255, D(G(z)): 0.2939 Epoch: [18/20], Batch Num: [112/600] Discriminator Loss: 0.7615, Generator Loss: 2.5288 D(x): 0.7543, D(G(z)): 0.2361 Epoch: [18/20], Batch Num: [113/600] Discriminator Loss: 0.8372, Generator Loss: 2.3439 D(x): 0.6842, D(G(z)): 0.1672 Epoch: [18/20], Batch Num: [114/600] Discriminator Loss: 0.9922, Generator Loss: 1.8902 D(x): 0.6943, D(G(z)): 0.2627 Epoch: [18/20], Batch Num: [115/600] Discriminator Loss: 0.7750, Generator Loss: 2.0699 D(x): 0.7582, D(G(z)): 0.2542 Epoch: [18/20], Batch Num: [116/600] Discriminator Loss: 0.8524, Generator Loss: 1.7424 D(x): 0.7685, D(G(z)): 0.2880 Epoch: [18/20], Batch Num: [117/600] Discriminator Loss: 0.9585, Generator Loss: 1.8089 D(x): 0.7173, D(G(z)): 0.2700 Epoch: [18/20], Batch Num: [118/600] Discriminator Loss: 1.3203, Generator Loss: 1.6994 D(x): 0.6492, D(G(z)): 0.3615 Epoch: [18/20], Batch Num: [119/600] Discriminator Loss: 0.8967, Generator Loss: 1.7741 D(x): 0.6856, D(G(z)): 0.2650 Epoch: [18/20], Batch Num: [120/600] Discriminator Loss: 1.2477, Generator Loss: 1.4891 D(x): 0.6174, D(G(z)): 0.3248 Epoch: [18/20], Batch Num: [121/600] Discriminator Loss: 1.3287, Generator Loss: 1.4196 D(x): 0.6287, D(G(z)): 0.3384 Epoch: [18/20], Batch Num: [122/600] Discriminator Loss: 1.1347, Generator Loss: 1.2588 D(x): 0.7087, D(G(z)): 0.3828 Epoch: [18/20], Batch Num: [123/600] Discriminator Loss: 1.2259, Generator Loss: 1.5047 D(x): 0.6693, D(G(z)): 0.3800 Epoch: [18/20], Batch Num: [124/600] Discriminator Loss: 1.1359, Generator Loss: 1.5243 D(x): 0.5938, D(G(z)): 0.2975 Epoch: [18/20], Batch Num: [125/600] Discriminator Loss: 1.2284, Generator Loss: 1.4120 D(x): 0.6098, D(G(z)): 0.3371 Epoch: [18/20], Batch Num: [126/600] Discriminator Loss: 1.0699, Generator Loss: 1.1730 D(x): 0.6509, D(G(z)): 0.3293 Epoch: [18/20], Batch Num: [127/600] Discriminator Loss: 1.0662, Generator Loss: 1.2586 D(x): 0.7137, D(G(z)): 0.4052 Epoch: [18/20], Batch Num: [128/600] Discriminator Loss: 1.2105, Generator Loss: 1.4492 D(x): 0.6813, D(G(z)): 0.4295 Epoch: [18/20], Batch Num: [129/600] Discriminator Loss: 1.2075, Generator Loss: 1.7152 D(x): 0.6377, D(G(z)): 0.3706 Epoch: [18/20], Batch Num: [130/600] Discriminator Loss: 1.0919, Generator Loss: 1.7169 D(x): 0.6057, D(G(z)): 0.2698 Epoch: [18/20], Batch Num: [131/600] Discriminator Loss: 1.0993, Generator Loss: 1.3651 D(x): 0.5753, D(G(z)): 0.2617 Epoch: [18/20], Batch Num: [132/600] Discriminator Loss: 1.1428, Generator Loss: 1.2331 D(x): 0.6421, D(G(z)): 0.3480 Epoch: [18/20], Batch Num: [133/600] Discriminator Loss: 0.9400, Generator Loss: 1.2257 D(x): 0.7173, D(G(z)): 0.3453 Epoch: [18/20], Batch Num: [134/600] Discriminator Loss: 1.1186, Generator Loss: 1.2584 D(x): 0.7079, D(G(z)): 0.4201 Epoch: [18/20], Batch Num: [135/600] Discriminator Loss: 0.8697, Generator Loss: 1.3878 D(x): 0.7377, D(G(z)): 0.3397 Epoch: [18/20], Batch Num: [136/600] Discriminator Loss: 1.0714, Generator Loss: 1.4483 D(x): 0.6346, D(G(z)): 0.3132 Epoch: [18/20], Batch Num: [137/600] Discriminator Loss: 0.8432, Generator Loss: 1.6143 D(x): 0.7255, D(G(z)): 0.2992 Epoch: [18/20], Batch Num: [138/600] Discriminator Loss: 0.8988, Generator Loss: 1.6838 D(x): 0.6826, D(G(z)): 0.2697 Epoch: [18/20], Batch Num: [139/600] Discriminator Loss: 0.8393, Generator Loss: 1.6889 D(x): 0.6914, D(G(z)): 0.2639 Epoch: [18/20], Batch Num: [140/600] Discriminator Loss: 0.8670, Generator Loss: 1.3909 D(x): 0.6582, D(G(z)): 0.2544 Epoch: [18/20], Batch Num: [141/600] Discriminator Loss: 0.7936, Generator Loss: 1.4030 D(x): 0.7379, D(G(z)): 0.2871 Epoch: [18/20], Batch Num: [142/600] Discriminator Loss: 0.7657, Generator Loss: 1.3306 D(x): 0.7387, D(G(z)): 0.2925 Epoch: [18/20], Batch Num: [143/600] Discriminator Loss: 0.9068, Generator Loss: 1.5485 D(x): 0.7288, D(G(z)): 0.3268 Epoch: [18/20], Batch Num: [144/600] Discriminator Loss: 0.7567, Generator Loss: 1.6211 D(x): 0.7625, D(G(z)): 0.2915 Epoch: [18/20], Batch Num: [145/600] Discriminator Loss: 0.7401, Generator Loss: 1.7376 D(x): 0.7464, D(G(z)): 0.2555 Epoch: [18/20], Batch Num: [146/600] Discriminator Loss: 0.7749, Generator Loss: 1.6131 D(x): 0.7589, D(G(z)): 0.2858 Epoch: [18/20], Batch Num: [147/600] Discriminator Loss: 0.7221, Generator Loss: 1.8235 D(x): 0.7098, D(G(z)): 0.2325 Epoch: [18/20], Batch Num: [148/600] Discriminator Loss: 0.7486, Generator Loss: 1.7039 D(x): 0.6840, D(G(z)): 0.2200 Epoch: [18/20], Batch Num: [149/600] Discriminator Loss: 0.7593, Generator Loss: 1.6184 D(x): 0.7487, D(G(z)): 0.2763 Epoch: [18/20], Batch Num: [150/600] Discriminator Loss: 0.6806, Generator Loss: 1.5402 D(x): 0.7578, D(G(z)): 0.2388 Epoch: [18/20], Batch Num: [151/600] Discriminator Loss: 0.7715, Generator Loss: 1.6659 D(x): 0.7685, D(G(z)): 0.3092 Epoch: [18/20], Batch Num: [152/600] Discriminator Loss: 0.7313, Generator Loss: 1.4878 D(x): 0.7525, D(G(z)): 0.2730 Epoch: [18/20], Batch Num: [153/600] Discriminator Loss: 0.7094, Generator Loss: 1.6767 D(x): 0.7962, D(G(z)): 0.3031 Epoch: [18/20], Batch Num: [154/600] Discriminator Loss: 0.6949, Generator Loss: 1.8771 D(x): 0.8095, D(G(z)): 0.3105 Epoch: [18/20], Batch Num: [155/600] Discriminator Loss: 0.6801, Generator Loss: 1.9237 D(x): 0.7430, D(G(z)): 0.2198 Epoch: [18/20], Batch Num: [156/600] Discriminator Loss: 0.7156, Generator Loss: 1.7934 D(x): 0.7047, D(G(z)): 0.2099 Epoch: [18/20], Batch Num: [157/600] Discriminator Loss: 0.8302, Generator Loss: 1.6250 D(x): 0.7158, D(G(z)): 0.2395 Epoch: [18/20], Batch Num: [158/600] Discriminator Loss: 0.7998, Generator Loss: 1.4655 D(x): 0.7092, D(G(z)): 0.2408 Epoch: [18/20], Batch Num: [159/600] Discriminator Loss: 0.8494, Generator Loss: 1.5296 D(x): 0.8025, D(G(z)): 0.3452 Epoch: [18/20], Batch Num: [160/600] Discriminator Loss: 0.8007, Generator Loss: 1.5586 D(x): 0.7943, D(G(z)): 0.3205 Epoch: [18/20], Batch Num: [161/600] Discriminator Loss: 0.8618, Generator Loss: 1.8565 D(x): 0.7455, D(G(z)): 0.2989 Epoch: [18/20], Batch Num: [162/600] Discriminator Loss: 0.9533, Generator Loss: 1.9355 D(x): 0.7014, D(G(z)): 0.3047 Epoch: [18/20], Batch Num: [163/600] Discriminator Loss: 0.9140, Generator Loss: 1.8141 D(x): 0.6933, D(G(z)): 0.2393 Epoch: [18/20], Batch Num: [164/600] Discriminator Loss: 1.0381, Generator Loss: 1.5010 D(x): 0.6145, D(G(z)): 0.2408 Epoch: [18/20], Batch Num: [165/600] Discriminator Loss: 0.7938, Generator Loss: 1.6405 D(x): 0.7787, D(G(z)): 0.3220 Epoch: [18/20], Batch Num: [166/600] Discriminator Loss: 1.0672, Generator Loss: 1.6029 D(x): 0.6629, D(G(z)): 0.3103 Epoch: [18/20], Batch Num: [167/600] Discriminator Loss: 1.1873, Generator Loss: 1.3933 D(x): 0.6256, D(G(z)): 0.3458 Epoch: [18/20], Batch Num: [168/600] Discriminator Loss: 1.0685, Generator Loss: 1.3248 D(x): 0.6929, D(G(z)): 0.3814 Epoch: [18/20], Batch Num: [169/600] Discriminator Loss: 1.1461, Generator Loss: 1.4755 D(x): 0.6692, D(G(z)): 0.3704 Epoch: [18/20], Batch Num: [170/600] Discriminator Loss: 1.1691, Generator Loss: 1.4690 D(x): 0.6302, D(G(z)): 0.3430 Epoch: [18/20], Batch Num: [171/600] Discriminator Loss: 1.1288, Generator Loss: 1.3444 D(x): 0.6336, D(G(z)): 0.3469 Epoch: [18/20], Batch Num: [172/600] Discriminator Loss: 1.0753, Generator Loss: 1.2601 D(x): 0.6013, D(G(z)): 0.2917 Epoch: [18/20], Batch Num: [173/600] Discriminator Loss: 1.1412, Generator Loss: 1.2307 D(x): 0.6365, D(G(z)): 0.3586 Epoch: [18/20], Batch Num: [174/600] Discriminator Loss: 1.0784, Generator Loss: 1.3852 D(x): 0.6810, D(G(z)): 0.3878 Epoch: [18/20], Batch Num: [175/600] Discriminator Loss: 0.9613, Generator Loss: 1.2304 D(x): 0.6531, D(G(z)): 0.3186 Epoch: [18/20], Batch Num: [176/600] Discriminator Loss: 1.1285, Generator Loss: 1.1636 D(x): 0.6494, D(G(z)): 0.3660 Epoch: [18/20], Batch Num: [177/600] Discriminator Loss: 1.1540, Generator Loss: 1.2512 D(x): 0.6439, D(G(z)): 0.3854 Epoch: [18/20], Batch Num: [178/600] Discriminator Loss: 1.1968, Generator Loss: 1.1824 D(x): 0.6704, D(G(z)): 0.4382 Epoch: [18/20], Batch Num: [179/600] Discriminator Loss: 0.9185, Generator Loss: 1.5922 D(x): 0.6977, D(G(z)): 0.3388 Epoch: [18/20], Batch Num: [180/600] Discriminator Loss: 1.0991, Generator Loss: 1.3771 D(x): 0.5822, D(G(z)): 0.3173 Epoch: [18/20], Batch Num: [181/600] Discriminator Loss: 0.9318, Generator Loss: 1.3367 D(x): 0.6710, D(G(z)): 0.3084 Epoch: [18/20], Batch Num: [182/600] Discriminator Loss: 1.1480, Generator Loss: 1.3479 D(x): 0.6172, D(G(z)): 0.3762 Epoch: [18/20], Batch Num: [183/600] Discriminator Loss: 0.9642, Generator Loss: 1.2567 D(x): 0.6689, D(G(z)): 0.3418 Epoch: [18/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [18/20], Batch Num: [200/600] Discriminator Loss: 0.8063, Generator Loss: 1.5657 D(x): 0.7636, D(G(z)): 0.3079 Epoch: [18/20], Batch Num: [201/600] Discriminator Loss: 0.6182, Generator Loss: 1.5041 D(x): 0.8033, D(G(z)): 0.2684 Epoch: [18/20], Batch Num: [202/600] Discriminator Loss: 0.7626, Generator Loss: 1.7966 D(x): 0.7878, D(G(z)): 0.3125 Epoch: [18/20], Batch Num: [203/600] Discriminator Loss: 0.7341, Generator Loss: 1.8360 D(x): 0.7450, D(G(z)): 0.2503 Epoch: [18/20], Batch Num: [204/600] Discriminator Loss: 0.6738, Generator Loss: 2.2826 D(x): 0.7780, D(G(z)): 0.2531 Epoch: [18/20], Batch Num: [205/600] Discriminator Loss: 0.8510, Generator Loss: 2.0449 D(x): 0.6503, D(G(z)): 0.2121 Epoch: [18/20], Batch Num: [206/600] Discriminator Loss: 0.8770, Generator Loss: 1.7132 D(x): 0.6696, D(G(z)): 0.2469 Epoch: [18/20], Batch Num: [207/600] Discriminator Loss: 0.6459, Generator Loss: 1.5319 D(x): 0.8045, D(G(z)): 0.2512 Epoch: [18/20], Batch Num: [208/600] Discriminator Loss: 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Discriminator Loss: 1.3095, Generator Loss: 1.4097 D(x): 0.5563, D(G(z)): 0.3297 Epoch: [18/20], Batch Num: [226/600] Discriminator Loss: 1.0637, Generator Loss: 1.3196 D(x): 0.6738, D(G(z)): 0.3384 Epoch: [18/20], Batch Num: [227/600] Discriminator Loss: 0.9876, Generator Loss: 1.3028 D(x): 0.6927, D(G(z)): 0.3551 Epoch: [18/20], Batch Num: [228/600] Discriminator Loss: 1.0207, Generator Loss: 1.2318 D(x): 0.6879, D(G(z)): 0.3385 Epoch: [18/20], Batch Num: [229/600] Discriminator Loss: 1.0545, Generator Loss: 1.3292 D(x): 0.7039, D(G(z)): 0.3689 Epoch: [18/20], Batch Num: [230/600] Discriminator Loss: 1.1368, Generator Loss: 1.5041 D(x): 0.6435, D(G(z)): 0.3575 Epoch: [18/20], Batch Num: [231/600] Discriminator Loss: 0.9832, Generator Loss: 1.5019 D(x): 0.6607, D(G(z)): 0.2760 Epoch: [18/20], Batch Num: [232/600] Discriminator Loss: 0.8578, Generator Loss: 1.5263 D(x): 0.7044, D(G(z)): 0.2847 Epoch: [18/20], Batch Num: [233/600] Discriminator Loss: 0.9826, Generator Loss: 1.5865 D(x): 0.6395, D(G(z)): 0.2913 Epoch: [18/20], Batch Num: [234/600] Discriminator Loss: 0.8262, Generator Loss: 1.5601 D(x): 0.7186, D(G(z)): 0.2941 Epoch: [18/20], Batch Num: [235/600] Discriminator Loss: 0.9447, Generator Loss: 1.5637 D(x): 0.6267, D(G(z)): 0.2693 Epoch: [18/20], Batch Num: [236/600] Discriminator Loss: 0.7602, Generator Loss: 1.4764 D(x): 0.7362, D(G(z)): 0.2821 Epoch: [18/20], Batch Num: [237/600] Discriminator Loss: 0.8360, Generator Loss: 1.3707 D(x): 0.7453, D(G(z)): 0.3217 Epoch: [18/20], Batch Num: [238/600] Discriminator Loss: 0.8355, Generator Loss: 1.4201 D(x): 0.7548, D(G(z)): 0.3184 Epoch: [18/20], Batch Num: [239/600] Discriminator Loss: 0.8336, Generator Loss: 1.5545 D(x): 0.7507, D(G(z)): 0.3089 Epoch: [18/20], Batch Num: [240/600] Discriminator Loss: 0.8137, Generator Loss: 1.5704 D(x): 0.7131, D(G(z)): 0.2797 Epoch: [18/20], Batch Num: [241/600] Discriminator Loss: 0.7047, Generator Loss: 1.8111 D(x): 0.7554, D(G(z)): 0.2440 Epoch: [18/20], Batch Num: [242/600] Discriminator Loss: 0.8091, Generator Loss: 1.8272 D(x): 0.7212, D(G(z)): 0.2646 Epoch: [18/20], Batch Num: [243/600] Discriminator Loss: 0.9089, Generator Loss: 1.7237 D(x): 0.6757, D(G(z)): 0.2720 Epoch: [18/20], Batch Num: [244/600] Discriminator Loss: 0.8272, Generator Loss: 1.5166 D(x): 0.7151, D(G(z)): 0.2322 Epoch: [18/20], Batch Num: [245/600] Discriminator Loss: 0.8637, Generator Loss: 1.5389 D(x): 0.6950, D(G(z)): 0.2551 Epoch: [18/20], Batch Num: [246/600] Discriminator Loss: 0.7500, Generator Loss: 1.4484 D(x): 0.7853, D(G(z)): 0.2896 Epoch: [18/20], Batch Num: [247/600] Discriminator Loss: 0.7699, Generator Loss: 1.5583 D(x): 0.8018, D(G(z)): 0.3332 Epoch: [18/20], Batch Num: [248/600] Discriminator Loss: 0.7753, Generator Loss: 1.7951 D(x): 0.7418, D(G(z)): 0.2834 Epoch: [18/20], Batch Num: [249/600] Discriminator Loss: 0.8293, Generator Loss: 1.6362 D(x): 0.6766, D(G(z)): 0.2313 Epoch: [18/20], Batch Num: [250/600] Discriminator Loss: 0.8608, Generator Loss: 1.5967 D(x): 0.7147, D(G(z)): 0.2698 Epoch: [18/20], Batch Num: [251/600] Discriminator Loss: 0.8643, Generator Loss: 1.4981 D(x): 0.7339, D(G(z)): 0.3053 Epoch: [18/20], Batch Num: [252/600] Discriminator Loss: 0.7217, Generator Loss: 1.4869 D(x): 0.7807, D(G(z)): 0.2985 Epoch: [18/20], Batch Num: [253/600] Discriminator Loss: 0.8983, Generator Loss: 1.4444 D(x): 0.7191, D(G(z)): 0.2983 Epoch: [18/20], Batch Num: [254/600] Discriminator Loss: 0.8060, Generator Loss: 1.6215 D(x): 0.7392, D(G(z)): 0.2860 Epoch: [18/20], Batch Num: [255/600] Discriminator Loss: 0.7369, Generator Loss: 1.6520 D(x): 0.7901, D(G(z)): 0.3047 Epoch: [18/20], Batch Num: [256/600] Discriminator Loss: 0.7555, Generator Loss: 1.7308 D(x): 0.7563, D(G(z)): 0.2840 Epoch: [18/20], Batch Num: [257/600] Discriminator Loss: 0.8792, Generator Loss: 1.9147 D(x): 0.6637, D(G(z)): 0.2233 Epoch: [18/20], Batch Num: [258/600] Discriminator Loss: 0.7905, Generator Loss: 1.5776 D(x): 0.7236, D(G(z)): 0.2498 Epoch: [18/20], Batch Num: [259/600] Discriminator Loss: 0.8620, Generator Loss: 1.4679 D(x): 0.7134, D(G(z)): 0.2738 Epoch: [18/20], Batch Num: [260/600] Discriminator Loss: 0.9813, Generator Loss: 1.6635 D(x): 0.7308, D(G(z)): 0.3578 Epoch: [18/20], Batch Num: [261/600] Discriminator Loss: 0.8556, Generator Loss: 1.5310 D(x): 0.7402, D(G(z)): 0.2838 Epoch: [18/20], Batch Num: [262/600] Discriminator Loss: 1.0335, Generator Loss: 1.8540 D(x): 0.6777, D(G(z)): 0.3221 Epoch: [18/20], Batch Num: [263/600] Discriminator Loss: 0.9471, Generator Loss: 1.5336 D(x): 0.6701, D(G(z)): 0.2847 Epoch: [18/20], Batch Num: [264/600] Discriminator Loss: 1.0052, Generator Loss: 1.6306 D(x): 0.7019, D(G(z)): 0.3355 Epoch: [18/20], Batch Num: [265/600] Discriminator Loss: 0.9153, Generator Loss: 1.5616 D(x): 0.7337, D(G(z)): 0.3282 Epoch: [18/20], Batch Num: [266/600] Discriminator Loss: 0.9467, Generator Loss: 1.6031 D(x): 0.7153, D(G(z)): 0.3183 Epoch: [18/20], Batch Num: [267/600] Discriminator Loss: 0.8399, Generator Loss: 1.7044 D(x): 0.7344, D(G(z)): 0.2757 Epoch: [18/20], Batch Num: [268/600] Discriminator Loss: 0.9818, Generator Loss: 1.8776 D(x): 0.7258, D(G(z)): 0.3300 Epoch: [18/20], Batch Num: [269/600] Discriminator Loss: 1.0805, Generator Loss: 1.9673 D(x): 0.6301, D(G(z)): 0.3049 Epoch: [18/20], Batch Num: [270/600] Discriminator Loss: 0.9375, Generator Loss: 1.7928 D(x): 0.6383, D(G(z)): 0.2370 Epoch: [18/20], Batch Num: [271/600] Discriminator Loss: 0.7568, Generator Loss: 1.4464 D(x): 0.7253, D(G(z)): 0.2329 Epoch: [18/20], Batch Num: [272/600] Discriminator Loss: 0.7566, Generator Loss: 1.3328 D(x): 0.7340, D(G(z)): 0.2720 Epoch: [18/20], Batch Num: [273/600] Discriminator Loss: 0.8734, Generator Loss: 1.2766 D(x): 0.7659, D(G(z)): 0.3521 Epoch: [18/20], Batch Num: [274/600] Discriminator Loss: 0.8506, Generator Loss: 1.4925 D(x): 0.7885, D(G(z)): 0.3488 Epoch: [18/20], Batch Num: [275/600] Discriminator Loss: 1.0909, Generator Loss: 1.7202 D(x): 0.7369, D(G(z)): 0.3890 Epoch: [18/20], Batch Num: [276/600] Discriminator Loss: 0.9200, Generator Loss: 1.7801 D(x): 0.6604, D(G(z)): 0.2573 Epoch: [18/20], Batch Num: [277/600] Discriminator Loss: 0.8129, Generator Loss: 1.8308 D(x): 0.6935, D(G(z)): 0.2405 Epoch: [18/20], Batch Num: [278/600] Discriminator Loss: 0.7266, Generator Loss: 1.6652 D(x): 0.7046, D(G(z)): 0.2071 Epoch: [18/20], Batch Num: [279/600] Discriminator Loss: 0.9164, Generator Loss: 1.3580 D(x): 0.6904, D(G(z)): 0.2971 Epoch: [18/20], Batch Num: [280/600] Discriminator Loss: 0.8336, Generator Loss: 1.4076 D(x): 0.7494, D(G(z)): 0.3192 Epoch: [18/20], Batch Num: [281/600] Discriminator Loss: 0.9107, Generator Loss: 1.5013 D(x): 0.7507, D(G(z)): 0.3495 Epoch: [18/20], Batch Num: [282/600] Discriminator Loss: 0.9483, Generator Loss: 1.5735 D(x): 0.7194, D(G(z)): 0.3384 Epoch: [18/20], Batch Num: [283/600] Discriminator Loss: 0.7679, Generator Loss: 1.7470 D(x): 0.7522, D(G(z)): 0.2819 Epoch: [18/20], Batch Num: [284/600] Discriminator Loss: 0.8737, Generator Loss: 1.9107 D(x): 0.6796, D(G(z)): 0.2576 Epoch: [18/20], Batch Num: [285/600] Discriminator Loss: 0.8076, Generator Loss: 1.8922 D(x): 0.7314, D(G(z)): 0.2652 Epoch: [18/20], Batch Num: [286/600] Discriminator Loss: 0.8644, Generator Loss: 1.7100 D(x): 0.6745, D(G(z)): 0.2405 Epoch: [18/20], Batch Num: [287/600] Discriminator Loss: 0.8046, Generator Loss: 1.7618 D(x): 0.7348, D(G(z)): 0.2786 Epoch: [18/20], Batch Num: [288/600] Discriminator Loss: 0.8802, Generator Loss: 1.5438 D(x): 0.6950, D(G(z)): 0.2658 Epoch: [18/20], Batch Num: [289/600] Discriminator Loss: 0.7589, Generator Loss: 1.4246 D(x): 0.7597, D(G(z)): 0.2910 Epoch: [18/20], Batch Num: [290/600] Discriminator Loss: 1.0561, Generator Loss: 1.3978 D(x): 0.7048, D(G(z)): 0.3574 Epoch: [18/20], Batch Num: [291/600] Discriminator Loss: 0.9076, Generator Loss: 1.4865 D(x): 0.7147, D(G(z)): 0.3069 Epoch: [18/20], Batch Num: [292/600] Discriminator Loss: 0.8296, Generator Loss: 1.7893 D(x): 0.7312, D(G(z)): 0.2949 Epoch: [18/20], Batch Num: [293/600] Discriminator Loss: 0.7842, Generator Loss: 1.8143 D(x): 0.7369, D(G(z)): 0.2612 Epoch: [18/20], Batch Num: [294/600] Discriminator Loss: 0.9938, Generator Loss: 1.8428 D(x): 0.6548, D(G(z)): 0.2651 Epoch: [18/20], Batch Num: [295/600] Discriminator Loss: 0.7182, Generator Loss: 1.6055 D(x): 0.7336, D(G(z)): 0.2395 Epoch: [18/20], Batch Num: [296/600] Discriminator Loss: 0.8239, Generator Loss: 1.5667 D(x): 0.7241, D(G(z)): 0.2871 Epoch: [18/20], Batch Num: [297/600] Discriminator Loss: 0.9565, Generator Loss: 1.4891 D(x): 0.7593, D(G(z)): 0.3584 Epoch: [18/20], Batch Num: [298/600] Discriminator Loss: 0.9475, Generator Loss: 1.4044 D(x): 0.7227, D(G(z)): 0.3197 Epoch: [18/20], Batch Num: [299/600] Discriminator Loss: 0.9202, Generator Loss: 1.6288 D(x): 0.7146, D(G(z)): 0.3067 Epoch: 18, Batch Num: [300/600]
Epoch: [18/20], Batch Num: [300/600] Discriminator Loss: 0.7430, Generator Loss: 1.5287 D(x): 0.7516, D(G(z)): 0.2590 Epoch: [18/20], Batch Num: [301/600] Discriminator Loss: 0.8934, Generator Loss: 1.4504 D(x): 0.7045, D(G(z)): 0.2764 Epoch: [18/20], Batch Num: [302/600] Discriminator Loss: 1.0098, Generator Loss: 1.5216 D(x): 0.6837, D(G(z)): 0.3106 Epoch: [18/20], Batch Num: [303/600] Discriminator Loss: 0.8715, Generator Loss: 1.5190 D(x): 0.7427, D(G(z)): 0.2750 Epoch: [18/20], Batch Num: [304/600] Discriminator Loss: 1.0266, Generator Loss: 1.7612 D(x): 0.7228, D(G(z)): 0.3320 Epoch: [18/20], Batch Num: [305/600] Discriminator Loss: 1.0635, Generator Loss: 1.5776 D(x): 0.6763, D(G(z)): 0.3161 Epoch: [18/20], Batch Num: [306/600] Discriminator Loss: 0.9536, Generator Loss: 1.4576 D(x): 0.6655, D(G(z)): 0.2764 Epoch: [18/20], Batch Num: [307/600] Discriminator Loss: 1.1373, Generator Loss: 1.6396 D(x): 0.6732, D(G(z)): 0.3661 Epoch: [18/20], Batch Num: [308/600] Discriminator Loss: 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0.2500 Epoch: [18/20], Batch Num: [317/600] Discriminator Loss: 0.9325, Generator Loss: 1.3281 D(x): 0.6972, D(G(z)): 0.2965 Epoch: [18/20], Batch Num: [318/600] Discriminator Loss: 0.8314, Generator Loss: 1.3733 D(x): 0.7319, D(G(z)): 0.2967 Epoch: [18/20], Batch Num: [319/600] Discriminator Loss: 0.9062, Generator Loss: 1.2753 D(x): 0.7402, D(G(z)): 0.3525 Epoch: [18/20], Batch Num: [320/600] Discriminator Loss: 0.9085, Generator Loss: 1.4142 D(x): 0.7371, D(G(z)): 0.3502 Epoch: [18/20], Batch Num: [321/600] Discriminator Loss: 0.9259, Generator Loss: 1.6801 D(x): 0.7644, D(G(z)): 0.3691 Epoch: [18/20], Batch Num: [322/600] Discriminator Loss: 0.9062, Generator Loss: 1.9149 D(x): 0.6700, D(G(z)): 0.2666 Epoch: [18/20], Batch Num: [323/600] Discriminator Loss: 0.9652, Generator Loss: 1.7705 D(x): 0.6479, D(G(z)): 0.2553 Epoch: [18/20], Batch Num: [324/600] Discriminator Loss: 0.8077, Generator Loss: 1.8113 D(x): 0.6979, D(G(z)): 0.2482 Epoch: [18/20], Batch Num: [325/600] Discriminator Loss: 0.9253, Generator Loss: 1.4571 D(x): 0.6328, D(G(z)): 0.2346 Epoch: [18/20], Batch Num: [326/600] Discriminator Loss: 0.8415, Generator Loss: 1.2814 D(x): 0.6977, D(G(z)): 0.2535 Epoch: [18/20], Batch Num: [327/600] Discriminator Loss: 0.9837, Generator Loss: 1.1820 D(x): 0.7768, D(G(z)): 0.4067 Epoch: [18/20], Batch Num: [328/600] Discriminator Loss: 0.7487, Generator Loss: 1.3946 D(x): 0.8453, D(G(z)): 0.3694 Epoch: [18/20], Batch Num: [329/600] Discriminator Loss: 0.7622, Generator Loss: 1.6566 D(x): 0.7879, D(G(z)): 0.3213 Epoch: [18/20], Batch Num: [330/600] Discriminator Loss: 0.7819, Generator Loss: 1.9368 D(x): 0.7019, D(G(z)): 0.2264 Epoch: [18/20], Batch Num: [331/600] Discriminator Loss: 0.8840, Generator Loss: 2.0492 D(x): 0.6668, D(G(z)): 0.2317 Epoch: [18/20], Batch Num: [332/600] Discriminator Loss: 0.8549, Generator Loss: 1.7998 D(x): 0.6724, D(G(z)): 0.2424 Epoch: [18/20], Batch Num: [333/600] Discriminator Loss: 0.9149, Generator Loss: 1.6401 D(x): 0.7027, D(G(z)): 0.2792 Epoch: [18/20], Batch Num: [334/600] Discriminator Loss: 0.8228, Generator Loss: 1.4699 D(x): 0.7308, D(G(z)): 0.2926 Epoch: [18/20], Batch Num: [335/600] Discriminator Loss: 0.8059, Generator Loss: 1.3401 D(x): 0.7315, D(G(z)): 0.2834 Epoch: [18/20], Batch Num: [336/600] Discriminator Loss: 0.7833, Generator Loss: 1.3816 D(x): 0.8426, D(G(z)): 0.3597 Epoch: [18/20], Batch Num: [337/600] Discriminator Loss: 0.7852, Generator Loss: 1.6714 D(x): 0.7693, D(G(z)): 0.2986 Epoch: [18/20], Batch Num: [338/600] Discriminator Loss: 0.9047, Generator Loss: 1.6576 D(x): 0.7369, D(G(z)): 0.3159 Epoch: [18/20], Batch Num: [339/600] Discriminator Loss: 0.9489, Generator Loss: 1.9368 D(x): 0.6505, D(G(z)): 0.2353 Epoch: [18/20], Batch Num: [340/600] Discriminator Loss: 0.9732, Generator Loss: 1.6272 D(x): 0.6071, D(G(z)): 0.2000 Epoch: [18/20], Batch Num: [341/600] Discriminator Loss: 0.7382, Generator Loss: 1.4482 D(x): 0.7443, D(G(z)): 0.2408 Epoch: [18/20], Batch Num: 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1.3094 D(x): 0.7573, D(G(z)): 0.3778 Epoch: [18/20], Batch Num: [351/600] Discriminator Loss: 0.8950, Generator Loss: 1.5126 D(x): 0.7374, D(G(z)): 0.3323 Epoch: [18/20], Batch Num: [352/600] Discriminator Loss: 0.8366, Generator Loss: 1.6514 D(x): 0.7393, D(G(z)): 0.3071 Epoch: [18/20], Batch Num: [353/600] Discriminator Loss: 0.8645, Generator Loss: 1.7220 D(x): 0.6541, D(G(z)): 0.2517 Epoch: [18/20], Batch Num: [354/600] Discriminator Loss: 0.9020, Generator Loss: 1.6516 D(x): 0.6986, D(G(z)): 0.2735 Epoch: [18/20], Batch Num: [355/600] Discriminator Loss: 0.7832, Generator Loss: 1.4962 D(x): 0.6950, D(G(z)): 0.2363 Epoch: [18/20], Batch Num: [356/600] Discriminator Loss: 0.9564, Generator Loss: 1.3194 D(x): 0.6632, D(G(z)): 0.2866 Epoch: [18/20], Batch Num: [357/600] Discriminator Loss: 0.9229, Generator Loss: 1.3014 D(x): 0.7220, D(G(z)): 0.3618 Epoch: [18/20], Batch Num: [358/600] Discriminator Loss: 0.9640, Generator Loss: 1.2923 D(x): 0.7138, D(G(z)): 0.3499 Epoch: [18/20], 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Generator Loss: 1.8104 D(x): 0.7166, D(G(z)): 0.2969 Epoch: [18/20], Batch Num: [368/600] Discriminator Loss: 0.9243, Generator Loss: 1.7759 D(x): 0.7136, D(G(z)): 0.3053 Epoch: [18/20], Batch Num: [369/600] Discriminator Loss: 0.9140, Generator Loss: 1.6466 D(x): 0.6179, D(G(z)): 0.2252 Epoch: [18/20], Batch Num: [370/600] Discriminator Loss: 0.9539, Generator Loss: 1.2799 D(x): 0.6377, D(G(z)): 0.2520 Epoch: [18/20], Batch Num: [371/600] Discriminator Loss: 0.9512, Generator Loss: 1.4305 D(x): 0.7180, D(G(z)): 0.3173 Epoch: [18/20], Batch Num: [372/600] Discriminator Loss: 0.8100, Generator Loss: 1.3789 D(x): 0.7733, D(G(z)): 0.3279 Epoch: [18/20], Batch Num: [373/600] Discriminator Loss: 1.0204, Generator Loss: 1.4574 D(x): 0.7116, D(G(z)): 0.3480 Epoch: [18/20], Batch Num: [374/600] Discriminator Loss: 0.8222, Generator Loss: 1.7042 D(x): 0.7584, D(G(z)): 0.3169 Epoch: [18/20], Batch Num: [375/600] Discriminator Loss: 0.8620, Generator Loss: 1.8548 D(x): 0.7027, D(G(z)): 0.2672 Epoch: [18/20], Batch Num: [376/600] Discriminator Loss: 0.9953, Generator Loss: 1.7066 D(x): 0.6906, D(G(z)): 0.2930 Epoch: [18/20], Batch Num: [377/600] Discriminator Loss: 0.8594, Generator Loss: 1.6299 D(x): 0.6732, D(G(z)): 0.2402 Epoch: [18/20], Batch Num: [378/600] Discriminator Loss: 0.8192, Generator Loss: 1.7088 D(x): 0.7267, D(G(z)): 0.2910 Epoch: [18/20], Batch Num: [379/600] Discriminator Loss: 0.7513, Generator Loss: 1.4218 D(x): 0.7611, D(G(z)): 0.2759 Epoch: [18/20], Batch Num: [380/600] Discriminator Loss: 0.8970, Generator Loss: 1.4023 D(x): 0.7559, D(G(z)): 0.3406 Epoch: [18/20], Batch Num: [381/600] Discriminator Loss: 0.8174, Generator Loss: 1.6160 D(x): 0.7827, D(G(z)): 0.3422 Epoch: [18/20], Batch Num: [382/600] Discriminator Loss: 0.9635, Generator Loss: 1.9322 D(x): 0.6829, D(G(z)): 0.2756 Epoch: [18/20], Batch Num: [383/600] Discriminator Loss: 1.0550, Generator Loss: 1.8342 D(x): 0.6532, D(G(z)): 0.2899 Epoch: [18/20], Batch Num: [384/600] Discriminator Loss: 0.9037, Generator Loss: 1.7817 D(x): 0.6997, D(G(z)): 0.2869 Epoch: [18/20], Batch Num: [385/600] Discriminator Loss: 1.0186, Generator Loss: 1.6213 D(x): 0.6471, D(G(z)): 0.2635 Epoch: [18/20], Batch Num: [386/600] Discriminator Loss: 0.8552, Generator Loss: 1.2786 D(x): 0.7264, D(G(z)): 0.2983 Epoch: [18/20], Batch Num: [387/600] Discriminator Loss: 0.8645, Generator Loss: 1.4079 D(x): 0.7298, D(G(z)): 0.2927 Epoch: [18/20], Batch Num: [388/600] Discriminator Loss: 1.0685, Generator Loss: 1.4111 D(x): 0.7549, D(G(z)): 0.4061 Epoch: [18/20], Batch Num: [389/600] Discriminator Loss: 1.0404, Generator Loss: 1.5644 D(x): 0.6932, D(G(z)): 0.3536 Epoch: [18/20], Batch Num: [390/600] Discriminator Loss: 1.1850, Generator Loss: 1.5817 D(x): 0.5891, D(G(z)): 0.2876 Epoch: [18/20], Batch Num: [391/600] Discriminator Loss: 1.1033, Generator Loss: 1.5414 D(x): 0.6887, D(G(z)): 0.3686 Epoch: [18/20], Batch Num: [392/600] Discriminator Loss: 1.0619, Generator Loss: 1.2815 D(x): 0.6454, D(G(z)): 0.2951 Epoch: [18/20], Batch Num: [393/600] Discriminator Loss: 1.0338, Generator Loss: 1.3281 D(x): 0.6705, D(G(z)): 0.3162 Epoch: [18/20], Batch Num: [394/600] Discriminator Loss: 0.9220, Generator Loss: 1.4843 D(x): 0.7035, D(G(z)): 0.3174 Epoch: [18/20], Batch Num: [395/600] Discriminator Loss: 1.2084, Generator Loss: 1.3956 D(x): 0.6837, D(G(z)): 0.3843 Epoch: [18/20], Batch Num: [396/600] Discriminator Loss: 1.0682, Generator Loss: 1.6232 D(x): 0.6844, D(G(z)): 0.3521 Epoch: [18/20], Batch Num: [397/600] Discriminator Loss: 0.9108, Generator Loss: 1.3779 D(x): 0.6788, D(G(z)): 0.2866 Epoch: [18/20], Batch Num: [398/600] Discriminator Loss: 0.9997, Generator Loss: 1.2784 D(x): 0.6727, D(G(z)): 0.3286 Epoch: [18/20], Batch Num: [399/600] Discriminator Loss: 1.1318, Generator Loss: 1.4720 D(x): 0.6977, D(G(z)): 0.3734 Epoch: 18, Batch Num: [400/600]
Epoch: [18/20], Batch Num: [400/600] Discriminator Loss: 1.0851, Generator Loss: 1.3695 D(x): 0.6370, D(G(z)): 0.3301 Epoch: [18/20], Batch Num: [401/600] Discriminator Loss: 0.7758, Generator Loss: 1.4059 D(x): 0.7160, D(G(z)): 0.2701 Epoch: [18/20], Batch Num: [402/600] Discriminator Loss: 0.9632, Generator Loss: 1.2686 D(x): 0.6705, D(G(z)): 0.3171 Epoch: [18/20], Batch Num: [403/600] Discriminator Loss: 0.9006, Generator Loss: 1.3233 D(x): 0.7239, D(G(z)): 0.3071 Epoch: [18/20], Batch Num: [404/600] Discriminator Loss: 0.9721, Generator Loss: 1.4122 D(x): 0.7223, D(G(z)): 0.3486 Epoch: [18/20], Batch Num: [405/600] Discriminator Loss: 0.8932, Generator Loss: 1.5361 D(x): 0.7196, D(G(z)): 0.3348 Epoch: [18/20], Batch Num: [406/600] Discriminator Loss: 0.9141, Generator Loss: 1.8076 D(x): 0.6903, D(G(z)): 0.3134 Epoch: [18/20], Batch Num: [407/600] Discriminator Loss: 0.8947, Generator Loss: 1.5462 D(x): 0.6612, D(G(z)): 0.2576 Epoch: [18/20], Batch Num: [408/600] Discriminator Loss: 0.9801, Generator Loss: 1.4874 D(x): 0.6394, D(G(z)): 0.2920 Epoch: [18/20], Batch Num: [409/600] Discriminator Loss: 0.9352, Generator Loss: 1.3810 D(x): 0.7152, D(G(z)): 0.3345 Epoch: [18/20], Batch Num: [410/600] Discriminator Loss: 0.7437, Generator Loss: 1.6399 D(x): 0.8008, D(G(z)): 0.3220 Epoch: [18/20], Batch Num: [411/600] Discriminator Loss: 0.7346, Generator Loss: 1.6759 D(x): 0.7506, D(G(z)): 0.2703 Epoch: [18/20], Batch Num: [412/600] Discriminator Loss: 0.7513, Generator Loss: 1.7190 D(x): 0.7484, D(G(z)): 0.2783 Epoch: [18/20], Batch Num: [413/600] Discriminator Loss: 0.7581, Generator Loss: 1.8739 D(x): 0.7400, D(G(z)): 0.2823 Epoch: [18/20], Batch Num: [414/600] Discriminator Loss: 0.7594, Generator Loss: 1.7902 D(x): 0.6924, D(G(z)): 0.2242 Epoch: [18/20], Batch Num: [415/600] Discriminator Loss: 0.7435, Generator Loss: 1.7058 D(x): 0.7466, D(G(z)): 0.2664 Epoch: [18/20], Batch Num: [416/600] Discriminator Loss: 0.7746, Generator Loss: 1.5372 D(x): 0.7241, D(G(z)): 0.2606 Epoch: [18/20], Batch Num: [417/600] Discriminator Loss: 0.7155, Generator Loss: 1.6183 D(x): 0.7687, D(G(z)): 0.2629 Epoch: [18/20], Batch Num: [418/600] Discriminator Loss: 0.8818, Generator Loss: 1.6765 D(x): 0.7231, D(G(z)): 0.3002 Epoch: [18/20], Batch Num: [419/600] Discriminator Loss: 0.8115, Generator Loss: 1.8470 D(x): 0.7348, D(G(z)): 0.2929 Epoch: [18/20], Batch Num: [420/600] Discriminator Loss: 0.7027, Generator Loss: 1.9308 D(x): 0.7870, D(G(z)): 0.2714 Epoch: [18/20], Batch Num: [421/600] Discriminator Loss: 0.7181, Generator Loss: 1.7908 D(x): 0.7278, D(G(z)): 0.2439 Epoch: [18/20], Batch Num: [422/600] Discriminator Loss: 0.8525, Generator Loss: 1.9067 D(x): 0.7066, D(G(z)): 0.2611 Epoch: [18/20], Batch Num: [423/600] Discriminator Loss: 0.7415, Generator Loss: 1.9381 D(x): 0.7529, D(G(z)): 0.2598 Epoch: [18/20], Batch Num: [424/600] Discriminator Loss: 0.8848, Generator Loss: 1.8124 D(x): 0.7131, D(G(z)): 0.2619 Epoch: [18/20], Batch Num: [425/600] Discriminator Loss: 0.7643, Generator Loss: 1.8700 D(x): 0.7643, D(G(z)): 0.2870 Epoch: [18/20], Batch Num: [426/600] Discriminator Loss: 0.8220, Generator Loss: 1.8938 D(x): 0.7696, D(G(z)): 0.3102 Epoch: [18/20], Batch Num: [427/600] Discriminator Loss: 0.9011, Generator Loss: 1.8157 D(x): 0.6807, D(G(z)): 0.2484 Epoch: [18/20], Batch Num: [428/600] Discriminator Loss: 1.0011, Generator Loss: 1.3517 D(x): 0.6525, D(G(z)): 0.2765 Epoch: [18/20], Batch Num: [429/600] Discriminator Loss: 0.9879, Generator Loss: 1.4226 D(x): 0.7660, D(G(z)): 0.3578 Epoch: [18/20], Batch Num: [430/600] Discriminator Loss: 1.0905, Generator Loss: 1.6944 D(x): 0.7173, D(G(z)): 0.3634 Epoch: [18/20], Batch Num: [431/600] Discriminator Loss: 0.9657, Generator Loss: 1.9355 D(x): 0.6956, D(G(z)): 0.2845 Epoch: [18/20], Batch Num: [432/600] Discriminator Loss: 1.0842, Generator Loss: 1.7012 D(x): 0.6357, D(G(z)): 0.2638 Epoch: [18/20], Batch Num: [433/600] Discriminator Loss: 1.1976, Generator Loss: 1.7791 D(x): 0.6222, D(G(z)): 0.2921 Epoch: [18/20], Batch Num: [434/600] Discriminator Loss: 1.2401, Generator Loss: 1.6060 D(x): 0.6158, D(G(z)): 0.3102 Epoch: [18/20], Batch Num: [435/600] Discriminator Loss: 1.0634, Generator Loss: 1.2348 D(x): 0.6929, D(G(z)): 0.3447 Epoch: [18/20], Batch Num: [436/600] Discriminator Loss: 0.9950, Generator Loss: 1.3974 D(x): 0.7325, D(G(z)): 0.3728 Epoch: [18/20], Batch Num: [437/600] Discriminator Loss: 1.1454, Generator Loss: 1.6113 D(x): 0.7201, D(G(z)): 0.4163 Epoch: [18/20], Batch Num: [438/600] Discriminator Loss: 1.1031, Generator Loss: 1.5067 D(x): 0.6169, D(G(z)): 0.2955 Epoch: [18/20], Batch Num: [439/600] Discriminator Loss: 1.1063, Generator Loss: 1.6180 D(x): 0.5982, D(G(z)): 0.2819 Epoch: [18/20], Batch Num: [440/600] Discriminator Loss: 1.1571, Generator Loss: 1.2833 D(x): 0.5808, D(G(z)): 0.3152 Epoch: [18/20], Batch Num: [441/600] Discriminator Loss: 1.1444, Generator Loss: 1.2369 D(x): 0.6497, D(G(z)): 0.3587 Epoch: [18/20], Batch Num: 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1.4470 D(x): 0.7459, D(G(z)): 0.2807 Epoch: [18/20], Batch Num: [451/600] Discriminator Loss: 0.7692, Generator Loss: 1.4806 D(x): 0.7934, D(G(z)): 0.3311 Epoch: [18/20], Batch Num: [452/600] Discriminator Loss: 0.7248, Generator Loss: 1.5656 D(x): 0.7878, D(G(z)): 0.3174 Epoch: [18/20], Batch Num: [453/600] Discriminator Loss: 0.6545, Generator Loss: 1.8499 D(x): 0.7933, D(G(z)): 0.2783 Epoch: [18/20], Batch Num: [454/600] Discriminator Loss: 0.5938, Generator Loss: 2.1809 D(x): 0.7691, D(G(z)): 0.2054 Epoch: [18/20], Batch Num: [455/600] Discriminator Loss: 0.5706, Generator Loss: 2.2926 D(x): 0.7898, D(G(z)): 0.2211 Epoch: [18/20], Batch Num: [456/600] Discriminator Loss: 0.5932, Generator Loss: 2.2773 D(x): 0.7568, D(G(z)): 0.1932 Epoch: [18/20], Batch Num: [457/600] Discriminator Loss: 0.6056, Generator Loss: 2.4190 D(x): 0.7224, D(G(z)): 0.1490 Epoch: [18/20], Batch Num: [458/600] Discriminator Loss: 0.5193, Generator Loss: 1.8992 D(x): 0.8053, D(G(z)): 0.1974 Epoch: [18/20], 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Generator Loss: 1.9166 D(x): 0.7890, D(G(z)): 0.2903 Epoch: [18/20], Batch Num: [468/600] Discriminator Loss: 0.8206, Generator Loss: 2.0881 D(x): 0.7671, D(G(z)): 0.2809 Epoch: [18/20], Batch Num: [469/600] Discriminator Loss: 0.8628, Generator Loss: 1.9888 D(x): 0.7080, D(G(z)): 0.2518 Epoch: [18/20], Batch Num: [470/600] Discriminator Loss: 1.0118, Generator Loss: 1.8205 D(x): 0.6621, D(G(z)): 0.2590 Epoch: [18/20], Batch Num: [471/600] Discriminator Loss: 0.9238, Generator Loss: 1.6161 D(x): 0.7137, D(G(z)): 0.2882 Epoch: [18/20], Batch Num: [472/600] Discriminator Loss: 1.2495, Generator Loss: 1.6051 D(x): 0.7201, D(G(z)): 0.4216 Epoch: [18/20], Batch Num: [473/600] Discriminator Loss: 1.1778, Generator Loss: 1.6473 D(x): 0.7353, D(G(z)): 0.3921 Epoch: [18/20], Batch Num: [474/600] Discriminator Loss: 1.2156, Generator Loss: 1.8788 D(x): 0.6004, D(G(z)): 0.2745 Epoch: [18/20], Batch Num: [475/600] Discriminator Loss: 1.3216, Generator Loss: 1.6495 D(x): 0.5760, D(G(z)): 0.2729 Epoch: [18/20], Batch Num: [476/600] Discriminator Loss: 1.3761, Generator Loss: 1.2985 D(x): 0.6041, D(G(z)): 0.3433 Epoch: [18/20], Batch Num: [477/600] Discriminator Loss: 1.1333, Generator Loss: 1.2615 D(x): 0.7330, D(G(z)): 0.3942 Epoch: [18/20], Batch Num: [478/600] Discriminator Loss: 1.3146, Generator Loss: 1.2781 D(x): 0.6435, D(G(z)): 0.4246 Epoch: [18/20], Batch Num: [479/600] Discriminator Loss: 1.1107, Generator Loss: 1.3327 D(x): 0.6886, D(G(z)): 0.3775 Epoch: [18/20], Batch Num: [480/600] Discriminator Loss: 1.2420, Generator Loss: 1.4911 D(x): 0.5746, D(G(z)): 0.3453 Epoch: [18/20], Batch Num: [481/600] Discriminator Loss: 1.2259, Generator Loss: 1.6639 D(x): 0.6124, D(G(z)): 0.3211 Epoch: [18/20], Batch Num: [482/600] Discriminator Loss: 1.1580, Generator Loss: 1.6163 D(x): 0.5910, D(G(z)): 0.2844 Epoch: [18/20], Batch Num: [483/600] Discriminator Loss: 1.1286, Generator Loss: 1.4246 D(x): 0.6701, D(G(z)): 0.3221 Epoch: [18/20], Batch Num: [484/600] Discriminator Loss: 1.1095, Generator Loss: 1.5179 D(x): 0.6511, D(G(z)): 0.3247 Epoch: [18/20], Batch Num: [485/600] Discriminator Loss: 1.0421, Generator Loss: 1.3415 D(x): 0.6536, D(G(z)): 0.3091 Epoch: [18/20], Batch Num: [486/600] Discriminator Loss: 0.8868, Generator Loss: 1.2162 D(x): 0.7198, D(G(z)): 0.3074 Epoch: [18/20], Batch Num: [487/600] Discriminator Loss: 0.9145, Generator Loss: 1.2855 D(x): 0.7472, D(G(z)): 0.3567 Epoch: [18/20], Batch Num: [488/600] Discriminator Loss: 1.0472, Generator Loss: 1.5430 D(x): 0.7107, D(G(z)): 0.3781 Epoch: [18/20], Batch Num: [489/600] Discriminator Loss: 0.9831, Generator Loss: 1.6769 D(x): 0.6927, D(G(z)): 0.3188 Epoch: [18/20], Batch Num: [490/600] Discriminator Loss: 0.8215, Generator Loss: 1.7247 D(x): 0.7031, D(G(z)): 0.2597 Epoch: [18/20], Batch Num: [491/600] Discriminator Loss: 0.8204, Generator Loss: 1.6140 D(x): 0.6523, D(G(z)): 0.2153 Epoch: [18/20], Batch Num: [492/600] Discriminator Loss: 0.9245, Generator Loss: 1.7214 D(x): 0.6544, D(G(z)): 0.2478 Epoch: [18/20], Batch Num: [493/600] Discriminator Loss: 0.7911, Generator Loss: 1.4614 D(x): 0.7412, D(G(z)): 0.2958 Epoch: [18/20], Batch Num: [494/600] Discriminator Loss: 0.8323, Generator Loss: 1.4390 D(x): 0.7770, D(G(z)): 0.3333 Epoch: [18/20], Batch Num: [495/600] Discriminator Loss: 0.7372, Generator Loss: 1.6239 D(x): 0.8062, D(G(z)): 0.3154 Epoch: [18/20], Batch Num: [496/600] Discriminator Loss: 0.6783, Generator Loss: 1.7719 D(x): 0.8070, D(G(z)): 0.2882 Epoch: [18/20], Batch Num: [497/600] Discriminator Loss: 0.7416, Generator Loss: 1.9953 D(x): 0.7396, D(G(z)): 0.2471 Epoch: [18/20], Batch Num: [498/600] Discriminator Loss: 0.7670, Generator Loss: 1.9689 D(x): 0.7318, D(G(z)): 0.2384 Epoch: [18/20], Batch Num: [499/600] Discriminator Loss: 0.7272, Generator Loss: 1.9289 D(x): 0.7411, D(G(z)): 0.2374 Epoch: 18, Batch Num: [500/600]
Epoch: [18/20], Batch Num: [500/600] Discriminator Loss: 0.7671, Generator Loss: 2.1376 D(x): 0.7304, D(G(z)): 0.2473 Epoch: [18/20], Batch Num: [501/600] Discriminator Loss: 0.6903, Generator Loss: 1.7244 D(x): 0.7443, D(G(z)): 0.2193 Epoch: [18/20], Batch Num: [502/600] Discriminator Loss: 0.7758, Generator Loss: 1.9445 D(x): 0.7914, D(G(z)): 0.2964 Epoch: [18/20], Batch Num: [503/600] Discriminator Loss: 0.7521, Generator Loss: 1.8163 D(x): 0.8072, D(G(z)): 0.3065 Epoch: [18/20], Batch Num: [504/600] Discriminator Loss: 0.8310, Generator Loss: 1.9296 D(x): 0.7468, D(G(z)): 0.2801 Epoch: [18/20], Batch Num: [505/600] Discriminator Loss: 0.6541, Generator Loss: 2.1627 D(x): 0.8049, D(G(z)): 0.2351 Epoch: [18/20], Batch Num: [506/600] Discriminator Loss: 0.7001, Generator Loss: 1.9350 D(x): 0.7658, D(G(z)): 0.2191 Epoch: [18/20], Batch Num: [507/600] Discriminator Loss: 0.8128, Generator Loss: 1.9661 D(x): 0.6813, D(G(z)): 0.2039 Epoch: [18/20], Batch Num: [508/600] Discriminator Loss: 0.8291, Generator Loss: 1.8610 D(x): 0.7456, D(G(z)): 0.2674 Epoch: [18/20], Batch Num: [509/600] Discriminator Loss: 0.7225, Generator Loss: 1.7667 D(x): 0.8252, D(G(z)): 0.2865 Epoch: [18/20], Batch Num: [510/600] Discriminator Loss: 0.8422, Generator Loss: 1.7967 D(x): 0.7412, D(G(z)): 0.2775 Epoch: [18/20], Batch Num: [511/600] Discriminator Loss: 0.8856, Generator Loss: 1.8008 D(x): 0.7543, D(G(z)): 0.3318 Epoch: [18/20], Batch Num: [512/600] Discriminator Loss: 0.9019, Generator Loss: 1.6759 D(x): 0.7195, D(G(z)): 0.2683 Epoch: [18/20], Batch Num: [513/600] Discriminator Loss: 0.8802, Generator Loss: 1.7180 D(x): 0.6828, D(G(z)): 0.2586 Epoch: [18/20], Batch Num: [514/600] Discriminator Loss: 0.9071, Generator Loss: 1.5535 D(x): 0.7155, D(G(z)): 0.3035 Epoch: [18/20], Batch Num: [515/600] Discriminator Loss: 1.0956, Generator Loss: 1.5054 D(x): 0.7012, D(G(z)): 0.3685 Epoch: [18/20], Batch Num: [516/600] Discriminator Loss: 0.9263, Generator Loss: 1.5144 D(x): 0.7725, D(G(z)): 0.3418 Epoch: [18/20], Batch Num: [517/600] Discriminator Loss: 1.0886, Generator Loss: 1.6027 D(x): 0.6980, D(G(z)): 0.3504 Epoch: [18/20], Batch Num: [518/600] Discriminator Loss: 0.9964, Generator Loss: 1.6350 D(x): 0.6555, D(G(z)): 0.2836 Epoch: [18/20], Batch Num: [519/600] Discriminator Loss: 0.9881, Generator Loss: 1.5821 D(x): 0.6734, D(G(z)): 0.2789 Epoch: [18/20], Batch Num: [520/600] Discriminator Loss: 1.0662, Generator Loss: 1.4763 D(x): 0.6957, D(G(z)): 0.3531 Epoch: [18/20], Batch Num: [521/600] Discriminator Loss: 0.9461, Generator Loss: 1.4898 D(x): 0.7339, D(G(z)): 0.3470 Epoch: [18/20], Batch Num: [522/600] Discriminator Loss: 0.8388, Generator Loss: 1.5882 D(x): 0.7660, D(G(z)): 0.3328 Epoch: [18/20], Batch Num: [523/600] Discriminator Loss: 0.9442, Generator Loss: 1.5664 D(x): 0.7070, D(G(z)): 0.3221 Epoch: [18/20], Batch Num: [524/600] Discriminator Loss: 1.0245, Generator Loss: 1.6605 D(x): 0.6895, D(G(z)): 0.3121 Epoch: [18/20], Batch Num: [525/600] Discriminator Loss: 1.1460, Generator Loss: 1.7940 D(x): 0.6800, D(G(z)): 0.3522 Epoch: [18/20], Batch Num: [526/600] Discriminator Loss: 0.8871, Generator Loss: 1.7256 D(x): 0.6843, D(G(z)): 0.2796 Epoch: [18/20], Batch Num: [527/600] Discriminator Loss: 0.8794, Generator Loss: 1.7136 D(x): 0.7000, D(G(z)): 0.2762 Epoch: [18/20], Batch Num: [528/600] Discriminator Loss: 0.9501, Generator Loss: 1.5916 D(x): 0.7143, D(G(z)): 0.3477 Epoch: [18/20], Batch Num: [529/600] Discriminator Loss: 1.0202, Generator Loss: 1.6776 D(x): 0.7125, D(G(z)): 0.3462 Epoch: [18/20], Batch Num: [530/600] Discriminator Loss: 0.9742, Generator Loss: 1.5367 D(x): 0.6848, D(G(z)): 0.3148 Epoch: [18/20], Batch Num: [531/600] Discriminator Loss: 0.9112, Generator Loss: 1.6301 D(x): 0.6969, D(G(z)): 0.3028 Epoch: [18/20], Batch Num: [532/600] Discriminator Loss: 0.8128, Generator Loss: 1.7248 D(x): 0.7324, D(G(z)): 0.2783 Epoch: [18/20], Batch Num: [533/600] Discriminator Loss: 0.8706, Generator Loss: 1.5990 D(x): 0.7126, D(G(z)): 0.3030 Epoch: [18/20], Batch Num: [534/600] Discriminator Loss: 0.9073, Generator Loss: 1.5244 D(x): 0.6947, D(G(z)): 0.3095 Epoch: [18/20], Batch Num: [535/600] Discriminator Loss: 0.9218, Generator Loss: 1.6160 D(x): 0.6815, D(G(z)): 0.2935 Epoch: [18/20], Batch Num: [536/600] Discriminator Loss: 0.8838, Generator Loss: 1.6288 D(x): 0.7289, D(G(z)): 0.3151 Epoch: [18/20], Batch Num: [537/600] Discriminator Loss: 0.7947, Generator Loss: 1.4561 D(x): 0.7158, D(G(z)): 0.2723 Epoch: [18/20], Batch Num: [538/600] Discriminator Loss: 0.8848, Generator Loss: 1.3638 D(x): 0.7313, D(G(z)): 0.3352 Epoch: [18/20], Batch Num: [539/600] Discriminator Loss: 0.7915, Generator Loss: 1.4984 D(x): 0.7396, D(G(z)): 0.2994 Epoch: [18/20], Batch Num: [540/600] Discriminator Loss: 0.8093, Generator Loss: 1.7333 D(x): 0.7530, D(G(z)): 0.3123 Epoch: [18/20], Batch Num: [541/600] Discriminator Loss: 0.8747, Generator Loss: 1.5590 D(x): 0.7114, D(G(z)): 0.3102 Epoch: [18/20], Batch Num: [542/600] Discriminator Loss: 0.6890, Generator Loss: 1.5678 D(x): 0.7348, D(G(z)): 0.2402 Epoch: [18/20], Batch Num: [543/600] Discriminator Loss: 0.7962, Generator Loss: 1.5131 D(x): 0.7303, D(G(z)): 0.2886 Epoch: [18/20], Batch Num: [544/600] Discriminator Loss: 0.8539, Generator Loss: 1.5636 D(x): 0.6745, D(G(z)): 0.2647 Epoch: [18/20], Batch Num: [545/600] Discriminator Loss: 0.8794, Generator Loss: 1.3293 D(x): 0.7729, D(G(z)): 0.3411 Epoch: [18/20], Batch Num: [546/600] Discriminator Loss: 0.8312, Generator Loss: 1.3123 D(x): 0.7343, D(G(z)): 0.2890 Epoch: [18/20], Batch Num: [547/600] Discriminator Loss: 0.7896, Generator Loss: 1.5260 D(x): 0.7643, D(G(z)): 0.3016 Epoch: [18/20], Batch Num: [548/600] Discriminator Loss: 0.9219, Generator Loss: 1.7091 D(x): 0.6976, D(G(z)): 0.3058 Epoch: [18/20], Batch Num: [549/600] Discriminator Loss: 0.8785, Generator Loss: 1.7058 D(x): 0.7075, D(G(z)): 0.3014 Epoch: [18/20], Batch Num: [550/600] Discriminator Loss: 0.8648, Generator Loss: 1.6163 D(x): 0.6847, D(G(z)): 0.2645 Epoch: [18/20], Batch Num: [551/600] Discriminator Loss: 0.7745, Generator Loss: 1.4642 D(x): 0.7226, D(G(z)): 0.2626 Epoch: [18/20], Batch Num: [552/600] Discriminator Loss: 0.8601, Generator Loss: 1.5806 D(x): 0.7051, D(G(z)): 0.2945 Epoch: [18/20], Batch Num: [553/600] Discriminator Loss: 0.9394, Generator Loss: 1.4691 D(x): 0.7323, D(G(z)): 0.3255 Epoch: [18/20], Batch Num: [554/600] Discriminator Loss: 0.9026, Generator Loss: 1.4370 D(x): 0.6927, D(G(z)): 0.3102 Epoch: [18/20], Batch Num: [555/600] Discriminator Loss: 0.9057, Generator Loss: 1.4255 D(x): 0.7280, D(G(z)): 0.3232 Epoch: [18/20], Batch Num: [556/600] Discriminator Loss: 0.8126, Generator Loss: 1.5944 D(x): 0.7272, D(G(z)): 0.2883 Epoch: [18/20], Batch Num: [557/600] Discriminator Loss: 0.9713, Generator Loss: 1.5431 D(x): 0.6955, D(G(z)): 0.3246 Epoch: [18/20], Batch Num: [558/600] Discriminator Loss: 0.8905, Generator Loss: 1.5992 D(x): 0.7002, D(G(z)): 0.2749 Epoch: [18/20], Batch Num: [559/600] Discriminator Loss: 0.8469, Generator Loss: 1.6455 D(x): 0.7004, D(G(z)): 0.2643 Epoch: [18/20], Batch Num: [560/600] Discriminator Loss: 0.7092, Generator Loss: 1.4840 D(x): 0.7292, D(G(z)): 0.2339 Epoch: [18/20], Batch Num: [561/600] Discriminator Loss: 0.7952, Generator Loss: 1.2913 D(x): 0.7501, D(G(z)): 0.2996 Epoch: [18/20], Batch Num: [562/600] Discriminator Loss: 0.7662, Generator Loss: 1.4495 D(x): 0.7538, D(G(z)): 0.2846 Epoch: [18/20], Batch Num: [563/600] Discriminator Loss: 0.7821, Generator Loss: 1.4154 D(x): 0.7467, D(G(z)): 0.2888 Epoch: [18/20], Batch Num: [564/600] Discriminator Loss: 0.7503, Generator Loss: 1.5201 D(x): 0.8092, D(G(z)): 0.3249 Epoch: [18/20], Batch Num: [565/600] Discriminator Loss: 0.7820, Generator Loss: 1.5684 D(x): 0.7405, D(G(z)): 0.2735 Epoch: [18/20], Batch Num: [566/600] Discriminator Loss: 0.6845, Generator Loss: 1.7464 D(x): 0.7719, D(G(z)): 0.2579 Epoch: [18/20], Batch Num: [567/600] Discriminator Loss: 0.7689, Generator Loss: 1.9196 D(x): 0.7508, D(G(z)): 0.2523 Epoch: [18/20], Batch Num: [568/600] Discriminator Loss: 0.8377, Generator Loss: 1.8726 D(x): 0.6813, D(G(z)): 0.2197 Epoch: [18/20], Batch Num: [569/600] Discriminator Loss: 0.7985, Generator Loss: 1.5868 D(x): 0.7208, D(G(z)): 0.2343 Epoch: [18/20], Batch Num: [570/600] Discriminator Loss: 0.7457, Generator Loss: 1.5124 D(x): 0.7942, D(G(z)): 0.2916 Epoch: [18/20], Batch Num: [571/600] Discriminator Loss: 0.7359, Generator Loss: 1.4482 D(x): 0.8162, D(G(z)): 0.3070 Epoch: [18/20], Batch Num: [572/600] Discriminator Loss: 0.8671, Generator Loss: 1.7578 D(x): 0.7624, D(G(z)): 0.3129 Epoch: [18/20], Batch Num: [573/600] Discriminator Loss: 0.6878, Generator Loss: 1.8516 D(x): 0.7567, D(G(z)): 0.2279 Epoch: [18/20], Batch Num: [574/600] Discriminator Loss: 0.7513, Generator Loss: 1.7052 D(x): 0.7165, D(G(z)): 0.2071 Epoch: [18/20], Batch Num: [575/600] Discriminator Loss: 0.6144, Generator Loss: 1.7305 D(x): 0.7747, D(G(z)): 0.2174 Epoch: [18/20], Batch Num: [576/600] Discriminator Loss: 0.7391, Generator Loss: 1.7356 D(x): 0.7578, D(G(z)): 0.2332 Epoch: [18/20], Batch Num: [577/600] Discriminator Loss: 0.7999, Generator Loss: 1.7672 D(x): 0.8024, D(G(z)): 0.2729 Epoch: [18/20], Batch Num: [578/600] Discriminator Loss: 0.6661, Generator Loss: 1.6993 D(x): 0.8016, D(G(z)): 0.2649 Epoch: [18/20], Batch Num: [579/600] Discriminator Loss: 0.7310, Generator Loss: 2.0720 D(x): 0.8091, D(G(z)): 0.2715 Epoch: [18/20], Batch Num: [580/600] Discriminator Loss: 0.7215, Generator Loss: 2.1385 D(x): 0.7587, D(G(z)): 0.2305 Epoch: [18/20], Batch Num: [581/600] Discriminator Loss: 0.7711, Generator Loss: 2.2761 D(x): 0.6998, D(G(z)): 0.1798 Epoch: [18/20], Batch Num: [582/600] Discriminator Loss: 0.7325, Generator Loss: 2.0871 D(x): 0.7513, D(G(z)): 0.2121 Epoch: [18/20], Batch Num: [583/600] Discriminator Loss: 0.8584, Generator Loss: 1.7651 D(x): 0.7047, D(G(z)): 0.2398 Epoch: [18/20], Batch Num: [584/600] Discriminator Loss: 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Epoch: [19/20], Batch Num: [0/600] Discriminator Loss: 0.8065, Generator Loss: 1.4889 D(x): 0.7634, D(G(z)): 0.2949 Epoch: [19/20], Batch Num: [1/600] Discriminator Loss: 0.8236, Generator Loss: 1.5840 D(x): 0.7798, D(G(z)): 0.3136 Epoch: [19/20], Batch Num: [2/600] Discriminator Loss: 0.8944, Generator Loss: 1.6808 D(x): 0.7277, D(G(z)): 0.2880 Epoch: [19/20], Batch Num: [3/600] Discriminator Loss: 0.7793, Generator Loss: 1.7087 D(x): 0.7372, D(G(z)): 0.2722 Epoch: [19/20], Batch Num: [4/600] Discriminator Loss: 1.0048, Generator Loss: 1.5033 D(x): 0.6214, D(G(z)): 0.2449 Epoch: [19/20], Batch Num: [5/600] Discriminator Loss: 0.8765, Generator Loss: 1.6611 D(x): 0.7104, D(G(z)): 0.2888 Epoch: [19/20], Batch Num: [6/600] Discriminator Loss: 0.7085, Generator Loss: 1.6443 D(x): 0.7849, D(G(z)): 0.2575 Epoch: [19/20], Batch Num: [7/600] Discriminator Loss: 1.0260, Generator Loss: 1.7832 D(x): 0.7366, D(G(z)): 0.3155 Epoch: [19/20], Batch Num: [8/600] Discriminator Loss: 0.8103, Generator 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Num: [17/600] Discriminator Loss: 0.7863, Generator Loss: 1.7719 D(x): 0.7548, D(G(z)): 0.2405 Epoch: [19/20], Batch Num: [18/600] Discriminator Loss: 0.9406, Generator Loss: 1.7554 D(x): 0.7007, D(G(z)): 0.2789 Epoch: [19/20], Batch Num: [19/600] Discriminator Loss: 0.9947, Generator Loss: 1.9316 D(x): 0.7263, D(G(z)): 0.3404 Epoch: [19/20], Batch Num: [20/600] Discriminator Loss: 0.7103, Generator Loss: 1.9422 D(x): 0.7660, D(G(z)): 0.2494 Epoch: [19/20], Batch Num: [21/600] Discriminator Loss: 0.8481, Generator Loss: 2.1508 D(x): 0.7243, D(G(z)): 0.2557 Epoch: [19/20], Batch Num: [22/600] Discriminator Loss: 0.7381, Generator Loss: 1.7825 D(x): 0.7341, D(G(z)): 0.2235 Epoch: [19/20], Batch Num: [23/600] Discriminator Loss: 0.7693, Generator Loss: 1.8799 D(x): 0.7014, D(G(z)): 0.2152 Epoch: [19/20], Batch Num: [24/600] Discriminator Loss: 0.9008, Generator Loss: 1.6473 D(x): 0.7070, D(G(z)): 0.2535 Epoch: [19/20], Batch Num: [25/600] Discriminator Loss: 0.8922, Generator Loss: 1.2551 D(x): 0.7633, D(G(z)): 0.3399 Epoch: [19/20], Batch Num: [26/600] Discriminator Loss: 0.9397, Generator Loss: 1.4166 D(x): 0.7553, D(G(z)): 0.3239 Epoch: [19/20], Batch Num: [27/600] Discriminator Loss: 0.7859, Generator Loss: 1.4850 D(x): 0.7784, D(G(z)): 0.2855 Epoch: [19/20], Batch Num: [28/600] Discriminator Loss: 1.0356, Generator Loss: 1.7624 D(x): 0.7153, D(G(z)): 0.3419 Epoch: [19/20], Batch Num: [29/600] Discriminator Loss: 0.9983, Generator Loss: 1.9158 D(x): 0.6329, D(G(z)): 0.2458 Epoch: [19/20], Batch Num: [30/600] Discriminator Loss: 0.8656, Generator Loss: 1.7409 D(x): 0.6596, D(G(z)): 0.2387 Epoch: [19/20], Batch Num: [31/600] Discriminator Loss: 0.9759, Generator Loss: 1.3177 D(x): 0.7008, D(G(z)): 0.3084 Epoch: [19/20], Batch Num: [32/600] Discriminator Loss: 1.0075, Generator Loss: 1.4641 D(x): 0.7231, D(G(z)): 0.3456 Epoch: [19/20], Batch Num: [33/600] Discriminator Loss: 1.0547, Generator Loss: 1.3753 D(x): 0.7050, D(G(z)): 0.3597 Epoch: [19/20], Batch Num: 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D(x): 0.7114, D(G(z)): 0.4529 Epoch: [19/20], Batch Num: [43/600] Discriminator Loss: 1.1307, Generator Loss: 1.4770 D(x): 0.6379, D(G(z)): 0.3419 Epoch: [19/20], Batch Num: [44/600] Discriminator Loss: 0.9525, Generator Loss: 1.5439 D(x): 0.6648, D(G(z)): 0.2850 Epoch: [19/20], Batch Num: [45/600] Discriminator Loss: 0.9674, Generator Loss: 1.5344 D(x): 0.6282, D(G(z)): 0.2711 Epoch: [19/20], Batch Num: [46/600] Discriminator Loss: 0.7841, Generator Loss: 1.5153 D(x): 0.6832, D(G(z)): 0.2304 Epoch: [19/20], Batch Num: [47/600] Discriminator Loss: 0.9416, Generator Loss: 1.3940 D(x): 0.6668, D(G(z)): 0.2951 Epoch: [19/20], Batch Num: [48/600] Discriminator Loss: 0.8781, Generator Loss: 1.3485 D(x): 0.6939, D(G(z)): 0.2960 Epoch: [19/20], Batch Num: [49/600] Discriminator Loss: 0.9282, Generator Loss: 1.3402 D(x): 0.7509, D(G(z)): 0.3644 Epoch: [19/20], Batch Num: [50/600] Discriminator Loss: 0.8604, Generator Loss: 1.3946 D(x): 0.7607, D(G(z)): 0.3456 Epoch: [19/20], Batch Num: 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D(x): 0.7874, D(G(z)): 0.2297 Epoch: [19/20], Batch Num: [60/600] Discriminator Loss: 0.6837, Generator Loss: 2.0686 D(x): 0.7289, D(G(z)): 0.2124 Epoch: [19/20], Batch Num: [61/600] Discriminator Loss: 0.5716, Generator Loss: 2.2225 D(x): 0.7602, D(G(z)): 0.1737 Epoch: [19/20], Batch Num: [62/600] Discriminator Loss: 0.7291, Generator Loss: 1.9038 D(x): 0.7431, D(G(z)): 0.2231 Epoch: [19/20], Batch Num: [63/600] Discriminator Loss: 0.6996, Generator Loss: 1.7017 D(x): 0.7602, D(G(z)): 0.2486 Epoch: [19/20], Batch Num: [64/600] Discriminator Loss: 0.8144, Generator Loss: 1.9092 D(x): 0.7510, D(G(z)): 0.2752 Epoch: [19/20], Batch Num: [65/600] Discriminator Loss: 0.8279, Generator Loss: 1.7650 D(x): 0.7782, D(G(z)): 0.2881 Epoch: [19/20], Batch Num: [66/600] Discriminator Loss: 0.7978, Generator Loss: 1.8810 D(x): 0.7395, D(G(z)): 0.2435 Epoch: [19/20], Batch Num: [67/600] Discriminator Loss: 1.0002, Generator Loss: 2.0395 D(x): 0.6829, D(G(z)): 0.2732 Epoch: [19/20], Batch Num: [68/600] Discriminator Loss: 0.9007, Generator Loss: 2.0050 D(x): 0.7239, D(G(z)): 0.2741 Epoch: [19/20], Batch Num: [69/600] Discriminator Loss: 0.8665, Generator Loss: 1.9148 D(x): 0.7193, D(G(z)): 0.2709 Epoch: [19/20], Batch Num: [70/600] Discriminator Loss: 0.9622, Generator Loss: 1.4599 D(x): 0.6600, D(G(z)): 0.2495 Epoch: [19/20], Batch Num: [71/600] Discriminator Loss: 0.8731, Generator Loss: 1.3821 D(x): 0.7395, D(G(z)): 0.3314 Epoch: [19/20], Batch Num: [72/600] Discriminator Loss: 1.0592, Generator Loss: 1.3875 D(x): 0.7018, D(G(z)): 0.3432 Epoch: [19/20], Batch Num: [73/600] Discriminator Loss: 1.0448, Generator Loss: 1.8816 D(x): 0.7524, D(G(z)): 0.3933 Epoch: [19/20], Batch Num: [74/600] Discriminator Loss: 1.2349, Generator Loss: 1.7531 D(x): 0.5737, D(G(z)): 0.2783 Epoch: [19/20], Batch Num: [75/600] Discriminator Loss: 1.3396, Generator Loss: 1.5970 D(x): 0.5674, D(G(z)): 0.3425 Epoch: [19/20], Batch Num: [76/600] Discriminator Loss: 1.0722, Generator Loss: 1.4616 D(x): 0.6424, D(G(z)): 0.2761 Epoch: [19/20], Batch Num: [77/600] Discriminator Loss: 1.2007, Generator Loss: 1.1620 D(x): 0.6487, D(G(z)): 0.3670 Epoch: [19/20], Batch Num: [78/600] Discriminator Loss: 1.3270, Generator Loss: 1.1871 D(x): 0.6608, D(G(z)): 0.4047 Epoch: [19/20], Batch Num: [79/600] Discriminator Loss: 1.3872, Generator Loss: 1.2187 D(x): 0.6695, D(G(z)): 0.4532 Epoch: [19/20], Batch Num: [80/600] Discriminator Loss: 1.2105, Generator Loss: 1.4933 D(x): 0.6586, D(G(z)): 0.3941 Epoch: [19/20], Batch Num: [81/600] Discriminator Loss: 1.3375, Generator Loss: 1.6639 D(x): 0.5871, D(G(z)): 0.3711 Epoch: [19/20], Batch Num: [82/600] Discriminator Loss: 1.1563, Generator Loss: 1.5470 D(x): 0.5982, D(G(z)): 0.3100 Epoch: [19/20], Batch Num: [83/600] Discriminator Loss: 1.0917, Generator Loss: 1.3955 D(x): 0.6081, D(G(z)): 0.2876 Epoch: [19/20], Batch Num: [84/600] Discriminator Loss: 1.0660, Generator Loss: 1.2237 D(x): 0.5937, D(G(z)): 0.2778 Epoch: [19/20], Batch Num: [85/600] Discriminator Loss: 1.2161, Generator Loss: 1.1543 D(x): 0.6812, D(G(z)): 0.4315 Epoch: [19/20], Batch Num: [86/600] Discriminator Loss: 0.9675, Generator Loss: 1.2425 D(x): 0.7163, D(G(z)): 0.3730 Epoch: [19/20], Batch Num: [87/600] Discriminator Loss: 1.0464, Generator Loss: 1.2352 D(x): 0.6772, D(G(z)): 0.3817 Epoch: [19/20], Batch Num: [88/600] Discriminator Loss: 0.8049, Generator Loss: 1.3117 D(x): 0.7351, D(G(z)): 0.3198 Epoch: [19/20], Batch Num: [89/600] Discriminator Loss: 0.8581, Generator Loss: 1.5317 D(x): 0.7018, D(G(z)): 0.2993 Epoch: [19/20], Batch Num: [90/600] Discriminator Loss: 0.8462, Generator Loss: 1.7719 D(x): 0.7069, D(G(z)): 0.2865 Epoch: [19/20], Batch Num: [91/600] Discriminator Loss: 0.8275, Generator Loss: 1.6311 D(x): 0.6584, D(G(z)): 0.2399 Epoch: [19/20], Batch Num: [92/600] Discriminator Loss: 0.8606, Generator Loss: 1.5739 D(x): 0.6546, D(G(z)): 0.2377 Epoch: [19/20], Batch Num: [93/600] Discriminator Loss: 0.8616, Generator Loss: 1.3670 D(x): 0.6793, D(G(z)): 0.2796 Epoch: [19/20], Batch Num: [94/600] Discriminator Loss: 0.7305, Generator Loss: 1.4723 D(x): 0.7888, D(G(z)): 0.3088 Epoch: [19/20], Batch Num: [95/600] Discriminator Loss: 0.8984, Generator Loss: 1.5595 D(x): 0.7783, D(G(z)): 0.3725 Epoch: [19/20], Batch Num: [96/600] Discriminator Loss: 0.7710, Generator Loss: 1.7966 D(x): 0.7649, D(G(z)): 0.2812 Epoch: [19/20], Batch Num: [97/600] Discriminator Loss: 0.7213, Generator Loss: 1.8804 D(x): 0.7336, D(G(z)): 0.2346 Epoch: [19/20], Batch Num: [98/600] Discriminator Loss: 0.8629, Generator Loss: 2.0306 D(x): 0.6527, D(G(z)): 0.2144 Epoch: [19/20], Batch Num: [99/600] Discriminator Loss: 0.6809, Generator Loss: 1.8251 D(x): 0.6934, D(G(z)): 0.1604 Epoch: 19, Batch Num: [100/600]
Epoch: [19/20], Batch Num: [100/600] Discriminator Loss: 0.7053, Generator Loss: 1.7620 D(x): 0.7292, D(G(z)): 0.2255 Epoch: [19/20], Batch Num: [101/600] Discriminator Loss: 0.6703, Generator Loss: 1.6210 D(x): 0.7671, D(G(z)): 0.2450 Epoch: [19/20], Batch Num: [102/600] Discriminator Loss: 0.6969, Generator Loss: 1.6197 D(x): 0.8145, D(G(z)): 0.2926 Epoch: [19/20], Batch Num: [103/600] Discriminator Loss: 0.6175, Generator Loss: 1.6030 D(x): 0.8060, D(G(z)): 0.2483 Epoch: [19/20], Batch Num: [104/600] Discriminator Loss: 0.6167, Generator Loss: 1.8721 D(x): 0.8397, D(G(z)): 0.2768 Epoch: [19/20], Batch Num: [105/600] Discriminator Loss: 0.5497, Generator Loss: 2.1355 D(x): 0.8033, D(G(z)): 0.2155 Epoch: [19/20], Batch Num: [106/600] Discriminator Loss: 0.6319, Generator Loss: 2.2003 D(x): 0.7946, D(G(z)): 0.2281 Epoch: [19/20], Batch Num: [107/600] Discriminator Loss: 0.7340, Generator Loss: 2.4192 D(x): 0.7296, D(G(z)): 0.2026 Epoch: [19/20], Batch Num: [108/600] Discriminator Loss: 0.6103, Generator Loss: 2.1323 D(x): 0.7585, D(G(z)): 0.1772 Epoch: [19/20], Batch Num: [109/600] Discriminator Loss: 0.6243, Generator Loss: 2.1507 D(x): 0.7397, D(G(z)): 0.1784 Epoch: [19/20], Batch Num: [110/600] Discriminator Loss: 0.5439, Generator Loss: 1.9665 D(x): 0.8012, D(G(z)): 0.1985 Epoch: [19/20], Batch Num: [111/600] Discriminator Loss: 0.6291, Generator Loss: 1.7002 D(x): 0.8302, D(G(z)): 0.2591 Epoch: [19/20], Batch Num: [112/600] Discriminator Loss: 0.5591, Generator Loss: 1.9355 D(x): 0.8481, D(G(z)): 0.2546 Epoch: [19/20], Batch Num: [113/600] Discriminator Loss: 0.7223, Generator Loss: 1.8268 D(x): 0.7953, D(G(z)): 0.3066 Epoch: [19/20], Batch Num: [114/600] Discriminator Loss: 0.7161, Generator Loss: 2.0952 D(x): 0.7614, D(G(z)): 0.2250 Epoch: [19/20], Batch Num: [115/600] Discriminator Loss: 0.8746, Generator Loss: 2.1795 D(x): 0.7160, D(G(z)): 0.2569 Epoch: [19/20], Batch Num: [116/600] Discriminator Loss: 0.9116, Generator Loss: 1.8085 D(x): 0.6805, D(G(z)): 0.2244 Epoch: [19/20], Batch Num: [117/600] Discriminator Loss: 0.9549, Generator Loss: 1.4758 D(x): 0.6901, D(G(z)): 0.2746 Epoch: [19/20], Batch Num: [118/600] Discriminator Loss: 0.9731, Generator Loss: 1.3865 D(x): 0.7332, D(G(z)): 0.3275 Epoch: [19/20], Batch Num: [119/600] Discriminator Loss: 1.1953, Generator Loss: 1.4456 D(x): 0.6565, D(G(z)): 0.3666 Epoch: [19/20], Batch Num: [120/600] Discriminator Loss: 1.1355, Generator Loss: 1.7622 D(x): 0.7345, D(G(z)): 0.3987 Epoch: [19/20], Batch Num: [121/600] Discriminator Loss: 1.3167, Generator Loss: 1.5885 D(x): 0.6173, D(G(z)): 0.3953 Epoch: [19/20], Batch Num: [122/600] Discriminator Loss: 1.1077, Generator Loss: 1.7562 D(x): 0.6292, D(G(z)): 0.2887 Epoch: [19/20], Batch Num: [123/600] Discriminator Loss: 1.5374, Generator Loss: 1.3534 D(x): 0.5080, D(G(z)): 0.3329 Epoch: [19/20], Batch Num: [124/600] Discriminator Loss: 1.2670, Generator Loss: 1.1318 D(x): 0.6410, D(G(z)): 0.3700 Epoch: [19/20], Batch Num: [125/600] Discriminator Loss: 1.4267, Generator Loss: 1.1237 D(x): 0.6430, D(G(z)): 0.4488 Epoch: [19/20], Batch Num: [126/600] Discriminator Loss: 1.3584, Generator Loss: 1.1378 D(x): 0.6711, D(G(z)): 0.4675 Epoch: [19/20], Batch Num: [127/600] Discriminator Loss: 1.3251, Generator Loss: 1.3506 D(x): 0.6414, D(G(z)): 0.4464 Epoch: [19/20], Batch Num: [128/600] Discriminator Loss: 1.2212, Generator Loss: 1.6069 D(x): 0.6526, D(G(z)): 0.3994 Epoch: [19/20], Batch Num: [129/600] Discriminator Loss: 1.3970, Generator Loss: 1.7207 D(x): 0.5053, D(G(z)): 0.3285 Epoch: [19/20], Batch Num: [130/600] Discriminator Loss: 1.3037, Generator Loss: 1.4927 D(x): 0.5068, D(G(z)): 0.2808 Epoch: [19/20], Batch Num: [131/600] Discriminator Loss: 1.0616, Generator Loss: 1.1732 D(x): 0.6388, D(G(z)): 0.3161 Epoch: [19/20], Batch Num: [132/600] Discriminator Loss: 1.1464, Generator Loss: 1.0926 D(x): 0.6146, D(G(z)): 0.3634 Epoch: [19/20], Batch Num: [133/600] Discriminator Loss: 1.2074, Generator Loss: 1.0473 D(x): 0.6502, D(G(z)): 0.4075 Epoch: [19/20], Batch Num: [134/600] Discriminator Loss: 1.0757, Generator Loss: 1.1641 D(x): 0.6919, D(G(z)): 0.3928 Epoch: [19/20], Batch Num: [135/600] Discriminator Loss: 0.8822, Generator Loss: 1.2301 D(x): 0.7524, D(G(z)): 0.3659 Epoch: [19/20], Batch Num: [136/600] Discriminator Loss: 0.9674, Generator Loss: 1.3495 D(x): 0.7011, D(G(z)): 0.3692 Epoch: [19/20], Batch Num: [137/600] Discriminator Loss: 0.7766, Generator Loss: 1.4787 D(x): 0.7376, D(G(z)): 0.2995 Epoch: [19/20], Batch Num: [138/600] Discriminator Loss: 0.8846, Generator Loss: 1.4911 D(x): 0.7004, D(G(z)): 0.3206 Epoch: [19/20], Batch Num: [139/600] Discriminator Loss: 0.9143, Generator Loss: 1.7573 D(x): 0.6108, D(G(z)): 0.2494 Epoch: [19/20], Batch Num: [140/600] Discriminator Loss: 0.7177, Generator Loss: 1.5953 D(x): 0.6902, D(G(z)): 0.2117 Epoch: [19/20], Batch Num: [141/600] Discriminator Loss: 0.7528, Generator Loss: 1.5514 D(x): 0.7256, D(G(z)): 0.2803 Epoch: [19/20], Batch Num: 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2.0754 D(x): 0.7378, D(G(z)): 0.1916 Epoch: [19/20], Batch Num: [151/600] Discriminator Loss: 0.6484, Generator Loss: 1.9411 D(x): 0.7346, D(G(z)): 0.1904 Epoch: [19/20], Batch Num: [152/600] Discriminator Loss: 0.5495, Generator Loss: 1.9704 D(x): 0.8159, D(G(z)): 0.2121 Epoch: [19/20], Batch Num: [153/600] Discriminator Loss: 0.6587, Generator Loss: 1.8447 D(x): 0.8207, D(G(z)): 0.2727 Epoch: [19/20], Batch Num: [154/600] Discriminator Loss: 0.5437, Generator Loss: 1.9331 D(x): 0.8461, D(G(z)): 0.2414 Epoch: [19/20], Batch Num: [155/600] Discriminator Loss: 0.5959, Generator Loss: 2.1290 D(x): 0.7925, D(G(z)): 0.2200 Epoch: [19/20], Batch Num: [156/600] Discriminator Loss: 0.6192, Generator Loss: 2.3818 D(x): 0.7768, D(G(z)): 0.2085 Epoch: [19/20], Batch Num: [157/600] Discriminator Loss: 0.5939, Generator Loss: 2.2606 D(x): 0.7978, D(G(z)): 0.2052 Epoch: [19/20], Batch Num: [158/600] Discriminator Loss: 0.6966, Generator Loss: 2.2866 D(x): 0.7269, D(G(z)): 0.1966 Epoch: [19/20], Batch Num: [159/600] Discriminator Loss: 0.6777, Generator Loss: 1.9915 D(x): 0.7441, D(G(z)): 0.1799 Epoch: [19/20], Batch Num: [160/600] Discriminator Loss: 0.8231, Generator Loss: 2.0169 D(x): 0.7832, D(G(z)): 0.3022 Epoch: [19/20], Batch Num: [161/600] Discriminator Loss: 0.7561, Generator Loss: 1.9345 D(x): 0.7998, D(G(z)): 0.2695 Epoch: [19/20], Batch Num: [162/600] Discriminator Loss: 0.8036, Generator Loss: 1.9335 D(x): 0.7625, D(G(z)): 0.2473 Epoch: [19/20], Batch Num: [163/600] Discriminator Loss: 0.9103, Generator Loss: 1.9233 D(x): 0.7045, D(G(z)): 0.2707 Epoch: [19/20], Batch Num: [164/600] Discriminator Loss: 0.8898, Generator Loss: 1.8999 D(x): 0.7335, D(G(z)): 0.2914 Epoch: [19/20], Batch Num: [165/600] Discriminator Loss: 0.8267, Generator Loss: 1.8609 D(x): 0.7075, D(G(z)): 0.2562 Epoch: [19/20], Batch Num: [166/600] Discriminator Loss: 1.2356, Generator Loss: 1.6197 D(x): 0.6717, D(G(z)): 0.3227 Epoch: [19/20], Batch Num: [167/600] Discriminator Loss: 1.2459, Generator Loss: 1.5400 D(x): 0.7117, D(G(z)): 0.3766 Epoch: [19/20], Batch Num: [168/600] Discriminator Loss: 1.1514, Generator Loss: 1.6152 D(x): 0.6868, D(G(z)): 0.3518 Epoch: [19/20], Batch Num: [169/600] Discriminator Loss: 1.1809, Generator Loss: 1.8555 D(x): 0.6348, D(G(z)): 0.2922 Epoch: [19/20], Batch Num: [170/600] Discriminator Loss: 1.0874, Generator Loss: 1.5936 D(x): 0.6516, D(G(z)): 0.2640 Epoch: [19/20], Batch Num: [171/600] Discriminator Loss: 1.4142, Generator Loss: 1.4673 D(x): 0.5796, D(G(z)): 0.3438 Epoch: [19/20], Batch Num: [172/600] Discriminator Loss: 1.4498, Generator Loss: 1.4484 D(x): 0.6213, D(G(z)): 0.4035 Epoch: [19/20], Batch Num: [173/600] Discriminator Loss: 1.2155, Generator Loss: 1.6547 D(x): 0.7162, D(G(z)): 0.4381 Epoch: [19/20], Batch Num: [174/600] Discriminator Loss: 1.3721, Generator Loss: 1.6907 D(x): 0.5764, D(G(z)): 0.3486 Epoch: [19/20], Batch Num: [175/600] Discriminator Loss: 1.3235, Generator Loss: 1.5954 D(x): 0.5553, D(G(z)): 0.2891 Epoch: [19/20], Batch Num: [176/600] Discriminator Loss: 1.0857, Generator Loss: 1.4266 D(x): 0.5772, D(G(z)): 0.2483 Epoch: [19/20], Batch Num: [177/600] Discriminator Loss: 1.2060, Generator Loss: 1.3625 D(x): 0.5769, D(G(z)): 0.3136 Epoch: [19/20], Batch Num: [178/600] Discriminator Loss: 1.3482, Generator Loss: 1.0332 D(x): 0.5889, D(G(z)): 0.3888 Epoch: [19/20], Batch Num: [179/600] Discriminator Loss: 1.2985, Generator Loss: 1.0412 D(x): 0.6925, D(G(z)): 0.4655 Epoch: [19/20], Batch Num: [180/600] Discriminator Loss: 1.3168, Generator Loss: 1.1626 D(x): 0.6501, D(G(z)): 0.4482 Epoch: [19/20], Batch Num: [181/600] Discriminator Loss: 1.1549, Generator Loss: 1.3964 D(x): 0.6736, D(G(z)): 0.4033 Epoch: [19/20], Batch Num: [182/600] Discriminator Loss: 0.9482, Generator Loss: 1.4515 D(x): 0.6616, D(G(z)): 0.3036 Epoch: [19/20], Batch Num: [183/600] Discriminator Loss: 1.0901, Generator Loss: 1.6642 D(x): 0.5987, D(G(z)): 0.2853 Epoch: [19/20], Batch Num: [184/600] Discriminator Loss: 1.1826, Generator Loss: 1.4314 D(x): 0.5554, D(G(z)): 0.2748 Epoch: [19/20], Batch Num: [185/600] Discriminator Loss: 1.0294, Generator Loss: 1.4563 D(x): 0.5938, D(G(z)): 0.2624 Epoch: [19/20], Batch Num: [186/600] Discriminator Loss: 1.0411, Generator Loss: 1.2679 D(x): 0.6031, D(G(z)): 0.2781 Epoch: [19/20], Batch Num: [187/600] Discriminator Loss: 0.8812, Generator Loss: 1.2294 D(x): 0.7081, D(G(z)): 0.3097 Epoch: [19/20], Batch Num: [188/600] Discriminator Loss: 0.9749, Generator Loss: 1.2769 D(x): 0.7019, D(G(z)): 0.3644 Epoch: [19/20], Batch Num: [189/600] Discriminator Loss: 0.8909, Generator Loss: 1.2007 D(x): 0.7217, D(G(z)): 0.3483 Epoch: [19/20], Batch Num: [190/600] Discriminator Loss: 0.9184, Generator Loss: 1.2286 D(x): 0.7426, D(G(z)): 0.3599 Epoch: [19/20], Batch Num: [191/600] Discriminator Loss: 0.7933, Generator Loss: 1.3149 D(x): 0.7175, D(G(z)): 0.2961 Epoch: [19/20], Batch Num: [192/600] Discriminator Loss: 0.8036, Generator Loss: 1.3562 D(x): 0.7156, D(G(z)): 0.2943 Epoch: [19/20], Batch Num: [193/600] Discriminator Loss: 0.9524, Generator Loss: 1.5617 D(x): 0.6671, D(G(z)): 0.3192 Epoch: [19/20], Batch Num: [194/600] Discriminator Loss: 0.9362, Generator Loss: 1.7083 D(x): 0.6643, D(G(z)): 0.2854 Epoch: [19/20], Batch Num: [195/600] Discriminator Loss: 0.8356, Generator Loss: 1.7340 D(x): 0.6896, D(G(z)): 0.2623 Epoch: [19/20], Batch Num: [196/600] Discriminator Loss: 0.8032, Generator Loss: 1.6039 D(x): 0.7060, D(G(z)): 0.2584 Epoch: [19/20], Batch Num: [197/600] Discriminator Loss: 0.7637, Generator Loss: 1.4490 D(x): 0.6900, D(G(z)): 0.2336 Epoch: [19/20], Batch Num: [198/600] Discriminator Loss: 0.6751, Generator Loss: 1.5234 D(x): 0.7995, D(G(z)): 0.2922 Epoch: [19/20], Batch Num: [199/600] Discriminator Loss: 0.6978, Generator Loss: 1.6748 D(x): 0.7588, D(G(z)): 0.2539 Epoch: 19, Batch Num: [200/600]
Epoch: [19/20], Batch Num: [200/600] Discriminator Loss: 0.8048, Generator Loss: 1.6375 D(x): 0.7213, D(G(z)): 0.2872 Epoch: [19/20], Batch Num: [201/600] Discriminator Loss: 0.7735, Generator Loss: 1.6536 D(x): 0.7479, D(G(z)): 0.2956 Epoch: [19/20], Batch Num: [202/600] Discriminator Loss: 0.7042, Generator Loss: 1.7043 D(x): 0.7368, D(G(z)): 0.2239 Epoch: [19/20], Batch Num: [203/600] Discriminator Loss: 0.7054, Generator Loss: 1.6667 D(x): 0.7291, D(G(z)): 0.2282 Epoch: [19/20], Batch Num: [204/600] Discriminator Loss: 0.6599, Generator Loss: 1.7062 D(x): 0.7630, D(G(z)): 0.2430 Epoch: [19/20], Batch Num: [205/600] Discriminator Loss: 0.6066, Generator Loss: 1.6894 D(x): 0.7858, D(G(z)): 0.2412 Epoch: [19/20], Batch Num: [206/600] Discriminator Loss: 0.6428, Generator Loss: 1.7828 D(x): 0.8005, D(G(z)): 0.2694 Epoch: [19/20], Batch Num: [207/600] Discriminator Loss: 0.7856, Generator Loss: 1.8369 D(x): 0.7446, D(G(z)): 0.2724 Epoch: [19/20], Batch Num: [208/600] Discriminator Loss: 0.6155, Generator Loss: 1.9584 D(x): 0.7955, D(G(z)): 0.2457 Epoch: [19/20], Batch Num: [209/600] Discriminator Loss: 0.6584, Generator Loss: 1.9791 D(x): 0.7772, D(G(z)): 0.2434 Epoch: [19/20], Batch Num: [210/600] Discriminator Loss: 0.7519, Generator Loss: 1.8356 D(x): 0.7316, D(G(z)): 0.2496 Epoch: [19/20], Batch Num: [211/600] Discriminator Loss: 0.6499, Generator Loss: 1.7829 D(x): 0.7881, D(G(z)): 0.2423 Epoch: [19/20], Batch Num: [212/600] Discriminator Loss: 0.7755, Generator Loss: 1.6819 D(x): 0.7509, D(G(z)): 0.2703 Epoch: [19/20], Batch Num: [213/600] Discriminator Loss: 0.7127, Generator Loss: 1.8099 D(x): 0.7548, D(G(z)): 0.2644 Epoch: [19/20], Batch Num: [214/600] Discriminator Loss: 0.8055, Generator Loss: 1.9379 D(x): 0.7570, D(G(z)): 0.2953 Epoch: [19/20], Batch Num: [215/600] Discriminator Loss: 0.8068, Generator Loss: 1.9731 D(x): 0.7513, D(G(z)): 0.2852 Epoch: [19/20], Batch Num: [216/600] Discriminator Loss: 0.7027, Generator Loss: 1.8751 D(x): 0.7227, D(G(z)): 0.1984 Epoch: [19/20], Batch Num: [217/600] Discriminator Loss: 0.7797, Generator Loss: 1.5773 D(x): 0.7411, D(G(z)): 0.2772 Epoch: [19/20], Batch Num: [218/600] Discriminator Loss: 0.9851, Generator Loss: 1.7302 D(x): 0.7405, D(G(z)): 0.3175 Epoch: [19/20], Batch Num: [219/600] Discriminator Loss: 1.0648, Generator Loss: 1.7146 D(x): 0.6885, D(G(z)): 0.3256 Epoch: [19/20], Batch Num: [220/600] Discriminator Loss: 0.9169, Generator Loss: 1.4877 D(x): 0.7129, D(G(z)): 0.2959 Epoch: [19/20], Batch Num: [221/600] Discriminator Loss: 1.0733, Generator Loss: 1.7320 D(x): 0.6794, D(G(z)): 0.3457 Epoch: [19/20], Batch Num: [222/600] Discriminator Loss: 1.0979, Generator Loss: 1.5881 D(x): 0.6897, D(G(z)): 0.3390 Epoch: [19/20], Batch Num: [223/600] Discriminator Loss: 1.0495, Generator Loss: 1.7649 D(x): 0.7061, D(G(z)): 0.3314 Epoch: [19/20], Batch Num: [224/600] Discriminator Loss: 1.0618, Generator Loss: 1.6935 D(x): 0.6528, D(G(z)): 0.3044 Epoch: [19/20], Batch Num: [225/600] Discriminator Loss: 1.1165, Generator Loss: 1.5028 D(x): 0.6262, D(G(z)): 0.3012 Epoch: [19/20], Batch Num: [226/600] Discriminator Loss: 1.2213, Generator Loss: 1.2566 D(x): 0.6094, D(G(z)): 0.3543 Epoch: [19/20], Batch Num: [227/600] Discriminator Loss: 1.1816, Generator Loss: 1.1567 D(x): 0.6577, D(G(z)): 0.3748 Epoch: [19/20], Batch Num: [228/600] Discriminator Loss: 1.3795, Generator Loss: 1.1448 D(x): 0.6102, D(G(z)): 0.4245 Epoch: [19/20], Batch Num: [229/600] Discriminator Loss: 1.1907, Generator Loss: 1.2037 D(x): 0.6988, D(G(z)): 0.4120 Epoch: [19/20], Batch Num: [230/600] Discriminator Loss: 1.3686, Generator Loss: 1.3378 D(x): 0.6895, D(G(z)): 0.4779 Epoch: [19/20], Batch Num: [231/600] Discriminator Loss: 1.3392, Generator Loss: 1.4030 D(x): 0.5558, D(G(z)): 0.3458 Epoch: [19/20], Batch Num: [232/600] Discriminator Loss: 1.2876, Generator Loss: 1.2534 D(x): 0.5309, D(G(z)): 0.3027 Epoch: [19/20], Batch Num: [233/600] Discriminator Loss: 1.3800, Generator Loss: 1.1632 D(x): 0.5605, D(G(z)): 0.3849 Epoch: [19/20], Batch Num: [234/600] Discriminator Loss: 1.3701, Generator Loss: 1.0235 D(x): 0.5618, D(G(z)): 0.3914 Epoch: [19/20], Batch Num: [235/600] Discriminator Loss: 1.2011, Generator Loss: 1.0859 D(x): 0.6451, D(G(z)): 0.4080 Epoch: [19/20], Batch Num: [236/600] Discriminator Loss: 1.3292, Generator Loss: 0.9709 D(x): 0.6081, D(G(z)): 0.4455 Epoch: [19/20], Batch Num: [237/600] Discriminator Loss: 1.3584, Generator Loss: 1.0510 D(x): 0.6701, D(G(z)): 0.4845 Epoch: [19/20], Batch Num: [238/600] Discriminator Loss: 1.1717, Generator Loss: 1.2176 D(x): 0.6059, D(G(z)): 0.3815 Epoch: [19/20], Batch Num: [239/600] Discriminator Loss: 1.1817, Generator Loss: 1.2989 D(x): 0.5939, D(G(z)): 0.3697 Epoch: [19/20], Batch Num: [240/600] Discriminator Loss: 1.1027, Generator Loss: 1.4470 D(x): 0.5931, D(G(z)): 0.3205 Epoch: [19/20], Batch Num: [241/600] Discriminator Loss: 1.1760, Generator Loss: 1.2113 D(x): 0.5537, D(G(z)): 0.2970 Epoch: [19/20], Batch Num: 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1.6816 D(x): 0.6304, D(G(z)): 0.3033 Epoch: [19/20], Batch Num: [251/600] Discriminator Loss: 0.8444, Generator Loss: 1.4860 D(x): 0.6918, D(G(z)): 0.2942 Epoch: [19/20], Batch Num: [252/600] Discriminator Loss: 0.7343, Generator Loss: 1.5240 D(x): 0.6950, D(G(z)): 0.2387 Epoch: [19/20], Batch Num: [253/600] Discriminator Loss: 0.7634, Generator Loss: 1.4091 D(x): 0.6962, D(G(z)): 0.2560 Epoch: [19/20], Batch Num: [254/600] Discriminator Loss: 0.7302, Generator Loss: 1.3793 D(x): 0.7883, D(G(z)): 0.3220 Epoch: [19/20], Batch Num: [255/600] Discriminator Loss: 0.7483, Generator Loss: 1.5009 D(x): 0.8038, D(G(z)): 0.3328 Epoch: [19/20], Batch Num: [256/600] Discriminator Loss: 0.7383, Generator Loss: 1.6104 D(x): 0.7588, D(G(z)): 0.2975 Epoch: [19/20], Batch Num: [257/600] Discriminator Loss: 0.7058, Generator Loss: 1.8217 D(x): 0.7340, D(G(z)): 0.2506 Epoch: [19/20], Batch Num: [258/600] Discriminator Loss: 0.7172, Generator Loss: 1.8825 D(x): 0.7435, D(G(z)): 0.2679 Epoch: [19/20], Batch Num: [259/600] Discriminator Loss: 0.7178, Generator Loss: 1.8826 D(x): 0.6853, D(G(z)): 0.1917 Epoch: [19/20], Batch Num: [260/600] Discriminator Loss: 0.6670, Generator Loss: 1.8957 D(x): 0.7540, D(G(z)): 0.2259 Epoch: [19/20], Batch Num: [261/600] Discriminator Loss: 0.7888, Generator Loss: 1.7844 D(x): 0.7523, D(G(z)): 0.2749 Epoch: [19/20], Batch Num: [262/600] Discriminator Loss: 0.6374, Generator Loss: 2.0400 D(x): 0.7789, D(G(z)): 0.2447 Epoch: [19/20], Batch Num: [263/600] Discriminator Loss: 0.7426, Generator Loss: 1.9547 D(x): 0.7899, D(G(z)): 0.2784 Epoch: [19/20], Batch Num: [264/600] Discriminator Loss: 0.6635, Generator Loss: 1.8671 D(x): 0.8249, D(G(z)): 0.2698 Epoch: [19/20], Batch Num: [265/600] Discriminator Loss: 0.7740, Generator Loss: 2.2324 D(x): 0.7406, D(G(z)): 0.2545 Epoch: [19/20], Batch Num: [266/600] Discriminator Loss: 0.7263, Generator Loss: 2.3991 D(x): 0.7641, D(G(z)): 0.2117 Epoch: [19/20], Batch Num: [267/600] Discriminator Loss: 0.7483, Generator Loss: 2.2157 D(x): 0.7402, D(G(z)): 0.2200 Epoch: [19/20], Batch Num: [268/600] Discriminator Loss: 0.6562, Generator Loss: 1.8679 D(x): 0.7467, D(G(z)): 0.1841 Epoch: [19/20], Batch Num: [269/600] Discriminator Loss: 0.6798, Generator Loss: 1.9238 D(x): 0.8055, D(G(z)): 0.2433 Epoch: [19/20], Batch Num: [270/600] Discriminator Loss: 0.8899, Generator Loss: 1.8052 D(x): 0.7357, D(G(z)): 0.2786 Epoch: [19/20], Batch Num: [271/600] Discriminator Loss: 0.7379, Generator Loss: 2.0400 D(x): 0.8231, D(G(z)): 0.2925 Epoch: [19/20], Batch Num: [272/600] Discriminator Loss: 0.9941, Generator Loss: 1.9821 D(x): 0.7021, D(G(z)): 0.2938 Epoch: [19/20], Batch Num: [273/600] Discriminator Loss: 0.9425, Generator Loss: 1.6935 D(x): 0.6311, D(G(z)): 0.2112 Epoch: [19/20], Batch Num: [274/600] Discriminator Loss: 0.8832, Generator Loss: 1.2359 D(x): 0.6834, D(G(z)): 0.2492 Epoch: [19/20], Batch Num: [275/600] Discriminator Loss: 1.2315, Generator Loss: 1.2138 D(x): 0.7658, D(G(z)): 0.4555 Epoch: [19/20], Batch Num: [276/600] Discriminator Loss: 1.0684, Generator Loss: 1.7421 D(x): 0.8112, D(G(z)): 0.4265 Epoch: [19/20], Batch Num: [277/600] Discriminator Loss: 1.2532, Generator Loss: 2.3071 D(x): 0.6698, D(G(z)): 0.3421 Epoch: [19/20], Batch Num: [278/600] Discriminator Loss: 1.2289, Generator Loss: 2.2757 D(x): 0.5598, D(G(z)): 0.2467 Epoch: [19/20], Batch Num: [279/600] Discriminator Loss: 1.3933, Generator Loss: 1.5096 D(x): 0.5017, D(G(z)): 0.2242 Epoch: [19/20], Batch Num: [280/600] Discriminator Loss: 1.0402, Generator Loss: 1.0766 D(x): 0.6729, D(G(z)): 0.3196 Epoch: [19/20], Batch Num: [281/600] Discriminator Loss: 1.1390, Generator Loss: 0.9661 D(x): 0.7458, D(G(z)): 0.4344 Epoch: [19/20], Batch Num: [282/600] Discriminator Loss: 1.0489, Generator Loss: 1.0450 D(x): 0.7870, D(G(z)): 0.4503 Epoch: [19/20], Batch Num: [283/600] Discriminator Loss: 1.2192, Generator Loss: 1.1717 D(x): 0.6836, D(G(z)): 0.4490 Epoch: [19/20], Batch Num: [284/600] Discriminator Loss: 1.1651, Generator Loss: 1.4287 D(x): 0.6339, D(G(z)): 0.3806 Epoch: [19/20], Batch Num: [285/600] Discriminator Loss: 1.2974, Generator Loss: 1.5166 D(x): 0.5379, D(G(z)): 0.3206 Epoch: [19/20], Batch Num: [286/600] Discriminator Loss: 1.3313, Generator Loss: 1.1698 D(x): 0.5235, D(G(z)): 0.3045 Epoch: [19/20], Batch Num: [287/600] Discriminator Loss: 1.2529, Generator Loss: 1.0547 D(x): 0.5747, D(G(z)): 0.3582 Epoch: [19/20], Batch Num: [288/600] Discriminator Loss: 1.0725, Generator Loss: 1.0175 D(x): 0.6805, D(G(z)): 0.3964 Epoch: [19/20], Batch Num: [289/600] Discriminator Loss: 1.1590, Generator Loss: 0.8877 D(x): 0.6864, D(G(z)): 0.4459 Epoch: [19/20], Batch Num: [290/600] Discriminator Loss: 0.9569, Generator Loss: 0.9556 D(x): 0.7471, D(G(z)): 0.4221 Epoch: [19/20], Batch Num: [291/600] Discriminator Loss: 1.0605, Generator Loss: 1.1293 D(x): 0.7049, D(G(z)): 0.4310 Epoch: [19/20], Batch Num: [292/600] Discriminator Loss: 1.0498, Generator Loss: 1.3404 D(x): 0.7015, D(G(z)): 0.4084 Epoch: [19/20], Batch Num: [293/600] Discriminator Loss: 1.0571, Generator Loss: 1.5319 D(x): 0.6310, D(G(z)): 0.3390 Epoch: [19/20], Batch Num: [294/600] Discriminator Loss: 1.1298, Generator Loss: 1.5511 D(x): 0.5573, D(G(z)): 0.2847 Epoch: [19/20], Batch Num: [295/600] Discriminator Loss: 0.9121, Generator Loss: 1.5457 D(x): 0.6328, D(G(z)): 0.2521 Epoch: [19/20], Batch Num: [296/600] Discriminator Loss: 0.9562, Generator Loss: 1.4115 D(x): 0.5935, D(G(z)): 0.2379 Epoch: [19/20], Batch Num: [297/600] Discriminator Loss: 0.8971, Generator Loss: 1.4356 D(x): 0.6360, D(G(z)): 0.2662 Epoch: [19/20], Batch Num: [298/600] Discriminator Loss: 0.8573, Generator Loss: 1.2141 D(x): 0.6744, D(G(z)): 0.2923 Epoch: [19/20], Batch Num: [299/600] Discriminator Loss: 0.8095, Generator Loss: 1.2579 D(x): 0.7213, D(G(z)): 0.3007 Epoch: 19, Batch Num: [300/600]
Epoch: [19/20], Batch Num: [300/600] Discriminator Loss: 0.7406, Generator Loss: 1.2175 D(x): 0.8158, D(G(z)): 0.3493 Epoch: [19/20], Batch Num: [301/600] Discriminator Loss: 0.7702, Generator Loss: 1.3613 D(x): 0.7834, D(G(z)): 0.3362 Epoch: [19/20], Batch Num: [302/600] Discriminator Loss: 0.7389, Generator Loss: 1.5799 D(x): 0.7708, D(G(z)): 0.3058 Epoch: [19/20], Batch Num: [303/600] Discriminator Loss: 0.7262, Generator Loss: 1.8607 D(x): 0.7600, D(G(z)): 0.2758 Epoch: [19/20], Batch Num: [304/600] Discriminator Loss: 0.6645, Generator Loss: 1.7655 D(x): 0.7574, D(G(z)): 0.2449 Epoch: [19/20], Batch Num: [305/600] Discriminator Loss: 0.7054, Generator Loss: 2.0519 D(x): 0.7137, D(G(z)): 0.2151 Epoch: [19/20], Batch Num: [306/600] Discriminator Loss: 0.7385, Generator Loss: 2.0903 D(x): 0.7063, D(G(z)): 0.2315 Epoch: [19/20], Batch Num: [307/600] Discriminator Loss: 0.6042, Generator Loss: 1.9334 D(x): 0.7797, D(G(z)): 0.2296 Epoch: [19/20], Batch Num: [308/600] Discriminator Loss: 0.6633, Generator Loss: 1.7645 D(x): 0.6916, D(G(z)): 0.1569 Epoch: [19/20], Batch Num: [309/600] Discriminator Loss: 0.6845, Generator Loss: 1.8759 D(x): 0.7644, D(G(z)): 0.2456 Epoch: [19/20], Batch Num: [310/600] Discriminator Loss: 0.7594, Generator Loss: 1.4064 D(x): 0.7726, D(G(z)): 0.2905 Epoch: [19/20], Batch Num: [311/600] Discriminator Loss: 0.5178, Generator Loss: 1.8764 D(x): 0.8439, D(G(z)): 0.2354 Epoch: [19/20], Batch Num: [312/600] Discriminator Loss: 0.6560, Generator Loss: 1.9100 D(x): 0.8345, D(G(z)): 0.2756 Epoch: [19/20], Batch Num: [313/600] Discriminator Loss: 0.6496, Generator Loss: 2.2883 D(x): 0.7878, D(G(z)): 0.2497 Epoch: [19/20], Batch Num: [314/600] Discriminator Loss: 0.5591, Generator Loss: 2.1716 D(x): 0.8410, D(G(z)): 0.2194 Epoch: [19/20], Batch Num: [315/600] Discriminator Loss: 0.5949, Generator Loss: 2.6169 D(x): 0.7847, D(G(z)): 0.1647 Epoch: [19/20], Batch Num: [316/600] Discriminator Loss: 0.6182, Generator Loss: 2.7224 D(x): 0.7402, D(G(z)): 0.1598 Epoch: [19/20], Batch Num: [317/600] Discriminator Loss: 0.7609, Generator Loss: 2.0311 D(x): 0.7185, D(G(z)): 0.1778 Epoch: [19/20], Batch Num: [318/600] Discriminator Loss: 0.6048, Generator Loss: 1.8678 D(x): 0.8118, D(G(z)): 0.2382 Epoch: [19/20], Batch Num: [319/600] Discriminator Loss: 0.6576, Generator Loss: 1.9534 D(x): 0.8255, D(G(z)): 0.2694 Epoch: [19/20], Batch Num: [320/600] Discriminator Loss: 0.6350, Generator Loss: 1.7597 D(x): 0.8440, D(G(z)): 0.2767 Epoch: [19/20], Batch Num: [321/600] Discriminator Loss: 0.8734, Generator Loss: 2.1202 D(x): 0.8072, D(G(z)): 0.3088 Epoch: [19/20], Batch Num: [322/600] Discriminator Loss: 0.8927, Generator Loss: 2.2110 D(x): 0.7203, D(G(z)): 0.2491 Epoch: [19/20], Batch Num: [323/600] Discriminator Loss: 1.0044, Generator Loss: 2.2287 D(x): 0.6364, D(G(z)): 0.2142 Epoch: [19/20], Batch Num: [324/600] Discriminator Loss: 1.0007, Generator Loss: 2.0007 D(x): 0.6845, D(G(z)): 0.2546 Epoch: [19/20], Batch Num: [325/600] Discriminator Loss: 0.9364, Generator Loss: 1.7679 D(x): 0.7056, D(G(z)): 0.2899 Epoch: [19/20], Batch Num: [326/600] Discriminator Loss: 0.9088, Generator Loss: 1.6924 D(x): 0.7573, D(G(z)): 0.3040 Epoch: [19/20], Batch Num: [327/600] Discriminator Loss: 1.0315, Generator Loss: 1.5254 D(x): 0.6530, D(G(z)): 0.2911 Epoch: [19/20], Batch Num: [328/600] Discriminator Loss: 1.1575, Generator Loss: 1.5268 D(x): 0.7111, D(G(z)): 0.3831 Epoch: [19/20], Batch Num: [329/600] Discriminator Loss: 1.0380, Generator Loss: 1.6413 D(x): 0.7330, D(G(z)): 0.3490 Epoch: [19/20], Batch Num: [330/600] Discriminator Loss: 1.2236, Generator Loss: 1.6012 D(x): 0.6230, D(G(z)): 0.3327 Epoch: [19/20], Batch Num: [331/600] Discriminator Loss: 0.9488, Generator Loss: 1.6197 D(x): 0.6827, D(G(z)): 0.2820 Epoch: [19/20], Batch Num: [332/600] Discriminator Loss: 1.0601, Generator Loss: 1.3667 D(x): 0.6784, D(G(z)): 0.3151 Epoch: [19/20], Batch Num: [333/600] Discriminator Loss: 1.1906, Generator Loss: 1.4689 D(x): 0.6644, D(G(z)): 0.3622 Epoch: [19/20], Batch Num: [334/600] Discriminator Loss: 1.1827, Generator Loss: 1.2485 D(x): 0.6977, D(G(z)): 0.3686 Epoch: [19/20], Batch Num: [335/600] Discriminator Loss: 1.2115, Generator Loss: 1.4837 D(x): 0.6521, D(G(z)): 0.3759 Epoch: [19/20], Batch Num: [336/600] Discriminator Loss: 1.1373, Generator Loss: 1.1967 D(x): 0.6173, D(G(z)): 0.3198 Epoch: [19/20], Batch Num: [337/600] Discriminator Loss: 1.2324, Generator Loss: 1.4200 D(x): 0.6190, D(G(z)): 0.3581 Epoch: [19/20], Batch Num: [338/600] Discriminator Loss: 1.1885, Generator Loss: 1.0914 D(x): 0.6261, D(G(z)): 0.3604 Epoch: [19/20], Batch Num: [339/600] Discriminator Loss: 1.3621, Generator Loss: 1.1496 D(x): 0.6104, D(G(z)): 0.4041 Epoch: [19/20], Batch Num: [340/600] Discriminator Loss: 1.1533, Generator Loss: 1.1132 D(x): 0.6975, D(G(z)): 0.4148 Epoch: [19/20], Batch Num: [341/600] Discriminator Loss: 1.3221, Generator Loss: 1.3235 D(x): 0.6771, D(G(z)): 0.4505 Epoch: [19/20], Batch Num: [342/600] Discriminator Loss: 1.1474, Generator Loss: 1.3428 D(x): 0.6473, D(G(z)): 0.3630 Epoch: [19/20], Batch Num: [343/600] Discriminator Loss: 1.2025, Generator Loss: 1.4419 D(x): 0.5830, D(G(z)): 0.3415 Epoch: [19/20], Batch Num: [344/600] Discriminator Loss: 1.2031, Generator Loss: 1.4385 D(x): 0.5530, D(G(z)): 0.3170 Epoch: [19/20], Batch Num: [345/600] Discriminator Loss: 1.1630, Generator Loss: 1.4274 D(x): 0.5883, D(G(z)): 0.3353 Epoch: [19/20], Batch Num: [346/600] Discriminator Loss: 1.0209, Generator Loss: 1.2482 D(x): 0.6108, D(G(z)): 0.3018 Epoch: [19/20], Batch Num: [347/600] Discriminator Loss: 1.0117, Generator Loss: 1.2806 D(x): 0.6680, D(G(z)): 0.3592 Epoch: [19/20], Batch Num: [348/600] Discriminator Loss: 1.0425, Generator Loss: 1.2048 D(x): 0.6890, D(G(z)): 0.3739 Epoch: [19/20], Batch Num: [349/600] Discriminator Loss: 1.1158, Generator Loss: 1.1573 D(x): 0.6893, D(G(z)): 0.4084 Epoch: [19/20], Batch Num: [350/600] Discriminator Loss: 1.0718, Generator Loss: 1.4089 D(x): 0.6525, D(G(z)): 0.3677 Epoch: [19/20], Batch Num: [351/600] Discriminator Loss: 1.0954, Generator Loss: 1.4015 D(x): 0.6136, D(G(z)): 0.3589 Epoch: [19/20], Batch Num: [352/600] Discriminator Loss: 1.0225, Generator Loss: 1.1015 D(x): 0.6171, D(G(z)): 0.3259 Epoch: [19/20], Batch Num: [353/600] Discriminator Loss: 1.0848, Generator Loss: 1.2838 D(x): 0.6533, D(G(z)): 0.3708 Epoch: [19/20], Batch Num: [354/600] Discriminator Loss: 1.0323, Generator Loss: 1.2376 D(x): 0.6081, D(G(z)): 0.3167 Epoch: [19/20], Batch Num: [355/600] Discriminator Loss: 0.8844, Generator Loss: 1.3935 D(x): 0.6892, D(G(z)): 0.3184 Epoch: [19/20], Batch Num: [356/600] Discriminator Loss: 0.9949, Generator Loss: 1.2427 D(x): 0.6600, D(G(z)): 0.3613 Epoch: [19/20], Batch Num: [357/600] Discriminator Loss: 1.0168, Generator Loss: 1.3054 D(x): 0.6756, D(G(z)): 0.3691 Epoch: [19/20], Batch Num: [358/600] Discriminator Loss: 0.9037, Generator Loss: 1.3318 D(x): 0.6985, D(G(z)): 0.3354 Epoch: [19/20], Batch Num: [359/600] Discriminator Loss: 0.9169, Generator Loss: 1.4794 D(x): 0.6989, D(G(z)): 0.3403 Epoch: [19/20], Batch Num: [360/600] Discriminator Loss: 0.7814, Generator Loss: 1.3775 D(x): 0.7172, D(G(z)): 0.2941 Epoch: [19/20], Batch Num: [361/600] Discriminator Loss: 0.8725, Generator Loss: 1.3793 D(x): 0.7287, D(G(z)): 0.3468 Epoch: [19/20], Batch Num: [362/600] Discriminator Loss: 0.9483, Generator Loss: 1.5057 D(x): 0.6424, D(G(z)): 0.2956 Epoch: [19/20], Batch Num: [363/600] Discriminator Loss: 0.9737, Generator Loss: 1.4630 D(x): 0.6206, D(G(z)): 0.2821 Epoch: [19/20], Batch Num: [364/600] Discriminator Loss: 0.8975, Generator Loss: 1.4589 D(x): 0.6778, D(G(z)): 0.2938 Epoch: [19/20], Batch Num: [365/600] Discriminator Loss: 0.8297, Generator Loss: 1.3705 D(x): 0.7090, D(G(z)): 0.3024 Epoch: [19/20], Batch Num: [366/600] Discriminator Loss: 0.9064, Generator Loss: 1.4395 D(x): 0.7029, D(G(z)): 0.3269 Epoch: [19/20], Batch Num: [367/600] Discriminator Loss: 0.8285, Generator Loss: 1.4950 D(x): 0.7093, D(G(z)): 0.3047 Epoch: [19/20], Batch Num: [368/600] Discriminator Loss: 0.8152, Generator Loss: 1.4417 D(x): 0.7408, D(G(z)): 0.3359 Epoch: [19/20], Batch Num: [369/600] Discriminator Loss: 0.8164, Generator Loss: 1.5468 D(x): 0.7620, D(G(z)): 0.3290 Epoch: [19/20], Batch Num: [370/600] Discriminator Loss: 0.8843, Generator Loss: 1.6996 D(x): 0.7064, D(G(z)): 0.3180 Epoch: [19/20], Batch Num: [371/600] Discriminator Loss: 0.7622, Generator Loss: 1.8586 D(x): 0.7258, D(G(z)): 0.2846 Epoch: [19/20], Batch Num: [372/600] Discriminator Loss: 0.8836, Generator Loss: 1.8462 D(x): 0.6597, D(G(z)): 0.2716 Epoch: [19/20], Batch Num: [373/600] Discriminator Loss: 0.7680, Generator Loss: 1.6433 D(x): 0.6890, D(G(z)): 0.2339 Epoch: [19/20], Batch Num: [374/600] Discriminator Loss: 0.8702, Generator Loss: 1.5495 D(x): 0.6554, D(G(z)): 0.2497 Epoch: [19/20], Batch Num: [375/600] Discriminator Loss: 0.8386, Generator Loss: 1.3985 D(x): 0.7380, D(G(z)): 0.3216 Epoch: [19/20], Batch Num: [376/600] Discriminator Loss: 0.7218, Generator Loss: 1.3592 D(x): 0.7939, D(G(z)): 0.3049 Epoch: [19/20], Batch Num: [377/600] Discriminator Loss: 0.9134, Generator Loss: 1.4886 D(x): 0.7507, D(G(z)): 0.3447 Epoch: [19/20], Batch Num: [378/600] Discriminator Loss: 0.8027, Generator Loss: 1.7541 D(x): 0.7341, D(G(z)): 0.2931 Epoch: [19/20], Batch Num: [379/600] Discriminator Loss: 0.7291, Generator Loss: 1.7840 D(x): 0.7358, D(G(z)): 0.2542 Epoch: [19/20], Batch Num: [380/600] Discriminator Loss: 0.7799, Generator Loss: 1.8754 D(x): 0.6929, D(G(z)): 0.2246 Epoch: [19/20], Batch Num: [381/600] Discriminator Loss: 0.7668, Generator Loss: 1.6391 D(x): 0.7057, D(G(z)): 0.2266 Epoch: [19/20], Batch Num: [382/600] Discriminator Loss: 0.7494, Generator Loss: 1.6067 D(x): 0.7291, D(G(z)): 0.2487 Epoch: [19/20], Batch Num: [383/600] Discriminator Loss: 0.8630, Generator Loss: 1.5376 D(x): 0.7605, D(G(z)): 0.3321 Epoch: [19/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [19/20], Batch Num: [400/600] Discriminator Loss: 1.1430, Generator Loss: 1.0576 D(x): 0.6478, D(G(z)): 0.3530 Epoch: [19/20], Batch Num: [401/600] Discriminator Loss: 1.2323, Generator Loss: 1.1386 D(x): 0.6442, D(G(z)): 0.4009 Epoch: [19/20], Batch Num: [402/600] Discriminator Loss: 1.2581, Generator Loss: 1.0183 D(x): 0.6598, D(G(z)): 0.4402 Epoch: [19/20], Batch Num: [403/600] Discriminator Loss: 1.0680, Generator Loss: 1.2100 D(x): 0.7040, D(G(z)): 0.3971 Epoch: [19/20], Batch Num: [404/600] Discriminator Loss: 1.1130, Generator Loss: 1.2472 D(x): 0.6196, D(G(z)): 0.3476 Epoch: [19/20], Batch Num: [405/600] Discriminator Loss: 1.0809, Generator Loss: 1.3481 D(x): 0.6059, D(G(z)): 0.3208 Epoch: [19/20], Batch Num: [406/600] Discriminator Loss: 1.0959, Generator Loss: 1.3706 D(x): 0.5934, D(G(z)): 0.3135 Epoch: [19/20], Batch Num: [407/600] Discriminator Loss: 1.0261, Generator Loss: 1.1407 D(x): 0.6133, D(G(z)): 0.3160 Epoch: [19/20], Batch Num: [408/600] Discriminator Loss: 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Discriminator Loss: 0.5977, Generator Loss: 1.8008 D(x): 0.7566, D(G(z)): 0.2077 Epoch: [19/20], Batch Num: [426/600] Discriminator Loss: 0.6499, Generator Loss: 1.6257 D(x): 0.7883, D(G(z)): 0.2694 Epoch: [19/20], Batch Num: [427/600] Discriminator Loss: 0.6591, Generator Loss: 1.7680 D(x): 0.8188, D(G(z)): 0.2974 Epoch: [19/20], Batch Num: [428/600] Discriminator Loss: 0.7065, Generator Loss: 1.8813 D(x): 0.8000, D(G(z)): 0.2924 Epoch: [19/20], Batch Num: [429/600] Discriminator Loss: 0.6618, Generator Loss: 2.3108 D(x): 0.8281, D(G(z)): 0.2681 Epoch: [19/20], Batch Num: [430/600] Discriminator Loss: 0.6638, Generator Loss: 2.0303 D(x): 0.7391, D(G(z)): 0.2084 Epoch: [19/20], Batch Num: [431/600] Discriminator Loss: 0.6141, Generator Loss: 1.9542 D(x): 0.7594, D(G(z)): 0.1953 Epoch: [19/20], Batch Num: [432/600] Discriminator Loss: 0.5891, Generator Loss: 1.8805 D(x): 0.7582, D(G(z)): 0.1829 Epoch: [19/20], Batch Num: [433/600] Discriminator Loss: 0.6981, Generator Loss: 1.8272 D(x): 0.7566, D(G(z)): 0.2226 Epoch: [19/20], Batch Num: [434/600] Discriminator Loss: 0.7153, Generator Loss: 1.7152 D(x): 0.8256, D(G(z)): 0.3081 Epoch: [19/20], Batch Num: [435/600] Discriminator Loss: 0.6168, Generator Loss: 1.8138 D(x): 0.8228, D(G(z)): 0.2652 Epoch: [19/20], Batch Num: [436/600] Discriminator Loss: 0.6450, Generator Loss: 1.8928 D(x): 0.8016, D(G(z)): 0.2542 Epoch: [19/20], Batch Num: [437/600] Discriminator Loss: 0.7351, Generator Loss: 2.0411 D(x): 0.7738, D(G(z)): 0.2630 Epoch: [19/20], Batch Num: [438/600] Discriminator Loss: 0.7690, Generator Loss: 2.0952 D(x): 0.7391, D(G(z)): 0.2153 Epoch: [19/20], Batch Num: [439/600] Discriminator Loss: 0.7085, Generator Loss: 2.0692 D(x): 0.7781, D(G(z)): 0.2516 Epoch: [19/20], Batch Num: [440/600] Discriminator Loss: 0.8413, Generator Loss: 1.8952 D(x): 0.7147, D(G(z)): 0.2542 Epoch: [19/20], Batch Num: [441/600] Discriminator Loss: 0.8348, Generator Loss: 1.5360 D(x): 0.7317, D(G(z)): 0.2655 Epoch: [19/20], Batch Num: [442/600] Discriminator Loss: 0.9459, Generator Loss: 1.5744 D(x): 0.7332, D(G(z)): 0.3095 Epoch: [19/20], Batch Num: [443/600] Discriminator Loss: 0.9906, Generator Loss: 1.6191 D(x): 0.7747, D(G(z)): 0.3675 Epoch: [19/20], Batch Num: [444/600] Discriminator Loss: 0.9593, Generator Loss: 1.9427 D(x): 0.7666, D(G(z)): 0.3358 Epoch: [19/20], Batch Num: [445/600] Discriminator Loss: 1.0675, Generator Loss: 2.0338 D(x): 0.6212, D(G(z)): 0.2480 Epoch: [19/20], Batch Num: [446/600] Discriminator Loss: 1.1503, Generator Loss: 1.6898 D(x): 0.6377, D(G(z)): 0.3052 Epoch: [19/20], Batch Num: [447/600] Discriminator Loss: 0.9727, Generator Loss: 1.5199 D(x): 0.7154, D(G(z)): 0.2879 Epoch: [19/20], Batch Num: [448/600] Discriminator Loss: 1.0455, Generator Loss: 1.3173 D(x): 0.6912, D(G(z)): 0.3206 Epoch: [19/20], Batch Num: [449/600] Discriminator Loss: 1.0581, Generator Loss: 1.3760 D(x): 0.7063, D(G(z)): 0.3542 Epoch: [19/20], Batch Num: [450/600] Discriminator Loss: 1.1476, Generator Loss: 1.2433 D(x): 0.6938, D(G(z)): 0.3791 Epoch: [19/20], Batch Num: [451/600] Discriminator Loss: 1.1482, Generator Loss: 1.5462 D(x): 0.6779, D(G(z)): 0.3397 Epoch: [19/20], Batch Num: [452/600] Discriminator Loss: 1.1667, Generator Loss: 1.6449 D(x): 0.6314, D(G(z)): 0.3135 Epoch: [19/20], Batch Num: [453/600] Discriminator Loss: 1.1297, Generator Loss: 1.2728 D(x): 0.6027, D(G(z)): 0.2870 Epoch: [19/20], Batch Num: [454/600] Discriminator Loss: 1.0613, Generator Loss: 1.1951 D(x): 0.6299, D(G(z)): 0.3030 Epoch: [19/20], Batch Num: [455/600] Discriminator Loss: 1.1492, Generator Loss: 1.4634 D(x): 0.7483, D(G(z)): 0.4276 Epoch: [19/20], Batch Num: [456/600] Discriminator Loss: 1.0190, Generator Loss: 1.3144 D(x): 0.7212, D(G(z)): 0.3662 Epoch: [19/20], Batch Num: [457/600] Discriminator Loss: 0.9295, Generator Loss: 1.4952 D(x): 0.6827, D(G(z)): 0.2877 Epoch: [19/20], Batch Num: [458/600] Discriminator Loss: 1.0894, Generator Loss: 1.4731 D(x): 0.6426, D(G(z)): 0.3166 Epoch: [19/20], Batch Num: [459/600] Discriminator Loss: 1.0709, Generator Loss: 1.5543 D(x): 0.6595, D(G(z)): 0.3264 Epoch: [19/20], Batch Num: [460/600] Discriminator Loss: 0.9085, Generator Loss: 1.4682 D(x): 0.6645, D(G(z)): 0.2660 Epoch: [19/20], Batch Num: [461/600] Discriminator Loss: 0.9505, Generator Loss: 1.5637 D(x): 0.6739, D(G(z)): 0.3042 Epoch: [19/20], Batch Num: [462/600] Discriminator Loss: 0.8298, Generator Loss: 1.4351 D(x): 0.7081, D(G(z)): 0.2676 Epoch: [19/20], Batch Num: [463/600] Discriminator Loss: 1.0004, Generator Loss: 1.2868 D(x): 0.7057, D(G(z)): 0.3518 Epoch: [19/20], Batch Num: [464/600] Discriminator Loss: 0.9397, Generator Loss: 1.3720 D(x): 0.7055, D(G(z)): 0.3352 Epoch: [19/20], Batch Num: [465/600] Discriminator Loss: 0.9144, Generator Loss: 1.4521 D(x): 0.7297, D(G(z)): 0.3241 Epoch: [19/20], Batch Num: [466/600] Discriminator Loss: 0.9396, Generator Loss: 1.6906 D(x): 0.7177, D(G(z)): 0.3271 Epoch: [19/20], Batch Num: [467/600] Discriminator Loss: 0.9663, Generator Loss: 1.6292 D(x): 0.7087, D(G(z)): 0.3034 Epoch: [19/20], Batch Num: [468/600] Discriminator Loss: 0.9335, Generator Loss: 1.6829 D(x): 0.6653, D(G(z)): 0.2775 Epoch: [19/20], Batch Num: [469/600] Discriminator Loss: 0.9745, Generator Loss: 1.7356 D(x): 0.6433, D(G(z)): 0.2740 Epoch: [19/20], Batch Num: [470/600] Discriminator Loss: 0.7545, Generator Loss: 1.5573 D(x): 0.7208, D(G(z)): 0.2520 Epoch: [19/20], Batch Num: [471/600] Discriminator Loss: 0.7792, Generator Loss: 1.5342 D(x): 0.7451, D(G(z)): 0.2847 Epoch: [19/20], Batch Num: [472/600] Discriminator Loss: 0.7459, Generator Loss: 1.4640 D(x): 0.7469, D(G(z)): 0.2667 Epoch: [19/20], Batch Num: [473/600] Discriminator Loss: 0.7776, Generator Loss: 1.4440 D(x): 0.7849, D(G(z)): 0.3315 Epoch: [19/20], Batch Num: [474/600] Discriminator Loss: 0.8483, Generator Loss: 1.7544 D(x): 0.7726, D(G(z)): 0.3447 Epoch: [19/20], Batch Num: [475/600] Discriminator Loss: 0.6893, Generator Loss: 1.8095 D(x): 0.7676, D(G(z)): 0.2563 Epoch: [19/20], Batch Num: [476/600] Discriminator Loss: 0.8335, Generator Loss: 1.8741 D(x): 0.6947, D(G(z)): 0.2701 Epoch: [19/20], Batch Num: [477/600] Discriminator Loss: 0.8141, Generator Loss: 1.7797 D(x): 0.6869, D(G(z)): 0.2287 Epoch: [19/20], Batch Num: [478/600] Discriminator Loss: 0.7571, Generator Loss: 1.6053 D(x): 0.6886, D(G(z)): 0.2069 Epoch: [19/20], Batch Num: [479/600] Discriminator Loss: 0.6979, Generator Loss: 1.4046 D(x): 0.7676, D(G(z)): 0.2496 Epoch: [19/20], Batch Num: [480/600] Discriminator Loss: 0.7928, Generator Loss: 1.3950 D(x): 0.7831, D(G(z)): 0.3182 Epoch: [19/20], Batch Num: [481/600] Discriminator Loss: 0.8393, Generator Loss: 1.4629 D(x): 0.8034, D(G(z)): 0.3549 Epoch: [19/20], Batch Num: [482/600] Discriminator Loss: 0.7367, Generator Loss: 1.6630 D(x): 0.7760, D(G(z)): 0.3118 Epoch: [19/20], Batch Num: [483/600] Discriminator Loss: 0.7875, Generator Loss: 1.8296 D(x): 0.7293, D(G(z)): 0.2751 Epoch: [19/20], Batch Num: [484/600] Discriminator Loss: 0.8203, Generator Loss: 1.7279 D(x): 0.6699, D(G(z)): 0.2269 Epoch: [19/20], Batch Num: [485/600] Discriminator Loss: 0.7582, Generator Loss: 1.6209 D(x): 0.7326, D(G(z)): 0.2425 Epoch: [19/20], Batch Num: [486/600] Discriminator Loss: 0.8538, Generator Loss: 1.5973 D(x): 0.6895, D(G(z)): 0.2650 Epoch: [19/20], Batch Num: [487/600] Discriminator Loss: 0.7973, Generator Loss: 1.3222 D(x): 0.7422, D(G(z)): 0.2947 Epoch: [19/20], Batch Num: [488/600] Discriminator Loss: 0.8141, Generator Loss: 1.4024 D(x): 0.7876, D(G(z)): 0.3308 Epoch: [19/20], Batch Num: [489/600] Discriminator Loss: 1.0341, Generator Loss: 1.6490 D(x): 0.6875, D(G(z)): 0.3482 Epoch: [19/20], Batch Num: [490/600] Discriminator Loss: 0.9442, Generator Loss: 1.4621 D(x): 0.6831, D(G(z)): 0.2941 Epoch: [19/20], Batch Num: [491/600] Discriminator Loss: 1.0052, Generator Loss: 1.7219 D(x): 0.6818, D(G(z)): 0.3289 Epoch: [19/20], Batch Num: [492/600] Discriminator Loss: 1.0702, Generator Loss: 1.9309 D(x): 0.6693, D(G(z)): 0.3191 Epoch: [19/20], Batch Num: [493/600] Discriminator Loss: 1.0141, Generator Loss: 1.5435 D(x): 0.6302, D(G(z)): 0.2468 Epoch: [19/20], Batch Num: [494/600] Discriminator Loss: 1.0456, Generator Loss: 1.5426 D(x): 0.6437, D(G(z)): 0.2824 Epoch: [19/20], Batch Num: [495/600] Discriminator Loss: 0.9256, Generator Loss: 1.3038 D(x): 0.7052, D(G(z)): 0.3039 Epoch: [19/20], Batch Num: [496/600] Discriminator Loss: 0.9601, Generator Loss: 1.4516 D(x): 0.7283, D(G(z)): 0.3472 Epoch: [19/20], Batch Num: [497/600] Discriminator Loss: 1.2345, Generator Loss: 1.2015 D(x): 0.6283, D(G(z)): 0.3880 Epoch: [19/20], Batch Num: [498/600] Discriminator Loss: 1.0582, Generator Loss: 1.4377 D(x): 0.6794, D(G(z)): 0.3522 Epoch: [19/20], Batch Num: [499/600] Discriminator Loss: 1.0993, Generator Loss: 1.4208 D(x): 0.6251, D(G(z)): 0.3039 Epoch: 19, Batch Num: [500/600]
Epoch: [19/20], Batch Num: [500/600] Discriminator Loss: 1.2123, Generator Loss: 1.4360 D(x): 0.6330, D(G(z)): 0.3629 Epoch: [19/20], Batch Num: [501/600] Discriminator Loss: 1.0496, Generator Loss: 1.3733 D(x): 0.6239, D(G(z)): 0.2911 Epoch: [19/20], Batch Num: [502/600] Discriminator Loss: 1.1945, Generator Loss: 1.2529 D(x): 0.6271, D(G(z)): 0.3651 Epoch: [19/20], Batch Num: [503/600] Discriminator Loss: 1.1034, Generator Loss: 1.2581 D(x): 0.7125, D(G(z)): 0.3953 Epoch: [19/20], Batch Num: [504/600] Discriminator Loss: 1.2862, Generator Loss: 1.3268 D(x): 0.5909, D(G(z)): 0.3629 Epoch: [19/20], Batch Num: [505/600] Discriminator Loss: 1.1162, Generator Loss: 1.2550 D(x): 0.6068, D(G(z)): 0.3426 Epoch: [19/20], Batch Num: [506/600] Discriminator Loss: 1.0175, Generator Loss: 1.2394 D(x): 0.6556, D(G(z)): 0.3306 Epoch: [19/20], Batch Num: [507/600] Discriminator Loss: 1.0836, Generator Loss: 1.3380 D(x): 0.6100, D(G(z)): 0.3190 Epoch: [19/20], Batch Num: [508/600] Discriminator Loss: 1.0662, Generator Loss: 1.2089 D(x): 0.6611, D(G(z)): 0.3618 Epoch: [19/20], Batch Num: [509/600] Discriminator Loss: 1.0378, Generator Loss: 1.3123 D(x): 0.6837, D(G(z)): 0.3590 Epoch: [19/20], Batch Num: [510/600] Discriminator Loss: 1.0663, Generator Loss: 1.4470 D(x): 0.6682, D(G(z)): 0.3707 Epoch: [19/20], Batch Num: [511/600] Discriminator Loss: 1.0660, Generator Loss: 1.5540 D(x): 0.6710, D(G(z)): 0.3304 Epoch: [19/20], Batch Num: [512/600] Discriminator Loss: 0.9384, Generator Loss: 1.5787 D(x): 0.6587, D(G(z)): 0.2944 Epoch: [19/20], Batch Num: [513/600] Discriminator Loss: 0.8867, Generator Loss: 1.5009 D(x): 0.6141, D(G(z)): 0.2429 Epoch: [19/20], Batch Num: [514/600] Discriminator Loss: 0.8903, Generator Loss: 1.3630 D(x): 0.6981, D(G(z)): 0.2898 Epoch: [19/20], Batch Num: [515/600] Discriminator Loss: 0.8867, Generator Loss: 1.4166 D(x): 0.7076, D(G(z)): 0.3239 Epoch: [19/20], Batch Num: [516/600] Discriminator Loss: 0.7876, Generator Loss: 1.5023 D(x): 0.7622, D(G(z)): 0.3181 Epoch: [19/20], Batch Num: [517/600] Discriminator Loss: 0.7420, Generator Loss: 1.6500 D(x): 0.7811, D(G(z)): 0.3152 Epoch: [19/20], Batch Num: [518/600] Discriminator Loss: 0.7894, Generator Loss: 1.7330 D(x): 0.7531, D(G(z)): 0.3187 Epoch: [19/20], Batch Num: [519/600] Discriminator Loss: 0.8346, Generator Loss: 1.8748 D(x): 0.6993, D(G(z)): 0.2770 Epoch: [19/20], Batch Num: [520/600] Discriminator Loss: 0.6639, Generator Loss: 1.9772 D(x): 0.7379, D(G(z)): 0.2100 Epoch: [19/20], Batch Num: [521/600] Discriminator Loss: 0.7018, Generator Loss: 1.9790 D(x): 0.7278, D(G(z)): 0.2111 Epoch: [19/20], Batch Num: [522/600] Discriminator Loss: 0.5861, Generator Loss: 1.9908 D(x): 0.7387, D(G(z)): 0.1699 Epoch: [19/20], Batch Num: [523/600] Discriminator Loss: 0.7945, Generator Loss: 1.9463 D(x): 0.6930, D(G(z)): 0.2218 Epoch: [19/20], Batch Num: [524/600] Discriminator Loss: 0.5872, Generator Loss: 1.7116 D(x): 0.8012, D(G(z)): 0.2407 Epoch: [19/20], Batch Num: [525/600] Discriminator Loss: 0.6589, Generator Loss: 1.7123 D(x): 0.8355, D(G(z)): 0.2963 Epoch: [19/20], Batch Num: [526/600] Discriminator Loss: 0.7329, Generator Loss: 1.9514 D(x): 0.8002, D(G(z)): 0.3004 Epoch: [19/20], Batch Num: [527/600] Discriminator Loss: 0.6749, Generator Loss: 2.1324 D(x): 0.7571, D(G(z)): 0.2069 Epoch: [19/20], Batch Num: [528/600] Discriminator Loss: 0.6914, Generator Loss: 1.8686 D(x): 0.7190, D(G(z)): 0.1763 Epoch: [19/20], Batch Num: [529/600] Discriminator Loss: 0.6486, Generator Loss: 1.8802 D(x): 0.7606, D(G(z)): 0.1965 Epoch: [19/20], Batch Num: [530/600] Discriminator Loss: 0.6989, Generator Loss: 1.6876 D(x): 0.7907, D(G(z)): 0.2654 Epoch: [19/20], Batch Num: [531/600] Discriminator Loss: 0.7234, Generator Loss: 1.8666 D(x): 0.8238, D(G(z)): 0.3087 Epoch: [19/20], Batch Num: [532/600] Discriminator Loss: 0.7718, Generator Loss: 2.1400 D(x): 0.7270, D(G(z)): 0.2467 Epoch: [19/20], Batch Num: [533/600] Discriminator Loss: 0.7613, Generator Loss: 2.2053 D(x): 0.7525, D(G(z)): 0.2491 Epoch: [19/20], Batch Num: [534/600] Discriminator Loss: 0.9341, Generator Loss: 2.0956 D(x): 0.7134, D(G(z)): 0.2753 Epoch: [19/20], Batch Num: [535/600] Discriminator Loss: 0.8480, Generator Loss: 2.1701 D(x): 0.6981, D(G(z)): 0.2299 Epoch: [19/20], Batch Num: [536/600] Discriminator Loss: 0.7552, Generator Loss: 1.9178 D(x): 0.7339, D(G(z)): 0.2262 Epoch: [19/20], Batch Num: [537/600] Discriminator Loss: 0.8879, Generator Loss: 1.6510 D(x): 0.7124, D(G(z)): 0.2949 Epoch: [19/20], Batch Num: [538/600] Discriminator Loss: 0.7806, Generator Loss: 1.5903 D(x): 0.7896, D(G(z)): 0.3059 Epoch: [19/20], Batch Num: [539/600] Discriminator Loss: 0.9595, Generator Loss: 1.8046 D(x): 0.7784, D(G(z)): 0.3265 Epoch: [19/20], Batch Num: [540/600] Discriminator Loss: 1.1138, Generator Loss: 1.9388 D(x): 0.6394, D(G(z)): 0.2879 Epoch: [19/20], Batch Num: [541/600] Discriminator Loss: 1.2334, Generator Loss: 1.6791 D(x): 0.6034, D(G(z)): 0.3172 Epoch: [19/20], Batch Num: [542/600] Discriminator Loss: 1.0401, Generator Loss: 1.3505 D(x): 0.6622, D(G(z)): 0.2785 Epoch: [19/20], Batch Num: [543/600] Discriminator Loss: 1.1570, Generator Loss: 1.3046 D(x): 0.6652, D(G(z)): 0.3673 Epoch: [19/20], Batch Num: [544/600] Discriminator Loss: 1.1744, Generator Loss: 1.3375 D(x): 0.7303, D(G(z)): 0.4003 Epoch: [19/20], Batch Num: [545/600] Discriminator Loss: 1.3463, Generator Loss: 1.5984 D(x): 0.6416, D(G(z)): 0.4152 Epoch: [19/20], Batch Num: [546/600] Discriminator Loss: 0.9980, Generator Loss: 1.6772 D(x): 0.6734, D(G(z)): 0.3107 Epoch: [19/20], Batch Num: [547/600] Discriminator Loss: 1.4051, Generator Loss: 1.5312 D(x): 0.5526, D(G(z)): 0.3443 Epoch: [19/20], Batch Num: [548/600] Discriminator Loss: 1.3780, Generator Loss: 1.2980 D(x): 0.5526, D(G(z)): 0.3417 Epoch: [19/20], Batch Num: [549/600] Discriminator Loss: 1.2995, Generator Loss: 1.0388 D(x): 0.6481, D(G(z)): 0.4267 Epoch: [19/20], Batch Num: [550/600] Discriminator Loss: 1.1723, Generator Loss: 1.0714 D(x): 0.6710, D(G(z)): 0.4061 Epoch: [19/20], Batch Num: [551/600] Discriminator Loss: 1.2337, Generator Loss: 1.3285 D(x): 0.6576, D(G(z)): 0.4215 Epoch: [19/20], Batch Num: [552/600] Discriminator Loss: 1.2417, Generator Loss: 1.2414 D(x): 0.6410, D(G(z)): 0.4041 Epoch: [19/20], Batch Num: [553/600] Discriminator Loss: 1.2684, Generator Loss: 1.2917 D(x): 0.5938, D(G(z)): 0.3777 Epoch: [19/20], Batch Num: [554/600] Discriminator Loss: 1.2039, Generator Loss: 1.2395 D(x): 0.5859, D(G(z)): 0.3543 Epoch: [19/20], Batch Num: [555/600] Discriminator Loss: 1.2787, Generator Loss: 1.2015 D(x): 0.5631, D(G(z)): 0.3466 Epoch: [19/20], Batch Num: [556/600] Discriminator Loss: 1.0911, Generator Loss: 1.1064 D(x): 0.6549, D(G(z)): 0.3786 Epoch: [19/20], Batch Num: [557/600] Discriminator Loss: 0.9817, Generator Loss: 1.1959 D(x): 0.7308, D(G(z)): 0.3911 Epoch: [19/20], Batch Num: [558/600] Discriminator Loss: 1.0052, Generator Loss: 1.3896 D(x): 0.6616, D(G(z)): 0.3508 Epoch: [19/20], Batch Num: [559/600] Discriminator Loss: 0.8900, Generator Loss: 1.3077 D(x): 0.6667, D(G(z)): 0.2905 Epoch: [19/20], Batch Num: [560/600] Discriminator Loss: 1.0482, Generator Loss: 1.3857 D(x): 0.6468, D(G(z)): 0.3460 Epoch: [19/20], Batch Num: [561/600] Discriminator Loss: 0.8739, Generator Loss: 1.4251 D(x): 0.6883, D(G(z)): 0.2966 Epoch: [19/20], Batch Num: [562/600] Discriminator Loss: 0.8516, Generator Loss: 1.3480 D(x): 0.6759, D(G(z)): 0.2875 Epoch: [19/20], Batch Num: [563/600] Discriminator Loss: 0.7960, Generator Loss: 1.3136 D(x): 0.7076, D(G(z)): 0.2881 Epoch: [19/20], Batch Num: [564/600] Discriminator Loss: 0.9109, Generator Loss: 1.3364 D(x): 0.6991, D(G(z)): 0.3265 Epoch: [19/20], Batch Num: [565/600] Discriminator Loss: 0.8068, Generator Loss: 1.3640 D(x): 0.7331, D(G(z)): 0.3050 Epoch: [19/20], Batch Num: [566/600] Discriminator Loss: 0.9127, Generator Loss: 1.5714 D(x): 0.7351, D(G(z)): 0.3619 Epoch: [19/20], Batch Num: [567/600] Discriminator Loss: 0.7679, Generator Loss: 1.6232 D(x): 0.7339, D(G(z)): 0.2937 Epoch: [19/20], Batch Num: [568/600] Discriminator Loss: 0.8184, Generator Loss: 1.7472 D(x): 0.7163, D(G(z)): 0.2828 Epoch: [19/20], Batch Num: [569/600] Discriminator Loss: 0.7515, Generator Loss: 1.7664 D(x): 0.7228, D(G(z)): 0.2626 Epoch: [19/20], Batch Num: [570/600] Discriminator Loss: 0.6611, Generator Loss: 1.7803 D(x): 0.7245, D(G(z)): 0.2049 Epoch: [19/20], Batch Num: [571/600] Discriminator Loss: 0.7443, Generator Loss: 1.6673 D(x): 0.7322, D(G(z)): 0.2594 Epoch: [19/20], Batch Num: [572/600] Discriminator Loss: 0.6545, Generator Loss: 1.6892 D(x): 0.7666, D(G(z)): 0.2439 Epoch: [19/20], Batch Num: [573/600] Discriminator Loss: 0.7020, Generator Loss: 1.6756 D(x): 0.7621, D(G(z)): 0.2649 Epoch: [19/20], Batch Num: [574/600] Discriminator Loss: 0.5811, Generator Loss: 1.7233 D(x): 0.8101, D(G(z)): 0.2381 Epoch: [19/20], Batch Num: [575/600] Discriminator Loss: 0.5564, Generator Loss: 1.6994 D(x): 0.8291, D(G(z)): 0.2477 Epoch: [19/20], Batch Num: [576/600] Discriminator Loss: 0.6270, Generator Loss: 1.8780 D(x): 0.8029, D(G(z)): 0.2497 Epoch: [19/20], Batch Num: [577/600] Discriminator Loss: 0.5730, Generator Loss: 1.9453 D(x): 0.8422, D(G(z)): 0.2302 Epoch: [19/20], Batch Num: [578/600] Discriminator Loss: 0.6666, Generator Loss: 2.1847 D(x): 0.7638, D(G(z)): 0.2106 Epoch: [19/20], Batch Num: [579/600] Discriminator Loss: 0.9107, Generator Loss: 2.1905 D(x): 0.6920, D(G(z)): 0.2115 Epoch: [19/20], Batch Num: [580/600] Discriminator Loss: 0.7421, Generator Loss: 2.0320 D(x): 0.7085, D(G(z)): 0.1799 Epoch: [19/20], Batch Num: [581/600] Discriminator Loss: 0.6634, Generator Loss: 1.7490 D(x): 0.7805, D(G(z)): 0.2348 Epoch: [19/20], Batch Num: [582/600] Discriminator Loss: 0.7568, Generator Loss: 1.5655 D(x): 0.8327, D(G(z)): 0.3090 Epoch: [19/20], Batch Num: [583/600] Discriminator Loss: 0.9200, Generator Loss: 2.0812 D(x): 0.8594, D(G(z)): 0.4153 Epoch: [19/20], Batch Num: [584/600] Discriminator Loss: 0.6927, Generator Loss: 2.2304 D(x): 0.7448, D(G(z)): 0.1930 Epoch: [19/20], Batch Num: [585/600] Discriminator Loss: 0.9652, Generator Loss: 2.1165 D(x): 0.6070, D(G(z)): 0.1502 Epoch: [19/20], Batch Num: [586/600] Discriminator Loss: 0.8291, Generator Loss: 1.8675 D(x): 0.6688, D(G(z)): 0.2002 Epoch: [19/20], Batch Num: [587/600] Discriminator Loss: 1.0683, Generator Loss: 1.4481 D(x): 0.6948, D(G(z)): 0.3280 Epoch: [19/20], Batch Num: [588/600] Discriminator Loss: 1.1444, Generator Loss: 1.6732 D(x): 0.7482, D(G(z)): 0.3918 Epoch: [19/20], Batch Num: [589/600] Discriminator Loss: 1.0642, Generator Loss: 1.6954 D(x): 0.7479, D(G(z)): 0.3794 Epoch: [19/20], Batch Num: [590/600] Discriminator Loss: 0.9667, Generator Loss: 2.1212 D(x): 0.6676, D(G(z)): 0.2773 Epoch: [19/20], Batch Num: [591/600] Discriminator Loss: 1.1921, Generator Loss: 1.9527 D(x): 0.5605, D(G(z)): 0.2443 Epoch: [19/20], Batch Num: [592/600] Discriminator Loss: 1.2480, Generator Loss: 1.4698 D(x): 0.6022, D(G(z)): 0.3037 Epoch: [19/20], Batch Num: [593/600] Discriminator Loss: 1.1892, Generator Loss: 1.4608 D(x): 0.6780, D(G(z)): 0.4041 Epoch: [19/20], Batch Num: [594/600] Discriminator Loss: 1.2107, Generator Loss: 1.2343 D(x): 0.6400, D(G(z)): 0.3724 Epoch: [19/20], Batch Num: [595/600] Discriminator Loss: 1.4099, Generator Loss: 1.3747 D(x): 0.6256, D(G(z)): 0.4273 Epoch: [19/20], Batch Num: [596/600] Discriminator Loss: 1.3370, Generator Loss: 1.3535 D(x): 0.5912, D(G(z)): 0.3709 Epoch: [19/20], Batch Num: [597/600] Discriminator Loss: 1.3764, Generator Loss: 1.3767 D(x): 0.5435, D(G(z)): 0.3305 Epoch: [19/20], Batch Num: [598/600] Discriminator Loss: 1.0920, Generator Loss: 1.5166 D(x): 0.6477, D(G(z)): 0.3489 Epoch: [19/20], Batch Num: [599/600] Discriminator Loss: 1.3190, Generator Loss: 1.4301 D(x): 0.5624, D(G(z)): 0.3680
Au début, les images générées sont du pur bruit, mais elles s'améliorent ensuite, jusqu'à ce qu'on obtienne des images assez bonnes.
Il est également possible de visualiser le processus d'apprentissage. Comme vous pouvez le voir dans le bloc précédent, l'erreur du discriminateur est très élevée au début, car il ne sait pas comment classer correctement les images comme étant réelles ou fausses. Au fur et à mesure que le discriminateur s'améliore et que son erreur diminue à environ 0,4 . Quant à l'erreur du générateur, elle augmente, ce qui prouve que le discriminateur est plus performant que le générateur et qu'il peut classer correctement les échantillons faux.
En poursuivant l'entraînement, l'erreur du générateur diminue, ce qui implique que les images qu'il génère sont de mieux en mieux. Alors que le générateur s'améliore, l'erreur du discriminateur augmente, car les images deviennent chaque fois plus réalistes.
def train_discriminator(optimizer, real_data, fake_data):
N = real_data.size(0)
# Reset gradients
optimizer.zero_grad()
# 1.1 Train on Real Data
prediction_real = discriminator(real_data)
# Calculate error and backpropagate
error_real = loss(prediction_real.cuda(), ones_target(N).cuda())
error_real.backward()
# 1.2 Train on Fake Data
prediction_fake = discriminator(fake_data)
# Calculate error and backpropagate
error_fake = loss(prediction_fake.cuda(), zeros_target(N).cuda())
error_fake.backward()
# 1.3 Update weights with gradients
optimizer.step()
# Return error and predictions for real and fake inputs
return error_real + error_fake, prediction_real, prediction_fake
def train_generator(optimizer, fake_data):
N = fake_data.size(0)
# Reset gradients
optimizer.zero_grad()
# Sample noise and generate fake data
prediction = discriminator(fake_data)
# Calculate error and backpropagate
error = loss(prediction.cuda(), ones_target(N).cuda())
error.backward()
# Update weights with gradients
optimizer.step()
# Return error
return error
Modifiez la structure des deux réseaux (discriminateur et générateur) en ajoutant ou enlevant des couches cachées, en modifiant le nombre de neurones dans ces couches et en jouant sur les paramètres des fonctions LeakyReLU et Dropout.
Après avoir expliqué comment évaluer la qualité des images générées par le générateur, comparez la qualité des résultats obtenus avec les modifications que vous avez testé et essayez d’obtenir le meilleur résultat possible.
class DiscriminatorNet(nn.Module):
#On ajoute une couche cachée + Modifiant le nombre de neurones dans ces couches
#LeakyReLU à alpha = 0.10 et Dropout0.5
def __init__(self):
super(DiscriminatorNet, self).__init__()
n_features = 784
n_out = 1
self.hidden0 = nn.Sequential(
nn.Linear(n_features, 1024),
nn.LeakyReLU(0.1),
nn.Dropout(0.5)
)
self.hidden1 = nn.Sequential(
nn.Linear(1024, 768),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden2 = nn.Sequential(
nn.Linear(768, 512),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.hidden3 = nn.Sequential(
nn.Linear(512, 256),
nn.LeakyReLU(0.2),
nn.Dropout(0.3)
)
self.out = nn.Sequential(
torch.nn.Linear(256, n_out),
torch.nn.Sigmoid()
)
def forward(self, x):
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.hidden3(x)
x = self.out(x)
return x
discriminator = DiscriminatorNet()
class GeneratorNet(nn.Module):
"""
4 couches cachées generative neural network
"""
def __init__(self):
super(GeneratorNet, self).__init__()
n_features = 100
n_out = 784
self.hidden0 = nn.Sequential(
nn.Linear(n_features, 256),
nn.LeakyReLU(0.1)
)
self.hidden1 = nn.Sequential(
nn.Linear(256, 512),
nn.LeakyReLU(0.1)
)
self.hidden2 = nn.Sequential(
nn.Linear(512, 768 ),
nn.LeakyReLU(0.1)
)
self.hidden3 = nn.Sequential(
nn.Linear(768, 1024),
nn.LeakyReLU(0.1),
)
self.out = nn.Sequential(
nn.Linear(1024, n_out),
nn.Tanh()
)
def forward(self, x):
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.hidden3(x)
x = self.out(x)
return x
generator = GeneratorNet()
if torch.cuda.is_available():
discriminator.cuda()
generator.cuda()
# Optimizers
d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)
g_optimizer = optim.Adam(generator.parameters(), lr=0.0002)
# Loss function
loss = nn.BCELoss()
def train_discriminator(optimizer, real_data, fake_data):
# Reset gradients
optimizer.zero_grad()
# 1.1 Train on Real Data
prediction_real = discriminator(real_data)
# Calculate error and backpropagate
error_real = loss(prediction_real, gt.real_data_target(real_data.size(0)))
error_real.backward()
# 1.2 Train on Fake Data
prediction_fake = discriminator(fake_data)
# Calculate error and backpropagate
error_fake = loss(prediction_fake, gt.fake_data_target(real_data.size(0)))
error_fake.backward()
# 1.3 Update weights with gradients
optimizer.step()
# Return error
return error_real + error_fake, prediction_real, prediction_fake
def train_generator(optimizer, fake_data):
# 2. Train Generator
# Reset gradients
optimizer.zero_grad()
# Sample noise and generate fake data
prediction = discriminator(fake_data)
# Calculate error and backpropagate
error = loss(prediction, gt.real_data_target(prediction.size(0)))
error.backward()
# Update weights with gradients
optimizer.step()
# Return error
return error
num_epochs=20
for epoch in range(num_epochs):
for n_batch, (real_batch,_) in enumerate(data_loader):
n = real_batch.size(0)
#Transformez le lot de données en variables Torch à l'aide des fonctions Variable et gt.images_to_vectors.
#Ce sont les vraies données pour l'étape suivante.
# 1 Train Discriminator
# 1.1 Prepare real data
real_data = Variable(gt.images_to_vectors(real_batch))
# 1.2 Generate fake data with the Generator
#""" A COMPLETER : Créer des vecteurs bruit """
#(les gradients ne sont donc pas calculés pour le générateur)
fake_data = generator(gt.noise(n)).detach()
#""" A COMPLETER : Générer des images avec le générateur """
# 1.3 Train Discriminator
# """ A COMPLETER : Entrainer le discriminateur """
d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, real_data, fake_data)
# 2 Train Generator
# 2.1 Generate noise
fake_data = generator(gt.noise(n))
# 2.2 Train Generator
#""" A COMPLETER : Entrainer le generateur """
g_error = train_generator(g_optimizer, fake_data)
# Generate images from a fixed noise input and visualize the output
gt.plot_gan(epoch, n_batch, num_batches, generator)
# Display status Logs
gt.logger.display_status(
epoch, num_epochs, n_batch, num_batches,
d_error, g_error, d_pred_real, d_pred_fake
)
Epoch: 0, Batch Num: [0/600]
Epoch: [0/20], Batch Num: [0/600] Discriminator Loss: 1.3811, Generator Loss: 0.7151 D(x): 0.4914, D(G(z)): 0.4885 Epoch: [0/20], Batch Num: [1/600] Discriminator Loss: 1.3041, Generator Loss: 0.7127 D(x): 0.5318, D(G(z)): 0.4896 Epoch: [0/20], Batch Num: [2/600] Discriminator Loss: 1.2142, Generator Loss: 0.7083 D(x): 0.5841, D(G(z)): 0.4914 Epoch: [0/20], Batch Num: [3/600] Discriminator Loss: 1.1055, Generator Loss: 0.7016 D(x): 0.6549, D(G(z)): 0.4943 Epoch: [0/20], Batch Num: [4/600] Discriminator Loss: 0.9954, Generator Loss: 0.6876 D(x): 0.7392, D(G(z)): 0.4996 Epoch: [0/20], Batch Num: [5/600] Discriminator Loss: 0.9013, Generator Loss: 0.6620 D(x): 0.8275, D(G(z)): 0.5090 Epoch: [0/20], Batch Num: [6/600] Discriminator Loss: 0.8470, Generator Loss: 0.6230 D(x): 0.9068, D(G(z)): 0.5270 Epoch: [0/20], Batch Num: [7/600] Discriminator Loss: 0.8443, Generator Loss: 0.5659 D(x): 0.9633, D(G(z)): 0.5536 Epoch: [0/20], Batch Num: [8/600] Discriminator Loss: 0.9213, Generator Loss: 0.5061 D(x): 0.9856, D(G(z)): 0.5959 Epoch: [0/20], Batch Num: [9/600] Discriminator Loss: 1.0543, Generator Loss: 0.4480 D(x): 0.9919, D(G(z)): 0.6479 Epoch: [0/20], Batch Num: [10/600] Discriminator Loss: 1.1811, Generator Loss: 0.3996 D(x): 0.9944, D(G(z)): 0.6900 Epoch: [0/20], Batch Num: [11/600] Discriminator Loss: 1.3527, Generator Loss: 0.3732 D(x): 0.9947, D(G(z)): 0.7378 Epoch: [0/20], Batch Num: [12/600] Discriminator Loss: 1.4119, Generator Loss: 0.3642 D(x): 0.9912, D(G(z)): 0.7519 Epoch: [0/20], Batch Num: [13/600] Discriminator Loss: 1.4805, Generator Loss: 0.3713 D(x): 0.9832, D(G(z)): 0.7657 Epoch: [0/20], Batch Num: [14/600] Discriminator Loss: 1.4603, Generator Loss: 0.4004 D(x): 0.9670, D(G(z)): 0.7574 Epoch: [0/20], Batch Num: [15/600] Discriminator Loss: 1.3578, Generator Loss: 0.4560 D(x): 0.9332, D(G(z)): 0.7224 Epoch: [0/20], Batch Num: [16/600] Discriminator Loss: 1.2953, Generator Loss: 0.5225 D(x): 0.8803, D(G(z)): 0.6873 Epoch: [0/20], Batch Num: [17/600] Discriminator Loss: 1.2335, Generator Loss: 0.6099 D(x): 0.8052, D(G(z)): 0.6367 Epoch: [0/20], Batch Num: [18/600] Discriminator Loss: 1.1781, Generator Loss: 0.7066 D(x): 0.7261, D(G(z)): 0.5746 Epoch: [0/20], Batch Num: [19/600] Discriminator Loss: 1.1698, Generator Loss: 0.7903 D(x): 0.6431, D(G(z)): 0.5161 Epoch: [0/20], Batch Num: [20/600] Discriminator Loss: 1.1910, Generator Loss: 0.8743 D(x): 0.5761, D(G(z)): 0.4711 Epoch: [0/20], Batch Num: [21/600] Discriminator Loss: 1.2092, Generator Loss: 0.9240 D(x): 0.5252, D(G(z)): 0.4307 Epoch: [0/20], Batch Num: [22/600] Discriminator Loss: 1.2407, Generator Loss: 0.9584 D(x): 0.4939, D(G(z)): 0.4131 Epoch: [0/20], Batch Num: [23/600] Discriminator Loss: 1.2493, Generator Loss: 0.9705 D(x): 0.4773, D(G(z)): 0.3976 Epoch: [0/20], Batch Num: [24/600] Discriminator Loss: 1.2410, Generator Loss: 0.9657 D(x): 0.4815, D(G(z)): 0.3975 Epoch: [0/20], Batch Num: [25/600] Discriminator Loss: 1.2087, Generator Loss: 0.9312 D(x): 0.5061, D(G(z)): 0.4073 Epoch: [0/20], Batch Num: [26/600] Discriminator Loss: 1.2134, Generator Loss: 0.8800 D(x): 0.5240, D(G(z)): 0.4296 Epoch: [0/20], Batch Num: [27/600] Discriminator Loss: 1.2263, Generator Loss: 0.8646 D(x): 0.5505, D(G(z)): 0.4634 Epoch: [0/20], Batch Num: [28/600] Discriminator Loss: 1.2099, Generator Loss: 0.8966 D(x): 0.5705, D(G(z)): 0.4733 Epoch: [0/20], Batch Num: [29/600] Discriminator Loss: 1.1969, Generator Loss: 1.0007 D(x): 0.5664, D(G(z)): 0.4590 Epoch: [0/20], Batch Num: [30/600] Discriminator Loss: 1.1857, Generator Loss: 1.1693 D(x): 0.5278, D(G(z)): 0.4103 Epoch: [0/20], Batch Num: [31/600] Discriminator Loss: 1.1678, Generator Loss: 1.3387 D(x): 0.4925, D(G(z)): 0.3506 Epoch: [0/20], Batch Num: [32/600] Discriminator Loss: 1.1432, Generator Loss: 1.4207 D(x): 0.4866, D(G(z)): 0.3130 Epoch: [0/20], Batch Num: [33/600] Discriminator Loss: 1.0824, Generator Loss: 1.3478 D(x): 0.5019, D(G(z)): 0.2987 Epoch: [0/20], Batch Num: [34/600] Discriminator Loss: 0.9774, Generator Loss: 1.2812 D(x): 0.5893, D(G(z)): 0.3355 Epoch: [0/20], Batch Num: [35/600] Discriminator Loss: 1.0033, Generator Loss: 1.4289 D(x): 0.6163, D(G(z)): 0.3760 Epoch: [0/20], Batch Num: [36/600] Discriminator Loss: 0.7920, Generator Loss: 1.9065 D(x): 0.6889, D(G(z)): 0.3233 Epoch: [0/20], Batch Num: [37/600] Discriminator Loss: 0.6211, Generator Loss: 2.5281 D(x): 0.7147, D(G(z)): 0.2273 Epoch: [0/20], Batch Num: [38/600] Discriminator Loss: 0.5293, Generator Loss: 2.7788 D(x): 0.7052, D(G(z)): 0.1295 Epoch: [0/20], Batch Num: [39/600] Discriminator Loss: 0.3876, Generator Loss: 2.9894 D(x): 0.7764, D(G(z)): 0.1047 Epoch: [0/20], Batch Num: [40/600] Discriminator Loss: 0.2676, Generator Loss: 3.0837 D(x): 0.8559, D(G(z)): 0.0945 Epoch: [0/20], Batch Num: [41/600] Discriminator Loss: 0.1882, Generator Loss: 3.5170 D(x): 0.9240, D(G(z)): 0.0972 Epoch: [0/20], Batch Num: [42/600] Discriminator Loss: 0.1259, Generator Loss: 4.3066 D(x): 0.9553, D(G(z)): 0.0742 Epoch: [0/20], Batch Num: [43/600] Discriminator Loss: 0.0847, Generator Loss: 5.3372 D(x): 0.9673, D(G(z)): 0.0480 Epoch: [0/20], Batch Num: [44/600] Discriminator Loss: 0.0377, Generator Loss: 6.3888 D(x): 0.9789, D(G(z)): 0.0154 Epoch: [0/20], Batch Num: [45/600] Discriminator Loss: 0.0258, Generator Loss: 7.2192 D(x): 0.9825, D(G(z)): 0.0078 Epoch: [0/20], Batch Num: [46/600] Discriminator Loss: 0.0162, Generator Loss: 7.7835 D(x): 0.9894, D(G(z)): 0.0054 Epoch: [0/20], Batch Num: [47/600] Discriminator Loss: 0.0097, Generator Loss: 8.2731 D(x): 0.9935, D(G(z)): 0.0031 Epoch: [0/20], Batch Num: [48/600] Discriminator Loss: 0.0122, Generator Loss: 7.8538 D(x): 0.9939, D(G(z)): 0.0058 Epoch: [0/20], Batch Num: [49/600] Discriminator Loss: 0.0146, Generator Loss: 7.8559 D(x): 0.9982, D(G(z)): 0.0120 Epoch: [0/20], Batch Num: [50/600] Discriminator Loss: 0.0248, Generator Loss: 8.0070 D(x): 0.9977, D(G(z)): 0.0205 Epoch: [0/20], Batch Num: [51/600] Discriminator Loss: 0.0654, Generator Loss: 8.2111 D(x): 0.9835, D(G(z)): 0.0363 Epoch: [0/20], Batch Num: [52/600] Discriminator Loss: 0.0496, Generator Loss: 8.4913 D(x): 0.9935, D(G(z)): 0.0402 Epoch: [0/20], Batch Num: [53/600] Discriminator Loss: 0.1565, Generator Loss: 8.1644 D(x): 0.9430, D(G(z)): 0.0508 Epoch: [0/20], Batch Num: [54/600] Discriminator Loss: 0.3960, Generator Loss: 8.5984 D(x): 0.9349, D(G(z)): 0.1189 Epoch: [0/20], Batch Num: [55/600] Discriminator Loss: 0.4522, Generator Loss: 6.3314 D(x): 0.8841, D(G(z)): 0.1173 Epoch: [0/20], Batch Num: [56/600] Discriminator Loss: 0.5697, Generator Loss: 5.7827 D(x): 0.8875, D(G(z)): 0.2431 Epoch: [0/20], Batch Num: [57/600] Discriminator Loss: 0.6221, Generator Loss: 5.7328 D(x): 0.9352, D(G(z)): 0.3180 Epoch: [0/20], Batch Num: [58/600] Discriminator Loss: 0.8373, Generator Loss: 4.0031 D(x): 0.8110, D(G(z)): 0.2348 Epoch: [0/20], Batch Num: [59/600] Discriminator Loss: 0.7660, Generator Loss: 2.9691 D(x): 0.8925, D(G(z)): 0.3443 Epoch: [0/20], Batch Num: [60/600] Discriminator Loss: 0.9578, Generator Loss: 2.3757 D(x): 0.9151, D(G(z)): 0.4391 Epoch: [0/20], Batch Num: [61/600] Discriminator Loss: 0.7068, Generator Loss: 1.9959 D(x): 0.9710, D(G(z)): 0.4663 Epoch: [0/20], Batch Num: [62/600] Discriminator Loss: 0.8702, Generator Loss: 1.4930 D(x): 0.9298, D(G(z)): 0.4644 Epoch: [0/20], Batch Num: [63/600] Discriminator Loss: 0.9684, Generator Loss: 1.1858 D(x): 0.9401, D(G(z)): 0.5088 Epoch: [0/20], Batch Num: [64/600] Discriminator Loss: 0.8099, Generator Loss: 0.8860 D(x): 0.9802, D(G(z)): 0.5329 Epoch: [0/20], Batch Num: [65/600] Discriminator Loss: 0.8279, Generator Loss: 0.7408 D(x): 0.9898, D(G(z)): 0.5550 Epoch: [0/20], Batch Num: [66/600] Discriminator Loss: 0.9195, Generator Loss: 0.6802 D(x): 0.9892, D(G(z)): 0.5523 Epoch: [0/20], Batch Num: [67/600] Discriminator Loss: 0.8246, Generator Loss: 0.6427 D(x): 0.9997, D(G(z)): 0.5609 Epoch: [0/20], Batch Num: [68/600] Discriminator Loss: 0.8403, Generator Loss: 0.6272 D(x): 0.9999, D(G(z)): 0.5680 Epoch: [0/20], Batch Num: [69/600] Discriminator Loss: 0.8843, Generator Loss: 0.5960 D(x): 0.9999, D(G(z)): 0.5862 Epoch: [0/20], Batch Num: [70/600] Discriminator Loss: 0.9385, Generator Loss: 0.5654 D(x): 1.0000, D(G(z)): 0.6072 Epoch: [0/20], Batch Num: [71/600] Discriminator Loss: 1.0292, Generator Loss: 0.5373 D(x): 1.0000, D(G(z)): 0.6399 Epoch: [0/20], Batch Num: [72/600] Discriminator Loss: 1.1522, Generator Loss: 0.4780 D(x): 1.0000, D(G(z)): 0.6806 Epoch: [0/20], Batch Num: [73/600] Discriminator Loss: 1.2476, Generator Loss: 0.4406 D(x): 1.0000, D(G(z)): 0.7088 Epoch: [0/20], Batch Num: [74/600] Discriminator Loss: 1.4058, Generator Loss: 0.3854 D(x): 1.0000, D(G(z)): 0.7488 Epoch: [0/20], Batch Num: [75/600] Discriminator Loss: 1.4416, Generator Loss: 0.3689 D(x): 1.0000, D(G(z)): 0.7588 Epoch: [0/20], Batch Num: [76/600] Discriminator Loss: 1.6021, Generator Loss: 0.3777 D(x): 1.0000, D(G(z)): 0.7919 Epoch: [0/20], Batch Num: [77/600] Discriminator Loss: 1.6174, Generator Loss: 0.3802 D(x): 0.9999, D(G(z)): 0.7950 Epoch: [0/20], Batch Num: [78/600] Discriminator Loss: 1.4825, Generator Loss: 0.4291 D(x): 0.9996, D(G(z)): 0.7658 Epoch: [0/20], Batch Num: [79/600] Discriminator Loss: 1.3366, Generator Loss: 0.4892 D(x): 0.9974, D(G(z)): 0.7321 Epoch: [0/20], Batch Num: [80/600] Discriminator Loss: 1.2044, Generator Loss: 0.5798 D(x): 0.9909, D(G(z)): 0.6936 Epoch: [0/20], Batch Num: [81/600] Discriminator Loss: 1.0091, Generator Loss: 0.6767 D(x): 0.9614, D(G(z)): 0.6180 Epoch: [0/20], Batch Num: [82/600] Discriminator Loss: 0.9657, Generator Loss: 0.7829 D(x): 0.8397, D(G(z)): 0.5431 Epoch: [0/20], Batch Num: [83/600] Discriminator Loss: 0.9927, Generator Loss: 0.8690 D(x): 0.7254, D(G(z)): 0.4826 Epoch: [0/20], Batch Num: [84/600] Discriminator Loss: 1.0003, Generator Loss: 0.9647 D(x): 0.6785, D(G(z)): 0.4493 Epoch: [0/20], Batch Num: [85/600] Discriminator Loss: 0.9166, Generator Loss: 1.0330 D(x): 0.6843, D(G(z)): 0.4073 Epoch: [0/20], Batch Num: [86/600] Discriminator Loss: 0.7847, Generator Loss: 1.1145 D(x): 0.7690, D(G(z)): 0.4018 Epoch: [0/20], Batch Num: [87/600] Discriminator Loss: 0.6644, Generator Loss: 1.2277 D(x): 0.8325, D(G(z)): 0.3783 Epoch: [0/20], Batch Num: [88/600] Discriminator Loss: 0.5876, Generator Loss: 1.4620 D(x): 0.8751, D(G(z)): 0.3608 Epoch: [0/20], Batch Num: [89/600] Discriminator Loss: 0.5050, Generator Loss: 1.7112 D(x): 0.8946, D(G(z)): 0.3200 Epoch: [0/20], Batch Num: [90/600] Discriminator Loss: 0.4506, Generator Loss: 1.9696 D(x): 0.9059, D(G(z)): 0.2905 Epoch: [0/20], Batch Num: [91/600] Discriminator Loss: 0.3998, Generator Loss: 2.3500 D(x): 0.9144, D(G(z)): 0.2610 Epoch: [0/20], Batch Num: [92/600] Discriminator Loss: 0.3741, Generator Loss: 2.6176 D(x): 0.9140, D(G(z)): 0.2412 Epoch: [0/20], Batch Num: [93/600] Discriminator Loss: 0.4029, Generator Loss: 2.9516 D(x): 0.8816, D(G(z)): 0.2330 Epoch: [0/20], Batch Num: [94/600] Discriminator Loss: 0.6148, Generator Loss: 2.4229 D(x): 0.8009, D(G(z)): 0.2919 Epoch: [0/20], Batch Num: [95/600] Discriminator Loss: 0.9966, Generator Loss: 1.5914 D(x): 0.6959, D(G(z)): 0.3994 Epoch: [0/20], Batch Num: [96/600] Discriminator Loss: 1.4322, Generator Loss: 0.6372 D(x): 0.6878, D(G(z)): 0.5953 Epoch: [0/20], Batch Num: [97/600] Discriminator Loss: 1.5342, Generator Loss: 0.4426 D(x): 0.8165, D(G(z)): 0.7154 Epoch: [0/20], Batch Num: [98/600] Discriminator Loss: 1.5331, Generator Loss: 0.4736 D(x): 0.8413, D(G(z)): 0.7262 Epoch: [0/20], Batch Num: [99/600] Discriminator Loss: 1.5111, Generator Loss: 0.5678 D(x): 0.8085, D(G(z)): 0.6997 Epoch: 0, Batch Num: [100/600]
Epoch: [0/20], Batch Num: [100/600] Discriminator Loss: 1.2226, Generator Loss: 1.1257 D(x): 0.8606, D(G(z)): 0.6253 Epoch: [0/20], Batch Num: [101/600] Discriminator Loss: 1.0580, Generator Loss: 1.7958 D(x): 0.8532, D(G(z)): 0.5280 Epoch: [0/20], Batch Num: [102/600] Discriminator Loss: 0.9436, Generator Loss: 2.2187 D(x): 0.8310, D(G(z)): 0.4480 Epoch: [0/20], Batch Num: [103/600] Discriminator Loss: 1.0432, Generator Loss: 2.2022 D(x): 0.8228, D(G(z)): 0.4535 Epoch: [0/20], Batch Num: [104/600] Discriminator Loss: 1.2752, Generator Loss: 1.4897 D(x): 0.7521, D(G(z)): 0.4728 Epoch: [0/20], Batch Num: [105/600] Discriminator Loss: 1.5371, Generator Loss: 0.8708 D(x): 0.8103, D(G(z)): 0.6400 Epoch: [0/20], Batch Num: [106/600] Discriminator Loss: 1.7817, Generator Loss: 0.4807 D(x): 0.8405, D(G(z)): 0.7410 Epoch: [0/20], Batch Num: [107/600] Discriminator Loss: 1.8604, Generator Loss: 0.3794 D(x): 0.8710, D(G(z)): 0.7891 Epoch: [0/20], Batch Num: [108/600] Discriminator Loss: 2.2591, Generator Loss: 0.3331 D(x): 0.8362, D(G(z)): 0.7944 Epoch: [0/20], Batch Num: [109/600] Discriminator Loss: 2.0341, Generator Loss: 0.3480 D(x): 0.8260, D(G(z)): 0.7920 Epoch: [0/20], Batch Num: [110/600] Discriminator Loss: 1.8190, Generator Loss: 0.3775 D(x): 0.8587, D(G(z)): 0.7838 Epoch: [0/20], Batch Num: [111/600] Discriminator Loss: 1.5523, Generator Loss: 0.4718 D(x): 0.8627, D(G(z)): 0.7357 Epoch: [0/20], Batch Num: [112/600] Discriminator Loss: 1.4621, Generator Loss: 0.5428 D(x): 0.8378, D(G(z)): 0.6782 Epoch: [0/20], Batch Num: [113/600] Discriminator Loss: 1.2057, Generator Loss: 0.6851 D(x): 0.8008, D(G(z)): 0.6159 Epoch: [0/20], Batch Num: [114/600] Discriminator Loss: 1.1665, Generator Loss: 0.8332 D(x): 0.7396, D(G(z)): 0.5434 Epoch: [0/20], Batch Num: [115/600] Discriminator Loss: 1.0243, Generator Loss: 1.0041 D(x): 0.7124, D(G(z)): 0.4854 Epoch: [0/20], Batch Num: [116/600] Discriminator Loss: 1.0438, Generator Loss: 1.1815 D(x): 0.6262, D(G(z)): 0.4236 Epoch: [0/20], Batch Num: [117/600] Discriminator Loss: 1.0243, Generator Loss: 1.3828 D(x): 0.6122, D(G(z)): 0.3641 Epoch: [0/20], Batch Num: [118/600] Discriminator Loss: 1.0774, Generator Loss: 1.4966 D(x): 0.5688, D(G(z)): 0.3384 Epoch: [0/20], Batch Num: [119/600] Discriminator Loss: 0.9338, Generator Loss: 1.5858 D(x): 0.5985, D(G(z)): 0.3216 Epoch: [0/20], Batch Num: [120/600] Discriminator Loss: 0.8181, Generator Loss: 1.5915 D(x): 0.6438, D(G(z)): 0.3000 Epoch: [0/20], Batch Num: [121/600] Discriminator Loss: 0.7450, Generator Loss: 1.7898 D(x): 0.7026, D(G(z)): 0.3117 Epoch: [0/20], Batch Num: [122/600] Discriminator Loss: 0.7151, Generator Loss: 1.7474 D(x): 0.7299, D(G(z)): 0.3177 Epoch: [0/20], Batch Num: [123/600] Discriminator Loss: 0.6915, Generator Loss: 1.9151 D(x): 0.7452, D(G(z)): 0.3121 Epoch: [0/20], Batch Num: [124/600] Discriminator Loss: 0.6439, Generator Loss: 2.1232 D(x): 0.7618, D(G(z)): 0.2961 Epoch: [0/20], Batch Num: [125/600] Discriminator Loss: 0.5640, Generator Loss: 2.3685 D(x): 0.8127, D(G(z)): 0.2869 Epoch: [0/20], Batch Num: [126/600] Discriminator Loss: 0.5303, Generator Loss: 2.7998 D(x): 0.7947, D(G(z)): 0.2366 Epoch: [0/20], Batch Num: [127/600] Discriminator Loss: 0.4648, Generator Loss: 3.3026 D(x): 0.8077, D(G(z)): 0.2017 Epoch: [0/20], Batch Num: [128/600] Discriminator Loss: 0.4462, Generator Loss: 3.7398 D(x): 0.7898, D(G(z)): 0.1584 Epoch: [0/20], Batch Num: [129/600] Discriminator Loss: 0.3589, Generator Loss: 3.9516 D(x): 0.8039, D(G(z)): 0.1177 Epoch: [0/20], Batch Num: [130/600] Discriminator Loss: 0.3681, Generator Loss: 4.2133 D(x): 0.7965, D(G(z)): 0.1023 Epoch: [0/20], Batch Num: [131/600] Discriminator Loss: 0.3288, Generator Loss: 4.0830 D(x): 0.8508, D(G(z)): 0.1350 Epoch: [0/20], Batch Num: [132/600] Discriminator Loss: 0.2671, Generator Loss: 4.1139 D(x): 0.8853, D(G(z)): 0.1193 Epoch: [0/20], Batch Num: [133/600] Discriminator Loss: 0.3151, Generator Loss: 4.0625 D(x): 0.8768, D(G(z)): 0.1367 Epoch: [0/20], Batch Num: [134/600] Discriminator Loss: 0.3683, Generator Loss: 4.1866 D(x): 0.8985, D(G(z)): 0.1992 Epoch: [0/20], Batch Num: [135/600] Discriminator Loss: 0.4387, Generator Loss: 4.2578 D(x): 0.8570, D(G(z)): 0.1934 Epoch: [0/20], Batch Num: [136/600] Discriminator Loss: 0.5400, Generator Loss: 3.9504 D(x): 0.8585, D(G(z)): 0.2392 Epoch: [0/20], Batch Num: [137/600] Discriminator Loss: 0.5750, Generator Loss: 3.3380 D(x): 0.8517, D(G(z)): 0.2621 Epoch: [0/20], Batch Num: [138/600] Discriminator Loss: 1.2209, Generator Loss: 2.5158 D(x): 0.7328, D(G(z)): 0.4037 Epoch: [0/20], Batch Num: [139/600] Discriminator Loss: 1.5892, Generator Loss: 1.6401 D(x): 0.7605, D(G(z)): 0.6195 Epoch: [0/20], Batch Num: [140/600] Discriminator Loss: 2.1549, Generator Loss: 0.7661 D(x): 0.7154, D(G(z)): 0.7296 Epoch: [0/20], Batch Num: [141/600] Discriminator Loss: 2.4962, Generator Loss: 0.3252 D(x): 0.7279, D(G(z)): 0.8351 Epoch: [0/20], Batch Num: [142/600] Discriminator Loss: 2.6563, Generator Loss: 0.2505 D(x): 0.7841, D(G(z)): 0.8752 Epoch: [0/20], Batch Num: [143/600] Discriminator Loss: 2.4679, Generator Loss: 0.2096 D(x): 0.8353, D(G(z)): 0.8760 Epoch: [0/20], Batch Num: [144/600] Discriminator Loss: 2.2033, Generator Loss: 0.2886 D(x): 0.9151, D(G(z)): 0.8694 Epoch: [0/20], Batch Num: [145/600] Discriminator Loss: 1.9329, Generator Loss: 0.3944 D(x): 0.8916, D(G(z)): 0.8234 Epoch: [0/20], Batch Num: [146/600] Discriminator Loss: 1.6615, Generator Loss: 0.5933 D(x): 0.8162, D(G(z)): 0.7470 Epoch: [0/20], Batch Num: [147/600] Discriminator Loss: 1.6146, Generator Loss: 0.7192 D(x): 0.6940, D(G(z)): 0.6618 Epoch: [0/20], Batch Num: [148/600] Discriminator Loss: 1.4078, Generator Loss: 0.8150 D(x): 0.6651, D(G(z)): 0.5901 Epoch: [0/20], Batch Num: [149/600] Discriminator Loss: 1.2749, Generator Loss: 0.8655 D(x): 0.6548, D(G(z)): 0.5341 Epoch: [0/20], Batch Num: [150/600] Discriminator Loss: 1.2782, Generator Loss: 0.8916 D(x): 0.6393, D(G(z)): 0.5051 Epoch: [0/20], Batch Num: [151/600] Discriminator Loss: 1.0848, Generator Loss: 0.8545 D(x): 0.7005, D(G(z)): 0.5024 Epoch: [0/20], Batch Num: [152/600] Discriminator Loss: 1.0440, Generator Loss: 0.8735 D(x): 0.7390, D(G(z)): 0.5119 Epoch: [0/20], Batch Num: [153/600] Discriminator Loss: 1.0660, Generator Loss: 0.9087 D(x): 0.7205, D(G(z)): 0.5040 Epoch: [0/20], Batch Num: [154/600] Discriminator Loss: 0.9943, Generator Loss: 0.9398 D(x): 0.7536, D(G(z)): 0.4983 Epoch: [0/20], Batch Num: [155/600] Discriminator Loss: 0.9955, Generator Loss: 0.9795 D(x): 0.7555, D(G(z)): 0.5018 Epoch: [0/20], Batch Num: [156/600] Discriminator Loss: 0.9993, Generator Loss: 1.1110 D(x): 0.7386, D(G(z)): 0.4911 Epoch: [0/20], Batch Num: [157/600] Discriminator Loss: 0.9151, Generator Loss: 1.1279 D(x): 0.7581, D(G(z)): 0.4599 Epoch: [0/20], Batch Num: [158/600] Discriminator Loss: 0.9775, Generator Loss: 1.2546 D(x): 0.7449, D(G(z)): 0.4839 Epoch: [0/20], Batch Num: [159/600] Discriminator Loss: 1.0250, Generator Loss: 1.1941 D(x): 0.7137, D(G(z)): 0.4781 Epoch: [0/20], Batch Num: [160/600] Discriminator Loss: 1.0837, Generator Loss: 1.2643 D(x): 0.7031, D(G(z)): 0.4995 Epoch: [0/20], Batch Num: [161/600] Discriminator Loss: 1.1205, Generator Loss: 1.1803 D(x): 0.6852, D(G(z)): 0.5080 Epoch: [0/20], Batch Num: [162/600] Discriminator Loss: 1.2976, Generator Loss: 1.1652 D(x): 0.6321, D(G(z)): 0.5302 Epoch: [0/20], Batch Num: [163/600] Discriminator Loss: 1.4703, Generator Loss: 0.9384 D(x): 0.6237, D(G(z)): 0.5798 Epoch: [0/20], Batch Num: [164/600] Discriminator Loss: 1.4982, Generator Loss: 0.8734 D(x): 0.6051, D(G(z)): 0.5887 Epoch: [0/20], Batch Num: [165/600] Discriminator Loss: 1.5670, Generator Loss: 0.9150 D(x): 0.6209, D(G(z)): 0.6042 Epoch: [0/20], Batch Num: [166/600] Discriminator Loss: 1.5260, Generator Loss: 0.9520 D(x): 0.6309, D(G(z)): 0.6149 Epoch: [0/20], Batch Num: [167/600] Discriminator Loss: 1.5269, Generator Loss: 1.0771 D(x): 0.6179, D(G(z)): 0.5878 Epoch: [0/20], Batch Num: [168/600] Discriminator Loss: 1.3705, Generator Loss: 1.4938 D(x): 0.6247, D(G(z)): 0.5285 Epoch: [0/20], Batch Num: [169/600] Discriminator Loss: 1.2055, Generator Loss: 2.1989 D(x): 0.6209, D(G(z)): 0.4349 Epoch: [0/20], Batch Num: [170/600] Discriminator Loss: 1.0931, Generator Loss: 3.2201 D(x): 0.6137, D(G(z)): 0.3427 Epoch: [0/20], Batch Num: [171/600] Discriminator Loss: 1.0912, Generator Loss: 3.4827 D(x): 0.5794, D(G(z)): 0.2449 Epoch: [0/20], Batch Num: [172/600] Discriminator Loss: 1.1341, Generator Loss: 3.2651 D(x): 0.5853, D(G(z)): 0.2664 Epoch: [0/20], Batch Num: [173/600] Discriminator Loss: 1.1046, Generator Loss: 2.9091 D(x): 0.5813, D(G(z)): 0.2552 Epoch: [0/20], Batch Num: [174/600] Discriminator Loss: 1.2303, Generator Loss: 2.2049 D(x): 0.5911, D(G(z)): 0.3463 Epoch: [0/20], Batch Num: [175/600] Discriminator Loss: 1.3462, Generator Loss: 1.5210 D(x): 0.6054, D(G(z)): 0.4495 Epoch: [0/20], Batch Num: [176/600] Discriminator Loss: 1.7968, Generator Loss: 1.1442 D(x): 0.6126, D(G(z)): 0.5968 Epoch: [0/20], Batch Num: [177/600] Discriminator Loss: 2.0809, Generator Loss: 0.9763 D(x): 0.6025, D(G(z)): 0.6671 Epoch: [0/20], Batch Num: [178/600] Discriminator Loss: 1.9045, Generator Loss: 0.8318 D(x): 0.5702, D(G(z)): 0.6632 Epoch: [0/20], Batch Num: [179/600] Discriminator Loss: 2.0982, Generator Loss: 0.7664 D(x): 0.5256, D(G(z)): 0.7081 Epoch: [0/20], Batch Num: [180/600] Discriminator Loss: 2.3049, Generator Loss: 0.6634 D(x): 0.4270, D(G(z)): 0.6958 Epoch: [0/20], Batch Num: [181/600] Discriminator Loss: 2.6431, Generator Loss: 0.5360 D(x): 0.3644, D(G(z)): 0.7054 Epoch: [0/20], Batch Num: [182/600] Discriminator Loss: 2.4155, Generator Loss: 0.5168 D(x): 0.3852, D(G(z)): 0.7112 Epoch: [0/20], Batch Num: [183/600] Discriminator Loss: 2.2592, Generator Loss: 0.5092 D(x): 0.4278, D(G(z)): 0.6988 Epoch: [0/20], Batch Num: [184/600] Discriminator Loss: 2.1095, Generator Loss: 0.5154 D(x): 0.4331, D(G(z)): 0.6777 Epoch: [0/20], Batch Num: [185/600] Discriminator Loss: 1.8175, Generator Loss: 0.5502 D(x): 0.4868, D(G(z)): 0.6561 Epoch: [0/20], Batch Num: [186/600] Discriminator Loss: 1.6045, Generator Loss: 0.6072 D(x): 0.5114, D(G(z)): 0.6022 Epoch: [0/20], Batch Num: [187/600] Discriminator Loss: 1.4761, Generator Loss: 0.6484 D(x): 0.5284, D(G(z)): 0.5632 Epoch: [0/20], Batch Num: [188/600] Discriminator Loss: 1.3613, Generator Loss: 0.6710 D(x): 0.5498, D(G(z)): 0.5309 Epoch: [0/20], Batch Num: [189/600] Discriminator Loss: 1.2875, Generator Loss: 0.6710 D(x): 0.5698, D(G(z)): 0.5132 Epoch: [0/20], Batch Num: [190/600] Discriminator Loss: 1.2248, Generator Loss: 0.6817 D(x): 0.6024, D(G(z)): 0.5095 Epoch: [0/20], Batch Num: [191/600] Discriminator Loss: 1.1848, Generator Loss: 0.6687 D(x): 0.6202, D(G(z)): 0.5036 Epoch: [0/20], Batch Num: [192/600] Discriminator Loss: 1.1614, Generator Loss: 0.6686 D(x): 0.6464, D(G(z)): 0.5126 Epoch: [0/20], Batch Num: [193/600] Discriminator Loss: 1.1196, Generator Loss: 0.6683 D(x): 0.6584, D(G(z)): 0.5000 Epoch: [0/20], Batch Num: [194/600] Discriminator Loss: 1.0945, Generator Loss: 0.7196 D(x): 0.6785, D(G(z)): 0.5017 Epoch: [0/20], Batch Num: [195/600] Discriminator Loss: 1.0430, Generator Loss: 0.7667 D(x): 0.7022, D(G(z)): 0.4925 Epoch: [0/20], Batch Num: [196/600] Discriminator Loss: 0.9774, Generator Loss: 0.8754 D(x): 0.7077, D(G(z)): 0.4628 Epoch: [0/20], Batch Num: [197/600] Discriminator Loss: 0.9269, Generator Loss: 0.9732 D(x): 0.7130, D(G(z)): 0.4384 Epoch: [0/20], Batch Num: [198/600] Discriminator Loss: 0.8342, Generator Loss: 1.1440 D(x): 0.7249, D(G(z)): 0.3936 Epoch: [0/20], Batch Num: [199/600] Discriminator Loss: 0.7731, Generator Loss: 1.3443 D(x): 0.7064, D(G(z)): 0.3382 Epoch: 0, Batch Num: [200/600]
Epoch: [0/20], Batch Num: [200/600] Discriminator Loss: 0.6372, Generator Loss: 1.6274 D(x): 0.7398, D(G(z)): 0.2765 Epoch: [0/20], Batch Num: [201/600] Discriminator Loss: 0.5831, Generator Loss: 1.7845 D(x): 0.7523, D(G(z)): 0.2470 Epoch: [0/20], Batch Num: [202/600] Discriminator Loss: 0.4591, Generator Loss: 2.0685 D(x): 0.7989, D(G(z)): 0.1995 Epoch: [0/20], Batch Num: [203/600] Discriminator Loss: 0.3488, Generator Loss: 2.3218 D(x): 0.8416, D(G(z)): 0.1542 Epoch: [0/20], Batch Num: [204/600] Discriminator Loss: 0.3318, Generator Loss: 2.6426 D(x): 0.8602, D(G(z)): 0.1545 Epoch: [0/20], Batch Num: [205/600] Discriminator Loss: 0.2266, Generator Loss: 2.7364 D(x): 0.9053, D(G(z)): 0.1133 Epoch: [0/20], Batch Num: [206/600] Discriminator Loss: 0.1769, Generator Loss: 3.2597 D(x): 0.9372, D(G(z)): 0.0999 Epoch: [0/20], Batch Num: [207/600] Discriminator Loss: 0.1393, Generator Loss: 3.7013 D(x): 0.9518, D(G(z)): 0.0809 Epoch: [0/20], Batch Num: [208/600] Discriminator Loss: 0.1107, Generator Loss: 4.1792 D(x): 0.9479, D(G(z)): 0.0517 Epoch: [0/20], Batch Num: [209/600] Discriminator Loss: 0.0941, Generator Loss: 4.5779 D(x): 0.9687, D(G(z)): 0.0543 Epoch: [0/20], Batch Num: [210/600] Discriminator Loss: 0.0732, Generator Loss: 5.0145 D(x): 0.9700, D(G(z)): 0.0383 Epoch: [0/20], Batch Num: [211/600] Discriminator Loss: 0.0553, Generator Loss: 5.7145 D(x): 0.9781, D(G(z)): 0.0307 Epoch: [0/20], Batch Num: [212/600] Discriminator Loss: 0.0658, Generator Loss: 6.0005 D(x): 0.9679, D(G(z)): 0.0266 Epoch: [0/20], Batch Num: [213/600] Discriminator Loss: 0.0870, Generator Loss: 6.2063 D(x): 0.9464, D(G(z)): 0.0224 Epoch: [0/20], Batch Num: [214/600] Discriminator Loss: 0.0587, Generator Loss: 6.0892 D(x): 0.9658, D(G(z)): 0.0197 Epoch: [0/20], Batch Num: [215/600] Discriminator Loss: 0.0942, Generator Loss: 6.0369 D(x): 0.9519, D(G(z)): 0.0179 Epoch: [0/20], Batch Num: [216/600] Discriminator Loss: 0.0614, Generator Loss: 5.5680 D(x): 0.9771, D(G(z)): 0.0315 Epoch: [0/20], Batch Num: [217/600] Discriminator Loss: 0.1094, Generator Loss: 5.6422 D(x): 0.9734, D(G(z)): 0.0632 Epoch: [0/20], Batch Num: [218/600] Discriminator Loss: 0.2140, Generator Loss: 6.6419 D(x): 0.9577, D(G(z)): 0.0910 Epoch: [0/20], Batch Num: [219/600] Discriminator Loss: 0.1712, Generator Loss: 6.9988 D(x): 0.9256, D(G(z)): 0.0205 Epoch: [0/20], Batch Num: [220/600] Discriminator Loss: 0.1803, Generator Loss: 6.4572 D(x): 0.9300, D(G(z)): 0.0504 Epoch: [0/20], Batch Num: [221/600] Discriminator Loss: 0.2764, Generator Loss: 5.2288 D(x): 0.9009, D(G(z)): 0.0648 Epoch: [0/20], Batch Num: [222/600] Discriminator Loss: 0.2473, Generator Loss: 5.3386 D(x): 0.9527, D(G(z)): 0.1203 Epoch: [0/20], Batch Num: [223/600] Discriminator Loss: 0.4398, Generator Loss: 7.4259 D(x): 0.9180, D(G(z)): 0.1365 Epoch: [0/20], Batch Num: [224/600] Discriminator Loss: 0.4645, Generator Loss: 5.8880 D(x): 0.8233, D(G(z)): 0.0443 Epoch: [0/20], Batch Num: [225/600] Discriminator Loss: 0.3669, Generator Loss: 4.6943 D(x): 0.8803, D(G(z)): 0.0917 Epoch: [0/20], Batch Num: [226/600] Discriminator Loss: 0.4313, Generator Loss: 5.2174 D(x): 0.9143, D(G(z)): 0.1687 Epoch: [0/20], Batch Num: [227/600] Discriminator Loss: 0.4307, Generator Loss: 6.6796 D(x): 0.9009, D(G(z)): 0.1328 Epoch: [0/20], Batch Num: [228/600] Discriminator Loss: 0.5489, Generator Loss: 6.3142 D(x): 0.8382, D(G(z)): 0.0777 Epoch: [0/20], Batch Num: [229/600] Discriminator Loss: 0.5555, Generator Loss: 5.0208 D(x): 0.8355, D(G(z)): 0.1094 Epoch: [0/20], Batch Num: [230/600] Discriminator Loss: 0.6557, Generator Loss: 4.6759 D(x): 0.8549, D(G(z)): 0.1733 Epoch: [0/20], Batch Num: [231/600] Discriminator Loss: 0.7421, Generator Loss: 4.1968 D(x): 0.8138, D(G(z)): 0.1888 Epoch: [0/20], Batch Num: [232/600] Discriminator Loss: 0.7906, Generator Loss: 5.4927 D(x): 0.8353, D(G(z)): 0.2665 Epoch: [0/20], Batch Num: [233/600] Discriminator Loss: 0.7946, Generator Loss: 4.6214 D(x): 0.7579, D(G(z)): 0.0923 Epoch: [0/20], Batch Num: [234/600] Discriminator Loss: 0.7870, Generator Loss: 4.0354 D(x): 0.8447, D(G(z)): 0.1844 Epoch: [0/20], Batch Num: [235/600] Discriminator Loss: 0.5195, Generator Loss: 4.5098 D(x): 0.8880, D(G(z)): 0.1957 Epoch: [0/20], Batch Num: [236/600] Discriminator Loss: 0.6381, Generator Loss: 4.9728 D(x): 0.8363, D(G(z)): 0.1552 Epoch: [0/20], Batch Num: [237/600] Discriminator Loss: 0.4628, Generator Loss: 4.4459 D(x): 0.8459, D(G(z)): 0.0970 Epoch: [0/20], Batch Num: [238/600] Discriminator Loss: 0.3829, Generator Loss: 4.0644 D(x): 0.8693, D(G(z)): 0.1097 Epoch: [0/20], Batch Num: [239/600] Discriminator Loss: 0.3358, Generator Loss: 3.7524 D(x): 0.9219, D(G(z)): 0.1506 Epoch: [0/20], Batch Num: [240/600] Discriminator Loss: 0.2820, Generator Loss: 4.2262 D(x): 0.9348, D(G(z)): 0.1442 Epoch: [0/20], Batch Num: [241/600] Discriminator Loss: 0.2246, Generator Loss: 4.4011 D(x): 0.9404, D(G(z)): 0.1135 Epoch: [0/20], Batch Num: [242/600] Discriminator Loss: 0.1366, Generator Loss: 4.8101 D(x): 0.9470, D(G(z)): 0.0677 Epoch: [0/20], Batch Num: [243/600] Discriminator Loss: 0.2233, Generator Loss: 4.6371 D(x): 0.9192, D(G(z)): 0.0602 Epoch: [0/20], Batch Num: [244/600] Discriminator Loss: 0.3001, Generator Loss: 3.8762 D(x): 0.8824, D(G(z)): 0.0718 Epoch: [0/20], Batch Num: [245/600] Discriminator Loss: 0.1701, Generator Loss: 3.7035 D(x): 0.9634, D(G(z)): 0.1124 Epoch: [0/20], Batch Num: [246/600] Discriminator Loss: 0.2444, Generator Loss: 3.9616 D(x): 0.9502, D(G(z)): 0.1422 Epoch: [0/20], Batch Num: [247/600] Discriminator Loss: 0.1228, Generator Loss: 4.7464 D(x): 0.9749, D(G(z)): 0.0832 Epoch: [0/20], Batch Num: [248/600] Discriminator Loss: 0.1068, Generator Loss: 5.1728 D(x): 0.9629, D(G(z)): 0.0535 Epoch: [0/20], Batch Num: [249/600] Discriminator Loss: 0.0908, Generator Loss: 5.6589 D(x): 0.9708, D(G(z)): 0.0493 Epoch: [0/20], Batch Num: [250/600] Discriminator Loss: 0.1111, Generator Loss: 6.1184 D(x): 0.9655, D(G(z)): 0.0555 Epoch: [0/20], Batch Num: [251/600] Discriminator Loss: 0.0883, Generator Loss: 6.1968 D(x): 0.9678, D(G(z)): 0.0387 Epoch: [0/20], Batch Num: [252/600] Discriminator Loss: 0.0493, Generator Loss: 6.8078 D(x): 0.9884, D(G(z)): 0.0335 Epoch: [0/20], Batch Num: [253/600] Discriminator Loss: 0.0422, Generator Loss: 6.5934 D(x): 0.9934, D(G(z)): 0.0316 Epoch: [0/20], Batch Num: [254/600] Discriminator Loss: 0.0358, Generator Loss: 7.2465 D(x): 0.9907, D(G(z)): 0.0240 Epoch: [0/20], Batch Num: [255/600] Discriminator Loss: 0.0299, Generator Loss: 7.3889 D(x): 0.9922, D(G(z)): 0.0200 Epoch: [0/20], Batch Num: [256/600] Discriminator Loss: 0.0197, Generator Loss: 7.3313 D(x): 0.9951, D(G(z)): 0.0143 Epoch: [0/20], Batch Num: [257/600] Discriminator Loss: 0.0431, Generator Loss: 7.5146 D(x): 0.9943, D(G(z)): 0.0336 Epoch: [0/20], Batch Num: [258/600] Discriminator Loss: 0.0764, Generator Loss: 8.0872 D(x): 0.9927, D(G(z)): 0.0548 Epoch: [0/20], Batch Num: [259/600] Discriminator Loss: 0.1265, Generator Loss: 8.7559 D(x): 0.9735, D(G(z)): 0.0557 Epoch: [0/20], Batch Num: [260/600] Discriminator Loss: 0.1126, Generator Loss: 8.4550 D(x): 0.9685, D(G(z)): 0.0492 Epoch: [0/20], Batch Num: [261/600] Discriminator Loss: 0.1072, Generator Loss: 8.6820 D(x): 0.9814, D(G(z)): 0.0581 Epoch: [0/20], Batch Num: [262/600] Discriminator Loss: 0.1566, Generator Loss: 8.4845 D(x): 0.9933, D(G(z)): 0.0728 Epoch: [0/20], Batch Num: [263/600] Discriminator Loss: 0.3011, Generator Loss: 8.2540 D(x): 0.9781, D(G(z)): 0.1517 Epoch: [0/20], Batch Num: [264/600] Discriminator Loss: 0.6373, Generator Loss: 8.2599 D(x): 0.9780, D(G(z)): 0.2763 Epoch: [0/20], Batch Num: [265/600] Discriminator Loss: 0.7151, Generator Loss: 7.1063 D(x): 0.9825, D(G(z)): 0.3511 Epoch: [0/20], Batch Num: [266/600] Discriminator Loss: 1.8357, Generator Loss: 4.8493 D(x): 0.9414, D(G(z)): 0.6756 Epoch: [0/20], Batch Num: [267/600] Discriminator Loss: 2.0325, Generator Loss: 2.3313 D(x): 0.9272, D(G(z)): 0.7632 Epoch: [0/20], Batch Num: [268/600] Discriminator Loss: 0.9170, Generator Loss: 3.3185 D(x): 0.8589, D(G(z)): 0.2861 Epoch: [0/20], Batch Num: [269/600] Discriminator Loss: 0.2973, Generator Loss: 4.7248 D(x): 0.9093, D(G(z)): 0.0463 Epoch: [0/20], Batch Num: [270/600] Discriminator Loss: 0.1790, Generator Loss: 3.6432 D(x): 0.9764, D(G(z)): 0.0785 Epoch: [0/20], Batch Num: [271/600] Discriminator Loss: 0.4566, Generator Loss: 2.0471 D(x): 0.9867, D(G(z)): 0.3000 Epoch: [0/20], Batch Num: [272/600] Discriminator Loss: 1.3940, Generator Loss: 0.4834 D(x): 0.9702, D(G(z)): 0.7084 Epoch: [0/20], Batch Num: [273/600] Discriminator Loss: 1.9744, Generator Loss: 0.2293 D(x): 0.9893, D(G(z)): 0.8466 Epoch: [0/20], Batch Num: [274/600] Discriminator Loss: 2.3239, Generator Loss: 0.1792 D(x): 0.9901, D(G(z)): 0.8915 Epoch: [0/20], Batch Num: [275/600] Discriminator Loss: 2.4418, Generator Loss: 0.1735 D(x): 0.9768, D(G(z)): 0.8921 Epoch: [0/20], Batch Num: [276/600] Discriminator Loss: 2.2875, Generator Loss: 0.2200 D(x): 0.9616, D(G(z)): 0.8729 Epoch: [0/20], Batch Num: [277/600] Discriminator Loss: 1.9377, Generator Loss: 0.2709 D(x): 0.9524, D(G(z)): 0.8390 Epoch: [0/20], Batch Num: [278/600] Discriminator Loss: 1.8433, Generator Loss: 0.3445 D(x): 0.9396, D(G(z)): 0.8009 Epoch: [0/20], Batch Num: [279/600] Discriminator Loss: 1.5646, Generator Loss: 0.4928 D(x): 0.9012, D(G(z)): 0.7467 Epoch: [0/20], Batch Num: [280/600] Discriminator Loss: 1.3410, Generator Loss: 0.6609 D(x): 0.8625, D(G(z)): 0.6733 Epoch: [0/20], Batch Num: [281/600] Discriminator Loss: 1.1437, Generator Loss: 0.8499 D(x): 0.8164, D(G(z)): 0.5808 Epoch: [0/20], Batch Num: [282/600] Discriminator Loss: 1.0199, Generator Loss: 1.0402 D(x): 0.7673, D(G(z)): 0.4977 Epoch: [0/20], Batch Num: [283/600] Discriminator Loss: 0.9153, Generator Loss: 1.2170 D(x): 0.7494, D(G(z)): 0.4399 Epoch: [0/20], Batch Num: [284/600] Discriminator Loss: 0.8321, Generator Loss: 1.4030 D(x): 0.6863, D(G(z)): 0.3539 Epoch: [0/20], Batch Num: [285/600] Discriminator Loss: 0.9474, Generator Loss: 1.4884 D(x): 0.5903, D(G(z)): 0.3157 Epoch: [0/20], Batch Num: [286/600] Discriminator Loss: 1.1746, Generator Loss: 1.4364 D(x): 0.4643, D(G(z)): 0.2789 Epoch: [0/20], Batch Num: [287/600] Discriminator Loss: 1.3331, Generator Loss: 1.3598 D(x): 0.4157, D(G(z)): 0.2982 Epoch: [0/20], Batch Num: [288/600] Discriminator Loss: 1.3499, Generator Loss: 1.1514 D(x): 0.4194, D(G(z)): 0.3328 Epoch: [0/20], Batch Num: [289/600] Discriminator Loss: 1.1918, Generator Loss: 0.9322 D(x): 0.5126, D(G(z)): 0.3843 Epoch: [0/20], Batch Num: [290/600] Discriminator Loss: 1.1948, Generator Loss: 0.8537 D(x): 0.5744, D(G(z)): 0.4597 Epoch: [0/20], Batch Num: [291/600] Discriminator Loss: 1.1258, Generator Loss: 0.8725 D(x): 0.6190, D(G(z)): 0.4620 Epoch: [0/20], Batch Num: [292/600] Discriminator Loss: 0.9844, Generator Loss: 1.3084 D(x): 0.6646, D(G(z)): 0.4234 Epoch: [0/20], Batch Num: [293/600] Discriminator Loss: 0.7258, Generator Loss: 2.4372 D(x): 0.7080, D(G(z)): 0.3012 Epoch: [0/20], Batch Num: [294/600] Discriminator Loss: 0.5627, Generator Loss: 3.3807 D(x): 0.7245, D(G(z)): 0.2011 Epoch: [0/20], Batch Num: [295/600] Discriminator Loss: 0.5262, Generator Loss: 4.0562 D(x): 0.7042, D(G(z)): 0.1471 Epoch: [0/20], Batch Num: [296/600] Discriminator Loss: 0.5038, Generator Loss: 4.4361 D(x): 0.6946, D(G(z)): 0.1170 Epoch: [0/20], Batch Num: [297/600] Discriminator Loss: 0.6264, Generator Loss: 4.0507 D(x): 0.6448, D(G(z)): 0.1359 Epoch: [0/20], Batch Num: [298/600] Discriminator Loss: 0.7965, Generator Loss: 3.0323 D(x): 0.6086, D(G(z)): 0.2166 Epoch: [0/20], Batch Num: [299/600] Discriminator Loss: 0.9575, Generator Loss: 1.7424 D(x): 0.5892, D(G(z)): 0.2955 Epoch: 0, Batch Num: [300/600]
Epoch: [0/20], Batch Num: [300/600] Discriminator Loss: 1.1457, Generator Loss: 1.0084 D(x): 0.6261, D(G(z)): 0.4423 Epoch: [0/20], Batch Num: [301/600] Discriminator Loss: 1.0656, Generator Loss: 0.8392 D(x): 0.7104, D(G(z)): 0.4872 Epoch: [0/20], Batch Num: [302/600] Discriminator Loss: 1.0440, Generator Loss: 0.9604 D(x): 0.7665, D(G(z)): 0.5038 Epoch: [0/20], Batch Num: [303/600] Discriminator Loss: 0.9350, Generator Loss: 1.1901 D(x): 0.7899, D(G(z)): 0.4815 Epoch: [0/20], Batch Num: [304/600] Discriminator Loss: 0.7046, Generator Loss: 1.7125 D(x): 0.7975, D(G(z)): 0.3646 Epoch: [0/20], Batch Num: [305/600] Discriminator Loss: 0.5929, Generator Loss: 2.0959 D(x): 0.7551, D(G(z)): 0.2400 Epoch: [0/20], Batch Num: [306/600] Discriminator Loss: 0.5200, Generator Loss: 2.4241 D(x): 0.7456, D(G(z)): 0.1697 Epoch: [0/20], Batch Num: [307/600] Discriminator Loss: 0.5281, Generator Loss: 2.5764 D(x): 0.7116, D(G(z)): 0.1395 Epoch: [0/20], Batch Num: [308/600] Discriminator Loss: 0.6211, Generator Loss: 2.4154 D(x): 0.6694, D(G(z)): 0.1216 Epoch: [0/20], Batch Num: [309/600] Discriminator Loss: 0.5815, Generator Loss: 2.1572 D(x): 0.7014, D(G(z)): 0.1485 Epoch: [0/20], Batch Num: [310/600] Discriminator Loss: 0.6035, Generator Loss: 1.7924 D(x): 0.7849, D(G(z)): 0.2728 Epoch: [0/20], Batch Num: [311/600] Discriminator Loss: 0.8159, Generator Loss: 1.6701 D(x): 0.7960, D(G(z)): 0.3971 Epoch: [0/20], Batch Num: [312/600] Discriminator Loss: 1.0370, Generator Loss: 2.1297 D(x): 0.7660, D(G(z)): 0.4664 Epoch: [0/20], Batch Num: [313/600] Discriminator Loss: 1.1238, Generator Loss: 2.4030 D(x): 0.6653, D(G(z)): 0.4159 Epoch: [0/20], Batch Num: [314/600] Discriminator Loss: 1.5520, Generator Loss: 2.1000 D(x): 0.4532, D(G(z)): 0.2957 Epoch: [0/20], Batch Num: [315/600] Discriminator Loss: 1.9792, Generator Loss: 1.4947 D(x): 0.4142, D(G(z)): 0.4412 Epoch: [0/20], Batch Num: [316/600] Discriminator Loss: 2.4088, Generator Loss: 0.9838 D(x): 0.4174, D(G(z)): 0.6109 Epoch: [0/20], Batch Num: [317/600] Discriminator Loss: 2.9703, Generator Loss: 0.5579 D(x): 0.4344, D(G(z)): 0.7902 Epoch: [0/20], Batch Num: [318/600] Discriminator Loss: 3.3154, Generator Loss: 0.4401 D(x): 0.4398, D(G(z)): 0.8697 Epoch: [0/20], Batch Num: [319/600] Discriminator Loss: 3.3722, Generator Loss: 0.2857 D(x): 0.4347, D(G(z)): 0.8579 Epoch: [0/20], Batch Num: [320/600] Discriminator Loss: 3.4677, Generator Loss: 0.2259 D(x): 0.4154, D(G(z)): 0.8689 Epoch: [0/20], Batch Num: [321/600] Discriminator Loss: 3.4438, Generator Loss: 0.2051 D(x): 0.4135, D(G(z)): 0.8953 Epoch: [0/20], Batch Num: [322/600] Discriminator Loss: 3.0935, Generator Loss: 0.2398 D(x): 0.5050, D(G(z)): 0.8815 Epoch: [0/20], Batch Num: [323/600] Discriminator Loss: 2.7907, Generator Loss: 0.2868 D(x): 0.5223, D(G(z)): 0.8491 Epoch: [0/20], Batch Num: [324/600] Discriminator Loss: 2.3830, Generator Loss: 0.3457 D(x): 0.5663, D(G(z)): 0.8150 Epoch: [0/20], Batch Num: [325/600] Discriminator Loss: 2.0538, Generator Loss: 0.4407 D(x): 0.5812, D(G(z)): 0.7560 Epoch: [0/20], Batch Num: [326/600] Discriminator Loss: 1.8237, Generator Loss: 0.6290 D(x): 0.5538, D(G(z)): 0.6824 Epoch: [0/20], Batch Num: [327/600] Discriminator Loss: 1.5107, Generator Loss: 0.9440 D(x): 0.5627, D(G(z)): 0.5707 Epoch: [0/20], Batch Num: [328/600] Discriminator Loss: 1.2231, Generator Loss: 1.9982 D(x): 0.5488, D(G(z)): 0.4268 Epoch: [0/20], Batch Num: [329/600] Discriminator Loss: 0.9789, Generator Loss: 4.3427 D(x): 0.4908, D(G(z)): 0.1862 Epoch: [0/20], Batch Num: [330/600] Discriminator Loss: 0.8078, Generator Loss: 6.1049 D(x): 0.4995, D(G(z)): 0.0747 Epoch: [0/20], Batch Num: [331/600] Discriminator Loss: 0.6897, Generator Loss: 7.7258 D(x): 0.5197, D(G(z)): 0.0126 Epoch: [0/20], Batch Num: [332/600] Discriminator Loss: 0.6267, Generator Loss: 9.1888 D(x): 0.5481, D(G(z)): 0.0094 Epoch: [0/20], Batch Num: [333/600] Discriminator Loss: 0.6237, Generator Loss: 9.2511 D(x): 0.5480, D(G(z)): 0.0075 Epoch: [0/20], Batch Num: [334/600] Discriminator Loss: 0.5581, Generator Loss: 9.1493 D(x): 0.5808, D(G(z)): 0.0081 Epoch: [0/20], Batch Num: [335/600] Discriminator Loss: 0.5307, Generator Loss: 7.5857 D(x): 0.6000, D(G(z)): 0.0105 Epoch: [0/20], Batch Num: [336/600] Discriminator Loss: 0.5177, Generator Loss: 6.4136 D(x): 0.6197, D(G(z)): 0.0304 Epoch: [0/20], Batch Num: [337/600] Discriminator Loss: 0.5070, Generator Loss: 5.3811 D(x): 0.6475, D(G(z)): 0.0594 Epoch: [0/20], Batch Num: [338/600] Discriminator Loss: 0.5448, Generator Loss: 4.3367 D(x): 0.6689, D(G(z)): 0.1114 Epoch: [0/20], Batch Num: [339/600] Discriminator Loss: 0.6527, Generator Loss: 3.4134 D(x): 0.6887, D(G(z)): 0.2158 Epoch: [0/20], Batch Num: [340/600] Discriminator Loss: 0.7259, Generator Loss: 3.2213 D(x): 0.6844, D(G(z)): 0.2580 Epoch: [0/20], Batch Num: [341/600] Discriminator Loss: 0.8634, Generator Loss: 2.9672 D(x): 0.6772, D(G(z)): 0.3353 Epoch: [0/20], Batch Num: [342/600] Discriminator Loss: 0.9209, Generator Loss: 2.9711 D(x): 0.6555, D(G(z)): 0.3551 Epoch: [0/20], Batch Num: [343/600] Discriminator Loss: 1.1496, Generator Loss: 2.4085 D(x): 0.5793, D(G(z)): 0.3068 Epoch: [0/20], Batch Num: [344/600] Discriminator Loss: 1.4808, Generator Loss: 1.3732 D(x): 0.4989, D(G(z)): 0.4272 Epoch: [0/20], Batch Num: [345/600] Discriminator Loss: 2.3843, Generator Loss: 0.4212 D(x): 0.4368, D(G(z)): 0.5712 Epoch: [0/20], Batch Num: [346/600] Discriminator Loss: 1.6690, Generator Loss: 0.3008 D(x): 0.6925, D(G(z)): 0.7000 Epoch: [0/20], Batch Num: [347/600] Discriminator Loss: 1.6785, Generator Loss: 0.2531 D(x): 0.7888, D(G(z)): 0.7559 Epoch: [0/20], Batch Num: [348/600] Discriminator Loss: 1.7653, Generator Loss: 0.2459 D(x): 0.8181, D(G(z)): 0.7830 Epoch: [0/20], Batch Num: [349/600] Discriminator Loss: 1.7488, Generator Loss: 0.2513 D(x): 0.8584, D(G(z)): 0.7916 Epoch: [0/20], Batch Num: [350/600] Discriminator Loss: 1.6800, Generator Loss: 0.2750 D(x): 0.8598, D(G(z)): 0.7781 Epoch: [0/20], Batch Num: [351/600] Discriminator Loss: 1.6354, Generator Loss: 0.2974 D(x): 0.8499, D(G(z)): 0.7667 Epoch: [0/20], Batch Num: [352/600] Discriminator Loss: 1.5932, Generator Loss: 0.3248 D(x): 0.8342, D(G(z)): 0.7512 Epoch: [0/20], Batch Num: [353/600] Discriminator Loss: 1.4724, Generator Loss: 0.3674 D(x): 0.8104, D(G(z)): 0.7124 Epoch: [0/20], Batch Num: [354/600] Discriminator Loss: 1.4055, Generator Loss: 0.4039 D(x): 0.7936, D(G(z)): 0.6876 Epoch: [0/20], Batch Num: [355/600] Discriminator Loss: 1.3743, Generator Loss: 0.4287 D(x): 0.7782, D(G(z)): 0.6725 Epoch: [0/20], Batch Num: [356/600] Discriminator Loss: 1.3383, Generator Loss: 0.4704 D(x): 0.7561, D(G(z)): 0.6510 Epoch: [0/20], Batch Num: [357/600] Discriminator Loss: 1.3189, Generator Loss: 0.4941 D(x): 0.7219, D(G(z)): 0.6274 Epoch: [0/20], Batch Num: [358/600] Discriminator Loss: 1.2910, Generator Loss: 0.5287 D(x): 0.7067, D(G(z)): 0.6094 Epoch: [0/20], Batch Num: [359/600] Discriminator Loss: 1.2859, Generator Loss: 0.5456 D(x): 0.6834, D(G(z)): 0.5938 Epoch: [0/20], Batch Num: [360/600] Discriminator Loss: 1.2776, Generator Loss: 0.5690 D(x): 0.6694, D(G(z)): 0.5821 Epoch: [0/20], Batch Num: [361/600] Discriminator Loss: 1.2804, Generator Loss: 0.5814 D(x): 0.6492, D(G(z)): 0.5706 Epoch: [0/20], Batch Num: [362/600] Discriminator Loss: 1.2620, Generator Loss: 0.5976 D(x): 0.6412, D(G(z)): 0.5574 Epoch: [0/20], Batch Num: [363/600] Discriminator Loss: 1.2599, Generator Loss: 0.6117 D(x): 0.6324, D(G(z)): 0.5503 Epoch: [0/20], Batch Num: [364/600] Discriminator Loss: 1.2541, Generator Loss: 0.6171 D(x): 0.6241, D(G(z)): 0.5419 Epoch: [0/20], Batch Num: [365/600] Discriminator Loss: 1.2522, Generator Loss: 0.6237 D(x): 0.6207, D(G(z)): 0.5385 Epoch: [0/20], Batch Num: [366/600] Discriminator Loss: 1.2657, Generator Loss: 0.6289 D(x): 0.6128, D(G(z)): 0.5387 Epoch: [0/20], Batch Num: [367/600] Discriminator Loss: 1.2555, Generator Loss: 0.6327 D(x): 0.6119, D(G(z)): 0.5330 Epoch: [0/20], Batch Num: [368/600] Discriminator Loss: 1.2428, Generator Loss: 0.6396 D(x): 0.6148, D(G(z)): 0.5294 Epoch: [0/20], Batch Num: [369/600] Discriminator Loss: 1.2369, Generator Loss: 0.6448 D(x): 0.6178, D(G(z)): 0.5290 Epoch: [0/20], Batch Num: [370/600] Discriminator Loss: 1.2378, Generator Loss: 0.6431 D(x): 0.6157, D(G(z)): 0.5279 Epoch: [0/20], Batch Num: [371/600] Discriminator Loss: 1.2351, Generator Loss: 0.6416 D(x): 0.6174, D(G(z)): 0.5276 Epoch: [0/20], Batch Num: [372/600] Discriminator Loss: 1.2090, Generator Loss: 0.6411 D(x): 0.6334, D(G(z)): 0.5275 Epoch: [0/20], Batch Num: [373/600] Discriminator Loss: 1.1976, Generator Loss: 0.6413 D(x): 0.6375, D(G(z)): 0.5250 Epoch: [0/20], Batch Num: [374/600] Discriminator Loss: 1.1977, Generator Loss: 0.6416 D(x): 0.6399, D(G(z)): 0.5270 Epoch: [0/20], Batch Num: [375/600] Discriminator Loss: 1.1725, Generator Loss: 0.6406 D(x): 0.6533, D(G(z)): 0.5247 Epoch: [0/20], Batch Num: [376/600] Discriminator Loss: 1.1703, Generator Loss: 0.6370 D(x): 0.6625, D(G(z)): 0.5302 Epoch: [0/20], Batch Num: [377/600] Discriminator Loss: 1.1649, Generator Loss: 0.6265 D(x): 0.6701, D(G(z)): 0.5329 Epoch: [0/20], Batch Num: [378/600] Discriminator Loss: 1.1457, Generator Loss: 0.6260 D(x): 0.6878, D(G(z)): 0.5361 Epoch: [0/20], Batch Num: [379/600] Discriminator Loss: 1.1451, Generator Loss: 0.6299 D(x): 0.6936, D(G(z)): 0.5395 Epoch: [0/20], Batch Num: [380/600] Discriminator Loss: 1.1376, Generator Loss: 0.6129 D(x): 0.7011, D(G(z)): 0.5408 Epoch: [0/20], Batch Num: [381/600] Discriminator Loss: 1.1158, Generator Loss: 0.6162 D(x): 0.7161, D(G(z)): 0.5406 Epoch: [0/20], Batch Num: [382/600] Discriminator Loss: 1.0887, Generator Loss: 0.6130 D(x): 0.7366, D(G(z)): 0.5412 Epoch: [0/20], Batch Num: [383/600] Discriminator Loss: 1.0828, Generator Loss: 0.6136 D(x): 0.7396, D(G(z)): 0.5403 Epoch: [0/20], Batch Num: [384/600] Discriminator Loss: 1.0903, Generator Loss: 0.6147 D(x): 0.7542, D(G(z)): 0.5523 Epoch: [0/20], Batch Num: [385/600] Discriminator Loss: 1.0645, Generator Loss: 0.6021 D(x): 0.7669, D(G(z)): 0.5481 Epoch: [0/20], Batch Num: [386/600] Discriminator Loss: 1.0498, Generator Loss: 0.6211 D(x): 0.7715, D(G(z)): 0.5444 Epoch: [0/20], Batch Num: [387/600] Discriminator Loss: 1.0523, Generator Loss: 0.6134 D(x): 0.7720, D(G(z)): 0.5455 Epoch: [0/20], Batch Num: [388/600] Discriminator Loss: 1.0219, Generator Loss: 0.6219 D(x): 0.7860, D(G(z)): 0.5401 Epoch: [0/20], Batch Num: [389/600] Discriminator Loss: 1.0236, Generator Loss: 0.6387 D(x): 0.7857, D(G(z)): 0.5408 Epoch: [0/20], Batch Num: [390/600] Discriminator Loss: 0.9933, Generator Loss: 0.6395 D(x): 0.7987, D(G(z)): 0.5340 Epoch: [0/20], Batch Num: [391/600] Discriminator Loss: 1.0031, Generator Loss: 0.6398 D(x): 0.7946, D(G(z)): 0.5361 Epoch: [0/20], Batch Num: [392/600] Discriminator Loss: 0.9773, Generator Loss: 0.6672 D(x): 0.8037, D(G(z)): 0.5291 Epoch: [0/20], Batch Num: [393/600] Discriminator Loss: 0.9529, Generator Loss: 0.6792 D(x): 0.8037, D(G(z)): 0.5170 Epoch: [0/20], Batch Num: [394/600] Discriminator Loss: 0.9629, Generator Loss: 0.6942 D(x): 0.8053, D(G(z)): 0.5230 Epoch: [0/20], Batch Num: [395/600] Discriminator Loss: 0.9287, Generator Loss: 0.7024 D(x): 0.8153, D(G(z)): 0.5124 Epoch: [0/20], Batch Num: [396/600] Discriminator Loss: 0.9314, Generator Loss: 0.7211 D(x): 0.8048, D(G(z)): 0.5060 Epoch: [0/20], Batch Num: [397/600] Discriminator Loss: 0.8918, Generator Loss: 0.7215 D(x): 0.8206, D(G(z)): 0.4974 Epoch: [0/20], Batch Num: [398/600] Discriminator Loss: 0.9088, Generator Loss: 0.7337 D(x): 0.8162, D(G(z)): 0.5016 Epoch: [0/20], Batch Num: [399/600] Discriminator Loss: 0.8977, Generator Loss: 0.7750 D(x): 0.8175, D(G(z)): 0.4968 Epoch: 0, Batch Num: [400/600]
Epoch: [0/20], Batch Num: [400/600] Discriminator Loss: 0.9080, Generator Loss: 0.7457 D(x): 0.7964, D(G(z)): 0.4868 Epoch: [0/20], Batch Num: [401/600] Discriminator Loss: 0.9137, Generator Loss: 0.7577 D(x): 0.8061, D(G(z)): 0.4957 Epoch: [0/20], Batch Num: [402/600] Discriminator Loss: 0.9307, Generator Loss: 0.8113 D(x): 0.8054, D(G(z)): 0.5029 Epoch: [0/20], Batch Num: [403/600] Discriminator Loss: 0.9400, Generator Loss: 0.7340 D(x): 0.8127, D(G(z)): 0.5127 Epoch: [0/20], Batch Num: [404/600] Discriminator Loss: 0.9554, Generator Loss: 0.6937 D(x): 0.8081, D(G(z)): 0.5148 Epoch: [0/20], Batch Num: [405/600] Discriminator Loss: 1.0475, Generator Loss: 0.7161 D(x): 0.7924, D(G(z)): 0.5439 Epoch: [0/20], Batch Num: [406/600] Discriminator Loss: 1.0959, Generator Loss: 0.6633 D(x): 0.7812, D(G(z)): 0.5597 Epoch: [0/20], Batch Num: [407/600] Discriminator Loss: 1.1660, Generator Loss: 0.6539 D(x): 0.7467, D(G(z)): 0.5670 Epoch: [0/20], Batch Num: [408/600] Discriminator Loss: 1.1915, Generator Loss: 0.6429 D(x): 0.7571, D(G(z)): 0.5837 Epoch: [0/20], Batch Num: [409/600] Discriminator Loss: 1.3029, Generator Loss: 0.7193 D(x): 0.6799, D(G(z)): 0.5745 Epoch: [0/20], Batch Num: [410/600] Discriminator Loss: 1.1391, Generator Loss: 1.2184 D(x): 0.7089, D(G(z)): 0.5207 Epoch: [0/20], Batch Num: [411/600] Discriminator Loss: 0.9690, Generator Loss: 1.9851 D(x): 0.6696, D(G(z)): 0.3877 Epoch: [0/20], Batch Num: [412/600] Discriminator Loss: 0.8616, Generator Loss: 2.9846 D(x): 0.6494, D(G(z)): 0.2928 Epoch: [0/20], Batch Num: [413/600] Discriminator Loss: 0.7003, Generator Loss: 3.9145 D(x): 0.6950, D(G(z)): 0.2269 Epoch: [0/20], Batch Num: [414/600] Discriminator Loss: 0.5345, Generator Loss: 4.0438 D(x): 0.7296, D(G(z)): 0.1632 Epoch: [0/20], Batch Num: [415/600] Discriminator Loss: 0.4451, Generator Loss: 3.7476 D(x): 0.7611, D(G(z)): 0.1273 Epoch: [0/20], Batch Num: [416/600] Discriminator Loss: 0.3780, Generator Loss: 3.7805 D(x): 0.8100, D(G(z)): 0.1346 Epoch: [0/20], Batch Num: [417/600] Discriminator Loss: 0.3761, Generator Loss: 3.9372 D(x): 0.8137, D(G(z)): 0.1315 Epoch: [0/20], Batch Num: [418/600] Discriminator Loss: 0.4404, Generator Loss: 3.6182 D(x): 0.7944, D(G(z)): 0.1559 Epoch: [0/20], Batch Num: [419/600] Discriminator Loss: 0.4435, Generator Loss: 3.5426 D(x): 0.8124, D(G(z)): 0.1591 Epoch: [0/20], Batch Num: [420/600] Discriminator Loss: 0.4450, Generator Loss: 2.9667 D(x): 0.8538, D(G(z)): 0.1838 Epoch: [0/20], Batch Num: [421/600] Discriminator Loss: 0.5976, Generator Loss: 2.6332 D(x): 0.8337, D(G(z)): 0.2426 Epoch: [0/20], Batch Num: [422/600] Discriminator Loss: 1.0486, Generator Loss: 1.8920 D(x): 0.7923, D(G(z)): 0.3331 Epoch: [0/20], Batch Num: [423/600] Discriminator Loss: 1.1211, Generator Loss: 1.5633 D(x): 0.8378, D(G(z)): 0.5391 Epoch: [0/20], Batch Num: [424/600] Discriminator Loss: 1.7658, Generator Loss: 0.8230 D(x): 0.7296, D(G(z)): 0.5843 Epoch: [0/20], Batch Num: [425/600] Discriminator Loss: 2.0318, Generator Loss: 0.4280 D(x): 0.7608, D(G(z)): 0.7237 Epoch: [0/20], Batch Num: [426/600] Discriminator Loss: 2.2572, Generator Loss: 0.2805 D(x): 0.7740, D(G(z)): 0.8038 Epoch: [0/20], Batch Num: [427/600] Discriminator Loss: 2.1700, Generator Loss: 0.2198 D(x): 0.8189, D(G(z)): 0.8289 Epoch: [0/20], Batch Num: [428/600] Discriminator Loss: 2.0922, Generator Loss: 0.2573 D(x): 0.8585, D(G(z)): 0.8367 Epoch: [0/20], Batch Num: [429/600] Discriminator Loss: 1.8686, Generator Loss: 0.3424 D(x): 0.8914, D(G(z)): 0.8127 Epoch: [0/20], Batch Num: [430/600] Discriminator Loss: 1.6376, Generator Loss: 0.5455 D(x): 0.8403, D(G(z)): 0.7437 Epoch: [0/20], Batch Num: [431/600] Discriminator Loss: 1.3476, Generator Loss: 1.2229 D(x): 0.8111, D(G(z)): 0.6384 Epoch: [0/20], Batch Num: [432/600] Discriminator Loss: 0.9832, Generator Loss: 2.4245 D(x): 0.7353, D(G(z)): 0.4192 Epoch: [0/20], Batch Num: [433/600] Discriminator Loss: 0.7819, Generator Loss: 3.5260 D(x): 0.6643, D(G(z)): 0.2256 Epoch: [0/20], Batch Num: [434/600] Discriminator Loss: 0.7203, Generator Loss: 3.7445 D(x): 0.6163, D(G(z)): 0.0962 Epoch: [0/20], Batch Num: [435/600] Discriminator Loss: 0.6157, Generator Loss: 3.6806 D(x): 0.6585, D(G(z)): 0.0998 Epoch: [0/20], Batch Num: [436/600] Discriminator Loss: 0.5115, Generator Loss: 3.4458 D(x): 0.7295, D(G(z)): 0.1470 Epoch: [0/20], Batch Num: [437/600] Discriminator Loss: 0.4979, Generator Loss: 2.8961 D(x): 0.7517, D(G(z)): 0.1662 Epoch: [0/20], Batch Num: [438/600] Discriminator Loss: 0.6132, Generator Loss: 2.4336 D(x): 0.7723, D(G(z)): 0.2562 Epoch: [0/20], Batch Num: [439/600] Discriminator Loss: 0.6801, Generator Loss: 2.2468 D(x): 0.7796, D(G(z)): 0.3106 Epoch: [0/20], Batch Num: [440/600] Discriminator Loss: 0.8018, Generator Loss: 2.3036 D(x): 0.7572, D(G(z)): 0.3663 Epoch: [0/20], Batch Num: [441/600] Discriminator Loss: 0.8693, Generator Loss: 2.5687 D(x): 0.7309, D(G(z)): 0.3836 Epoch: [0/20], Batch Num: [442/600] Discriminator Loss: 1.1729, Generator Loss: 2.1257 D(x): 0.6255, D(G(z)): 0.4038 Epoch: [0/20], Batch Num: [443/600] Discriminator Loss: 1.5810, Generator Loss: 1.1754 D(x): 0.5609, D(G(z)): 0.4728 Epoch: [0/20], Batch Num: [444/600] Discriminator Loss: 1.5162, Generator Loss: 0.7394 D(x): 0.6361, D(G(z)): 0.5802 Epoch: [0/20], Batch Num: [445/600] Discriminator Loss: 1.6795, Generator Loss: 0.4857 D(x): 0.6526, D(G(z)): 0.6725 Epoch: [0/20], Batch Num: [446/600] Discriminator Loss: 1.7753, Generator Loss: 0.3625 D(x): 0.6986, D(G(z)): 0.7206 Epoch: [0/20], Batch Num: [447/600] Discriminator Loss: 1.6902, Generator Loss: 0.3287 D(x): 0.7656, D(G(z)): 0.7462 Epoch: [0/20], Batch Num: [448/600] Discriminator Loss: 1.7167, Generator Loss: 0.3197 D(x): 0.7631, D(G(z)): 0.7522 Epoch: [0/20], Batch Num: [449/600] Discriminator Loss: 1.5830, Generator Loss: 0.3299 D(x): 0.8147, D(G(z)): 0.7395 Epoch: [0/20], Batch Num: [450/600] Discriminator Loss: 1.6295, Generator Loss: 0.3551 D(x): 0.7834, D(G(z)): 0.7406 Epoch: [0/20], Batch Num: [451/600] Discriminator Loss: 1.5478, Generator Loss: 0.3932 D(x): 0.7851, D(G(z)): 0.7202 Epoch: [0/20], Batch Num: [452/600] Discriminator Loss: 1.4792, Generator Loss: 0.4502 D(x): 0.7656, D(G(z)): 0.6954 Epoch: [0/20], Batch Num: [453/600] Discriminator Loss: 1.3862, Generator Loss: 0.5153 D(x): 0.7260, D(G(z)): 0.6477 Epoch: [0/20], Batch Num: [454/600] Discriminator Loss: 1.3460, Generator Loss: 0.5767 D(x): 0.6870, D(G(z)): 0.6144 Epoch: [0/20], Batch Num: [455/600] Discriminator Loss: 1.3381, Generator Loss: 0.6763 D(x): 0.6330, D(G(z)): 0.5696 Epoch: [0/20], Batch Num: [456/600] Discriminator Loss: 1.2417, Generator Loss: 0.7737 D(x): 0.6191, D(G(z)): 0.5256 Epoch: [0/20], Batch Num: [457/600] Discriminator Loss: 1.3081, Generator Loss: 0.8549 D(x): 0.5482, D(G(z)): 0.4840 Epoch: [0/20], Batch Num: [458/600] Discriminator Loss: 1.2467, Generator Loss: 0.9327 D(x): 0.5329, D(G(z)): 0.4478 Epoch: [0/20], Batch Num: [459/600] Discriminator Loss: 1.2695, Generator Loss: 0.9662 D(x): 0.4983, D(G(z)): 0.4147 Epoch: [0/20], Batch Num: [460/600] Discriminator Loss: 1.2858, Generator Loss: 1.0517 D(x): 0.4736, D(G(z)): 0.4031 Epoch: [0/20], Batch Num: [461/600] Discriminator Loss: 1.2511, Generator Loss: 0.9759 D(x): 0.4900, D(G(z)): 0.3982 Epoch: [0/20], Batch Num: [462/600] Discriminator Loss: 1.2982, Generator Loss: 0.9833 D(x): 0.4777, D(G(z)): 0.4153 Epoch: [0/20], Batch Num: [463/600] Discriminator Loss: 1.2452, Generator Loss: 0.9261 D(x): 0.5052, D(G(z)): 0.4142 Epoch: [0/20], Batch Num: [464/600] Discriminator Loss: 1.2245, Generator Loss: 0.8822 D(x): 0.5280, D(G(z)): 0.4339 Epoch: [0/20], Batch Num: [465/600] Discriminator Loss: 1.2031, Generator Loss: 0.8425 D(x): 0.5493, D(G(z)): 0.4413 Epoch: [0/20], Batch Num: [466/600] Discriminator Loss: 1.1893, Generator Loss: 0.8188 D(x): 0.5689, D(G(z)): 0.4541 Epoch: [0/20], Batch Num: [467/600] Discriminator Loss: 1.1707, Generator Loss: 0.8093 D(x): 0.6009, D(G(z)): 0.4682 Epoch: [0/20], Batch Num: [468/600] Discriminator Loss: 1.1170, Generator Loss: 0.7851 D(x): 0.6240, D(G(z)): 0.4671 Epoch: [0/20], Batch Num: [469/600] Discriminator Loss: 1.1454, Generator Loss: 0.7987 D(x): 0.6105, D(G(z)): 0.4715 Epoch: [0/20], Batch Num: [470/600] Discriminator Loss: 1.0922, Generator Loss: 0.9043 D(x): 0.6356, D(G(z)): 0.4647 Epoch: [0/20], Batch Num: [471/600] Discriminator Loss: 1.0707, Generator Loss: 0.9480 D(x): 0.6388, D(G(z)): 0.4524 Epoch: [0/20], Batch Num: [472/600] Discriminator Loss: 1.0608, Generator Loss: 1.0214 D(x): 0.6164, D(G(z)): 0.4144 Epoch: [0/20], Batch Num: [473/600] Discriminator Loss: 0.9693, Generator Loss: 1.1568 D(x): 0.6144, D(G(z)): 0.3655 Epoch: [0/20], Batch Num: [474/600] Discriminator Loss: 1.0134, Generator Loss: 1.3134 D(x): 0.5816, D(G(z)): 0.3382 Epoch: [0/20], Batch Num: [475/600] Discriminator Loss: 0.9288, Generator Loss: 1.3948 D(x): 0.6123, D(G(z)): 0.3353 Epoch: [0/20], Batch Num: [476/600] Discriminator Loss: 0.9284, Generator Loss: 1.4610 D(x): 0.5886, D(G(z)): 0.3014 Epoch: [0/20], Batch Num: [477/600] Discriminator Loss: 0.9579, Generator Loss: 1.4042 D(x): 0.5876, D(G(z)): 0.3154 Epoch: [0/20], Batch Num: [478/600] Discriminator Loss: 0.9478, Generator Loss: 1.3496 D(x): 0.6139, D(G(z)): 0.3387 Epoch: [0/20], Batch Num: [479/600] Discriminator Loss: 0.9340, Generator Loss: 1.2818 D(x): 0.6298, D(G(z)): 0.3506 Epoch: [0/20], Batch Num: [480/600] Discriminator Loss: 1.0639, Generator Loss: 1.1846 D(x): 0.6208, D(G(z)): 0.3998 Epoch: [0/20], Batch Num: [481/600] Discriminator Loss: 1.0147, Generator Loss: 1.1496 D(x): 0.6759, D(G(z)): 0.4407 Epoch: [0/20], Batch Num: [482/600] Discriminator Loss: 1.0783, Generator Loss: 1.0985 D(x): 0.6600, D(G(z)): 0.4592 Epoch: [0/20], Batch Num: [483/600] Discriminator Loss: 1.1376, Generator Loss: 1.1901 D(x): 0.6418, D(G(z)): 0.4669 Epoch: [0/20], Batch Num: [484/600] Discriminator Loss: 1.1094, Generator Loss: 1.2858 D(x): 0.6293, D(G(z)): 0.4457 Epoch: [0/20], Batch Num: [485/600] Discriminator Loss: 1.0986, Generator Loss: 1.3110 D(x): 0.6175, D(G(z)): 0.4179 Epoch: [0/20], Batch Num: [486/600] Discriminator Loss: 1.0792, Generator Loss: 1.3372 D(x): 0.5772, D(G(z)): 0.3579 Epoch: [0/20], Batch Num: [487/600] Discriminator Loss: 1.1029, Generator Loss: 1.2867 D(x): 0.6104, D(G(z)): 0.4011 Epoch: [0/20], Batch Num: [488/600] Discriminator Loss: 0.9504, Generator Loss: 1.2427 D(x): 0.6405, D(G(z)): 0.3600 Epoch: [0/20], Batch Num: [489/600] Discriminator Loss: 0.9137, Generator Loss: 1.2832 D(x): 0.6912, D(G(z)): 0.3831 Epoch: [0/20], Batch Num: [490/600] Discriminator Loss: 1.0033, Generator Loss: 1.4633 D(x): 0.6876, D(G(z)): 0.4231 Epoch: [0/20], Batch Num: [491/600] Discriminator Loss: 1.1423, Generator Loss: 1.6607 D(x): 0.6611, D(G(z)): 0.4593 Epoch: [0/20], Batch Num: [492/600] Discriminator Loss: 1.3096, Generator Loss: 1.5127 D(x): 0.6177, D(G(z)): 0.4860 Epoch: [0/20], Batch Num: [493/600] Discriminator Loss: 1.5487, Generator Loss: 1.2265 D(x): 0.5769, D(G(z)): 0.5465 Epoch: [0/20], Batch Num: [494/600] Discriminator Loss: 1.9133, Generator Loss: 0.7441 D(x): 0.5141, D(G(z)): 0.6274 Epoch: [0/20], Batch Num: [495/600] Discriminator Loss: 2.2534, Generator Loss: 0.4057 D(x): 0.4911, D(G(z)): 0.7207 Epoch: [0/20], Batch Num: [496/600] Discriminator Loss: 2.2768, Generator Loss: 0.3147 D(x): 0.5284, D(G(z)): 0.7586 Epoch: [0/20], Batch Num: [497/600] Discriminator Loss: 2.0257, Generator Loss: 0.2993 D(x): 0.6234, D(G(z)): 0.7694 Epoch: [0/20], Batch Num: [498/600] Discriminator Loss: 1.8243, Generator Loss: 0.3429 D(x): 0.7017, D(G(z)): 0.7570 Epoch: [0/20], Batch Num: [499/600] Discriminator Loss: 1.5677, Generator Loss: 0.5353 D(x): 0.7418, D(G(z)): 0.7064 Epoch: 0, Batch Num: [500/600]
Epoch: [0/20], Batch Num: [500/600] Discriminator Loss: 1.2793, Generator Loss: 1.4929 D(x): 0.7467, D(G(z)): 0.6055 Epoch: [0/20], Batch Num: [501/600] Discriminator Loss: 1.0480, Generator Loss: 3.2172 D(x): 0.7159, D(G(z)): 0.4722 Epoch: [0/20], Batch Num: [502/600] Discriminator Loss: 0.6565, Generator Loss: 4.7493 D(x): 0.7027, D(G(z)): 0.2333 Epoch: [0/20], Batch Num: [503/600] Discriminator Loss: 0.5683, Generator Loss: 6.1685 D(x): 0.6740, D(G(z)): 0.1390 Epoch: [0/20], Batch Num: [504/600] Discriminator Loss: 0.5298, Generator Loss: 7.8163 D(x): 0.6362, D(G(z)): 0.0579 Epoch: [0/20], Batch Num: [505/600] Discriminator Loss: 0.5798, Generator Loss: 8.1771 D(x): 0.5876, D(G(z)): 0.0301 Epoch: [0/20], Batch Num: [506/600] Discriminator Loss: 0.7035, Generator Loss: 7.7513 D(x): 0.5457, D(G(z)): 0.0592 Epoch: [0/20], Batch Num: [507/600] Discriminator Loss: 0.8863, Generator Loss: 5.1463 D(x): 0.5007, D(G(z)): 0.0934 Epoch: [0/20], Batch Num: [508/600] Discriminator Loss: 1.1779, Generator Loss: 3.0169 D(x): 0.4853, D(G(z)): 0.1962 Epoch: [0/20], Batch Num: [509/600] Discriminator Loss: 1.1522, Generator Loss: 1.3227 D(x): 0.5784, D(G(z)): 0.3952 Epoch: [0/20], Batch Num: [510/600] Discriminator Loss: 1.3270, Generator Loss: 0.7961 D(x): 0.5995, D(G(z)): 0.5210 Epoch: [0/20], Batch Num: [511/600] Discriminator Loss: 1.4325, Generator Loss: 0.5789 D(x): 0.6314, D(G(z)): 0.6003 Epoch: [0/20], Batch Num: [512/600] Discriminator Loss: 1.4720, Generator Loss: 0.4752 D(x): 0.6679, D(G(z)): 0.6441 Epoch: [0/20], Batch Num: [513/600] Discriminator Loss: 1.4190, Generator Loss: 0.4607 D(x): 0.7034, D(G(z)): 0.6475 Epoch: [0/20], Batch Num: [514/600] Discriminator Loss: 1.4773, Generator Loss: 0.4723 D(x): 0.6997, D(G(z)): 0.6657 Epoch: [0/20], Batch Num: [515/600] Discriminator Loss: 1.4308, Generator Loss: 0.4849 D(x): 0.6952, D(G(z)): 0.6467 Epoch: [0/20], Batch Num: [516/600] Discriminator Loss: 1.3861, Generator Loss: 0.5145 D(x): 0.6968, D(G(z)): 0.6351 Epoch: [0/20], Batch Num: [517/600] Discriminator Loss: 1.3651, Generator Loss: 0.5403 D(x): 0.6827, D(G(z)): 0.6172 Epoch: [0/20], Batch Num: [518/600] Discriminator Loss: 1.3264, Generator Loss: 0.5971 D(x): 0.6706, D(G(z)): 0.5962 Epoch: [0/20], Batch Num: [519/600] Discriminator Loss: 1.2548, Generator Loss: 0.6513 D(x): 0.6599, D(G(z)): 0.5602 Epoch: [0/20], Batch Num: [520/600] Discriminator Loss: 1.2243, Generator Loss: 0.7207 D(x): 0.6444, D(G(z)): 0.5362 Epoch: [0/20], Batch Num: [521/600] Discriminator Loss: 1.1959, Generator Loss: 0.8279 D(x): 0.6422, D(G(z)): 0.5211 Epoch: [0/20], Batch Num: [522/600] Discriminator Loss: 1.1132, Generator Loss: 0.9050 D(x): 0.6178, D(G(z)): 0.4556 Epoch: [0/20], Batch Num: [523/600] Discriminator Loss: 1.1174, Generator Loss: 1.0518 D(x): 0.6034, D(G(z)): 0.4463 Epoch: [0/20], Batch Num: [524/600] Discriminator Loss: 1.0950, Generator Loss: 1.2249 D(x): 0.5833, D(G(z)): 0.4100 Epoch: [0/20], Batch Num: [525/600] Discriminator Loss: 1.0648, Generator Loss: 1.4039 D(x): 0.5662, D(G(z)): 0.3648 Epoch: [0/20], Batch Num: [526/600] Discriminator Loss: 1.0323, Generator Loss: 1.3902 D(x): 0.5565, D(G(z)): 0.3358 Epoch: [0/20], Batch Num: [527/600] Discriminator Loss: 1.0399, Generator Loss: 1.4118 D(x): 0.5718, D(G(z)): 0.3455 Epoch: [0/20], Batch Num: [528/600] Discriminator Loss: 1.0252, Generator Loss: 1.4250 D(x): 0.5838, D(G(z)): 0.3643 Epoch: [0/20], Batch Num: [529/600] Discriminator Loss: 1.0294, Generator Loss: 1.3783 D(x): 0.6026, D(G(z)): 0.3801 Epoch: [0/20], Batch Num: [530/600] Discriminator Loss: 1.1210, Generator Loss: 0.9469 D(x): 0.6044, D(G(z)): 0.4333 Epoch: [0/20], Batch Num: [531/600] Discriminator Loss: 1.1565, Generator Loss: 0.8788 D(x): 0.6314, D(G(z)): 0.4843 Epoch: [0/20], Batch Num: [532/600] Discriminator Loss: 1.3270, Generator Loss: 0.7121 D(x): 0.6100, D(G(z)): 0.5437 Epoch: [0/20], Batch Num: [533/600] Discriminator Loss: 1.2950, Generator Loss: 0.5836 D(x): 0.6738, D(G(z)): 0.5832 Epoch: [0/20], Batch Num: [534/600] Discriminator Loss: 1.4353, Generator Loss: 0.5390 D(x): 0.6311, D(G(z)): 0.6061 Epoch: [0/20], Batch Num: [535/600] Discriminator Loss: 1.5094, Generator Loss: 0.4804 D(x): 0.6604, D(G(z)): 0.6515 Epoch: [0/20], Batch Num: [536/600] Discriminator Loss: 1.4509, Generator Loss: 0.4596 D(x): 0.6765, D(G(z)): 0.6419 Epoch: [0/20], Batch Num: [537/600] Discriminator Loss: 1.4499, Generator Loss: 0.4401 D(x): 0.6945, D(G(z)): 0.6524 Epoch: [0/20], Batch Num: [538/600] Discriminator Loss: 1.4911, Generator Loss: 0.4549 D(x): 0.6972, D(G(z)): 0.6671 Epoch: [0/20], Batch Num: [539/600] Discriminator Loss: 1.5097, Generator Loss: 0.4594 D(x): 0.6883, D(G(z)): 0.6685 Epoch: [0/20], Batch Num: [540/600] Discriminator Loss: 1.4516, Generator Loss: 0.4782 D(x): 0.6919, D(G(z)): 0.6529 Epoch: [0/20], Batch Num: [541/600] Discriminator Loss: 1.4684, Generator Loss: 0.5081 D(x): 0.6562, D(G(z)): 0.6380 Epoch: [0/20], Batch Num: [542/600] Discriminator Loss: 1.3940, Generator Loss: 0.5446 D(x): 0.6618, D(G(z)): 0.6149 Epoch: [0/20], Batch Num: [543/600] Discriminator Loss: 1.3598, Generator Loss: 0.5782 D(x): 0.6413, D(G(z)): 0.5886 Epoch: [0/20], Batch Num: [544/600] Discriminator Loss: 1.3604, Generator Loss: 0.6002 D(x): 0.6473, D(G(z)): 0.5928 Epoch: [0/20], Batch Num: [545/600] Discriminator Loss: 1.4142, Generator Loss: 0.6092 D(x): 0.5882, D(G(z)): 0.5682 Epoch: [0/20], Batch Num: [546/600] Discriminator Loss: 1.2658, Generator Loss: 0.6269 D(x): 0.6158, D(G(z)): 0.5328 Epoch: [0/20], Batch Num: [547/600] Discriminator Loss: 1.2629, Generator Loss: 0.6631 D(x): 0.6141, D(G(z)): 0.5308 Epoch: [0/20], Batch Num: [548/600] Discriminator Loss: 1.2586, Generator Loss: 0.6988 D(x): 0.6009, D(G(z)): 0.5187 Epoch: [0/20], Batch Num: [549/600] Discriminator Loss: 1.2561, Generator Loss: 0.7655 D(x): 0.5979, D(G(z)): 0.5154 Epoch: [0/20], Batch Num: [550/600] Discriminator Loss: 1.2464, Generator Loss: 0.7612 D(x): 0.5892, D(G(z)): 0.5019 Epoch: [0/20], Batch Num: [551/600] Discriminator Loss: 1.2191, Generator Loss: 0.7410 D(x): 0.5851, D(G(z)): 0.4859 Epoch: [0/20], Batch Num: [552/600] Discriminator Loss: 1.1918, Generator Loss: 0.7875 D(x): 0.5827, D(G(z)): 0.4692 Epoch: [0/20], Batch Num: [553/600] Discriminator Loss: 1.2067, Generator Loss: 0.8476 D(x): 0.5894, D(G(z)): 0.4815 Epoch: [0/20], Batch Num: [554/600] Discriminator Loss: 1.1893, Generator Loss: 0.7849 D(x): 0.5856, D(G(z)): 0.4595 Epoch: [0/20], Batch Num: [555/600] Discriminator Loss: 1.1549, Generator Loss: 0.8224 D(x): 0.6065, D(G(z)): 0.4666 Epoch: [0/20], Batch Num: [556/600] Discriminator Loss: 1.1522, Generator Loss: 0.8294 D(x): 0.5985, D(G(z)): 0.4588 Epoch: [0/20], Batch Num: [557/600] Discriminator Loss: 1.0762, Generator Loss: 0.9237 D(x): 0.6134, D(G(z)): 0.4291 Epoch: [0/20], Batch Num: [558/600] Discriminator Loss: 1.1696, Generator Loss: 0.9288 D(x): 0.5948, D(G(z)): 0.4574 Epoch: [0/20], Batch Num: [559/600] Discriminator Loss: 1.1231, Generator Loss: 0.9538 D(x): 0.6043, D(G(z)): 0.4472 Epoch: [0/20], Batch Num: [560/600] Discriminator Loss: 1.1591, Generator Loss: 0.9679 D(x): 0.5951, D(G(z)): 0.4467 Epoch: [0/20], Batch Num: [561/600] Discriminator Loss: 1.1428, Generator Loss: 0.9257 D(x): 0.6086, D(G(z)): 0.4495 Epoch: [0/20], Batch Num: [562/600] Discriminator Loss: 0.9943, Generator Loss: 1.0142 D(x): 0.6377, D(G(z)): 0.3999 Epoch: [0/20], Batch Num: [563/600] Discriminator Loss: 1.0881, Generator Loss: 1.0055 D(x): 0.6315, D(G(z)): 0.4449 Epoch: [0/20], Batch Num: [564/600] Discriminator Loss: 1.0645, Generator Loss: 1.0331 D(x): 0.6345, D(G(z)): 0.4292 Epoch: [0/20], Batch Num: [565/600] Discriminator Loss: 1.1160, Generator Loss: 1.1204 D(x): 0.6080, D(G(z)): 0.4340 Epoch: [0/20], Batch Num: [566/600] Discriminator Loss: 1.0942, Generator Loss: 1.0848 D(x): 0.6126, D(G(z)): 0.4132 Epoch: [0/20], Batch Num: [567/600] Discriminator Loss: 1.0479, Generator Loss: 1.1650 D(x): 0.6317, D(G(z)): 0.4138 Epoch: [0/20], Batch Num: [568/600] Discriminator Loss: 1.1241, Generator Loss: 1.1133 D(x): 0.5868, D(G(z)): 0.3963 Epoch: [0/20], Batch Num: [569/600] Discriminator Loss: 1.2152, Generator Loss: 0.9942 D(x): 0.5888, D(G(z)): 0.4418 Epoch: [0/20], Batch Num: [570/600] Discriminator Loss: 1.2518, Generator Loss: 0.7934 D(x): 0.6302, D(G(z)): 0.4758 Epoch: [0/20], Batch Num: [571/600] Discriminator Loss: 1.2667, Generator Loss: 0.8806 D(x): 0.6622, D(G(z)): 0.5397 Epoch: [0/20], Batch Num: [572/600] Discriminator Loss: 1.2452, Generator Loss: 0.8889 D(x): 0.6773, D(G(z)): 0.5419 Epoch: [0/20], Batch Num: [573/600] Discriminator Loss: 1.3073, Generator Loss: 0.8598 D(x): 0.6581, D(G(z)): 0.5588 Epoch: [0/20], Batch Num: [574/600] Discriminator Loss: 1.3463, Generator Loss: 0.9907 D(x): 0.6154, D(G(z)): 0.5246 Epoch: [0/20], Batch Num: [575/600] Discriminator Loss: 1.2217, Generator Loss: 0.9946 D(x): 0.6380, D(G(z)): 0.4988 Epoch: [0/20], Batch Num: [576/600] Discriminator Loss: 1.2903, Generator Loss: 0.8921 D(x): 0.5952, D(G(z)): 0.4910 Epoch: [0/20], Batch Num: [577/600] Discriminator Loss: 1.3676, Generator Loss: 0.8966 D(x): 0.5915, D(G(z)): 0.5095 Epoch: [0/20], Batch Num: [578/600] Discriminator Loss: 1.3087, Generator Loss: 0.8960 D(x): 0.5852, D(G(z)): 0.4899 Epoch: [0/20], Batch Num: [579/600] Discriminator Loss: 1.3688, Generator Loss: 0.8410 D(x): 0.5873, D(G(z)): 0.5100 Epoch: [0/20], Batch Num: [580/600] Discriminator Loss: 1.2834, Generator Loss: 0.8794 D(x): 0.6406, D(G(z)): 0.5395 Epoch: [0/20], Batch Num: [581/600] Discriminator Loss: 1.2745, Generator Loss: 0.8826 D(x): 0.6330, D(G(z)): 0.5236 Epoch: [0/20], Batch Num: [582/600] Discriminator Loss: 1.3258, Generator Loss: 0.9011 D(x): 0.5764, D(G(z)): 0.5013 Epoch: [0/20], Batch Num: [583/600] Discriminator Loss: 1.2215, Generator Loss: 0.9795 D(x): 0.5960, D(G(z)): 0.4712 Epoch: [0/20], Batch Num: [584/600] Discriminator Loss: 1.2937, Generator Loss: 1.0437 D(x): 0.5462, D(G(z)): 0.4529 Epoch: [0/20], Batch Num: [585/600] Discriminator Loss: 1.2064, Generator Loss: 1.1578 D(x): 0.5761, D(G(z)): 0.4375 Epoch: [0/20], Batch Num: [586/600] Discriminator Loss: 1.2008, Generator Loss: 1.0913 D(x): 0.5587, D(G(z)): 0.4125 Epoch: [0/20], Batch Num: [587/600] Discriminator Loss: 1.1162, Generator Loss: 1.2551 D(x): 0.5957, D(G(z)): 0.4070 Epoch: [0/20], Batch Num: [588/600] Discriminator Loss: 0.9638, Generator Loss: 1.2607 D(x): 0.6516, D(G(z)): 0.3794 Epoch: [0/20], Batch Num: [589/600] Discriminator Loss: 0.9937, Generator Loss: 1.3894 D(x): 0.6172, D(G(z)): 0.3589 Epoch: [0/20], Batch Num: [590/600] Discriminator Loss: 0.8836, Generator Loss: 1.5058 D(x): 0.6537, D(G(z)): 0.3375 Epoch: [0/20], Batch Num: [591/600] Discriminator Loss: 0.7659, Generator Loss: 1.8083 D(x): 0.6752, D(G(z)): 0.2879 Epoch: [0/20], Batch Num: [592/600] Discriminator Loss: 0.7129, Generator Loss: 2.0388 D(x): 0.6614, D(G(z)): 0.2241 Epoch: [0/20], Batch Num: [593/600] Discriminator Loss: 0.6161, Generator Loss: 2.3831 D(x): 0.6847, D(G(z)): 0.1835 Epoch: [0/20], Batch Num: [594/600] Discriminator Loss: 0.6159, Generator Loss: 2.8345 D(x): 0.6827, D(G(z)): 0.1756 Epoch: [0/20], Batch Num: [595/600] Discriminator Loss: 0.5369, Generator Loss: 3.0440 D(x): 0.7340, D(G(z)): 0.1715 Epoch: [0/20], Batch Num: [596/600] Discriminator Loss: 0.4819, Generator Loss: 2.9098 D(x): 0.7618, D(G(z)): 0.1689 Epoch: [0/20], Batch Num: [597/600] Discriminator Loss: 0.4655, Generator Loss: 3.6687 D(x): 0.7608, D(G(z)): 0.1533 Epoch: [0/20], Batch Num: [598/600] Discriminator Loss: 0.4523, Generator Loss: 3.5715 D(x): 0.7576, D(G(z)): 0.1301 Epoch: [0/20], Batch Num: [599/600] Discriminator Loss: 0.3859, Generator Loss: 4.1045 D(x): 0.7923, D(G(z)): 0.1262 Epoch: 1, Batch Num: [0/600]
Epoch: [1/20], Batch Num: [0/600] Discriminator Loss: 0.5202, Generator Loss: 4.0752 D(x): 0.7597, D(G(z)): 0.1811 Epoch: [1/20], Batch Num: [1/600] Discriminator Loss: 0.5835, Generator Loss: 4.1026 D(x): 0.7446, D(G(z)): 0.1850 Epoch: [1/20], Batch Num: [2/600] Discriminator Loss: 0.6986, Generator Loss: 3.1937 D(x): 0.6775, D(G(z)): 0.1940 Epoch: [1/20], Batch Num: [3/600] Discriminator Loss: 0.9460, Generator Loss: 2.7345 D(x): 0.6192, D(G(z)): 0.2588 Epoch: [1/20], Batch Num: [4/600] Discriminator Loss: 1.2503, Generator Loss: 1.9828 D(x): 0.6789, D(G(z)): 0.4707 Epoch: [1/20], Batch Num: [5/600] Discriminator Loss: 1.7229, Generator Loss: 1.3584 D(x): 0.6017, D(G(z)): 0.5766 Epoch: [1/20], Batch Num: [6/600] Discriminator Loss: 1.9445, Generator Loss: 0.7158 D(x): 0.5811, D(G(z)): 0.6461 Epoch: [1/20], Batch Num: [7/600] Discriminator Loss: 1.8127, Generator Loss: 0.7706 D(x): 0.6881, D(G(z)): 0.7034 Epoch: [1/20], Batch Num: [8/600] Discriminator Loss: 1.7337, Generator Loss: 0.6013 D(x): 0.6748, D(G(z)): 0.6827 Epoch: [1/20], Batch Num: [9/600] Discriminator Loss: 1.7597, Generator Loss: 0.8823 D(x): 0.6844, D(G(z)): 0.6763 Epoch: [1/20], Batch Num: [10/600] Discriminator Loss: 1.4669, Generator Loss: 1.2611 D(x): 0.7432, D(G(z)): 0.6355 Epoch: [1/20], Batch Num: [11/600] Discriminator Loss: 1.3161, Generator Loss: 1.7202 D(x): 0.7341, D(G(z)): 0.5414 Epoch: [1/20], Batch Num: [12/600] Discriminator Loss: 1.3231, Generator Loss: 2.0086 D(x): 0.6429, D(G(z)): 0.4615 Epoch: [1/20], Batch Num: [13/600] Discriminator Loss: 1.4075, Generator Loss: 2.0898 D(x): 0.6203, D(G(z)): 0.4373 Epoch: [1/20], Batch Num: [14/600] Discriminator Loss: 1.2873, Generator Loss: 1.7170 D(x): 0.6721, D(G(z)): 0.4369 Epoch: [1/20], Batch Num: [15/600] Discriminator Loss: 1.2319, Generator Loss: 1.7165 D(x): 0.6899, D(G(z)): 0.4640 Epoch: [1/20], Batch Num: [16/600] Discriminator Loss: 1.0518, Generator Loss: 2.3877 D(x): 0.7247, D(G(z)): 0.4387 Epoch: [1/20], Batch Num: [17/600] Discriminator Loss: 1.0331, Generator Loss: 2.4319 D(x): 0.7140, D(G(z)): 0.4036 Epoch: [1/20], Batch Num: [18/600] Discriminator Loss: 0.9002, Generator Loss: 2.9957 D(x): 0.7166, D(G(z)): 0.3490 Epoch: [1/20], Batch Num: [19/600] Discriminator Loss: 0.9703, Generator Loss: 2.8555 D(x): 0.6764, D(G(z)): 0.3222 Epoch: [1/20], Batch Num: [20/600] Discriminator Loss: 0.9891, Generator Loss: 3.1089 D(x): 0.6197, D(G(z)): 0.2850 Epoch: [1/20], Batch Num: [21/600] Discriminator Loss: 0.9314, Generator Loss: 2.9224 D(x): 0.6416, D(G(z)): 0.2627 Epoch: [1/20], Batch Num: [22/600] Discriminator Loss: 1.1196, Generator Loss: 2.6932 D(x): 0.6314, D(G(z)): 0.3501 Epoch: [1/20], Batch Num: [23/600] Discriminator Loss: 1.2226, Generator Loss: 1.6825 D(x): 0.6279, D(G(z)): 0.3938 Epoch: [1/20], Batch Num: [24/600] Discriminator Loss: 1.1206, Generator Loss: 1.6601 D(x): 0.6634, D(G(z)): 0.4191 Epoch: [1/20], Batch Num: [25/600] Discriminator Loss: 1.2520, Generator Loss: 1.5955 D(x): 0.6796, D(G(z)): 0.5049 Epoch: [1/20], Batch Num: [26/600] Discriminator Loss: 1.3500, Generator Loss: 1.5838 D(x): 0.6370, D(G(z)): 0.4963 Epoch: [1/20], Batch Num: [27/600] Discriminator Loss: 1.2013, Generator Loss: 1.9252 D(x): 0.6596, D(G(z)): 0.4681 Epoch: [1/20], Batch Num: [28/600] Discriminator Loss: 1.2736, Generator Loss: 2.6324 D(x): 0.6054, D(G(z)): 0.4439 Epoch: [1/20], Batch Num: [29/600] Discriminator Loss: 1.1796, Generator Loss: 2.8806 D(x): 0.5967, D(G(z)): 0.3633 Epoch: [1/20], Batch Num: [30/600] Discriminator Loss: 0.9137, Generator Loss: 3.3054 D(x): 0.6389, D(G(z)): 0.2888 Epoch: [1/20], Batch Num: [31/600] Discriminator Loss: 0.8566, Generator Loss: 3.6358 D(x): 0.6220, D(G(z)): 0.2421 Epoch: [1/20], Batch Num: [32/600] Discriminator Loss: 0.7948, Generator Loss: 5.1350 D(x): 0.6418, D(G(z)): 0.2046 Epoch: [1/20], Batch Num: [33/600] Discriminator Loss: 0.7489, Generator Loss: 4.9568 D(x): 0.6584, D(G(z)): 0.2106 Epoch: [1/20], Batch Num: [34/600] Discriminator Loss: 0.6576, Generator Loss: 5.0740 D(x): 0.6745, D(G(z)): 0.1687 Epoch: [1/20], Batch Num: [35/600] Discriminator Loss: 0.6565, Generator Loss: 5.0232 D(x): 0.6920, D(G(z)): 0.2013 Epoch: [1/20], Batch Num: [36/600] Discriminator Loss: 0.6218, Generator Loss: 5.2012 D(x): 0.7254, D(G(z)): 0.2099 Epoch: [1/20], Batch Num: [37/600] Discriminator Loss: 0.5728, Generator Loss: 5.7543 D(x): 0.7267, D(G(z)): 0.1826 Epoch: [1/20], Batch Num: [38/600] Discriminator Loss: 0.6491, Generator Loss: 5.4521 D(x): 0.6872, D(G(z)): 0.1760 Epoch: [1/20], Batch Num: [39/600] Discriminator Loss: 0.7195, Generator Loss: 4.7701 D(x): 0.6819, D(G(z)): 0.1971 Epoch: [1/20], Batch Num: [40/600] Discriminator Loss: 0.9755, Generator Loss: 3.4073 D(x): 0.6637, D(G(z)): 0.2708 Epoch: [1/20], Batch Num: [41/600] Discriminator Loss: 1.2665, Generator Loss: 2.0785 D(x): 0.6326, D(G(z)): 0.3429 Epoch: [1/20], Batch Num: [42/600] Discriminator Loss: 1.4815, Generator Loss: 1.3432 D(x): 0.6633, D(G(z)): 0.5174 Epoch: [1/20], Batch Num: [43/600] Discriminator Loss: 1.6972, Generator Loss: 0.7350 D(x): 0.6853, D(G(z)): 0.6258 Epoch: [1/20], Batch Num: [44/600] Discriminator Loss: 1.8736, Generator Loss: 0.3980 D(x): 0.6664, D(G(z)): 0.6930 Epoch: [1/20], Batch Num: [45/600] Discriminator Loss: 1.7926, Generator Loss: 0.3035 D(x): 0.7193, D(G(z)): 0.7272 Epoch: [1/20], Batch Num: [46/600] Discriminator Loss: 1.8389, Generator Loss: 0.2684 D(x): 0.7919, D(G(z)): 0.7705 Epoch: [1/20], Batch Num: [47/600] Discriminator Loss: 1.8936, Generator Loss: 0.2442 D(x): 0.8211, D(G(z)): 0.7930 Epoch: [1/20], Batch Num: [48/600] Discriminator Loss: 1.8088, Generator Loss: 0.2539 D(x): 0.8613, D(G(z)): 0.8016 Epoch: [1/20], Batch Num: [49/600] Discriminator Loss: 1.7578, Generator Loss: 0.2450 D(x): 0.8582, D(G(z)): 0.7914 Epoch: [1/20], Batch Num: [50/600] Discriminator Loss: 1.7549, Generator Loss: 0.2689 D(x): 0.8555, D(G(z)): 0.7892 Epoch: [1/20], Batch Num: [51/600] Discriminator Loss: 1.7663, Generator Loss: 0.2998 D(x): 0.8150, D(G(z)): 0.7693 Epoch: [1/20], Batch Num: [52/600] Discriminator Loss: 1.6604, Generator Loss: 0.3107 D(x): 0.8221, D(G(z)): 0.7626 Epoch: [1/20], Batch Num: [53/600] Discriminator Loss: 1.5759, Generator Loss: 0.3461 D(x): 0.8041, D(G(z)): 0.7357 Epoch: [1/20], Batch Num: [54/600] Discriminator Loss: 1.5736, Generator Loss: 0.3840 D(x): 0.7670, D(G(z)): 0.7217 Epoch: [1/20], Batch Num: [55/600] Discriminator Loss: 1.5024, Generator Loss: 0.4235 D(x): 0.7361, D(G(z)): 0.6909 Epoch: [1/20], Batch Num: [56/600] Discriminator Loss: 1.5094, Generator Loss: 0.4458 D(x): 0.7081, D(G(z)): 0.6672 Epoch: [1/20], Batch Num: [57/600] Discriminator Loss: 1.4675, Generator Loss: 0.4759 D(x): 0.6728, D(G(z)): 0.6501 Epoch: [1/20], Batch Num: [58/600] Discriminator Loss: 1.4830, Generator Loss: 0.4909 D(x): 0.6405, D(G(z)): 0.6325 Epoch: [1/20], Batch Num: [59/600] Discriminator Loss: 1.4441, Generator Loss: 0.5135 D(x): 0.6289, D(G(z)): 0.6194 Epoch: [1/20], Batch Num: [60/600] Discriminator Loss: 1.4176, Generator Loss: 0.5328 D(x): 0.6229, D(G(z)): 0.6022 Epoch: [1/20], Batch Num: [61/600] Discriminator Loss: 1.3845, Generator Loss: 0.5401 D(x): 0.6277, D(G(z)): 0.5968 Epoch: [1/20], Batch Num: [62/600] Discriminator Loss: 1.3892, Generator Loss: 0.5613 D(x): 0.6101, D(G(z)): 0.5869 Epoch: [1/20], Batch Num: [63/600] Discriminator Loss: 1.4288, Generator Loss: 0.5664 D(x): 0.5938, D(G(z)): 0.5765 Epoch: [1/20], Batch Num: [64/600] Discriminator Loss: 1.3700, Generator Loss: 0.5876 D(x): 0.5901, D(G(z)): 0.5660 Epoch: [1/20], Batch Num: [65/600] Discriminator Loss: 1.3624, Generator Loss: 0.6034 D(x): 0.5870, D(G(z)): 0.5587 Epoch: [1/20], Batch Num: [66/600] Discriminator Loss: 1.3594, Generator Loss: 0.5974 D(x): 0.5784, D(G(z)): 0.5532 Epoch: [1/20], Batch Num: [67/600] Discriminator Loss: 1.3697, Generator Loss: 0.6154 D(x): 0.5760, D(G(z)): 0.5565 Epoch: [1/20], Batch Num: [68/600] Discriminator Loss: 1.3400, Generator Loss: 0.6231 D(x): 0.5777, D(G(z)): 0.5447 Epoch: [1/20], Batch Num: [69/600] Discriminator Loss: 1.3529, Generator Loss: 0.6278 D(x): 0.5682, D(G(z)): 0.5419 Epoch: [1/20], Batch Num: [70/600] Discriminator Loss: 1.3350, Generator Loss: 0.6336 D(x): 0.5644, D(G(z)): 0.5325 Epoch: [1/20], Batch Num: [71/600] Discriminator Loss: 1.3427, Generator Loss: 0.6345 D(x): 0.5602, D(G(z)): 0.5323 Epoch: [1/20], Batch Num: [72/600] Discriminator Loss: 1.3511, Generator Loss: 0.6483 D(x): 0.5534, D(G(z)): 0.5308 Epoch: [1/20], Batch Num: [73/600] Discriminator Loss: 1.3353, Generator Loss: 0.6467 D(x): 0.5511, D(G(z)): 0.5211 Epoch: [1/20], Batch Num: [74/600] Discriminator Loss: 1.3486, Generator Loss: 0.6592 D(x): 0.5482, D(G(z)): 0.5250 Epoch: [1/20], Batch Num: [75/600] Discriminator Loss: 1.3330, Generator Loss: 0.6556 D(x): 0.5502, D(G(z)): 0.5194 Epoch: [1/20], Batch Num: [76/600] Discriminator Loss: 1.3314, Generator Loss: 0.6572 D(x): 0.5492, D(G(z)): 0.5176 Epoch: [1/20], Batch Num: [77/600] Discriminator Loss: 1.3295, Generator Loss: 0.6648 D(x): 0.5487, D(G(z)): 0.5165 Epoch: [1/20], Batch Num: [78/600] Discriminator Loss: 1.3317, Generator Loss: 0.6587 D(x): 0.5440, D(G(z)): 0.5135 Epoch: [1/20], Batch Num: [79/600] Discriminator Loss: 1.3227, Generator Loss: 0.6648 D(x): 0.5488, D(G(z)): 0.5133 Epoch: [1/20], Batch Num: [80/600] Discriminator Loss: 1.3210, Generator Loss: 0.6692 D(x): 0.5484, D(G(z)): 0.5122 Epoch: [1/20], Batch Num: [81/600] Discriminator Loss: 1.3409, Generator Loss: 0.6735 D(x): 0.5415, D(G(z)): 0.5157 Epoch: [1/20], Batch Num: [82/600] Discriminator Loss: 1.3305, Generator Loss: 0.6761 D(x): 0.5418, D(G(z)): 0.5106 Epoch: [1/20], Batch Num: [83/600] Discriminator Loss: 1.3376, Generator Loss: 0.6739 D(x): 0.5420, D(G(z)): 0.5144 Epoch: [1/20], Batch Num: [84/600] Discriminator Loss: 1.3212, Generator Loss: 0.6795 D(x): 0.5440, D(G(z)): 0.5082 Epoch: [1/20], Batch Num: [85/600] Discriminator Loss: 1.3222, Generator Loss: 0.6800 D(x): 0.5441, D(G(z)): 0.5090 Epoch: [1/20], Batch Num: [86/600] Discriminator Loss: 1.3183, Generator Loss: 0.6817 D(x): 0.5444, D(G(z)): 0.5072 Epoch: [1/20], Batch Num: [87/600] Discriminator Loss: 1.3160, Generator Loss: 0.6792 D(x): 0.5491, D(G(z)): 0.5105 Epoch: [1/20], Batch Num: [88/600] Discriminator Loss: 1.3116, Generator Loss: 0.6754 D(x): 0.5502, D(G(z)): 0.5091 Epoch: [1/20], Batch Num: [89/600] Discriminator Loss: 1.3108, Generator Loss: 0.6804 D(x): 0.5491, D(G(z)): 0.5075 Epoch: [1/20], Batch Num: [90/600] Discriminator Loss: 1.3012, Generator Loss: 0.6803 D(x): 0.5532, D(G(z)): 0.5067 Epoch: [1/20], Batch Num: [91/600] Discriminator Loss: 1.2992, Generator Loss: 0.6821 D(x): 0.5527, D(G(z)): 0.5051 Epoch: [1/20], Batch Num: [92/600] Discriminator Loss: 1.3106, Generator Loss: 0.6830 D(x): 0.5492, D(G(z)): 0.5073 Epoch: [1/20], Batch Num: [93/600] Discriminator Loss: 1.2951, Generator Loss: 0.6993 D(x): 0.5512, D(G(z)): 0.5014 Epoch: [1/20], Batch Num: [94/600] Discriminator Loss: 1.2865, Generator Loss: 0.6918 D(x): 0.5553, D(G(z)): 0.5008 Epoch: [1/20], Batch Num: [95/600] Discriminator Loss: 1.2884, Generator Loss: 0.7035 D(x): 0.5586, D(G(z)): 0.5042 Epoch: [1/20], Batch Num: [96/600] Discriminator Loss: 1.2736, Generator Loss: 0.7191 D(x): 0.5592, D(G(z)): 0.4974 Epoch: [1/20], Batch Num: [97/600] Discriminator Loss: 1.2548, Generator Loss: 0.7233 D(x): 0.5605, D(G(z)): 0.4892 Epoch: [1/20], Batch Num: [98/600] Discriminator Loss: 1.2556, Generator Loss: 0.7573 D(x): 0.5535, D(G(z)): 0.4828 Epoch: [1/20], Batch Num: [99/600] Discriminator Loss: 1.2242, Generator Loss: 0.7525 D(x): 0.5645, D(G(z)): 0.4755 Epoch: 1, Batch Num: [100/600]
Epoch: [1/20], Batch Num: [100/600] Discriminator Loss: 1.2127, Generator Loss: 0.7949 D(x): 0.5596, D(G(z)): 0.4638 Epoch: [1/20], Batch Num: [101/600] Discriminator Loss: 1.2433, Generator Loss: 0.7931 D(x): 0.5500, D(G(z)): 0.4712 Epoch: [1/20], Batch Num: [102/600] Discriminator Loss: 1.2014, Generator Loss: 0.8524 D(x): 0.5515, D(G(z)): 0.4491 Epoch: [1/20], Batch Num: [103/600] Discriminator Loss: 1.1858, Generator Loss: 0.9535 D(x): 0.5590, D(G(z)): 0.4459 Epoch: [1/20], Batch Num: [104/600] Discriminator Loss: 1.1612, Generator Loss: 1.0995 D(x): 0.5397, D(G(z)): 0.4081 Epoch: [1/20], Batch Num: [105/600] Discriminator Loss: 1.1587, Generator Loss: 1.2319 D(x): 0.5274, D(G(z)): 0.3851 Epoch: [1/20], Batch Num: [106/600] Discriminator Loss: 1.1223, Generator Loss: 1.3173 D(x): 0.5332, D(G(z)): 0.3725 Epoch: [1/20], Batch Num: [107/600] Discriminator Loss: 1.0942, Generator Loss: 1.5326 D(x): 0.5243, D(G(z)): 0.3395 Epoch: [1/20], Batch Num: [108/600] Discriminator Loss: 1.0918, Generator Loss: 1.6980 D(x): 0.4965, D(G(z)): 0.2891 Epoch: [1/20], Batch Num: [109/600] Discriminator Loss: 1.1004, Generator Loss: 1.3550 D(x): 0.5096, D(G(z)): 0.3198 Epoch: [1/20], Batch Num: [110/600] Discriminator Loss: 1.1559, Generator Loss: 1.2692 D(x): 0.4979, D(G(z)): 0.3340 Epoch: [1/20], Batch Num: [111/600] Discriminator Loss: 1.0940, Generator Loss: 1.1842 D(x): 0.5279, D(G(z)): 0.3345 Epoch: [1/20], Batch Num: [112/600] Discriminator Loss: 1.0604, Generator Loss: 1.0497 D(x): 0.5566, D(G(z)): 0.3563 Epoch: [1/20], Batch Num: [113/600] Discriminator Loss: 1.1005, Generator Loss: 0.9857 D(x): 0.5648, D(G(z)): 0.3807 Epoch: [1/20], Batch Num: [114/600] Discriminator Loss: 1.0810, Generator Loss: 0.9391 D(x): 0.5902, D(G(z)): 0.4056 Epoch: [1/20], Batch Num: [115/600] Discriminator Loss: 1.0503, Generator Loss: 0.9387 D(x): 0.6195, D(G(z)): 0.4217 Epoch: [1/20], Batch Num: [116/600] Discriminator Loss: 1.0605, Generator Loss: 0.9683 D(x): 0.6214, D(G(z)): 0.4293 Epoch: [1/20], Batch Num: [117/600] Discriminator Loss: 1.0472, Generator Loss: 1.1055 D(x): 0.6317, D(G(z)): 0.4304 Epoch: [1/20], Batch Num: [118/600] Discriminator Loss: 0.9682, Generator Loss: 1.0705 D(x): 0.6209, D(G(z)): 0.3751 Epoch: [1/20], Batch Num: [119/600] Discriminator Loss: 1.0113, Generator Loss: 1.2978 D(x): 0.5937, D(G(z)): 0.3602 Epoch: [1/20], Batch Num: [120/600] Discriminator Loss: 0.9846, Generator Loss: 1.2431 D(x): 0.6039, D(G(z)): 0.3660 Epoch: [1/20], Batch Num: [121/600] Discriminator Loss: 0.9513, Generator Loss: 1.2718 D(x): 0.6083, D(G(z)): 0.3437 Epoch: [1/20], Batch Num: [122/600] Discriminator Loss: 0.9604, Generator Loss: 1.1973 D(x): 0.6141, D(G(z)): 0.3483 Epoch: [1/20], Batch Num: [123/600] Discriminator Loss: 0.9191, Generator Loss: 1.1974 D(x): 0.6278, D(G(z)): 0.3446 Epoch: [1/20], Batch Num: [124/600] Discriminator Loss: 0.9607, Generator Loss: 1.0769 D(x): 0.6469, D(G(z)): 0.3862 Epoch: [1/20], Batch Num: [125/600] Discriminator Loss: 0.9933, Generator Loss: 1.0249 D(x): 0.6554, D(G(z)): 0.4191 Epoch: [1/20], Batch Num: [126/600] Discriminator Loss: 1.0287, Generator Loss: 0.9983 D(x): 0.6652, D(G(z)): 0.4464 Epoch: [1/20], Batch Num: [127/600] Discriminator Loss: 1.1149, Generator Loss: 0.9319 D(x): 0.6492, D(G(z)): 0.4727 Epoch: [1/20], Batch Num: [128/600] Discriminator Loss: 1.1933, Generator Loss: 0.9156 D(x): 0.6469, D(G(z)): 0.5107 Epoch: [1/20], Batch Num: [129/600] Discriminator Loss: 1.3053, Generator Loss: 0.7473 D(x): 0.6058, D(G(z)): 0.5225 Epoch: [1/20], Batch Num: [130/600] Discriminator Loss: 1.3507, Generator Loss: 0.6419 D(x): 0.6358, D(G(z)): 0.5727 Epoch: [1/20], Batch Num: [131/600] Discriminator Loss: 1.4902, Generator Loss: 0.5417 D(x): 0.6058, D(G(z)): 0.6017 Epoch: [1/20], Batch Num: [132/600] Discriminator Loss: 1.5908, Generator Loss: 0.4204 D(x): 0.6240, D(G(z)): 0.6533 Epoch: [1/20], Batch Num: [133/600] Discriminator Loss: 1.5894, Generator Loss: 0.3748 D(x): 0.6713, D(G(z)): 0.6848 Epoch: [1/20], Batch Num: [134/600] Discriminator Loss: 1.6468, Generator Loss: 0.3430 D(x): 0.6864, D(G(z)): 0.7101 Epoch: [1/20], Batch Num: [135/600] Discriminator Loss: 1.6953, Generator Loss: 0.3263 D(x): 0.7154, D(G(z)): 0.7321 Epoch: [1/20], Batch Num: [136/600] Discriminator Loss: 1.6102, Generator Loss: 0.3242 D(x): 0.7690, D(G(z)): 0.7357 Epoch: [1/20], Batch Num: [137/600] Discriminator Loss: 1.6625, Generator Loss: 0.3205 D(x): 0.7629, D(G(z)): 0.7469 Epoch: [1/20], Batch Num: [138/600] Discriminator Loss: 1.5921, Generator Loss: 0.3375 D(x): 0.7770, D(G(z)): 0.7329 Epoch: [1/20], Batch Num: [139/600] Discriminator Loss: 1.5574, Generator Loss: 0.3281 D(x): 0.7880, D(G(z)): 0.7294 Epoch: [1/20], Batch Num: [140/600] Discriminator Loss: 1.5168, Generator Loss: 0.3658 D(x): 0.7803, D(G(z)): 0.7156 Epoch: [1/20], Batch Num: [141/600] Discriminator Loss: 1.5134, Generator Loss: 0.3897 D(x): 0.7638, D(G(z)): 0.7082 Epoch: [1/20], Batch Num: [142/600] Discriminator Loss: 1.4316, Generator Loss: 0.4188 D(x): 0.7575, D(G(z)): 0.6819 Epoch: [1/20], Batch Num: [143/600] Discriminator Loss: 1.4038, Generator Loss: 0.4469 D(x): 0.7390, D(G(z)): 0.6643 Epoch: [1/20], Batch Num: [144/600] Discriminator Loss: 1.3339, Generator Loss: 0.4979 D(x): 0.7285, D(G(z)): 0.6358 Epoch: [1/20], Batch Num: [145/600] Discriminator Loss: 1.2989, Generator Loss: 0.5376 D(x): 0.7137, D(G(z)): 0.6153 Epoch: [1/20], Batch Num: [146/600] Discriminator Loss: 1.2895, Generator Loss: 0.6357 D(x): 0.6964, D(G(z)): 0.6008 Epoch: [1/20], Batch Num: [147/600] Discriminator Loss: 1.2083, Generator Loss: 0.7566 D(x): 0.6842, D(G(z)): 0.5580 Epoch: [1/20], Batch Num: [148/600] Discriminator Loss: 1.1345, Generator Loss: 0.8850 D(x): 0.6711, D(G(z)): 0.5149 Epoch: [1/20], Batch Num: [149/600] Discriminator Loss: 1.1136, Generator Loss: 1.2621 D(x): 0.6500, D(G(z)): 0.4862 Epoch: [1/20], Batch Num: [150/600] Discriminator Loss: 1.0501, Generator Loss: 1.6938 D(x): 0.6329, D(G(z)): 0.4363 Epoch: [1/20], Batch Num: [151/600] Discriminator Loss: 0.9636, Generator Loss: 2.0644 D(x): 0.6139, D(G(z)): 0.3624 Epoch: [1/20], Batch Num: [152/600] Discriminator Loss: 0.9651, Generator Loss: 2.4625 D(x): 0.5749, D(G(z)): 0.3184 Epoch: [1/20], Batch Num: [153/600] Discriminator Loss: 1.0070, Generator Loss: 3.0981 D(x): 0.4949, D(G(z)): 0.2276 Epoch: [1/20], Batch Num: [154/600] Discriminator Loss: 1.1956, Generator Loss: 2.7561 D(x): 0.4174, D(G(z)): 0.2172 Epoch: [1/20], Batch Num: [155/600] Discriminator Loss: 1.4286, Generator Loss: 1.5570 D(x): 0.3860, D(G(z)): 0.2606 Epoch: [1/20], Batch Num: [156/600] Discriminator Loss: 1.4919, Generator Loss: 0.9784 D(x): 0.3950, D(G(z)): 0.3596 Epoch: [1/20], Batch Num: [157/600] Discriminator Loss: 1.5951, Generator Loss: 0.6597 D(x): 0.4271, D(G(z)): 0.4803 Epoch: [1/20], Batch Num: [158/600] Discriminator Loss: 1.5612, Generator Loss: 0.5554 D(x): 0.4853, D(G(z)): 0.5489 Epoch: [1/20], Batch Num: [159/600] Discriminator Loss: 1.5450, Generator Loss: 0.4967 D(x): 0.5423, D(G(z)): 0.5970 Epoch: [1/20], Batch Num: [160/600] Discriminator Loss: 1.4986, Generator Loss: 0.4961 D(x): 0.5918, D(G(z)): 0.6185 Epoch: [1/20], Batch Num: [161/600] Discriminator Loss: 1.4715, Generator Loss: 0.4598 D(x): 0.6202, D(G(z)): 0.6266 Epoch: [1/20], Batch Num: [162/600] Discriminator Loss: 1.4793, Generator Loss: 0.4776 D(x): 0.6338, D(G(z)): 0.6372 Epoch: [1/20], Batch Num: [163/600] Discriminator Loss: 1.4486, Generator Loss: 0.4994 D(x): 0.6432, D(G(z)): 0.6300 Epoch: [1/20], Batch Num: [164/600] Discriminator Loss: 1.4156, Generator Loss: 0.5764 D(x): 0.6366, D(G(z)): 0.6123 Epoch: [1/20], Batch Num: [165/600] Discriminator Loss: 1.3755, Generator Loss: 0.6332 D(x): 0.6383, D(G(z)): 0.5993 Epoch: [1/20], Batch Num: [166/600] Discriminator Loss: 1.2853, Generator Loss: 0.8119 D(x): 0.6309, D(G(z)): 0.5541 Epoch: [1/20], Batch Num: [167/600] Discriminator Loss: 1.1430, Generator Loss: 1.0548 D(x): 0.6125, D(G(z)): 0.4661 Epoch: [1/20], Batch Num: [168/600] Discriminator Loss: 1.0235, Generator Loss: 1.4837 D(x): 0.5902, D(G(z)): 0.3768 Epoch: [1/20], Batch Num: [169/600] Discriminator Loss: 0.9339, Generator Loss: 2.0363 D(x): 0.5479, D(G(z)): 0.2626 Epoch: [1/20], Batch Num: [170/600] Discriminator Loss: 0.9436, Generator Loss: 2.4489 D(x): 0.4933, D(G(z)): 0.1767 Epoch: [1/20], Batch Num: [171/600] Discriminator Loss: 0.9290, Generator Loss: 2.5117 D(x): 0.4821, D(G(z)): 0.1434 Epoch: [1/20], Batch Num: [172/600] Discriminator Loss: 1.0127, Generator Loss: 2.5089 D(x): 0.4370, D(G(z)): 0.1233 Epoch: [1/20], Batch Num: [173/600] Discriminator Loss: 1.0585, Generator Loss: 2.5250 D(x): 0.4206, D(G(z)): 0.1183 Epoch: [1/20], Batch Num: [174/600] Discriminator Loss: 0.9467, Generator Loss: 2.0686 D(x): 0.4705, D(G(z)): 0.1324 Epoch: [1/20], Batch Num: [175/600] Discriminator Loss: 0.9256, Generator Loss: 1.9222 D(x): 0.5186, D(G(z)): 0.2022 Epoch: [1/20], Batch Num: [176/600] Discriminator Loss: 0.8703, Generator Loss: 1.4527 D(x): 0.5684, D(G(z)): 0.2412 Epoch: [1/20], Batch Num: [177/600] Discriminator Loss: 0.9224, Generator Loss: 1.2455 D(x): 0.6021, D(G(z)): 0.3164 Epoch: [1/20], Batch Num: [178/600] Discriminator Loss: 0.9973, Generator Loss: 1.2469 D(x): 0.6047, D(G(z)): 0.3672 Epoch: [1/20], Batch Num: [179/600] Discriminator Loss: 1.0308, Generator Loss: 1.2991 D(x): 0.6232, D(G(z)): 0.4054 Epoch: [1/20], Batch Num: [180/600] Discriminator Loss: 1.0033, Generator Loss: 1.3497 D(x): 0.6185, D(G(z)): 0.3795 Epoch: [1/20], Batch Num: [181/600] Discriminator Loss: 1.0039, Generator Loss: 1.7160 D(x): 0.5975, D(G(z)): 0.3602 Epoch: [1/20], Batch Num: [182/600] Discriminator Loss: 0.9508, Generator Loss: 1.9729 D(x): 0.5744, D(G(z)): 0.2937 Epoch: [1/20], Batch Num: [183/600] Discriminator Loss: 1.0776, Generator Loss: 2.1702 D(x): 0.5161, D(G(z)): 0.2854 Epoch: [1/20], Batch Num: [184/600] Discriminator Loss: 1.1253, Generator Loss: 2.0718 D(x): 0.4746, D(G(z)): 0.2549 Epoch: [1/20], Batch Num: [185/600] Discriminator Loss: 1.2499, Generator Loss: 1.6579 D(x): 0.4332, D(G(z)): 0.2472 Epoch: [1/20], Batch Num: [186/600] Discriminator Loss: 1.3662, Generator Loss: 1.3006 D(x): 0.4389, D(G(z)): 0.3301 Epoch: [1/20], Batch Num: [187/600] Discriminator Loss: 1.3480, Generator Loss: 0.9564 D(x): 0.5004, D(G(z)): 0.4245 Epoch: [1/20], Batch Num: [188/600] Discriminator Loss: 1.4118, Generator Loss: 0.7060 D(x): 0.5398, D(G(z)): 0.4938 Epoch: [1/20], Batch Num: [189/600] Discriminator Loss: 1.4669, Generator Loss: 0.5853 D(x): 0.5878, D(G(z)): 0.5858 Epoch: [1/20], Batch Num: [190/600] Discriminator Loss: 1.4867, Generator Loss: 0.5272 D(x): 0.6235, D(G(z)): 0.6236 Epoch: [1/20], Batch Num: [191/600] Discriminator Loss: 1.5006, Generator Loss: 0.4910 D(x): 0.6274, D(G(z)): 0.6317 Epoch: [1/20], Batch Num: [192/600] Discriminator Loss: 1.5030, Generator Loss: 0.4936 D(x): 0.6319, D(G(z)): 0.6396 Epoch: [1/20], Batch Num: [193/600] Discriminator Loss: 1.5034, Generator Loss: 0.5069 D(x): 0.6363, D(G(z)): 0.6396 Epoch: [1/20], Batch Num: [194/600] Discriminator Loss: 1.4401, Generator Loss: 0.5166 D(x): 0.6435, D(G(z)): 0.6221 Epoch: [1/20], Batch Num: [195/600] Discriminator Loss: 1.4373, Generator Loss: 0.5587 D(x): 0.6338, D(G(z)): 0.6125 Epoch: [1/20], Batch Num: [196/600] Discriminator Loss: 1.3813, Generator Loss: 0.6202 D(x): 0.6384, D(G(z)): 0.5957 Epoch: [1/20], Batch Num: [197/600] Discriminator Loss: 1.3150, Generator Loss: 0.6650 D(x): 0.6456, D(G(z)): 0.5778 Epoch: [1/20], Batch Num: [198/600] Discriminator Loss: 1.3272, Generator Loss: 0.7199 D(x): 0.6101, D(G(z)): 0.5494 Epoch: [1/20], Batch Num: [199/600] Discriminator Loss: 1.3272, Generator Loss: 0.8165 D(x): 0.5812, D(G(z)): 0.5202 Epoch: 1, Batch Num: [200/600]
Epoch: [1/20], Batch Num: [200/600] Discriminator Loss: 1.2657, Generator Loss: 0.9199 D(x): 0.5667, D(G(z)): 0.4802 Epoch: [1/20], Batch Num: [201/600] Discriminator Loss: 1.2972, Generator Loss: 0.9938 D(x): 0.5336, D(G(z)): 0.4603 Epoch: [1/20], Batch Num: [202/600] Discriminator Loss: 1.1520, Generator Loss: 1.0635 D(x): 0.5433, D(G(z)): 0.3924 Epoch: [1/20], Batch Num: [203/600] Discriminator Loss: 1.1581, Generator Loss: 1.1073 D(x): 0.5279, D(G(z)): 0.3707 Epoch: [1/20], Batch Num: [204/600] Discriminator Loss: 1.1245, Generator Loss: 1.0642 D(x): 0.5521, D(G(z)): 0.3881 Epoch: [1/20], Batch Num: [205/600] Discriminator Loss: 1.0734, Generator Loss: 1.0393 D(x): 0.5720, D(G(z)): 0.3822 Epoch: [1/20], Batch Num: [206/600] Discriminator Loss: 1.0457, Generator Loss: 1.0726 D(x): 0.5747, D(G(z)): 0.3663 Epoch: [1/20], Batch Num: [207/600] Discriminator Loss: 0.9892, Generator Loss: 1.0875 D(x): 0.5966, D(G(z)): 0.3631 Epoch: [1/20], Batch Num: [208/600] Discriminator Loss: 0.9731, Generator Loss: 1.1189 D(x): 0.6077, D(G(z)): 0.3652 Epoch: [1/20], Batch Num: [209/600] Discriminator Loss: 0.9658, Generator Loss: 1.2410 D(x): 0.6129, D(G(z)): 0.3611 Epoch: [1/20], Batch Num: [210/600] Discriminator Loss: 0.9540, Generator Loss: 1.2404 D(x): 0.6027, D(G(z)): 0.3461 Epoch: [1/20], Batch Num: [211/600] Discriminator Loss: 0.9353, Generator Loss: 1.3282 D(x): 0.6108, D(G(z)): 0.3354 Epoch: [1/20], Batch Num: [212/600] Discriminator Loss: 0.8973, Generator Loss: 1.3318 D(x): 0.6162, D(G(z)): 0.3207 Epoch: [1/20], Batch Num: [213/600] Discriminator Loss: 0.8540, Generator Loss: 1.3760 D(x): 0.6311, D(G(z)): 0.3070 Epoch: [1/20], Batch Num: [214/600] Discriminator Loss: 0.8643, Generator Loss: 1.3023 D(x): 0.6469, D(G(z)): 0.3268 Epoch: [1/20], Batch Num: [215/600] Discriminator Loss: 0.8145, Generator Loss: 1.2581 D(x): 0.6743, D(G(z)): 0.3283 Epoch: [1/20], Batch Num: [216/600] Discriminator Loss: 0.9078, Generator Loss: 1.1437 D(x): 0.6447, D(G(z)): 0.3485 Epoch: [1/20], Batch Num: [217/600] Discriminator Loss: 0.9643, Generator Loss: 1.1265 D(x): 0.6638, D(G(z)): 0.3912 Epoch: [1/20], Batch Num: [218/600] Discriminator Loss: 1.0627, Generator Loss: 0.9930 D(x): 0.6530, D(G(z)): 0.4251 Epoch: [1/20], Batch Num: [219/600] Discriminator Loss: 1.0570, Generator Loss: 0.8468 D(x): 0.6734, D(G(z)): 0.4554 Epoch: [1/20], Batch Num: [220/600] Discriminator Loss: 1.0901, Generator Loss: 0.7998 D(x): 0.6922, D(G(z)): 0.4929 Epoch: [1/20], Batch Num: [221/600] Discriminator Loss: 1.2089, Generator Loss: 0.7008 D(x): 0.6784, D(G(z)): 0.5288 Epoch: [1/20], Batch Num: [222/600] Discriminator Loss: 1.1619, Generator Loss: 0.7053 D(x): 0.7029, D(G(z)): 0.5306 Epoch: [1/20], Batch Num: [223/600] Discriminator Loss: 1.1734, Generator Loss: 0.7215 D(x): 0.6892, D(G(z)): 0.5269 Epoch: [1/20], Batch Num: [224/600] Discriminator Loss: 1.2345, Generator Loss: 0.7271 D(x): 0.6769, D(G(z)): 0.5322 Epoch: [1/20], Batch Num: [225/600] Discriminator Loss: 1.2815, Generator Loss: 0.6866 D(x): 0.6736, D(G(z)): 0.5476 Epoch: [1/20], Batch Num: [226/600] Discriminator Loss: 1.2978, Generator Loss: 0.6424 D(x): 0.6733, D(G(z)): 0.5488 Epoch: [1/20], Batch Num: [227/600] Discriminator Loss: 1.2926, Generator Loss: 0.6543 D(x): 0.6853, D(G(z)): 0.5609 Epoch: [1/20], Batch Num: [228/600] Discriminator Loss: 1.2008, Generator Loss: 0.6765 D(x): 0.6841, D(G(z)): 0.5104 Epoch: [1/20], Batch Num: [229/600] Discriminator Loss: 1.2448, Generator Loss: 0.6960 D(x): 0.7098, D(G(z)): 0.5645 Epoch: [1/20], Batch Num: [230/600] Discriminator Loss: 1.1810, Generator Loss: 0.8411 D(x): 0.6953, D(G(z)): 0.5206 Epoch: [1/20], Batch Num: [231/600] Discriminator Loss: 1.1980, Generator Loss: 0.9774 D(x): 0.6444, D(G(z)): 0.4877 Epoch: [1/20], Batch Num: [232/600] Discriminator Loss: 1.1168, Generator Loss: 1.1022 D(x): 0.6376, D(G(z)): 0.4412 Epoch: [1/20], Batch Num: [233/600] Discriminator Loss: 1.2522, Generator Loss: 0.9918 D(x): 0.5694, D(G(z)): 0.4351 Epoch: [1/20], Batch Num: [234/600] Discriminator Loss: 1.0986, Generator Loss: 0.9638 D(x): 0.6612, D(G(z)): 0.4551 Epoch: [1/20], Batch Num: [235/600] Discriminator Loss: 1.0983, Generator Loss: 0.9549 D(x): 0.6785, D(G(z)): 0.4605 Epoch: [1/20], Batch Num: [236/600] Discriminator Loss: 1.0898, Generator Loss: 1.0483 D(x): 0.6833, D(G(z)): 0.4698 Epoch: [1/20], Batch Num: [237/600] Discriminator Loss: 1.0379, Generator Loss: 1.3223 D(x): 0.6797, D(G(z)): 0.4373 Epoch: [1/20], Batch Num: [238/600] Discriminator Loss: 1.0651, Generator Loss: 1.3179 D(x): 0.6201, D(G(z)): 0.3874 Epoch: [1/20], Batch Num: [239/600] Discriminator Loss: 1.0222, Generator Loss: 1.1994 D(x): 0.6158, D(G(z)): 0.3633 Epoch: [1/20], Batch Num: [240/600] Discriminator Loss: 0.9695, Generator Loss: 1.0511 D(x): 0.6395, D(G(z)): 0.3649 Epoch: [1/20], Batch Num: [241/600] Discriminator Loss: 1.0274, Generator Loss: 0.9670 D(x): 0.6948, D(G(z)): 0.4435 Epoch: [1/20], Batch Num: [242/600] Discriminator Loss: 0.9566, Generator Loss: 1.1470 D(x): 0.7039, D(G(z)): 0.4154 Epoch: [1/20], Batch Num: [243/600] Discriminator Loss: 1.0208, Generator Loss: 1.2228 D(x): 0.6530, D(G(z)): 0.3958 Epoch: [1/20], Batch Num: [244/600] Discriminator Loss: 1.0801, Generator Loss: 1.3187 D(x): 0.6361, D(G(z)): 0.4140 Epoch: [1/20], Batch Num: [245/600] Discriminator Loss: 0.9987, Generator Loss: 1.2469 D(x): 0.6619, D(G(z)): 0.3920 Epoch: [1/20], Batch Num: [246/600] Discriminator Loss: 0.9490, Generator Loss: 1.2624 D(x): 0.6889, D(G(z)): 0.3914 Epoch: [1/20], Batch Num: [247/600] Discriminator Loss: 1.0646, Generator Loss: 1.1396 D(x): 0.6256, D(G(z)): 0.3803 Epoch: [1/20], Batch Num: [248/600] Discriminator Loss: 0.9897, Generator Loss: 1.2099 D(x): 0.6992, D(G(z)): 0.4213 Epoch: [1/20], Batch Num: [249/600] Discriminator Loss: 1.0967, Generator Loss: 1.1478 D(x): 0.6585, D(G(z)): 0.4380 Epoch: [1/20], Batch Num: [250/600] Discriminator Loss: 1.0130, Generator Loss: 1.2354 D(x): 0.7068, D(G(z)): 0.4329 Epoch: [1/20], Batch Num: [251/600] Discriminator Loss: 0.9812, Generator Loss: 1.3668 D(x): 0.6919, D(G(z)): 0.4029 Epoch: [1/20], Batch Num: [252/600] Discriminator Loss: 0.8765, Generator Loss: 1.4844 D(x): 0.7372, D(G(z)): 0.3912 Epoch: [1/20], Batch Num: [253/600] Discriminator Loss: 0.8685, Generator Loss: 1.6243 D(x): 0.6826, D(G(z)): 0.3140 Epoch: [1/20], Batch Num: [254/600] Discriminator Loss: 1.0906, Generator Loss: 1.3823 D(x): 0.6238, D(G(z)): 0.3278 Epoch: [1/20], Batch Num: [255/600] Discriminator Loss: 0.8677, Generator Loss: 1.5151 D(x): 0.7589, D(G(z)): 0.4026 Epoch: [1/20], Batch Num: [256/600] Discriminator Loss: 0.7735, Generator Loss: 1.7677 D(x): 0.7788, D(G(z)): 0.3692 Epoch: [1/20], Batch Num: [257/600] Discriminator Loss: 0.7601, Generator Loss: 2.1521 D(x): 0.7238, D(G(z)): 0.2869 Epoch: [1/20], Batch Num: [258/600] Discriminator Loss: 0.8405, Generator Loss: 2.0535 D(x): 0.6754, D(G(z)): 0.2290 Epoch: [1/20], Batch Num: [259/600] Discriminator Loss: 0.6788, Generator Loss: 1.8268 D(x): 0.7181, D(G(z)): 0.2236 Epoch: [1/20], Batch Num: [260/600] Discriminator Loss: 0.6549, Generator Loss: 1.6944 D(x): 0.8097, D(G(z)): 0.3227 Epoch: [1/20], Batch Num: [261/600] Discriminator Loss: 0.6078, Generator Loss: 2.0194 D(x): 0.8331, D(G(z)): 0.3182 Epoch: [1/20], Batch Num: [262/600] Discriminator Loss: 0.4949, Generator Loss: 2.3889 D(x): 0.8361, D(G(z)): 0.2453 Epoch: [1/20], Batch Num: [263/600] Discriminator Loss: 0.5350, Generator Loss: 2.5188 D(x): 0.7766, D(G(z)): 0.1713 Epoch: [1/20], Batch Num: [264/600] Discriminator Loss: 0.3506, Generator Loss: 2.6415 D(x): 0.8372, D(G(z)): 0.1317 Epoch: [1/20], Batch Num: [265/600] Discriminator Loss: 0.2995, Generator Loss: 2.7922 D(x): 0.8696, D(G(z)): 0.1247 Epoch: [1/20], Batch Num: [266/600] Discriminator Loss: 0.3353, Generator Loss: 2.6894 D(x): 0.8782, D(G(z)): 0.1482 Epoch: [1/20], Batch Num: [267/600] Discriminator Loss: 0.2618, Generator Loss: 2.5906 D(x): 0.9168, D(G(z)): 0.1463 Epoch: [1/20], Batch Num: [268/600] Discriminator Loss: 0.2657, Generator Loss: 3.3239 D(x): 0.9418, D(G(z)): 0.1684 Epoch: [1/20], Batch Num: [269/600] Discriminator Loss: 0.2066, Generator Loss: 3.6477 D(x): 0.9245, D(G(z)): 0.1101 Epoch: [1/20], Batch Num: [270/600] Discriminator Loss: 0.2224, Generator Loss: 3.6712 D(x): 0.8899, D(G(z)): 0.0797 Epoch: [1/20], Batch Num: [271/600] Discriminator Loss: 0.2132, Generator Loss: 3.6768 D(x): 0.9187, D(G(z)): 0.1030 Epoch: [1/20], Batch Num: [272/600] Discriminator Loss: 0.2600, Generator Loss: 3.7040 D(x): 0.9274, D(G(z)): 0.1477 Epoch: [1/20], Batch Num: [273/600] Discriminator Loss: 0.3722, Generator Loss: 3.9102 D(x): 0.9100, D(G(z)): 0.2002 Epoch: [1/20], Batch Num: [274/600] Discriminator Loss: 0.4801, Generator Loss: 3.5706 D(x): 0.8538, D(G(z)): 0.2078 Epoch: [1/20], Batch Num: [275/600] Discriminator Loss: 0.6245, Generator Loss: 3.0540 D(x): 0.8050, D(G(z)): 0.2304 Epoch: [1/20], Batch Num: [276/600] Discriminator Loss: 0.8077, Generator Loss: 2.4884 D(x): 0.8531, D(G(z)): 0.3594 Epoch: [1/20], Batch Num: [277/600] Discriminator Loss: 1.0671, Generator Loss: 2.3911 D(x): 0.8296, D(G(z)): 0.4997 Epoch: [1/20], Batch Num: [278/600] Discriminator Loss: 1.1866, Generator Loss: 2.5445 D(x): 0.8245, D(G(z)): 0.5305 Epoch: [1/20], Batch Num: [279/600] Discriminator Loss: 1.2398, Generator Loss: 3.1605 D(x): 0.7914, D(G(z)): 0.5256 Epoch: [1/20], Batch Num: [280/600] Discriminator Loss: 1.3269, Generator Loss: 3.3551 D(x): 0.7612, D(G(z)): 0.5033 Epoch: [1/20], Batch Num: [281/600] Discriminator Loss: 1.0807, Generator Loss: 3.8384 D(x): 0.7532, D(G(z)): 0.3977 Epoch: [1/20], Batch Num: [282/600] Discriminator Loss: 1.2515, Generator Loss: 3.9048 D(x): 0.7168, D(G(z)): 0.4205 Epoch: [1/20], Batch Num: [283/600] Discriminator Loss: 1.3069, Generator Loss: 4.6962 D(x): 0.7815, D(G(z)): 0.4802 Epoch: [1/20], Batch Num: [284/600] Discriminator Loss: 1.1262, Generator Loss: 6.3817 D(x): 0.7899, D(G(z)): 0.3945 Epoch: [1/20], Batch Num: [285/600] Discriminator Loss: 0.9135, Generator Loss: 6.6890 D(x): 0.7486, D(G(z)): 0.2500 Epoch: [1/20], Batch Num: [286/600] Discriminator Loss: 1.1410, Generator Loss: 7.7410 D(x): 0.6843, D(G(z)): 0.2088 Epoch: [1/20], Batch Num: [287/600] Discriminator Loss: 1.0326, Generator Loss: 6.6451 D(x): 0.6863, D(G(z)): 0.2197 Epoch: [1/20], Batch Num: [288/600] Discriminator Loss: 1.3118, Generator Loss: 3.4448 D(x): 0.6443, D(G(z)): 0.2590 Epoch: [1/20], Batch Num: [289/600] Discriminator Loss: 1.9575, Generator Loss: 2.4339 D(x): 0.6350, D(G(z)): 0.4708 Epoch: [1/20], Batch Num: [290/600] Discriminator Loss: 2.0204, Generator Loss: 2.0913 D(x): 0.7945, D(G(z)): 0.6998 Epoch: [1/20], Batch Num: [291/600] Discriminator Loss: 2.4954, Generator Loss: 2.7800 D(x): 0.6733, D(G(z)): 0.7301 Epoch: [1/20], Batch Num: [292/600] Discriminator Loss: 3.0558, Generator Loss: 1.6742 D(x): 0.4259, D(G(z)): 0.5813 Epoch: [1/20], Batch Num: [293/600] Discriminator Loss: 2.4081, Generator Loss: 1.1915 D(x): 0.4918, D(G(z)): 0.5584 Epoch: [1/20], Batch Num: [294/600] Discriminator Loss: 2.1651, Generator Loss: 0.8055 D(x): 0.6146, D(G(z)): 0.6472 Epoch: [1/20], Batch Num: [295/600] Discriminator Loss: 1.7864, Generator Loss: 1.0328 D(x): 0.6996, D(G(z)): 0.6619 Epoch: [1/20], Batch Num: [296/600] Discriminator Loss: 1.3613, Generator Loss: 1.3560 D(x): 0.6860, D(G(z)): 0.5246 Epoch: [1/20], Batch Num: [297/600] Discriminator Loss: 1.1738, Generator Loss: 1.7661 D(x): 0.6027, D(G(z)): 0.3682 Epoch: [1/20], Batch Num: [298/600] Discriminator Loss: 1.1314, Generator Loss: 1.7094 D(x): 0.5612, D(G(z)): 0.2907 Epoch: [1/20], Batch Num: [299/600] Discriminator Loss: 0.9483, Generator Loss: 1.5732 D(x): 0.5983, D(G(z)): 0.2728 Epoch: 1, Batch Num: [300/600]
Epoch: [1/20], Batch Num: [300/600] Discriminator Loss: 0.9845, Generator Loss: 1.4027 D(x): 0.6229, D(G(z)): 0.2852 Epoch: [1/20], Batch Num: [301/600] Discriminator Loss: 0.8060, Generator Loss: 1.2329 D(x): 0.7367, D(G(z)): 0.3468 Epoch: [1/20], Batch Num: [302/600] Discriminator Loss: 0.7723, Generator Loss: 1.1407 D(x): 0.7889, D(G(z)): 0.3664 Epoch: [1/20], Batch Num: [303/600] Discriminator Loss: 0.7332, Generator Loss: 1.1277 D(x): 0.8398, D(G(z)): 0.3992 Epoch: [1/20], Batch Num: [304/600] Discriminator Loss: 0.8023, Generator Loss: 1.2286 D(x): 0.8239, D(G(z)): 0.4173 Epoch: [1/20], Batch Num: [305/600] Discriminator Loss: 0.6520, Generator Loss: 1.3443 D(x): 0.8588, D(G(z)): 0.3619 Epoch: [1/20], Batch Num: [306/600] Discriminator Loss: 0.6940, Generator Loss: 1.3691 D(x): 0.8269, D(G(z)): 0.3638 Epoch: [1/20], Batch Num: [307/600] Discriminator Loss: 0.5981, Generator Loss: 1.4443 D(x): 0.8376, D(G(z)): 0.3153 Epoch: [1/20], Batch Num: [308/600] Discriminator Loss: 0.6275, Generator Loss: 1.5170 D(x): 0.8138, D(G(z)): 0.3060 Epoch: [1/20], Batch Num: [309/600] Discriminator Loss: 0.7068, Generator Loss: 1.5392 D(x): 0.7341, D(G(z)): 0.2687 Epoch: [1/20], Batch Num: [310/600] Discriminator Loss: 0.7499, Generator Loss: 1.3021 D(x): 0.7390, D(G(z)): 0.2866 Epoch: [1/20], Batch Num: [311/600] Discriminator Loss: 0.7966, Generator Loss: 0.9991 D(x): 0.8339, D(G(z)): 0.4083 Epoch: [1/20], Batch Num: [312/600] Discriminator Loss: 0.8260, Generator Loss: 0.9349 D(x): 0.8549, D(G(z)): 0.4411 Epoch: [1/20], Batch Num: [313/600] Discriminator Loss: 0.9856, Generator Loss: 1.0540 D(x): 0.8476, D(G(z)): 0.5054 Epoch: [1/20], Batch Num: [314/600] Discriminator Loss: 1.1174, Generator Loss: 1.0001 D(x): 0.8360, D(G(z)): 0.5535 Epoch: [1/20], Batch Num: [315/600] Discriminator Loss: 1.1299, Generator Loss: 0.9539 D(x): 0.7633, D(G(z)): 0.5000 Epoch: [1/20], Batch Num: [316/600] Discriminator Loss: 1.4070, Generator Loss: 0.9826 D(x): 0.7052, D(G(z)): 0.5298 Epoch: [1/20], Batch Num: [317/600] Discriminator Loss: 1.3759, Generator Loss: 0.7463 D(x): 0.6774, D(G(z)): 0.5245 Epoch: [1/20], Batch Num: [318/600] Discriminator Loss: 1.3286, Generator Loss: 0.7324 D(x): 0.7508, D(G(z)): 0.5803 Epoch: [1/20], Batch Num: [319/600] Discriminator Loss: 1.4075, Generator Loss: 0.6556 D(x): 0.7549, D(G(z)): 0.5940 Epoch: [1/20], Batch Num: [320/600] Discriminator Loss: 1.3988, Generator Loss: 0.7881 D(x): 0.7706, D(G(z)): 0.6368 Epoch: [1/20], Batch Num: [321/600] Discriminator Loss: 1.3641, Generator Loss: 0.8590 D(x): 0.6991, D(G(z)): 0.5533 Epoch: [1/20], Batch Num: [322/600] Discriminator Loss: 1.2613, Generator Loss: 0.9876 D(x): 0.7252, D(G(z)): 0.5388 Epoch: [1/20], Batch Num: [323/600] Discriminator Loss: 1.2581, Generator Loss: 0.9818 D(x): 0.6624, D(G(z)): 0.4916 Epoch: [1/20], Batch Num: [324/600] Discriminator Loss: 1.0897, Generator Loss: 1.0453 D(x): 0.7296, D(G(z)): 0.4769 Epoch: [1/20], Batch Num: [325/600] Discriminator Loss: 0.9736, Generator Loss: 1.1159 D(x): 0.7592, D(G(z)): 0.4481 Epoch: [1/20], Batch Num: [326/600] Discriminator Loss: 0.9501, Generator Loss: 1.1244 D(x): 0.7151, D(G(z)): 0.4097 Epoch: [1/20], Batch Num: [327/600] Discriminator Loss: 0.9520, Generator Loss: 1.1154 D(x): 0.7182, D(G(z)): 0.3938 Epoch: [1/20], Batch Num: [328/600] Discriminator Loss: 1.0003, Generator Loss: 1.3197 D(x): 0.6878, D(G(z)): 0.4018 Epoch: [1/20], Batch Num: [329/600] Discriminator Loss: 1.0562, Generator Loss: 1.1408 D(x): 0.6778, D(G(z)): 0.4079 Epoch: [1/20], Batch Num: [330/600] Discriminator Loss: 1.0261, Generator Loss: 1.0849 D(x): 0.7380, D(G(z)): 0.4530 Epoch: [1/20], Batch Num: [331/600] Discriminator Loss: 1.0489, Generator Loss: 1.1409 D(x): 0.7372, D(G(z)): 0.4593 Epoch: [1/20], Batch Num: [332/600] Discriminator Loss: 1.1460, Generator Loss: 1.1996 D(x): 0.6793, D(G(z)): 0.4586 Epoch: [1/20], Batch Num: [333/600] Discriminator Loss: 1.3522, Generator Loss: 1.1272 D(x): 0.5984, D(G(z)): 0.4540 Epoch: 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Generator Loss: 0.3887 D(x): 0.4345, D(G(z)): 0.7303 Epoch: [1/20], Batch Num: [343/600] Discriminator Loss: 2.2319, Generator Loss: 0.4158 D(x): 0.4529, D(G(z)): 0.7338 Epoch: [1/20], Batch Num: [344/600] Discriminator Loss: 2.0545, Generator Loss: 0.4705 D(x): 0.4718, D(G(z)): 0.7029 Epoch: [1/20], Batch Num: [345/600] Discriminator Loss: 1.8705, Generator Loss: 0.5353 D(x): 0.4752, D(G(z)): 0.6574 Epoch: [1/20], Batch Num: [346/600] Discriminator Loss: 1.7614, Generator Loss: 0.6037 D(x): 0.4632, D(G(z)): 0.6100 Epoch: [1/20], Batch Num: [347/600] Discriminator Loss: 1.6668, Generator Loss: 0.6634 D(x): 0.4607, D(G(z)): 0.5754 Epoch: [1/20], Batch Num: [348/600] Discriminator Loss: 1.5257, Generator Loss: 0.6762 D(x): 0.4901, D(G(z)): 0.5470 Epoch: [1/20], Batch Num: [349/600] Discriminator Loss: 1.4403, Generator Loss: 0.7107 D(x): 0.4973, D(G(z)): 0.5155 Epoch: [1/20], Batch Num: [350/600] Discriminator Loss: 1.3704, Generator Loss: 0.7146 D(x): 0.5146, D(G(z)): 0.4996 Epoch: [1/20], Batch Num: [351/600] Discriminator Loss: 1.2755, Generator Loss: 0.7527 D(x): 0.5509, D(G(z)): 0.4883 Epoch: [1/20], Batch Num: [352/600] Discriminator Loss: 1.1633, Generator Loss: 0.8938 D(x): 0.5938, D(G(z)): 0.4659 Epoch: [1/20], Batch Num: [353/600] Discriminator Loss: 1.0891, Generator Loss: 1.1814 D(x): 0.6018, D(G(z)): 0.4325 Epoch: [1/20], Batch Num: [354/600] Discriminator Loss: 0.9958, Generator Loss: 1.4902 D(x): 0.6389, D(G(z)): 0.4099 Epoch: [1/20], Batch Num: [355/600] Discriminator Loss: 0.9175, Generator Loss: 2.0094 D(x): 0.6594, D(G(z)): 0.3796 Epoch: [1/20], Batch Num: [356/600] Discriminator Loss: 0.8303, Generator Loss: 2.6586 D(x): 0.6688, D(G(z)): 0.3297 Epoch: [1/20], Batch Num: [357/600] Discriminator Loss: 0.6888, Generator Loss: 3.5252 D(x): 0.6924, D(G(z)): 0.2530 Epoch: [1/20], Batch Num: [358/600] Discriminator Loss: 0.6490, Generator Loss: 4.7828 D(x): 0.6940, D(G(z)): 0.2231 Epoch: [1/20], Batch Num: [359/600] Discriminator Loss: 0.6020, Generator Loss: 6.8767 D(x): 0.6749, D(G(z)): 0.1616 Epoch: [1/20], Batch Num: [360/600] Discriminator Loss: 0.5558, Generator Loss: 7.2687 D(x): 0.6405, D(G(z)): 0.0829 Epoch: [1/20], Batch Num: [361/600] Discriminator Loss: 0.6251, Generator Loss: 7.9414 D(x): 0.6007, D(G(z)): 0.0735 Epoch: [1/20], Batch Num: [362/600] Discriminator Loss: 0.7357, Generator Loss: 6.9619 D(x): 0.5434, D(G(z)): 0.0507 Epoch: [1/20], Batch Num: [363/600] Discriminator Loss: 1.0041, Generator Loss: 4.2641 D(x): 0.5191, D(G(z)): 0.1208 Epoch: [1/20], Batch Num: [364/600] Discriminator Loss: 0.8811, Generator Loss: 2.3094 D(x): 0.6430, D(G(z)): 0.2631 Epoch: [1/20], Batch Num: [365/600] Discriminator Loss: 1.0260, Generator Loss: 1.4768 D(x): 0.6757, D(G(z)): 0.3916 Epoch: [1/20], Batch Num: [366/600] Discriminator Loss: 1.2698, Generator Loss: 1.0260 D(x): 0.6663, D(G(z)): 0.5051 Epoch: [1/20], Batch Num: [367/600] Discriminator Loss: 1.2993, Generator Loss: 1.2714 D(x): 0.6857, D(G(z)): 0.5330 Epoch: [1/20], Batch Num: [368/600] Discriminator Loss: 1.5233, Generator Loss: 1.6297 D(x): 0.6240, D(G(z)): 0.5628 Epoch: [1/20], Batch Num: [369/600] Discriminator Loss: 1.3916, Generator Loss: 1.8663 D(x): 0.5968, D(G(z)): 0.4894 Epoch: [1/20], Batch Num: [370/600] Discriminator Loss: 1.8521, Generator Loss: 1.2579 D(x): 0.4671, D(G(z)): 0.4638 Epoch: [1/20], Batch Num: [371/600] Discriminator Loss: 1.5719, Generator Loss: 0.9667 D(x): 0.5739, D(G(z)): 0.5069 Epoch: [1/20], Batch Num: [372/600] Discriminator Loss: 1.7113, Generator Loss: 0.6284 D(x): 0.6072, D(G(z)): 0.6089 Epoch: [1/20], Batch Num: [373/600] Discriminator Loss: 1.5111, Generator Loss: 0.7441 D(x): 0.6932, D(G(z)): 0.6234 Epoch: [1/20], Batch Num: [374/600] Discriminator Loss: 1.5119, Generator Loss: 1.0832 D(x): 0.6727, D(G(z)): 0.6141 Epoch: [1/20], Batch Num: [375/600] Discriminator Loss: 1.4434, Generator Loss: 1.1326 D(x): 0.6310, D(G(z)): 0.5433 Epoch: [1/20], Batch Num: [376/600] Discriminator Loss: 1.4228, Generator Loss: 1.3796 D(x): 0.6013, D(G(z)): 0.4797 Epoch: [1/20], Batch Num: [377/600] Discriminator Loss: 1.4831, Generator Loss: 1.2624 D(x): 0.5772, D(G(z)): 0.5249 Epoch: [1/20], Batch Num: [378/600] Discriminator Loss: 1.4997, Generator Loss: 1.4580 D(x): 0.5588, D(G(z)): 0.4774 Epoch: [1/20], Batch Num: [379/600] Discriminator Loss: 1.3290, Generator Loss: 1.4256 D(x): 0.5692, D(G(z)): 0.4065 Epoch: [1/20], Batch Num: [380/600] Discriminator Loss: 1.1608, Generator Loss: 1.4232 D(x): 0.5928, D(G(z)): 0.3968 Epoch: [1/20], Batch Num: [381/600] Discriminator Loss: 1.2241, Generator Loss: 1.2325 D(x): 0.5921, D(G(z)): 0.4390 Epoch: [1/20], Batch Num: [382/600] Discriminator Loss: 1.2079, Generator Loss: 1.2108 D(x): 0.6187, D(G(z)): 0.4541 Epoch: [1/20], Batch Num: [383/600] Discriminator Loss: 1.1552, Generator Loss: 1.2963 D(x): 0.6448, D(G(z)): 0.4502 Epoch: [1/20], Batch Num: [384/600] Discriminator Loss: 1.2208, Generator Loss: 1.6714 D(x): 0.6181, D(G(z)): 0.4551 Epoch: [1/20], Batch Num: [385/600] Discriminator Loss: 1.1586, Generator Loss: 1.3539 D(x): 0.6468, D(G(z)): 0.4630 Epoch: [1/20], Batch Num: [386/600] Discriminator Loss: 1.0373, Generator Loss: 1.4064 D(x): 0.6230, D(G(z)): 0.3767 Epoch: [1/20], Batch Num: [387/600] Discriminator Loss: 1.2379, Generator Loss: 1.6102 D(x): 0.5586, D(G(z)): 0.3873 Epoch: [1/20], Batch Num: [388/600] Discriminator Loss: 1.1778, Generator Loss: 1.4576 D(x): 0.5608, D(G(z)): 0.3518 Epoch: [1/20], Batch Num: [389/600] Discriminator Loss: 1.1357, Generator Loss: 1.0752 D(x): 0.5917, D(G(z)): 0.3881 Epoch: [1/20], Batch Num: [390/600] Discriminator Loss: 1.1620, Generator Loss: 1.0857 D(x): 0.6272, D(G(z)): 0.4619 Epoch: [1/20], Batch Num: [391/600] Discriminator Loss: 1.3163, Generator Loss: 1.1582 D(x): 0.5983, D(G(z)): 0.5048 Epoch: [1/20], Batch Num: [392/600] Discriminator Loss: 1.1879, Generator Loss: 0.9340 D(x): 0.6157, D(G(z)): 0.4586 Epoch: [1/20], Batch Num: [393/600] Discriminator Loss: 1.2460, Generator Loss: 0.9350 D(x): 0.5950, D(G(z)): 0.4726 Epoch: [1/20], Batch Num: [394/600] Discriminator Loss: 1.1858, Generator Loss: 0.8934 D(x): 0.6195, D(G(z)): 0.4667 Epoch: [1/20], Batch Num: [395/600] Discriminator Loss: 1.2797, Generator Loss: 0.9056 D(x): 0.6118, D(G(z)): 0.5030 Epoch: [1/20], Batch Num: [396/600] Discriminator Loss: 1.3097, Generator Loss: 0.7790 D(x): 0.5850, D(G(z)): 0.4948 Epoch: [1/20], Batch Num: [397/600] Discriminator Loss: 1.3507, Generator Loss: 0.8060 D(x): 0.5888, D(G(z)): 0.5295 Epoch: [1/20], Batch Num: [398/600] Discriminator Loss: 1.3276, Generator Loss: 0.7505 D(x): 0.6013, D(G(z)): 0.5200 Epoch: [1/20], Batch Num: [399/600] Discriminator Loss: 1.2232, Generator Loss: 0.8078 D(x): 0.6138, D(G(z)): 0.4958 Epoch: 1, Batch Num: [400/600]
Epoch: [1/20], Batch Num: [400/600] Discriminator Loss: 1.2960, Generator Loss: 0.8277 D(x): 0.6102, D(G(z)): 0.5299 Epoch: [1/20], Batch Num: [401/600] Discriminator Loss: 1.3824, Generator Loss: 0.8350 D(x): 0.5795, D(G(z)): 0.5365 Epoch: [1/20], Batch Num: [402/600] Discriminator Loss: 1.3611, Generator Loss: 0.6972 D(x): 0.5897, D(G(z)): 0.5318 Epoch: [1/20], Batch Num: [403/600] Discriminator Loss: 1.2887, Generator Loss: 0.7094 D(x): 0.6037, D(G(z)): 0.5158 Epoch: [1/20], Batch Num: [404/600] Discriminator Loss: 1.3842, Generator Loss: 0.6554 D(x): 0.5806, D(G(z)): 0.5360 Epoch: [1/20], Batch Num: [405/600] Discriminator Loss: 1.3286, Generator Loss: 0.6575 D(x): 0.5838, D(G(z)): 0.5101 Epoch: [1/20], Batch Num: [406/600] Discriminator Loss: 1.3552, Generator Loss: 0.6725 D(x): 0.5840, D(G(z)): 0.5395 Epoch: [1/20], Batch Num: [407/600] Discriminator Loss: 1.3539, Generator Loss: 0.6668 D(x): 0.5874, D(G(z)): 0.5418 Epoch: [1/20], Batch Num: [408/600] Discriminator Loss: 1.3928, Generator Loss: 0.6369 D(x): 0.5893, D(G(z)): 0.5506 Epoch: [1/20], Batch Num: [409/600] Discriminator Loss: 1.3829, Generator Loss: 0.6662 D(x): 0.5884, D(G(z)): 0.5605 Epoch: [1/20], Batch Num: [410/600] Discriminator Loss: 1.3741, Generator Loss: 0.6351 D(x): 0.5897, D(G(z)): 0.5466 Epoch: [1/20], Batch Num: [411/600] Discriminator Loss: 1.4053, Generator Loss: 0.6848 D(x): 0.5745, D(G(z)): 0.5513 Epoch: [1/20], Batch Num: [412/600] Discriminator Loss: 1.3837, Generator Loss: 0.6841 D(x): 0.5890, D(G(z)): 0.5466 Epoch: [1/20], Batch Num: [413/600] Discriminator Loss: 1.4073, Generator Loss: 0.7211 D(x): 0.5574, D(G(z)): 0.5380 Epoch: [1/20], Batch Num: [414/600] Discriminator Loss: 1.3020, Generator Loss: 0.7121 D(x): 0.5862, D(G(z)): 0.5163 Epoch: [1/20], Batch Num: [415/600] Discriminator Loss: 1.3149, Generator Loss: 0.7085 D(x): 0.5844, D(G(z)): 0.5066 Epoch: [1/20], Batch Num: [416/600] Discriminator Loss: 1.2617, Generator Loss: 0.7466 D(x): 0.5846, D(G(z)): 0.4985 Epoch: [1/20], Batch Num: [417/600] Discriminator Loss: 1.2592, Generator Loss: 0.7857 D(x): 0.5808, D(G(z)): 0.4950 Epoch: [1/20], Batch Num: [418/600] Discriminator Loss: 1.2544, Generator Loss: 0.8473 D(x): 0.5824, D(G(z)): 0.4880 Epoch: [1/20], Batch Num: [419/600] Discriminator Loss: 1.2467, Generator Loss: 0.8211 D(x): 0.5770, D(G(z)): 0.4828 Epoch: [1/20], Batch Num: [420/600] Discriminator Loss: 1.1818, Generator Loss: 0.9559 D(x): 0.5764, D(G(z)): 0.4489 Epoch: [1/20], Batch Num: [421/600] Discriminator Loss: 1.2150, Generator Loss: 0.9345 D(x): 0.5491, D(G(z)): 0.4345 Epoch: [1/20], Batch Num: [422/600] Discriminator Loss: 1.1852, Generator Loss: 0.9425 D(x): 0.5580, D(G(z)): 0.4244 Epoch: [1/20], Batch Num: [423/600] Discriminator Loss: 1.1981, Generator Loss: 0.9906 D(x): 0.5446, D(G(z)): 0.4214 Epoch: [1/20], Batch Num: [424/600] Discriminator Loss: 1.1383, Generator Loss: 1.0644 D(x): 0.5535, D(G(z)): 0.3904 Epoch: [1/20], Batch Num: [425/600] Discriminator Loss: 1.1281, Generator Loss: 1.0709 D(x): 0.5500, D(G(z)): 0.3855 Epoch: [1/20], Batch Num: [426/600] Discriminator Loss: 1.0933, Generator Loss: 1.0133 D(x): 0.5673, D(G(z)): 0.3882 Epoch: [1/20], Batch Num: [427/600] Discriminator Loss: 1.1271, Generator Loss: 1.0629 D(x): 0.5532, D(G(z)): 0.3754 Epoch: [1/20], Batch Num: [428/600] Discriminator Loss: 1.0619, Generator Loss: 1.0846 D(x): 0.5760, D(G(z)): 0.3711 Epoch: [1/20], Batch Num: [429/600] Discriminator Loss: 1.0547, Generator Loss: 1.0907 D(x): 0.6027, D(G(z)): 0.3896 Epoch: [1/20], Batch Num: [430/600] Discriminator Loss: 1.0141, Generator Loss: 1.1360 D(x): 0.6124, D(G(z)): 0.3859 Epoch: [1/20], Batch Num: [431/600] Discriminator Loss: 0.9982, Generator Loss: 1.1367 D(x): 0.6163, D(G(z)): 0.3730 Epoch: [1/20], Batch Num: [432/600] Discriminator Loss: 0.9246, Generator Loss: 1.2240 D(x): 0.6452, D(G(z)): 0.3625 Epoch: [1/20], Batch Num: [433/600] Discriminator Loss: 0.8614, Generator Loss: 1.1703 D(x): 0.6631, D(G(z)): 0.3389 Epoch: [1/20], Batch Num: [434/600] Discriminator Loss: 0.8743, Generator Loss: 1.1841 D(x): 0.6716, D(G(z)): 0.3581 Epoch: [1/20], Batch Num: [435/600] Discriminator Loss: 0.7857, Generator Loss: 1.3489 D(x): 0.6996, D(G(z)): 0.3326 Epoch: [1/20], Batch Num: [436/600] Discriminator Loss: 0.7197, Generator Loss: 1.3878 D(x): 0.7115, D(G(z)): 0.2957 Epoch: [1/20], Batch Num: [437/600] Discriminator Loss: 0.6899, Generator Loss: 1.5726 D(x): 0.7047, D(G(z)): 0.2648 Epoch: [1/20], Batch Num: [438/600] Discriminator Loss: 0.7119, Generator Loss: 1.6469 D(x): 0.7035, D(G(z)): 0.2806 Epoch: [1/20], Batch Num: [439/600] Discriminator Loss: 0.6523, Generator Loss: 1.6496 D(x): 0.7388, D(G(z)): 0.2763 Epoch: [1/20], Batch Num: [440/600] Discriminator Loss: 0.6447, Generator Loss: 1.6546 D(x): 0.7441, D(G(z)): 0.2693 Epoch: [1/20], Batch Num: [441/600] Discriminator Loss: 0.6933, Generator Loss: 1.6015 D(x): 0.7436, D(G(z)): 0.3065 Epoch: [1/20], Batch Num: [442/600] Discriminator Loss: 0.6710, Generator Loss: 1.4932 D(x): 0.7696, D(G(z)): 0.3158 Epoch: [1/20], Batch Num: [443/600] Discriminator Loss: 0.6809, Generator Loss: 1.3384 D(x): 0.7859, D(G(z)): 0.3387 Epoch: [1/20], Batch Num: [444/600] Discriminator Loss: 0.7326, Generator Loss: 1.1286 D(x): 0.7746, D(G(z)): 0.3608 Epoch: [1/20], Batch Num: [445/600] Discriminator Loss: 0.8028, Generator Loss: 1.1939 D(x): 0.7788, D(G(z)): 0.3970 Epoch: [1/20], Batch Num: [446/600] Discriminator Loss: 0.7497, Generator Loss: 1.0513 D(x): 0.8065, D(G(z)): 0.3949 Epoch: [1/20], Batch Num: [447/600] Discriminator Loss: 0.8164, Generator Loss: 0.9387 D(x): 0.8088, D(G(z)): 0.4286 Epoch: [1/20], Batch Num: [448/600] Discriminator Loss: 0.8126, Generator Loss: 0.9688 D(x): 0.8084, D(G(z)): 0.4317 Epoch: [1/20], Batch Num: [449/600] Discriminator Loss: 0.8238, Generator Loss: 0.9273 D(x): 0.8121, D(G(z)): 0.4328 Epoch: [1/20], Batch Num: [450/600] Discriminator Loss: 0.7839, Generator Loss: 0.9802 D(x): 0.8357, D(G(z)): 0.4359 Epoch: [1/20], Batch Num: [451/600] Discriminator Loss: 0.9192, Generator Loss: 0.9935 D(x): 0.7865, D(G(z)): 0.4400 Epoch: [1/20], Batch Num: [452/600] Discriminator Loss: 0.8361, Generator Loss: 1.0530 D(x): 0.8222, D(G(z)): 0.4429 Epoch: [1/20], Batch Num: [453/600] Discriminator Loss: 0.7548, Generator Loss: 1.0981 D(x): 0.8531, D(G(z)): 0.4303 Epoch: [1/20], Batch Num: [454/600] Discriminator Loss: 0.7553, Generator Loss: 1.2100 D(x): 0.8182, D(G(z)): 0.3866 Epoch: [1/20], Batch Num: [455/600] Discriminator Loss: 0.6326, Generator Loss: 1.4650 D(x): 0.8528, D(G(z)): 0.3510 Epoch: [1/20], Batch Num: [456/600] Discriminator Loss: 0.6420, Generator Loss: 1.6678 D(x): 0.8369, D(G(z)): 0.3198 Epoch: [1/20], Batch Num: [457/600] Discriminator Loss: 0.5515, Generator Loss: 1.8972 D(x): 0.8368, D(G(z)): 0.2498 Epoch: [1/20], Batch Num: [458/600] Discriminator Loss: 0.5158, Generator Loss: 1.9659 D(x): 0.8396, D(G(z)): 0.2493 Epoch: [1/20], Batch Num: [459/600] Discriminator Loss: 0.5020, Generator Loss: 2.1869 D(x): 0.8676, D(G(z)): 0.2386 Epoch: [1/20], Batch Num: [460/600] Discriminator Loss: 0.4381, Generator Loss: 2.3808 D(x): 0.8622, D(G(z)): 0.1990 Epoch: [1/20], Batch Num: [461/600] Discriminator Loss: 0.4649, Generator Loss: 2.4491 D(x): 0.8793, D(G(z)): 0.2290 Epoch: [1/20], Batch Num: [462/600] Discriminator Loss: 0.3792, Generator Loss: 3.0717 D(x): 0.9373, D(G(z)): 0.2353 Epoch: [1/20], Batch Num: [463/600] Discriminator Loss: 0.4563, Generator Loss: 2.9641 D(x): 0.8789, D(G(z)): 0.2093 Epoch: [1/20], Batch Num: [464/600] Discriminator Loss: 0.3726, Generator Loss: 3.3696 D(x): 0.8780, D(G(z)): 0.1486 Epoch: [1/20], Batch Num: [465/600] Discriminator Loss: 0.5436, Generator Loss: 3.6107 D(x): 0.8403, D(G(z)): 0.1770 Epoch: [1/20], Batch Num: [466/600] Discriminator Loss: 0.7009, Generator Loss: 2.9398 D(x): 0.8127, D(G(z)): 0.2456 Epoch: [1/20], Batch Num: [467/600] Discriminator Loss: 0.6341, Generator Loss: 2.6836 D(x): 0.8613, D(G(z)): 0.2379 Epoch: [1/20], Batch Num: [468/600] Discriminator Loss: 0.7963, Generator Loss: 2.6437 D(x): 0.8334, D(G(z)): 0.3286 Epoch: [1/20], Batch Num: [469/600] Discriminator Loss: 1.1465, Generator Loss: 2.2374 D(x): 0.7646, D(G(z)): 0.3903 Epoch: [1/20], Batch Num: [470/600] Discriminator Loss: 1.3735, Generator Loss: 1.7113 D(x): 0.6766, D(G(z)): 0.3979 Epoch: [1/20], Batch Num: [471/600] Discriminator Loss: 1.6047, Generator Loss: 1.4584 D(x): 0.7702, D(G(z)): 0.5576 Epoch: [1/20], Batch Num: [472/600] Discriminator Loss: 1.9355, Generator Loss: 1.1107 D(x): 0.6219, D(G(z)): 0.5863 Epoch: [1/20], Batch Num: [473/600] Discriminator Loss: 2.2801, Generator Loss: 0.7256 D(x): 0.5719, D(G(z)): 0.6395 Epoch: [1/20], Batch Num: [474/600] Discriminator Loss: 2.4973, Generator Loss: 0.5283 D(x): 0.6348, D(G(z)): 0.7600 Epoch: [1/20], Batch Num: [475/600] Discriminator Loss: 2.6726, Generator Loss: 0.4414 D(x): 0.5695, D(G(z)): 0.7550 Epoch: [1/20], Batch Num: [476/600] Discriminator Loss: 2.2299, Generator Loss: 0.5442 D(x): 0.6004, D(G(z)): 0.7082 Epoch: [1/20], Batch Num: [477/600] Discriminator Loss: 1.7872, Generator Loss: 1.0829 D(x): 0.6659, D(G(z)): 0.6281 Epoch: [1/20], Batch Num: [478/600] Discriminator Loss: 1.3511, Generator Loss: 1.6837 D(x): 0.6459, D(G(z)): 0.4676 Epoch: [1/20], Batch Num: [479/600] Discriminator Loss: 1.1958, Generator Loss: 1.8532 D(x): 0.5859, D(G(z)): 0.3259 Epoch: [1/20], Batch Num: [480/600] Discriminator Loss: 1.0631, Generator Loss: 1.9824 D(x): 0.5872, D(G(z)): 0.2464 Epoch: [1/20], Batch Num: [481/600] Discriminator Loss: 0.9821, Generator Loss: 1.8023 D(x): 0.6605, D(G(z)): 0.3193 Epoch: [1/20], Batch Num: [482/600] Discriminator Loss: 0.8244, Generator Loss: 1.9807 D(x): 0.7171, D(G(z)): 0.2995 Epoch: [1/20], Batch Num: [483/600] Discriminator Loss: 0.9527, Generator Loss: 1.5921 D(x): 0.6691, D(G(z)): 0.3223 Epoch: [1/20], Batch Num: [484/600] Discriminator Loss: 0.9946, Generator Loss: 1.7110 D(x): 0.7039, D(G(z)): 0.3826 Epoch: [1/20], Batch Num: [485/600] Discriminator Loss: 0.9396, Generator Loss: 1.8088 D(x): 0.6754, D(G(z)): 0.3421 Epoch: [1/20], Batch Num: [486/600] Discriminator Loss: 0.9075, Generator Loss: 1.7866 D(x): 0.6739, D(G(z)): 0.3191 Epoch: [1/20], Batch Num: [487/600] Discriminator Loss: 0.9838, Generator Loss: 1.6867 D(x): 0.6137, D(G(z)): 0.2770 Epoch: [1/20], Batch Num: [488/600] Discriminator Loss: 0.8696, Generator Loss: 1.7544 D(x): 0.6638, D(G(z)): 0.2828 Epoch: [1/20], Batch Num: [489/600] Discriminator Loss: 0.9358, Generator Loss: 1.5716 D(x): 0.6307, D(G(z)): 0.3040 Epoch: [1/20], Batch Num: [490/600] Discriminator Loss: 0.7898, Generator Loss: 1.5926 D(x): 0.7476, D(G(z)): 0.3402 Epoch: [1/20], Batch Num: [491/600] Discriminator Loss: 0.7673, Generator Loss: 1.7468 D(x): 0.7479, D(G(z)): 0.3213 Epoch: [1/20], Batch Num: [492/600] Discriminator Loss: 0.8093, Generator Loss: 1.8916 D(x): 0.7510, D(G(z)): 0.3592 Epoch: [1/20], Batch Num: [493/600] Discriminator Loss: 0.6252, Generator Loss: 2.2637 D(x): 0.7675, D(G(z)): 0.2613 Epoch: [1/20], Batch Num: [494/600] Discriminator Loss: 0.6156, Generator Loss: 2.5885 D(x): 0.7628, D(G(z)): 0.2417 Epoch: [1/20], Batch Num: [495/600] Discriminator Loss: 0.6653, Generator Loss: 2.3540 D(x): 0.7204, D(G(z)): 0.2138 Epoch: [1/20], Batch Num: [496/600] Discriminator Loss: 0.7166, Generator Loss: 2.6636 D(x): 0.6959, D(G(z)): 0.2311 Epoch: [1/20], Batch Num: [497/600] Discriminator Loss: 0.6578, Generator Loss: 2.3183 D(x): 0.7325, D(G(z)): 0.2252 Epoch: [1/20], Batch Num: [498/600] Discriminator Loss: 0.8107, Generator Loss: 2.1832 D(x): 0.7010, D(G(z)): 0.2643 Epoch: [1/20], Batch Num: [499/600] Discriminator Loss: 0.7243, Generator Loss: 1.8306 D(x): 0.7521, D(G(z)): 0.2730 Epoch: 1, Batch Num: [500/600]
Epoch: [1/20], Batch Num: [500/600] Discriminator Loss: 0.8847, Generator Loss: 1.5490 D(x): 0.7315, D(G(z)): 0.3343 Epoch: [1/20], Batch Num: [501/600] Discriminator Loss: 0.9817, Generator Loss: 1.6268 D(x): 0.7897, D(G(z)): 0.4579 Epoch: [1/20], Batch Num: [502/600] Discriminator Loss: 0.9240, Generator Loss: 1.6969 D(x): 0.7370, D(G(z)): 0.3553 Epoch: [1/20], Batch Num: [503/600] Discriminator Loss: 0.9282, Generator Loss: 2.0692 D(x): 0.7322, D(G(z)): 0.3635 Epoch: [1/20], Batch Num: [504/600] Discriminator Loss: 1.1017, Generator Loss: 2.0699 D(x): 0.6843, D(G(z)): 0.3745 Epoch: [1/20], Batch Num: [505/600] Discriminator Loss: 1.2934, Generator Loss: 1.5448 D(x): 0.6181, D(G(z)): 0.4039 Epoch: [1/20], Batch Num: [506/600] Discriminator Loss: 1.0177, Generator Loss: 1.3812 D(x): 0.7263, D(G(z)): 0.4010 Epoch: [1/20], Batch Num: [507/600] Discriminator Loss: 1.2561, Generator Loss: 1.1738 D(x): 0.7459, D(G(z)): 0.5061 Epoch: [1/20], Batch Num: [508/600] Discriminator Loss: 1.2813, Generator Loss: 1.3362 D(x): 0.7317, D(G(z)): 0.5335 Epoch: [1/20], Batch Num: [509/600] Discriminator Loss: 1.2482, Generator Loss: 1.3705 D(x): 0.7025, D(G(z)): 0.4902 Epoch: [1/20], Batch Num: [510/600] Discriminator Loss: 1.3666, Generator Loss: 1.2643 D(x): 0.6472, D(G(z)): 0.4929 Epoch: [1/20], Batch Num: [511/600] Discriminator Loss: 1.2931, Generator Loss: 1.3696 D(x): 0.6602, D(G(z)): 0.4694 Epoch: [1/20], Batch Num: [512/600] Discriminator Loss: 1.1847, Generator Loss: 1.5043 D(x): 0.7129, D(G(z)): 0.4950 Epoch: [1/20], Batch Num: [513/600] Discriminator Loss: 1.2161, Generator Loss: 1.5656 D(x): 0.6736, D(G(z)): 0.4712 Epoch: [1/20], Batch Num: [514/600] Discriminator Loss: 1.1984, Generator Loss: 1.3411 D(x): 0.6482, D(G(z)): 0.4579 Epoch: [1/20], Batch Num: [515/600] Discriminator Loss: 1.0429, Generator Loss: 1.4100 D(x): 0.7014, D(G(z)): 0.4313 Epoch: [1/20], Batch Num: [516/600] Discriminator Loss: 1.2905, Generator Loss: 1.3083 D(x): 0.6026, D(G(z)): 0.4353 Epoch: [1/20], Batch Num: [517/600] Discriminator Loss: 1.0806, Generator Loss: 1.4267 D(x): 0.6324, D(G(z)): 0.4013 Epoch: [1/20], Batch Num: [518/600] Discriminator Loss: 1.2158, Generator Loss: 1.3356 D(x): 0.5825, D(G(z)): 0.3839 Epoch: [1/20], Batch Num: [519/600] Discriminator Loss: 1.0907, Generator Loss: 1.2844 D(x): 0.6388, D(G(z)): 0.3806 Epoch: [1/20], Batch Num: [520/600] Discriminator Loss: 0.8852, Generator Loss: 1.3418 D(x): 0.7298, D(G(z)): 0.3954 Epoch: [1/20], Batch Num: [521/600] Discriminator Loss: 0.8945, Generator Loss: 1.5466 D(x): 0.6871, D(G(z)): 0.3593 Epoch: [1/20], Batch Num: [522/600] Discriminator Loss: 0.7884, Generator Loss: 1.8791 D(x): 0.7227, D(G(z)): 0.3305 Epoch: [1/20], Batch Num: [523/600] Discriminator Loss: 0.7454, Generator Loss: 1.8490 D(x): 0.6833, D(G(z)): 0.2587 Epoch: [1/20], Batch Num: [524/600] Discriminator Loss: 0.7882, Generator Loss: 2.0158 D(x): 0.6554, D(G(z)): 0.2276 Epoch: [1/20], Batch Num: [525/600] Discriminator Loss: 0.6866, Generator Loss: 2.0584 D(x): 0.6948, D(G(z)): 0.2209 Epoch: [1/20], Batch Num: [526/600] Discriminator Loss: 0.6985, Generator Loss: 1.7937 D(x): 0.7239, D(G(z)): 0.2638 Epoch: [1/20], Batch Num: [527/600] Discriminator Loss: 0.6897, Generator Loss: 1.8320 D(x): 0.7400, D(G(z)): 0.2788 Epoch: [1/20], Batch Num: [528/600] Discriminator Loss: 0.7675, Generator Loss: 1.9842 D(x): 0.7562, D(G(z)): 0.3195 Epoch: [1/20], Batch Num: [529/600] Discriminator Loss: 0.8018, Generator Loss: 2.2542 D(x): 0.7239, D(G(z)): 0.3030 Epoch: [1/20], Batch Num: [530/600] Discriminator Loss: 0.9145, Generator Loss: 2.0802 D(x): 0.6948, D(G(z)): 0.2901 Epoch: [1/20], Batch Num: [531/600] Discriminator Loss: 1.0531, Generator Loss: 1.6809 D(x): 0.6049, D(G(z)): 0.2799 Epoch: [1/20], Batch Num: [532/600] Discriminator Loss: 0.7729, Generator Loss: 1.8392 D(x): 0.7756, D(G(z)): 0.3463 Epoch: [1/20], Batch Num: [533/600] Discriminator Loss: 0.9039, Generator Loss: 2.0496 D(x): 0.7631, D(G(z)): 0.4002 Epoch: [1/20], Batch Num: [534/600] Discriminator Loss: 1.0711, Generator Loss: 2.1733 D(x): 0.6522, D(G(z)): 0.3330 Epoch: [1/20], Batch Num: [535/600] Discriminator Loss: 1.3109, Generator Loss: 1.7968 D(x): 0.5634, D(G(z)): 0.3334 Epoch: [1/20], Batch Num: [536/600] Discriminator Loss: 1.4365, Generator Loss: 1.0809 D(x): 0.5609, D(G(z)): 0.4112 Epoch: [1/20], Batch Num: [537/600] Discriminator Loss: 1.7577, Generator Loss: 1.0441 D(x): 0.6356, D(G(z)): 0.6160 Epoch: [1/20], Batch Num: [538/600] Discriminator Loss: 1.7935, Generator Loss: 1.0546 D(x): 0.6265, D(G(z)): 0.6332 Epoch: [1/20], Batch Num: [539/600] Discriminator Loss: 1.9421, Generator Loss: 1.3907 D(x): 0.5293, D(G(z)): 0.5826 Epoch: [1/20], Batch Num: [540/600] Discriminator Loss: 1.9784, Generator Loss: 1.5049 D(x): 0.4148, D(G(z)): 0.4833 Epoch: [1/20], Batch Num: [541/600] Discriminator Loss: 2.0425, Generator Loss: 0.8590 D(x): 0.3987, D(G(z)): 0.4879 Epoch: [1/20], Batch Num: [542/600] Discriminator Loss: 2.0124, Generator Loss: 0.8357 D(x): 0.4869, D(G(z)): 0.5712 Epoch: [1/20], Batch Num: [543/600] Discriminator Loss: 1.9955, Generator Loss: 0.7472 D(x): 0.4765, D(G(z)): 0.5926 Epoch: [1/20], Batch Num: [544/600] Discriminator Loss: 1.8543, Generator Loss: 0.8641 D(x): 0.5258, D(G(z)): 0.6148 Epoch: [1/20], Batch Num: [545/600] Discriminator Loss: 1.7138, Generator Loss: 1.0896 D(x): 0.5127, D(G(z)): 0.5629 Epoch: [1/20], Batch Num: [546/600] Discriminator Loss: 1.4568, Generator Loss: 1.5084 D(x): 0.5099, D(G(z)): 0.4513 Epoch: [1/20], Batch Num: [547/600] Discriminator Loss: 1.2522, Generator Loss: 1.4768 D(x): 0.5173, D(G(z)): 0.3424 Epoch: [1/20], Batch Num: [548/600] Discriminator Loss: 1.1291, Generator Loss: 1.5224 D(x): 0.5128, D(G(z)): 0.2812 Epoch: [1/20], Batch Num: [549/600] Discriminator Loss: 1.0365, Generator Loss: 1.5199 D(x): 0.5657, D(G(z)): 0.3085 Epoch: [1/20], Batch Num: [550/600] Discriminator Loss: 0.9048, Generator Loss: 1.5436 D(x): 0.5827, D(G(z)): 0.2427 Epoch: [1/20], Batch Num: [551/600] Discriminator Loss: 0.8677, Generator Loss: 1.7414 D(x): 0.6442, D(G(z)): 0.2968 Epoch: [1/20], Batch Num: [552/600] Discriminator Loss: 0.9256, Generator Loss: 1.7904 D(x): 0.6536, D(G(z)): 0.3430 Epoch: [1/20], Batch Num: [553/600] Discriminator Loss: 0.7091, Generator Loss: 2.1661 D(x): 0.6979, D(G(z)): 0.2520 Epoch: [1/20], Batch Num: [554/600] Discriminator Loss: 0.7820, Generator Loss: 2.3006 D(x): 0.7065, D(G(z)): 0.3000 Epoch: [1/20], Batch Num: [555/600] Discriminator Loss: 0.7472, Generator Loss: 2.6905 D(x): 0.6970, D(G(z)): 0.2580 Epoch: [1/20], Batch Num: [556/600] Discriminator Loss: 0.6234, Generator Loss: 3.0739 D(x): 0.7224, D(G(z)): 0.2067 Epoch: [1/20], Batch Num: [557/600] Discriminator Loss: 0.6881, Generator Loss: 3.3001 D(x): 0.6902, D(G(z)): 0.1764 Epoch: [1/20], Batch Num: [558/600] Discriminator Loss: 0.6320, Generator Loss: 3.1124 D(x): 0.7040, D(G(z)): 0.1738 Epoch: [1/20], Batch Num: [559/600] Discriminator Loss: 0.5911, Generator Loss: 3.0463 D(x): 0.7569, D(G(z)): 0.1949 Epoch: [1/20], Batch Num: [560/600] Discriminator Loss: 0.6654, Generator Loss: 3.3979 D(x): 0.7343, D(G(z)): 0.2210 Epoch: [1/20], Batch Num: [561/600] Discriminator Loss: 0.6795, Generator Loss: 4.1509 D(x): 0.7297, D(G(z)): 0.2215 Epoch: [1/20], Batch Num: [562/600] Discriminator Loss: 0.6263, Generator Loss: 3.8873 D(x): 0.7353, D(G(z)): 0.1734 Epoch: [1/20], Batch Num: [563/600] Discriminator Loss: 0.4889, Generator Loss: 4.6411 D(x): 0.7834, D(G(z)): 0.1551 Epoch: [1/20], Batch Num: [564/600] Discriminator Loss: 0.5531, Generator Loss: 4.9367 D(x): 0.7691, D(G(z)): 0.1666 Epoch: [1/20], Batch Num: [565/600] Discriminator Loss: 0.5465, Generator Loss: 4.5496 D(x): 0.7791, D(G(z)): 0.1578 Epoch: [1/20], Batch Num: [566/600] Discriminator Loss: 0.5018, Generator Loss: 5.1563 D(x): 0.7839, D(G(z)): 0.1401 Epoch: [1/20], Batch Num: [567/600] Discriminator Loss: 0.3932, Generator Loss: 5.1858 D(x): 0.8128, D(G(z)): 0.1060 Epoch: [1/20], Batch Num: [568/600] Discriminator Loss: 0.3728, Generator Loss: 6.1567 D(x): 0.8451, D(G(z)): 0.1157 Epoch: [1/20], Batch Num: [569/600] Discriminator Loss: 0.2940, Generator Loss: 5.4881 D(x): 0.8426, D(G(z)): 0.0674 Epoch: [1/20], Batch Num: [570/600] Discriminator Loss: 0.3360, Generator Loss: 6.5283 D(x): 0.8592, D(G(z)): 0.1164 Epoch: [1/20], Batch Num: [571/600] Discriminator Loss: 0.2798, Generator Loss: 6.9242 D(x): 0.8950, D(G(z)): 0.0940 Epoch: [1/20], Batch Num: [572/600] Discriminator Loss: 0.2294, Generator Loss: 7.3134 D(x): 0.8714, D(G(z)): 0.0424 Epoch: [1/20], Batch Num: [573/600] Discriminator Loss: 0.1797, Generator Loss: 6.7237 D(x): 0.9025, D(G(z)): 0.0512 Epoch: [1/20], Batch Num: [574/600] Discriminator Loss: 0.2594, Generator Loss: 7.5498 D(x): 0.8945, D(G(z)): 0.0882 Epoch: [1/20], Batch Num: [575/600] Discriminator Loss: 0.2978, Generator Loss: 7.0456 D(x): 0.9084, D(G(z)): 0.1319 Epoch: [1/20], Batch Num: [576/600] Discriminator Loss: 0.3373, Generator Loss: 6.7933 D(x): 0.9084, D(G(z)): 0.1463 Epoch: [1/20], Batch Num: [577/600] Discriminator Loss: 0.3145, Generator Loss: 7.2811 D(x): 0.8795, D(G(z)): 0.1048 Epoch: [1/20], Batch Num: [578/600] Discriminator Loss: 0.3887, Generator Loss: 6.1874 D(x): 0.8457, D(G(z)): 0.1185 Epoch: [1/20], Batch Num: [579/600] Discriminator Loss: 0.5126, Generator Loss: 5.9549 D(x): 0.7882, D(G(z)): 0.1430 Epoch: [1/20], Batch Num: [580/600] Discriminator Loss: 0.7321, Generator Loss: 4.4613 D(x): 0.7566, D(G(z)): 0.2159 Epoch: [1/20], Batch Num: [581/600] Discriminator Loss: 0.8346, Generator Loss: 3.1574 D(x): 0.7507, D(G(z)): 0.2521 Epoch: [1/20], Batch Num: [582/600] Discriminator Loss: 1.3541, Generator Loss: 2.6909 D(x): 0.7438, D(G(z)): 0.4834 Epoch: [1/20], Batch Num: [583/600] Discriminator Loss: 1.6264, Generator Loss: 2.0849 D(x): 0.6663, D(G(z)): 0.5400 Epoch: [1/20], Batch Num: [584/600] Discriminator Loss: 2.2454, Generator Loss: 1.4400 D(x): 0.5067, D(G(z)): 0.5795 Epoch: [1/20], Batch Num: [585/600] Discriminator Loss: 2.1866, Generator Loss: 1.0235 D(x): 0.5388, D(G(z)): 0.6149 Epoch: [1/20], Batch Num: [586/600] Discriminator Loss: 2.1303, Generator Loss: 0.9382 D(x): 0.5340, D(G(z)): 0.6085 Epoch: [1/20], Batch Num: [587/600] Discriminator Loss: 1.8317, Generator Loss: 0.8946 D(x): 0.5986, D(G(z)): 0.6029 Epoch: [1/20], Batch Num: [588/600] Discriminator Loss: 1.3463, Generator Loss: 0.9755 D(x): 0.7077, D(G(z)): 0.5727 Epoch: [1/20], Batch Num: [589/600] Discriminator Loss: 1.1750, Generator Loss: 1.2001 D(x): 0.6663, D(G(z)): 0.4506 Epoch: [1/20], Batch Num: [590/600] Discriminator Loss: 1.0667, Generator Loss: 1.2103 D(x): 0.6260, D(G(z)): 0.3739 Epoch: [1/20], Batch Num: [591/600] Discriminator Loss: 1.0735, Generator Loss: 1.1796 D(x): 0.6180, D(G(z)): 0.3790 Epoch: [1/20], Batch Num: [592/600] Discriminator Loss: 0.9087, Generator Loss: 1.1807 D(x): 0.7007, D(G(z)): 0.3601 Epoch: [1/20], Batch Num: [593/600] Discriminator Loss: 0.7572, Generator Loss: 1.1321 D(x): 0.8036, D(G(z)): 0.3779 Epoch: [1/20], Batch Num: [594/600] Discriminator Loss: 0.8128, Generator Loss: 1.0493 D(x): 0.8183, D(G(z)): 0.4224 Epoch: [1/20], Batch Num: [595/600] Discriminator Loss: 0.7681, Generator Loss: 1.0935 D(x): 0.8501, D(G(z)): 0.4304 Epoch: [1/20], Batch Num: [596/600] Discriminator Loss: 0.7986, Generator Loss: 1.0743 D(x): 0.8359, D(G(z)): 0.4357 Epoch: [1/20], Batch Num: [597/600] Discriminator Loss: 0.8724, Generator Loss: 1.0958 D(x): 0.8113, D(G(z)): 0.4405 Epoch: [1/20], Batch Num: [598/600] Discriminator Loss: 0.8755, Generator Loss: 1.1316 D(x): 0.8033, D(G(z)): 0.4387 Epoch: [1/20], Batch Num: [599/600] Discriminator Loss: 0.8456, Generator Loss: 1.0794 D(x): 0.7952, D(G(z)): 0.4166 Epoch: 2, Batch Num: [0/600]
Epoch: [2/20], Batch Num: [0/600] Discriminator Loss: 1.0240, Generator Loss: 1.0688 D(x): 0.6854, D(G(z)): 0.4050 Epoch: [2/20], Batch Num: [1/600] Discriminator Loss: 0.8763, Generator Loss: 1.0513 D(x): 0.7550, D(G(z)): 0.3923 Epoch: [2/20], Batch Num: [2/600] Discriminator Loss: 1.0262, Generator Loss: 1.0592 D(x): 0.7182, D(G(z)): 0.4298 Epoch: [2/20], Batch Num: [3/600] Discriminator Loss: 0.9234, Generator Loss: 1.0159 D(x): 0.7818, D(G(z)): 0.4178 Epoch: [2/20], Batch Num: [4/600] Discriminator Loss: 0.8967, Generator Loss: 0.9759 D(x): 0.7867, D(G(z)): 0.4378 Epoch: [2/20], Batch Num: [5/600] Discriminator Loss: 0.8720, Generator Loss: 1.0680 D(x): 0.8048, D(G(z)): 0.4265 Epoch: [2/20], Batch Num: [6/600] Discriminator Loss: 0.8679, Generator Loss: 1.1260 D(x): 0.7768, D(G(z)): 0.4013 Epoch: [2/20], Batch Num: [7/600] Discriminator Loss: 0.8070, Generator Loss: 1.4186 D(x): 0.8120, D(G(z)): 0.3970 Epoch: [2/20], Batch Num: [8/600] Discriminator Loss: 0.7428, Generator Loss: 1.4844 D(x): 0.7668, D(G(z)): 0.3054 Epoch: [2/20], Batch Num: [9/600] Discriminator Loss: 0.8298, Generator Loss: 1.4786 D(x): 0.7170, D(G(z)): 0.3013 Epoch: [2/20], Batch Num: [10/600] Discriminator Loss: 0.6409, Generator Loss: 1.6399 D(x): 0.7962, D(G(z)): 0.2849 Epoch: [2/20], Batch Num: [11/600] Discriminator Loss: 0.7103, Generator Loss: 1.6174 D(x): 0.7622, D(G(z)): 0.2735 Epoch: [2/20], Batch Num: [12/600] Discriminator Loss: 0.6936, Generator Loss: 1.7003 D(x): 0.8225, D(G(z)): 0.3264 Epoch: [2/20], Batch Num: [13/600] Discriminator Loss: 0.7050, Generator Loss: 1.7615 D(x): 0.8212, D(G(z)): 0.3163 Epoch: [2/20], Batch Num: [14/600] Discriminator Loss: 0.7210, Generator Loss: 1.9694 D(x): 0.8172, D(G(z)): 0.3294 Epoch: [2/20], Batch Num: [15/600] Discriminator Loss: 0.7028, Generator Loss: 2.2003 D(x): 0.8261, D(G(z)): 0.3256 Epoch: [2/20], Batch Num: [16/600] Discriminator Loss: 0.6290, Generator Loss: 2.1478 D(x): 0.7869, D(G(z)): 0.2597 Epoch: [2/20], Batch Num: [17/600] Discriminator Loss: 0.7483, Generator Loss: 1.9565 D(x): 0.7228, D(G(z)): 0.2370 Epoch: [2/20], Batch Num: [18/600] Discriminator Loss: 0.7872, Generator Loss: 2.0596 D(x): 0.7808, D(G(z)): 0.3267 Epoch: [2/20], Batch Num: [19/600] Discriminator Loss: 0.8098, Generator Loss: 1.6071 D(x): 0.7765, D(G(z)): 0.3364 Epoch: [2/20], Batch Num: [20/600] Discriminator Loss: 0.8758, Generator Loss: 1.7123 D(x): 0.7721, D(G(z)): 0.3719 Epoch: [2/20], Batch Num: [21/600] Discriminator Loss: 1.0188, Generator Loss: 1.6495 D(x): 0.7028, D(G(z)): 0.3717 Epoch: [2/20], Batch Num: [22/600] Discriminator Loss: 1.0771, Generator Loss: 1.3324 D(x): 0.6610, D(G(z)): 0.3564 Epoch: [2/20], Batch Num: [23/600] Discriminator Loss: 1.2665, Generator Loss: 1.1642 D(x): 0.6549, D(G(z)): 0.4167 Epoch: [2/20], Batch Num: [24/600] Discriminator Loss: 1.5265, Generator Loss: 0.9623 D(x): 0.6423, D(G(z)): 0.5250 Epoch: [2/20], Batch Num: [25/600] Discriminator Loss: 1.5092, Generator Loss: 0.9650 D(x): 0.7173, D(G(z)): 0.6008 Epoch: [2/20], Batch Num: [26/600] Discriminator Loss: 1.3945, Generator Loss: 1.3128 D(x): 0.6825, D(G(z)): 0.5423 Epoch: [2/20], Batch Num: [27/600] Discriminator Loss: 1.6109, Generator Loss: 1.3689 D(x): 0.5593, D(G(z)): 0.4522 Epoch: [2/20], Batch Num: [28/600] Discriminator Loss: 1.6061, Generator Loss: 1.1743 D(x): 0.5417, D(G(z)): 0.4576 Epoch: [2/20], Batch Num: [29/600] Discriminator Loss: 1.4175, Generator Loss: 1.0491 D(x): 0.6477, D(G(z)): 0.5125 Epoch: [2/20], Batch Num: [30/600] Discriminator Loss: 1.2434, Generator Loss: 1.0866 D(x): 0.6954, D(G(z)): 0.4983 Epoch: [2/20], Batch Num: [31/600] Discriminator Loss: 1.3273, Generator Loss: 1.2763 D(x): 0.6436, D(G(z)): 0.4799 Epoch: [2/20], Batch Num: [32/600] Discriminator Loss: 1.1276, Generator Loss: 1.5204 D(x): 0.6881, D(G(z)): 0.4209 Epoch: [2/20], Batch Num: [33/600] Discriminator Loss: 1.1096, Generator Loss: 1.6044 D(x): 0.6443, D(G(z)): 0.3650 Epoch: [2/20], Batch Num: [34/600] Discriminator Loss: 0.9780, Generator Loss: 1.5817 D(x): 0.6988, D(G(z)): 0.3408 Epoch: [2/20], Batch Num: [35/600] Discriminator Loss: 0.8751, Generator Loss: 1.5658 D(x): 0.6925, D(G(z)): 0.3248 Epoch: [2/20], Batch Num: [36/600] Discriminator Loss: 0.8661, Generator Loss: 1.9324 D(x): 0.6845, D(G(z)): 0.3148 Epoch: [2/20], Batch Num: [37/600] Discriminator Loss: 1.0013, Generator Loss: 1.9651 D(x): 0.6657, D(G(z)): 0.3366 Epoch: [2/20], Batch Num: [38/600] Discriminator Loss: 1.0019, Generator Loss: 1.8424 D(x): 0.6425, D(G(z)): 0.3127 Epoch: [2/20], Batch Num: [39/600] Discriminator Loss: 1.1565, Generator Loss: 1.7767 D(x): 0.5986, D(G(z)): 0.3226 Epoch: [2/20], Batch Num: [40/600] Discriminator Loss: 1.1569, Generator Loss: 1.4072 D(x): 0.6260, D(G(z)): 0.3754 Epoch: [2/20], Batch Num: [41/600] Discriminator Loss: 1.2530, Generator Loss: 1.4840 D(x): 0.6532, D(G(z)): 0.4305 Epoch: [2/20], Batch Num: [42/600] Discriminator Loss: 1.1595, Generator Loss: 1.6840 D(x): 0.6350, D(G(z)): 0.4121 Epoch: [2/20], Batch Num: [43/600] Discriminator Loss: 1.1885, Generator Loss: 1.8866 D(x): 0.6175, D(G(z)): 0.3699 Epoch: [2/20], Batch Num: [44/600] Discriminator Loss: 1.1511, Generator Loss: 2.1285 D(x): 0.5671, D(G(z)): 0.2979 Epoch: [2/20], Batch Num: [45/600] Discriminator Loss: 1.1056, Generator Loss: 2.3483 D(x): 0.5738, D(G(z)): 0.2898 Epoch: [2/20], Batch Num: [46/600] Discriminator Loss: 1.2425, Generator Loss: 1.9560 D(x): 0.5739, D(G(z)): 0.3169 Epoch: [2/20], Batch Num: [47/600] Discriminator Loss: 1.0935, Generator Loss: 2.0297 D(x): 0.6139, D(G(z)): 0.3258 Epoch: [2/20], Batch Num: [48/600] Discriminator Loss: 1.1904, Generator Loss: 1.9707 D(x): 0.6568, D(G(z)): 0.4077 Epoch: [2/20], Batch Num: [49/600] Discriminator Loss: 1.1141, Generator Loss: 1.9736 D(x): 0.6808, D(G(z)): 0.4044 Epoch: [2/20], Batch Num: [50/600] Discriminator Loss: 1.3883, Generator Loss: 2.1530 D(x): 0.5973, D(G(z)): 0.4516 Epoch: [2/20], Batch Num: [51/600] Discriminator Loss: 1.3708, Generator Loss: 1.7636 D(x): 0.6008, D(G(z)): 0.4517 Epoch: [2/20], Batch Num: [52/600] Discriminator Loss: 1.3551, Generator Loss: 2.0723 D(x): 0.6170, D(G(z)): 0.4568 Epoch: [2/20], Batch Num: [53/600] Discriminator Loss: 1.4392, Generator Loss: 1.8336 D(x): 0.5740, D(G(z)): 0.4709 Epoch: [2/20], Batch Num: [54/600] Discriminator Loss: 1.5818, Generator Loss: 1.5177 D(x): 0.5539, D(G(z)): 0.5073 Epoch: [2/20], Batch Num: [55/600] Discriminator Loss: 1.7818, Generator Loss: 1.1531 D(x): 0.5384, D(G(z)): 0.5769 Epoch: [2/20], Batch Num: [56/600] Discriminator Loss: 1.9141, Generator Loss: 1.0352 D(x): 0.4922, D(G(z)): 0.5655 Epoch: [2/20], Batch Num: [57/600] Discriminator Loss: 2.1137, Generator Loss: 0.7768 D(x): 0.5271, D(G(z)): 0.6556 Epoch: [2/20], Batch Num: [58/600] Discriminator Loss: 2.2849, Generator Loss: 0.6060 D(x): 0.5448, D(G(z)): 0.7076 Epoch: [2/20], Batch Num: [59/600] Discriminator Loss: 1.7242, Generator Loss: 0.7029 D(x): 0.6118, D(G(z)): 0.6370 Epoch: [2/20], Batch Num: [60/600] Discriminator Loss: 1.7646, Generator Loss: 0.8943 D(x): 0.6129, D(G(z)): 0.6268 Epoch: [2/20], Batch Num: [61/600] Discriminator Loss: 1.4393, Generator Loss: 1.0023 D(x): 0.6038, D(G(z)): 0.5466 Epoch: [2/20], Batch Num: [62/600] Discriminator Loss: 1.3470, Generator Loss: 1.4008 D(x): 0.6031, D(G(z)): 0.4889 Epoch: [2/20], Batch Num: [63/600] Discriminator Loss: 1.2851, Generator Loss: 1.7936 D(x): 0.5925, D(G(z)): 0.4144 Epoch: [2/20], Batch Num: [64/600] Discriminator Loss: 0.9000, Generator Loss: 2.1793 D(x): 0.6714, D(G(z)): 0.3378 Epoch: [2/20], Batch Num: [65/600] Discriminator Loss: 0.7247, Generator Loss: 3.5202 D(x): 0.6912, D(G(z)): 0.2545 Epoch: [2/20], Batch Num: [66/600] Discriminator Loss: 0.7025, Generator Loss: 3.4735 D(x): 0.6645, D(G(z)): 0.1878 Epoch: [2/20], Batch Num: [67/600] Discriminator Loss: 0.6521, Generator Loss: 4.3486 D(x): 0.7000, D(G(z)): 0.2129 Epoch: [2/20], Batch Num: [68/600] Discriminator Loss: 0.5654, Generator Loss: 3.9689 D(x): 0.6922, D(G(z)): 0.1420 Epoch: [2/20], Batch Num: [69/600] Discriminator Loss: 0.6501, Generator Loss: 4.3305 D(x): 0.6838, D(G(z)): 0.1908 Epoch: [2/20], Batch Num: [70/600] Discriminator Loss: 0.5740, Generator Loss: 4.6725 D(x): 0.7212, D(G(z)): 0.1728 Epoch: [2/20], Batch Num: [71/600] Discriminator Loss: 0.5425, Generator Loss: 4.2481 D(x): 0.7095, D(G(z)): 0.1407 Epoch: [2/20], Batch Num: [72/600] Discriminator Loss: 0.6461, Generator Loss: 3.8396 D(x): 0.7011, D(G(z)): 0.1859 Epoch: [2/20], Batch Num: [73/600] Discriminator Loss: 0.6709, Generator Loss: 3.8049 D(x): 0.6923, D(G(z)): 0.2070 Epoch: [2/20], Batch Num: [74/600] Discriminator Loss: 0.8011, Generator Loss: 3.0118 D(x): 0.6943, D(G(z)): 0.2825 Epoch: [2/20], Batch Num: [75/600] Discriminator Loss: 0.8511, Generator Loss: 2.8632 D(x): 0.6709, D(G(z)): 0.2725 Epoch: [2/20], Batch Num: [76/600] Discriminator Loss: 0.9176, Generator Loss: 2.5440 D(x): 0.7119, D(G(z)): 0.3690 Epoch: [2/20], Batch Num: [77/600] Discriminator Loss: 1.0962, Generator Loss: 2.0821 D(x): 0.6202, D(G(z)): 0.3430 Epoch: [2/20], Batch Num: [78/600] Discriminator Loss: 1.1464, Generator Loss: 1.6457 D(x): 0.6251, D(G(z)): 0.3783 Epoch: [2/20], Batch Num: [79/600] Discriminator Loss: 1.4565, Generator Loss: 1.2371 D(x): 0.6297, D(G(z)): 0.5007 Epoch: [2/20], Batch Num: [80/600] Discriminator Loss: 1.5061, Generator Loss: 0.8715 D(x): 0.6446, D(G(z)): 0.5715 Epoch: [2/20], Batch Num: [81/600] Discriminator Loss: 1.5925, Generator Loss: 0.6923 D(x): 0.6256, D(G(z)): 0.5961 Epoch: [2/20], Batch Num: [82/600] Discriminator Loss: 1.5971, Generator Loss: 0.7409 D(x): 0.6708, D(G(z)): 0.6486 Epoch: [2/20], Batch Num: [83/600] Discriminator Loss: 1.6176, Generator Loss: 0.6715 D(x): 0.6256, D(G(z)): 0.5955 Epoch: [2/20], Batch Num: [84/600] Discriminator Loss: 1.7087, Generator Loss: 0.5648 D(x): 0.5772, D(G(z)): 0.6117 Epoch: [2/20], Batch Num: [85/600] Discriminator Loss: 1.7493, Generator Loss: 0.5444 D(x): 0.6173, D(G(z)): 0.6451 Epoch: [2/20], Batch Num: [86/600] Discriminator Loss: 1.6112, Generator Loss: 0.5554 D(x): 0.6377, D(G(z)): 0.6444 Epoch: [2/20], Batch Num: [87/600] Discriminator Loss: 1.7207, Generator Loss: 0.5346 D(x): 0.5921, D(G(z)): 0.6450 Epoch: [2/20], Batch Num: [88/600] Discriminator Loss: 1.4851, Generator Loss: 0.5552 D(x): 0.6349, D(G(z)): 0.5948 Epoch: [2/20], Batch Num: [89/600] Discriminator Loss: 1.4719, Generator Loss: 0.6409 D(x): 0.6416, D(G(z)): 0.6083 Epoch: [2/20], Batch Num: [90/600] Discriminator Loss: 1.3410, Generator Loss: 0.7152 D(x): 0.6091, D(G(z)): 0.5444 Epoch: [2/20], Batch Num: [91/600] Discriminator Loss: 1.3934, Generator Loss: 0.7282 D(x): 0.5655, D(G(z)): 0.5342 Epoch: [2/20], Batch Num: [92/600] Discriminator Loss: 1.2941, Generator Loss: 0.7305 D(x): 0.5898, D(G(z)): 0.5075 Epoch: [2/20], Batch Num: [93/600] Discriminator Loss: 1.3444, Generator Loss: 0.7406 D(x): 0.5656, D(G(z)): 0.5093 Epoch: [2/20], Batch Num: [94/600] Discriminator Loss: 1.3486, Generator Loss: 0.7245 D(x): 0.5710, D(G(z)): 0.5228 Epoch: [2/20], Batch Num: [95/600] Discriminator Loss: 1.2929, Generator Loss: 0.7231 D(x): 0.5824, D(G(z)): 0.5055 Epoch: [2/20], Batch Num: [96/600] Discriminator Loss: 1.2931, Generator Loss: 0.7235 D(x): 0.5832, D(G(z)): 0.5133 Epoch: [2/20], Batch Num: [97/600] Discriminator Loss: 1.2129, Generator Loss: 0.7086 D(x): 0.6140, D(G(z)): 0.5019 Epoch: [2/20], Batch Num: [98/600] Discriminator Loss: 1.2439, Generator Loss: 0.6945 D(x): 0.5964, D(G(z)): 0.5031 Epoch: [2/20], Batch Num: [99/600] Discriminator Loss: 1.2120, Generator Loss: 0.6890 D(x): 0.6289, D(G(z)): 0.5148 Epoch: 2, Batch Num: [100/600]
Epoch: [2/20], Batch Num: [100/600] Discriminator Loss: 1.2216, Generator Loss: 0.7269 D(x): 0.6161, D(G(z)): 0.5112 Epoch: [2/20], Batch Num: [101/600] Discriminator Loss: 1.1936, Generator Loss: 0.6971 D(x): 0.6218, D(G(z)): 0.5005 Epoch: [2/20], Batch Num: [102/600] Discriminator Loss: 1.1821, Generator Loss: 0.7190 D(x): 0.6268, D(G(z)): 0.5007 Epoch: [2/20], Batch Num: [103/600] Discriminator Loss: 1.1475, Generator Loss: 0.7350 D(x): 0.6499, D(G(z)): 0.5012 Epoch: [2/20], Batch Num: [104/600] Discriminator Loss: 1.1152, Generator Loss: 0.7673 D(x): 0.6555, D(G(z)): 0.4876 Epoch: [2/20], Batch Num: [105/600] Discriminator Loss: 1.0933, Generator Loss: 0.7797 D(x): 0.6421, D(G(z)): 0.4649 Epoch: [2/20], Batch Num: [106/600] Discriminator Loss: 1.1252, Generator Loss: 0.7872 D(x): 0.6222, D(G(z)): 0.4650 Epoch: [2/20], Batch Num: [107/600] Discriminator Loss: 1.1267, Generator Loss: 0.8079 D(x): 0.6178, D(G(z)): 0.4607 Epoch: [2/20], Batch Num: [108/600] Discriminator Loss: 1.0905, Generator Loss: 0.8119 D(x): 0.6292, D(G(z)): 0.4531 Epoch: [2/20], Batch Num: [109/600] Discriminator Loss: 1.0438, Generator Loss: 0.8336 D(x): 0.6608, D(G(z)): 0.4536 Epoch: [2/20], Batch Num: [110/600] Discriminator Loss: 1.0993, Generator Loss: 0.8189 D(x): 0.6252, D(G(z)): 0.4499 Epoch: [2/20], Batch Num: [111/600] Discriminator Loss: 1.0249, Generator Loss: 0.8323 D(x): 0.6741, D(G(z)): 0.4519 Epoch: [2/20], Batch Num: [112/600] Discriminator Loss: 1.0226, Generator Loss: 0.8442 D(x): 0.6766, D(G(z)): 0.4534 Epoch: [2/20], Batch Num: [113/600] Discriminator Loss: 1.0241, Generator Loss: 0.8658 D(x): 0.6780, D(G(z)): 0.4513 Epoch: [2/20], Batch Num: [114/600] Discriminator Loss: 0.9950, Generator Loss: 0.8617 D(x): 0.6984, D(G(z)): 0.4503 Epoch: [2/20], Batch Num: [115/600] Discriminator Loss: 1.0134, Generator Loss: 0.9329 D(x): 0.6616, D(G(z)): 0.4308 Epoch: [2/20], Batch Num: [116/600] Discriminator Loss: 0.9741, Generator Loss: 1.0410 D(x): 0.6818, D(G(z)): 0.4195 Epoch: [2/20], Batch Num: [117/600] Discriminator Loss: 0.9088, Generator Loss: 1.0435 D(x): 0.7197, D(G(z)): 0.4114 Epoch: [2/20], Batch Num: [118/600] Discriminator Loss: 0.9226, Generator Loss: 1.1435 D(x): 0.6619, D(G(z)): 0.3688 Epoch: [2/20], Batch Num: [119/600] Discriminator Loss: 0.9316, Generator Loss: 1.1057 D(x): 0.6660, D(G(z)): 0.3727 Epoch: [2/20], Batch Num: [120/600] Discriminator Loss: 0.8651, Generator Loss: 1.0988 D(x): 0.6917, D(G(z)): 0.3575 Epoch: [2/20], Batch Num: [121/600] Discriminator Loss: 0.7850, Generator Loss: 1.1665 D(x): 0.7322, D(G(z)): 0.3457 Epoch: [2/20], Batch Num: [122/600] Discriminator Loss: 0.8334, Generator Loss: 1.1551 D(x): 0.7234, D(G(z)): 0.3633 Epoch: [2/20], Batch Num: [123/600] Discriminator Loss: 0.8414, Generator Loss: 1.1993 D(x): 0.7244, D(G(z)): 0.3612 Epoch: [2/20], Batch Num: [124/600] Discriminator Loss: 0.8522, Generator Loss: 1.2546 D(x): 0.7364, D(G(z)): 0.3783 Epoch: [2/20], Batch Num: [125/600] Discriminator Loss: 0.7556, Generator Loss: 1.4038 D(x): 0.7629, D(G(z)): 0.3371 Epoch: [2/20], Batch Num: [126/600] Discriminator Loss: 0.8448, Generator Loss: 1.3384 D(x): 0.7077, D(G(z)): 0.3239 Epoch: [2/20], Batch Num: [127/600] Discriminator Loss: 0.7672, Generator Loss: 1.3446 D(x): 0.7252, D(G(z)): 0.3079 Epoch: [2/20], Batch Num: [128/600] Discriminator Loss: 0.7995, Generator Loss: 1.4763 D(x): 0.7458, D(G(z)): 0.3334 Epoch: [2/20], Batch Num: [129/600] Discriminator Loss: 0.8442, Generator Loss: 1.4104 D(x): 0.7233, D(G(z)): 0.3294 Epoch: [2/20], Batch Num: [130/600] Discriminator Loss: 0.8505, Generator Loss: 1.4422 D(x): 0.7264, D(G(z)): 0.3527 Epoch: [2/20], Batch Num: [131/600] Discriminator Loss: 0.8613, Generator Loss: 1.4177 D(x): 0.7306, D(G(z)): 0.3348 Epoch: [2/20], Batch Num: [132/600] Discriminator Loss: 0.8717, Generator Loss: 1.4433 D(x): 0.7103, D(G(z)): 0.3350 Epoch: [2/20], Batch Num: [133/600] Discriminator Loss: 0.8954, Generator Loss: 1.4931 D(x): 0.7155, D(G(z)): 0.3292 Epoch: 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Generator Loss: 1.8779 D(x): 0.7856, D(G(z)): 0.2990 Epoch: [2/20], Batch Num: [143/600] Discriminator Loss: 0.7246, Generator Loss: 1.9706 D(x): 0.7581, D(G(z)): 0.2330 Epoch: [2/20], Batch Num: [144/600] Discriminator Loss: 0.7074, Generator Loss: 2.0144 D(x): 0.7202, D(G(z)): 0.2061 Epoch: [2/20], Batch Num: [145/600] Discriminator Loss: 0.7008, Generator Loss: 1.6846 D(x): 0.7262, D(G(z)): 0.2028 Epoch: [2/20], Batch Num: [146/600] Discriminator Loss: 0.6445, Generator Loss: 1.8232 D(x): 0.8138, D(G(z)): 0.2815 Epoch: [2/20], Batch Num: [147/600] Discriminator Loss: 0.5758, Generator Loss: 2.3862 D(x): 0.8616, D(G(z)): 0.2985 Epoch: [2/20], Batch Num: [148/600] Discriminator Loss: 0.4371, Generator Loss: 2.6734 D(x): 0.8297, D(G(z)): 0.1621 Epoch: [2/20], Batch Num: [149/600] Discriminator Loss: 0.3831, Generator Loss: 2.8323 D(x): 0.8212, D(G(z)): 0.1121 Epoch: [2/20], Batch Num: [150/600] Discriminator Loss: 0.3439, Generator Loss: 2.8658 D(x): 0.8519, D(G(z)): 0.1180 Epoch: [2/20], Batch Num: [151/600] Discriminator Loss: 0.3785, Generator Loss: 2.7806 D(x): 0.8337, D(G(z)): 0.1067 Epoch: [2/20], Batch Num: [152/600] Discriminator Loss: 0.3305, Generator Loss: 2.3434 D(x): 0.8749, D(G(z)): 0.1321 Epoch: [2/20], Batch Num: [153/600] Discriminator Loss: 0.2640, Generator Loss: 2.3467 D(x): 0.9471, D(G(z)): 0.1690 Epoch: [2/20], Batch Num: [154/600] Discriminator Loss: 0.2821, Generator Loss: 2.7527 D(x): 0.9233, D(G(z)): 0.1429 Epoch: [2/20], Batch Num: [155/600] Discriminator Loss: 0.2577, Generator Loss: 3.3981 D(x): 0.9155, D(G(z)): 0.1285 Epoch: [2/20], Batch Num: [156/600] Discriminator Loss: 0.2602, Generator Loss: 3.4536 D(x): 0.8885, D(G(z)): 0.0795 Epoch: [2/20], Batch Num: [157/600] Discriminator Loss: 0.2851, Generator Loss: 3.6702 D(x): 0.8805, D(G(z)): 0.0726 Epoch: [2/20], Batch Num: [158/600] Discriminator Loss: 0.2417, Generator Loss: 3.5472 D(x): 0.8872, D(G(z)): 0.0638 Epoch: [2/20], Batch Num: [159/600] Discriminator Loss: 0.1936, Generator Loss: 3.2389 D(x): 0.9278, D(G(z)): 0.0856 Epoch: [2/20], Batch Num: [160/600] Discriminator Loss: 0.2009, Generator Loss: 2.8930 D(x): 0.9443, D(G(z)): 0.1137 Epoch: [2/20], Batch Num: [161/600] Discriminator Loss: 0.2378, Generator Loss: 3.2490 D(x): 0.9457, D(G(z)): 0.1344 Epoch: [2/20], Batch Num: [162/600] Discriminator Loss: 0.2831, Generator Loss: 3.2153 D(x): 0.8856, D(G(z)): 0.1090 Epoch: [2/20], Batch Num: [163/600] Discriminator Loss: 0.2765, Generator Loss: 3.1942 D(x): 0.9093, D(G(z)): 0.1041 Epoch: [2/20], Batch Num: [164/600] Discriminator Loss: 0.2416, Generator Loss: 3.4386 D(x): 0.9233, D(G(z)): 0.0984 Epoch: [2/20], Batch Num: [165/600] Discriminator Loss: 0.3815, Generator Loss: 3.0889 D(x): 0.8588, D(G(z)): 0.1055 Epoch: [2/20], Batch Num: [166/600] Discriminator Loss: 0.2821, Generator Loss: 3.1096 D(x): 0.9390, D(G(z)): 0.1648 Epoch: [2/20], Batch Num: [167/600] Discriminator Loss: 0.3089, Generator Loss: 3.4440 D(x): 0.9184, D(G(z)): 0.1620 Epoch: [2/20], Batch Num: [168/600] Discriminator Loss: 0.4145, Generator Loss: 3.0830 D(x): 0.8444, D(G(z)): 0.1167 Epoch: [2/20], Batch Num: [169/600] Discriminator Loss: 0.3937, Generator Loss: 2.6504 D(x): 0.8651, D(G(z)): 0.1134 Epoch: [2/20], Batch Num: [170/600] Discriminator Loss: 0.4067, Generator Loss: 2.3166 D(x): 0.8609, D(G(z)): 0.1607 Epoch: [2/20], Batch Num: [171/600] Discriminator Loss: 0.3961, Generator Loss: 2.3707 D(x): 0.9167, D(G(z)): 0.2199 Epoch: [2/20], Batch Num: [172/600] Discriminator Loss: 0.4846, Generator Loss: 2.4148 D(x): 0.8544, D(G(z)): 0.2116 Epoch: [2/20], Batch Num: [173/600] Discriminator Loss: 0.4911, Generator Loss: 2.2355 D(x): 0.8697, D(G(z)): 0.2311 Epoch: [2/20], Batch Num: [174/600] Discriminator Loss: 0.5836, Generator Loss: 2.2701 D(x): 0.8338, D(G(z)): 0.2206 Epoch: [2/20], Batch Num: [175/600] Discriminator Loss: 0.6273, Generator Loss: 2.0311 D(x): 0.8305, D(G(z)): 0.2299 Epoch: [2/20], Batch Num: [176/600] Discriminator Loss: 0.6566, Generator Loss: 1.7431 D(x): 0.7990, D(G(z)): 0.2241 Epoch: [2/20], Batch Num: [177/600] Discriminator Loss: 0.5562, Generator Loss: 1.8414 D(x): 0.9034, D(G(z)): 0.3170 Epoch: [2/20], Batch Num: [178/600] Discriminator Loss: 0.6213, Generator Loss: 2.0356 D(x): 0.8502, D(G(z)): 0.2980 Epoch: [2/20], Batch Num: [179/600] Discriminator Loss: 0.7616, Generator Loss: 2.0139 D(x): 0.7860, D(G(z)): 0.2564 Epoch: [2/20], Batch Num: [180/600] Discriminator Loss: 0.9603, Generator Loss: 1.4052 D(x): 0.6923, D(G(z)): 0.2320 Epoch: [2/20], Batch Num: [181/600] Discriminator Loss: 0.9537, Generator Loss: 1.0084 D(x): 0.8060, D(G(z)): 0.3825 Epoch: [2/20], Batch Num: [182/600] Discriminator Loss: 0.9977, Generator Loss: 1.4183 D(x): 0.8548, D(G(z)): 0.4859 Epoch: [2/20], Batch Num: [183/600] Discriminator Loss: 0.8942, Generator Loss: 1.6151 D(x): 0.8363, D(G(z)): 0.3990 Epoch: [2/20], Batch Num: [184/600] Discriminator Loss: 0.8580, Generator Loss: 1.7019 D(x): 0.7283, D(G(z)): 0.2600 Epoch: [2/20], Batch Num: [185/600] Discriminator Loss: 1.0662, Generator Loss: 1.4666 D(x): 0.6641, D(G(z)): 0.2512 Epoch: [2/20], Batch Num: [186/600] Discriminator Loss: 0.7843, Generator Loss: 1.3124 D(x): 0.8222, D(G(z)): 0.3407 Epoch: [2/20], Batch Num: [187/600] Discriminator Loss: 0.7525, Generator Loss: 1.2557 D(x): 0.8733, D(G(z)): 0.4135 Epoch: [2/20], Batch Num: [188/600] Discriminator Loss: 0.8128, Generator Loss: 1.4593 D(x): 0.8329, D(G(z)): 0.3787 Epoch: [2/20], Batch Num: [189/600] Discriminator Loss: 0.5217, Generator Loss: 1.6942 D(x): 0.8733, D(G(z)): 0.2617 Epoch: [2/20], Batch Num: [190/600] Discriminator Loss: 0.6199, Generator Loss: 1.8003 D(x): 0.8189, D(G(z)): 0.2408 Epoch: [2/20], Batch Num: [191/600] Discriminator Loss: 0.5184, Generator Loss: 1.8276 D(x): 0.8504, D(G(z)): 0.2262 Epoch: [2/20], Batch Num: [192/600] Discriminator Loss: 0.5503, Generator Loss: 1.7021 D(x): 0.8313, D(G(z)): 0.2289 Epoch: [2/20], Batch Num: [193/600] Discriminator Loss: 0.4570, Generator Loss: 1.6527 D(x): 0.8858, D(G(z)): 0.2276 Epoch: [2/20], Batch Num: [194/600] Discriminator Loss: 0.4335, Generator Loss: 1.8009 D(x): 0.9103, D(G(z)): 0.2512 Epoch: [2/20], Batch Num: [195/600] Discriminator Loss: 0.4418, Generator Loss: 1.8827 D(x): 0.9067, D(G(z)): 0.2394 Epoch: [2/20], Batch Num: [196/600] Discriminator Loss: 0.3577, Generator Loss: 2.2083 D(x): 0.9343, D(G(z)): 0.2121 Epoch: [2/20], Batch Num: [197/600] Discriminator Loss: 0.3532, Generator Loss: 2.2960 D(x): 0.8930, D(G(z)): 0.1618 Epoch: [2/20], Batch Num: [198/600] Discriminator Loss: 0.3038, Generator Loss: 2.4703 D(x): 0.9049, D(G(z)): 0.1360 Epoch: [2/20], Batch Num: [199/600] Discriminator Loss: 0.2994, Generator Loss: 2.4640 D(x): 0.9175, D(G(z)): 0.1213 Epoch: 2, Batch Num: [200/600]
Epoch: [2/20], Batch Num: [200/600] Discriminator Loss: 0.1966, Generator Loss: 2.5392 D(x): 0.9532, D(G(z)): 0.1199 Epoch: [2/20], Batch Num: [201/600] Discriminator Loss: 0.2374, Generator Loss: 2.5643 D(x): 0.9336, D(G(z)): 0.1113 Epoch: [2/20], Batch Num: [202/600] Discriminator Loss: 0.2348, Generator Loss: 2.6261 D(x): 0.9582, D(G(z)): 0.1305 Epoch: [2/20], Batch Num: [203/600] Discriminator Loss: 0.1315, Generator Loss: 2.6071 D(x): 0.9754, D(G(z)): 0.0970 Epoch: [2/20], Batch Num: [204/600] Discriminator Loss: 0.1988, Generator Loss: 2.8531 D(x): 0.9478, D(G(z)): 0.0997 Epoch: [2/20], Batch Num: [205/600] Discriminator Loss: 0.2003, Generator Loss: 2.9138 D(x): 0.9605, D(G(z)): 0.1184 Epoch: [2/20], Batch Num: [206/600] Discriminator Loss: 0.2128, Generator Loss: 3.0314 D(x): 0.9402, D(G(z)): 0.0850 Epoch: [2/20], Batch Num: [207/600] Discriminator Loss: 0.1497, Generator Loss: 3.1428 D(x): 0.9634, D(G(z)): 0.0855 Epoch: [2/20], Batch Num: [208/600] Discriminator Loss: 0.2075, Generator Loss: 3.2338 D(x): 0.9627, D(G(z)): 0.0927 Epoch: [2/20], Batch Num: [209/600] Discriminator Loss: 0.3507, Generator Loss: 2.8292 D(x): 0.8789, D(G(z)): 0.0721 Epoch: [2/20], Batch Num: [210/600] Discriminator Loss: 0.2419, Generator Loss: 2.8379 D(x): 0.9437, D(G(z)): 0.1209 Epoch: [2/20], Batch Num: [211/600] Discriminator Loss: 0.4057, Generator Loss: 2.4527 D(x): 0.9158, D(G(z)): 0.1510 Epoch: [2/20], Batch Num: [212/600] Discriminator Loss: 0.3752, Generator Loss: 2.8444 D(x): 0.9506, D(G(z)): 0.1986 Epoch: [2/20], Batch Num: [213/600] Discriminator Loss: 0.3685, Generator Loss: 3.0581 D(x): 0.9120, D(G(z)): 0.1355 Epoch: [2/20], Batch Num: [214/600] Discriminator Loss: 0.3537, Generator Loss: 3.1022 D(x): 0.9145, D(G(z)): 0.1213 Epoch: [2/20], Batch Num: [215/600] Discriminator Loss: 0.3431, Generator Loss: 3.1960 D(x): 0.9184, D(G(z)): 0.1347 Epoch: [2/20], Batch Num: [216/600] Discriminator Loss: 0.4126, Generator Loss: 2.9471 D(x): 0.8732, D(G(z)): 0.1034 Epoch: [2/20], Batch Num: [217/600] Discriminator Loss: 0.2373, Generator Loss: 3.1037 D(x): 0.9446, D(G(z)): 0.1308 Epoch: [2/20], Batch Num: [218/600] Discriminator Loss: 0.3489, Generator Loss: 3.1286 D(x): 0.9340, D(G(z)): 0.1660 Epoch: [2/20], Batch Num: [219/600] Discriminator Loss: 0.4888, Generator Loss: 3.4540 D(x): 0.8839, D(G(z)): 0.1425 Epoch: [2/20], Batch Num: [220/600] Discriminator Loss: 0.3311, Generator Loss: 3.0338 D(x): 0.8868, D(G(z)): 0.0892 Epoch: [2/20], Batch Num: [221/600] Discriminator Loss: 0.3246, Generator Loss: 3.0436 D(x): 0.9306, D(G(z)): 0.1344 Epoch: [2/20], Batch Num: [222/600] Discriminator Loss: 0.3966, Generator Loss: 3.2423 D(x): 0.9002, D(G(z)): 0.1455 Epoch: [2/20], Batch Num: [223/600] Discriminator Loss: 0.4510, Generator Loss: 3.3934 D(x): 0.8764, D(G(z)): 0.1342 Epoch: [2/20], Batch Num: [224/600] Discriminator Loss: 0.6325, Generator Loss: 3.3811 D(x): 0.8651, D(G(z)): 0.1905 Epoch: [2/20], Batch Num: [225/600] Discriminator Loss: 0.7398, Generator Loss: 3.3653 D(x): 0.8903, D(G(z)): 0.2500 Epoch: [2/20], Batch Num: [226/600] Discriminator Loss: 0.9046, Generator Loss: 4.2680 D(x): 0.8742, D(G(z)): 0.2994 Epoch: [2/20], Batch Num: [227/600] Discriminator Loss: 1.3982, Generator Loss: 3.4123 D(x): 0.6796, D(G(z)): 0.1927 Epoch: [2/20], Batch Num: [228/600] Discriminator Loss: 1.5795, Generator Loss: 1.9823 D(x): 0.7014, D(G(z)): 0.2888 Epoch: [2/20], Batch Num: [229/600] Discriminator Loss: 2.3389, Generator Loss: 1.5948 D(x): 0.7364, D(G(z)): 0.6237 Epoch: [2/20], Batch Num: [230/600] Discriminator Loss: 3.0946, Generator Loss: 2.0579 D(x): 0.7588, D(G(z)): 0.7958 Epoch: [2/20], Batch Num: [231/600] Discriminator Loss: 4.0759, Generator Loss: 2.0412 D(x): 0.4372, D(G(z)): 0.7109 Epoch: [2/20], Batch Num: [232/600] Discriminator Loss: 3.9451, Generator Loss: 2.5325 D(x): 0.4120, D(G(z)): 0.5843 Epoch: [2/20], Batch Num: [233/600] Discriminator Loss: 3.1698, Generator Loss: 4.2848 D(x): 0.3922, D(G(z)): 0.4359 Epoch: [2/20], Batch Num: [234/600] Discriminator Loss: 2.2166, Generator Loss: 3.6660 D(x): 0.4254, D(G(z)): 0.3113 Epoch: [2/20], Batch Num: [235/600] Discriminator Loss: 1.2165, Generator Loss: 4.8817 D(x): 0.6187, D(G(z)): 0.2423 Epoch: [2/20], Batch Num: [236/600] Discriminator Loss: 0.8926, Generator Loss: 4.4287 D(x): 0.7323, D(G(z)): 0.2743 Epoch: [2/20], Batch Num: [237/600] Discriminator Loss: 0.7124, Generator Loss: 4.7720 D(x): 0.8268, D(G(z)): 0.2834 Epoch: [2/20], Batch Num: [238/600] Discriminator Loss: 0.4692, Generator Loss: 6.2570 D(x): 0.8452, D(G(z)): 0.1981 Epoch: [2/20], Batch Num: [239/600] Discriminator Loss: 0.4025, Generator Loss: 7.2809 D(x): 0.8238, D(G(z)): 0.1310 Epoch: [2/20], Batch Num: [240/600] Discriminator Loss: 0.3531, Generator Loss: 7.8365 D(x): 0.8136, D(G(z)): 0.0882 Epoch: [2/20], Batch Num: [241/600] Discriminator Loss: 0.4581, Generator Loss: 8.6382 D(x): 0.7606, D(G(z)): 0.0870 Epoch: [2/20], Batch Num: [242/600] Discriminator Loss: 0.5483, Generator Loss: 7.1468 D(x): 0.7066, D(G(z)): 0.0516 Epoch: [2/20], Batch Num: [243/600] Discriminator Loss: 0.7615, Generator Loss: 5.9177 D(x): 0.6544, D(G(z)): 0.1129 Epoch: [2/20], Batch Num: [244/600] Discriminator Loss: 0.8805, Generator Loss: 4.8607 D(x): 0.7313, D(G(z)): 0.2245 Epoch: [2/20], Batch Num: [245/600] Discriminator Loss: 0.9160, Generator Loss: 4.0113 D(x): 0.7501, D(G(z)): 0.3070 Epoch: [2/20], Batch Num: [246/600] Discriminator Loss: 1.0085, Generator Loss: 4.6245 D(x): 0.6730, D(G(z)): 0.2389 Epoch: [2/20], Batch Num: [247/600] Discriminator Loss: 0.9361, Generator Loss: 4.2224 D(x): 0.6682, D(G(z)): 0.1970 Epoch: [2/20], Batch Num: [248/600] Discriminator Loss: 0.9090, Generator Loss: 3.4674 D(x): 0.6790, D(G(z)): 0.1769 Epoch: [2/20], Batch Num: [249/600] Discriminator Loss: 0.8156, Generator Loss: 3.2916 D(x): 0.6600, D(G(z)): 0.1882 Epoch: [2/20], Batch Num: [250/600] Discriminator Loss: 0.7969, Generator Loss: 2.7988 D(x): 0.7500, D(G(z)): 0.2723 Epoch: [2/20], Batch Num: [251/600] Discriminator Loss: 0.6964, Generator Loss: 2.9720 D(x): 0.7935, D(G(z)): 0.2780 Epoch: [2/20], Batch Num: [252/600] Discriminator Loss: 0.6371, Generator Loss: 4.0764 D(x): 0.7730, D(G(z)): 0.2197 Epoch: [2/20], Batch Num: [253/600] Discriminator Loss: 0.6632, Generator Loss: 3.9257 D(x): 0.7209, D(G(z)): 0.1374 Epoch: [2/20], Batch Num: [254/600] Discriminator Loss: 0.6331, Generator Loss: 3.6517 D(x): 0.7384, D(G(z)): 0.1713 Epoch: [2/20], Batch Num: [255/600] Discriminator Loss: 0.6546, Generator Loss: 2.5474 D(x): 0.7412, D(G(z)): 0.2095 Epoch: [2/20], Batch Num: [256/600] Discriminator Loss: 0.6187, Generator Loss: 2.7062 D(x): 0.7735, D(G(z)): 0.2140 Epoch: [2/20], Batch Num: [257/600] Discriminator Loss: 0.5855, Generator Loss: 3.1919 D(x): 0.8016, D(G(z)): 0.2230 Epoch: [2/20], Batch Num: [258/600] Discriminator Loss: 0.8087, Generator Loss: 2.6573 D(x): 0.7386, D(G(z)): 0.2450 Epoch: [2/20], Batch Num: [259/600] Discriminator Loss: 0.6764, Generator Loss: 2.4484 D(x): 0.7440, D(G(z)): 0.2138 Epoch: [2/20], Batch Num: [260/600] Discriminator Loss: 0.8945, Generator Loss: 2.0469 D(x): 0.7010, D(G(z)): 0.2493 Epoch: [2/20], Batch Num: [261/600] Discriminator Loss: 0.8705, Generator Loss: 2.4741 D(x): 0.7484, D(G(z)): 0.2911 Epoch: [2/20], Batch Num: [262/600] Discriminator Loss: 0.6924, Generator Loss: 2.7228 D(x): 0.7762, D(G(z)): 0.2550 Epoch: [2/20], Batch Num: [263/600] Discriminator Loss: 0.6681, Generator Loss: 3.4741 D(x): 0.8062, D(G(z)): 0.2307 Epoch: [2/20], Batch Num: [264/600] Discriminator Loss: 0.6603, Generator Loss: 3.5759 D(x): 0.7909, D(G(z)): 0.2095 Epoch: [2/20], Batch Num: [265/600] Discriminator Loss: 0.5911, Generator Loss: 3.1890 D(x): 0.7763, D(G(z)): 0.1635 Epoch: [2/20], Batch Num: [266/600] Discriminator Loss: 0.6026, Generator Loss: 2.8332 D(x): 0.7997, D(G(z)): 0.1853 Epoch: [2/20], Batch Num: [267/600] Discriminator Loss: 0.5120, Generator Loss: 2.7729 D(x): 0.8559, D(G(z)): 0.2173 Epoch: [2/20], Batch Num: [268/600] Discriminator Loss: 0.5473, Generator Loss: 3.1741 D(x): 0.8585, D(G(z)): 0.2336 Epoch: [2/20], Batch Num: [269/600] Discriminator Loss: 0.5142, Generator Loss: 2.8032 D(x): 0.8256, D(G(z)): 0.1810 Epoch: [2/20], Batch Num: [270/600] Discriminator Loss: 0.6834, Generator Loss: 2.1869 D(x): 0.7515, D(G(z)): 0.1819 Epoch: [2/20], Batch Num: [271/600] Discriminator Loss: 0.5074, Generator Loss: 2.2038 D(x): 0.8922, D(G(z)): 0.2545 Epoch: [2/20], Batch Num: [272/600] Discriminator Loss: 0.6096, Generator Loss: 2.3273 D(x): 0.8565, D(G(z)): 0.2799 Epoch: [2/20], Batch Num: [273/600] Discriminator Loss: 0.7676, Generator Loss: 2.5513 D(x): 0.7941, D(G(z)): 0.2477 Epoch: [2/20], Batch Num: [274/600] Discriminator Loss: 0.5270, Generator Loss: 2.3053 D(x): 0.8444, D(G(z)): 0.2267 Epoch: [2/20], Batch Num: [275/600] Discriminator Loss: 0.6790, Generator Loss: 2.2341 D(x): 0.8132, D(G(z)): 0.2532 Epoch: [2/20], Batch Num: [276/600] Discriminator Loss: 0.6038, Generator Loss: 2.5104 D(x): 0.8383, D(G(z)): 0.2590 Epoch: [2/20], Batch Num: [277/600] Discriminator Loss: 0.8125, Generator Loss: 2.0005 D(x): 0.7369, D(G(z)): 0.2248 Epoch: [2/20], Batch Num: [278/600] Discriminator Loss: 0.5907, Generator Loss: 1.8110 D(x): 0.8679, D(G(z)): 0.2823 Epoch: [2/20], Batch Num: [279/600] Discriminator Loss: 0.8563, Generator Loss: 1.9892 D(x): 0.8373, D(G(z)): 0.3939 Epoch: [2/20], Batch Num: [280/600] Discriminator Loss: 0.7812, Generator Loss: 2.4035 D(x): 0.8399, D(G(z)): 0.3167 Epoch: [2/20], Batch Num: [281/600] Discriminator Loss: 0.8930, Generator Loss: 2.2704 D(x): 0.7350, D(G(z)): 0.2540 Epoch: [2/20], Batch Num: [282/600] Discriminator Loss: 0.9244, Generator Loss: 1.8976 D(x): 0.7481, D(G(z)): 0.3245 Epoch: [2/20], Batch Num: [283/600] Discriminator Loss: 0.9397, Generator Loss: 1.5626 D(x): 0.7823, D(G(z)): 0.3690 Epoch: [2/20], Batch Num: [284/600] Discriminator Loss: 1.1227, Generator Loss: 1.5499 D(x): 0.7755, D(G(z)): 0.4360 Epoch: [2/20], Batch Num: [285/600] Discriminator Loss: 1.2688, Generator Loss: 1.7622 D(x): 0.7452, D(G(z)): 0.4482 Epoch: [2/20], Batch Num: [286/600] Discriminator Loss: 1.2031, Generator Loss: 1.5334 D(x): 0.7048, D(G(z)): 0.3939 Epoch: [2/20], Batch Num: [287/600] Discriminator Loss: 1.3510, Generator Loss: 1.4211 D(x): 0.6960, D(G(z)): 0.4389 Epoch: [2/20], Batch Num: [288/600] Discriminator Loss: 1.2711, Generator Loss: 1.5439 D(x): 0.7112, D(G(z)): 0.4442 Epoch: [2/20], Batch Num: [289/600] Discriminator Loss: 1.4759, Generator Loss: 1.2785 D(x): 0.6875, D(G(z)): 0.4475 Epoch: [2/20], Batch Num: [290/600] Discriminator Loss: 1.4272, Generator Loss: 1.1597 D(x): 0.6437, D(G(z)): 0.4698 Epoch: [2/20], Batch Num: [291/600] Discriminator Loss: 1.5918, Generator Loss: 0.9736 D(x): 0.6618, D(G(z)): 0.5123 Epoch: [2/20], Batch Num: [292/600] Discriminator Loss: 1.5854, Generator Loss: 1.1409 D(x): 0.6081, D(G(z)): 0.5367 Epoch: [2/20], Batch Num: [293/600] Discriminator Loss: 1.4588, Generator Loss: 1.0359 D(x): 0.6276, D(G(z)): 0.4903 Epoch: [2/20], Batch Num: [294/600] Discriminator Loss: 1.4301, Generator Loss: 1.0644 D(x): 0.6017, D(G(z)): 0.4764 Epoch: [2/20], Batch Num: [295/600] Discriminator Loss: 1.2677, Generator Loss: 1.0736 D(x): 0.6752, D(G(z)): 0.4935 Epoch: [2/20], Batch Num: [296/600] Discriminator Loss: 1.2858, Generator Loss: 1.1075 D(x): 0.5944, D(G(z)): 0.4294 Epoch: [2/20], Batch Num: [297/600] Discriminator Loss: 1.2168, Generator Loss: 1.0986 D(x): 0.6211, D(G(z)): 0.4309 Epoch: [2/20], Batch Num: [298/600] Discriminator Loss: 1.1458, Generator Loss: 1.0245 D(x): 0.6283, D(G(z)): 0.4135 Epoch: [2/20], Batch Num: [299/600] Discriminator Loss: 1.1468, Generator Loss: 0.9937 D(x): 0.6433, D(G(z)): 0.4370 Epoch: 2, Batch Num: [300/600]
Epoch: [2/20], Batch Num: [300/600] Discriminator Loss: 1.0567, Generator Loss: 0.9578 D(x): 0.6908, D(G(z)): 0.4397 Epoch: [2/20], Batch Num: [301/600] Discriminator Loss: 1.0512, Generator Loss: 1.0031 D(x): 0.6910, D(G(z)): 0.4374 Epoch: [2/20], Batch Num: [302/600] Discriminator Loss: 0.9977, Generator Loss: 0.9954 D(x): 0.7004, D(G(z)): 0.4311 Epoch: [2/20], Batch Num: [303/600] Discriminator Loss: 0.9576, Generator Loss: 1.0091 D(x): 0.7451, D(G(z)): 0.4498 Epoch: [2/20], Batch Num: [304/600] Discriminator Loss: 1.0066, Generator Loss: 1.0242 D(x): 0.6915, D(G(z)): 0.4240 Epoch: [2/20], Batch Num: [305/600] Discriminator Loss: 0.9938, Generator Loss: 1.0587 D(x): 0.6586, D(G(z)): 0.3889 Epoch: [2/20], Batch Num: [306/600] Discriminator Loss: 0.9736, Generator Loss: 1.1313 D(x): 0.6880, D(G(z)): 0.3930 Epoch: [2/20], Batch Num: [307/600] Discriminator Loss: 0.9586, Generator Loss: 1.1593 D(x): 0.6945, D(G(z)): 0.3963 Epoch: [2/20], Batch Num: [308/600] Discriminator Loss: 0.9230, Generator Loss: 1.1899 D(x): 0.6917, D(G(z)): 0.3701 Epoch: [2/20], Batch Num: [309/600] Discriminator Loss: 0.8684, Generator Loss: 1.2617 D(x): 0.7051, D(G(z)): 0.3477 Epoch: [2/20], Batch Num: [310/600] Discriminator Loss: 0.9120, Generator Loss: 1.2183 D(x): 0.6981, D(G(z)): 0.3664 Epoch: [2/20], Batch Num: [311/600] Discriminator Loss: 0.9908, Generator Loss: 1.2407 D(x): 0.6792, D(G(z)): 0.3841 Epoch: [2/20], Batch Num: [312/600] Discriminator Loss: 0.9503, Generator Loss: 1.3097 D(x): 0.7384, D(G(z)): 0.4020 Epoch: [2/20], Batch Num: [313/600] Discriminator Loss: 0.8806, Generator Loss: 1.4785 D(x): 0.7176, D(G(z)): 0.3613 Epoch: [2/20], Batch Num: [314/600] Discriminator Loss: 0.9685, Generator Loss: 1.4071 D(x): 0.6977, D(G(z)): 0.3809 Epoch: [2/20], Batch Num: [315/600] Discriminator Loss: 0.9304, Generator Loss: 1.4115 D(x): 0.6458, D(G(z)): 0.3134 Epoch: [2/20], Batch Num: [316/600] Discriminator Loss: 0.7850, Generator Loss: 1.4478 D(x): 0.7144, D(G(z)): 0.2903 Epoch: [2/20], Batch Num: [317/600] Discriminator Loss: 0.7944, Generator Loss: 1.6323 D(x): 0.7336, D(G(z)): 0.3183 Epoch: [2/20], Batch Num: [318/600] Discriminator Loss: 0.7391, Generator Loss: 1.5790 D(x): 0.7736, D(G(z)): 0.3168 Epoch: [2/20], Batch Num: [319/600] Discriminator Loss: 0.7212, Generator Loss: 1.5747 D(x): 0.7612, D(G(z)): 0.2902 Epoch: [2/20], Batch Num: [320/600] Discriminator Loss: 0.8145, Generator Loss: 1.8822 D(x): 0.7279, D(G(z)): 0.3037 Epoch: [2/20], Batch Num: [321/600] Discriminator Loss: 0.6245, Generator Loss: 2.0327 D(x): 0.7651, D(G(z)): 0.2327 Epoch: [2/20], Batch Num: [322/600] Discriminator Loss: 0.6227, Generator Loss: 2.2540 D(x): 0.7660, D(G(z)): 0.2143 Epoch: [2/20], Batch Num: [323/600] Discriminator Loss: 0.5413, Generator Loss: 2.1911 D(x): 0.7866, D(G(z)): 0.1985 Epoch: [2/20], Batch Num: [324/600] Discriminator Loss: 0.5519, Generator Loss: 2.1145 D(x): 0.8341, D(G(z)): 0.2465 Epoch: [2/20], Batch Num: [325/600] Discriminator Loss: 0.6038, Generator Loss: 2.5374 D(x): 0.7736, D(G(z)): 0.2142 Epoch: [2/20], Batch Num: [326/600] Discriminator Loss: 0.5389, Generator Loss: 2.5130 D(x): 0.8416, D(G(z)): 0.2427 Epoch: [2/20], Batch Num: [327/600] Discriminator Loss: 0.4334, Generator Loss: 2.9547 D(x): 0.8492, D(G(z)): 0.1835 Epoch: [2/20], Batch Num: [328/600] Discriminator Loss: 0.3759, Generator Loss: 3.4240 D(x): 0.8603, D(G(z)): 0.1540 Epoch: [2/20], Batch Num: [329/600] Discriminator Loss: 0.3554, Generator Loss: 3.7256 D(x): 0.8549, D(G(z)): 0.1290 Epoch: [2/20], Batch Num: [330/600] Discriminator Loss: 0.3398, Generator Loss: 3.5663 D(x): 0.8708, D(G(z)): 0.1273 Epoch: [2/20], Batch Num: [331/600] Discriminator Loss: 0.3394, Generator Loss: 3.9599 D(x): 0.8752, D(G(z)): 0.1342 Epoch: [2/20], Batch Num: [332/600] Discriminator Loss: 0.4018, Generator Loss: 3.4140 D(x): 0.8301, D(G(z)): 0.1209 Epoch: [2/20], Batch Num: [333/600] Discriminator Loss: 0.6029, Generator Loss: 3.3662 D(x): 0.7860, D(G(z)): 0.1663 Epoch: [2/20], Batch Num: [334/600] Discriminator Loss: 0.3930, Generator Loss: 3.1205 D(x): 0.8833, D(G(z)): 0.1741 Epoch: [2/20], Batch Num: [335/600] Discriminator Loss: 0.4727, Generator Loss: 3.2295 D(x): 0.8898, D(G(z)): 0.2327 Epoch: [2/20], Batch Num: [336/600] Discriminator Loss: 0.5214, Generator Loss: 3.0566 D(x): 0.8512, D(G(z)): 0.2093 Epoch: [2/20], Batch Num: [337/600] Discriminator Loss: 0.6377, Generator Loss: 3.0273 D(x): 0.8442, D(G(z)): 0.2680 Epoch: [2/20], Batch Num: [338/600] Discriminator Loss: 0.5392, Generator Loss: 3.2758 D(x): 0.8066, D(G(z)): 0.1635 Epoch: [2/20], Batch Num: [339/600] Discriminator Loss: 0.5141, Generator Loss: 3.2806 D(x): 0.8311, D(G(z)): 0.1878 Epoch: [2/20], Batch Num: [340/600] Discriminator Loss: 0.5553, Generator Loss: 3.6376 D(x): 0.8092, D(G(z)): 0.1769 Epoch: [2/20], Batch Num: [341/600] Discriminator Loss: 0.5829, Generator Loss: 3.3564 D(x): 0.7769, D(G(z)): 0.1808 Epoch: [2/20], Batch Num: [342/600] Discriminator Loss: 0.5075, Generator Loss: 3.0666 D(x): 0.8095, D(G(z)): 0.1680 Epoch: [2/20], Batch Num: [343/600] Discriminator Loss: 0.5697, Generator Loss: 2.7632 D(x): 0.8150, D(G(z)): 0.2027 Epoch: [2/20], Batch Num: [344/600] Discriminator Loss: 0.6537, Generator Loss: 2.6159 D(x): 0.8209, D(G(z)): 0.2583 Epoch: [2/20], Batch Num: [345/600] Discriminator Loss: 0.7501, Generator Loss: 2.9296 D(x): 0.7866, D(G(z)): 0.2808 Epoch: [2/20], Batch Num: [346/600] Discriminator Loss: 0.5853, Generator Loss: 3.2945 D(x): 0.8432, D(G(z)): 0.2554 Epoch: [2/20], Batch Num: [347/600] Discriminator Loss: 0.5283, Generator Loss: 3.4034 D(x): 0.8132, D(G(z)): 0.1936 Epoch: [2/20], Batch Num: [348/600] Discriminator Loss: 0.4703, Generator Loss: 3.7964 D(x): 0.7955, D(G(z)): 0.1238 Epoch: [2/20], Batch Num: [349/600] Discriminator Loss: 0.8883, Generator Loss: 3.1566 D(x): 0.7256, D(G(z)): 0.1798 Epoch: [2/20], Batch Num: [350/600] Discriminator Loss: 0.7122, Generator Loss: 2.3841 D(x): 0.7703, D(G(z)): 0.1883 Epoch: [2/20], Batch Num: [351/600] Discriminator Loss: 0.8566, Generator Loss: 2.2246 D(x): 0.7801, D(G(z)): 0.2966 Epoch: [2/20], Batch Num: [352/600] Discriminator Loss: 0.9554, Generator Loss: 1.8300 D(x): 0.7604, D(G(z)): 0.3165 Epoch: [2/20], Batch Num: [353/600] Discriminator Loss: 0.8756, Generator Loss: 2.1016 D(x): 0.7653, D(G(z)): 0.3067 Epoch: [2/20], Batch Num: [354/600] Discriminator Loss: 0.9446, Generator Loss: 2.0095 D(x): 0.7458, D(G(z)): 0.3174 Epoch: [2/20], Batch Num: [355/600] Discriminator Loss: 0.9266, Generator Loss: 1.9333 D(x): 0.7526, D(G(z)): 0.3398 Epoch: [2/20], Batch Num: [356/600] Discriminator Loss: 0.9797, Generator Loss: 1.7456 D(x): 0.7202, D(G(z)): 0.3140 Epoch: [2/20], Batch Num: [357/600] Discriminator Loss: 0.9235, Generator Loss: 1.5439 D(x): 0.7406, D(G(z)): 0.3292 Epoch: [2/20], Batch Num: [358/600] Discriminator Loss: 1.1168, Generator Loss: 1.3020 D(x): 0.6780, D(G(z)): 0.3713 Epoch: [2/20], Batch Num: [359/600] Discriminator Loss: 1.2368, Generator Loss: 1.2939 D(x): 0.6708, D(G(z)): 0.4201 Epoch: [2/20], Batch Num: [360/600] Discriminator Loss: 1.2819, Generator Loss: 1.0516 D(x): 0.6863, D(G(z)): 0.4668 Epoch: [2/20], Batch Num: [361/600] Discriminator Loss: 1.3485, Generator Loss: 1.0065 D(x): 0.6693, D(G(z)): 0.4622 Epoch: [2/20], Batch Num: [362/600] Discriminator Loss: 1.3692, Generator Loss: 0.9342 D(x): 0.6520, D(G(z)): 0.4328 Epoch: [2/20], Batch Num: [363/600] Discriminator Loss: 1.1573, Generator Loss: 0.8806 D(x): 0.7399, D(G(z)): 0.4784 Epoch: [2/20], Batch Num: [364/600] Discriminator Loss: 0.9351, Generator Loss: 0.9142 D(x): 0.7994, D(G(z)): 0.4570 Epoch: [2/20], Batch Num: [365/600] Discriminator Loss: 0.9302, Generator Loss: 0.8872 D(x): 0.7734, D(G(z)): 0.4229 Epoch: [2/20], Batch Num: [366/600] Discriminator Loss: 0.8973, Generator Loss: 0.9312 D(x): 0.7953, D(G(z)): 0.4209 Epoch: [2/20], Batch Num: [367/600] Discriminator Loss: 0.8513, Generator Loss: 0.9471 D(x): 0.7948, D(G(z)): 0.4100 Epoch: [2/20], Batch Num: [368/600] Discriminator Loss: 0.8911, Generator Loss: 0.9374 D(x): 0.7838, D(G(z)): 0.4249 Epoch: [2/20], Batch Num: [369/600] Discriminator Loss: 0.8445, Generator Loss: 0.9330 D(x): 0.8047, D(G(z)): 0.4175 Epoch: [2/20], Batch Num: [370/600] Discriminator Loss: 0.7713, Generator Loss: 0.9550 D(x): 0.8443, D(G(z)): 0.4181 Epoch: [2/20], Batch Num: [371/600] Discriminator Loss: 0.7105, Generator Loss: 0.9557 D(x): 0.8430, D(G(z)): 0.3882 Epoch: [2/20], Batch Num: [372/600] Discriminator Loss: 0.7257, Generator Loss: 1.0453 D(x): 0.8288, D(G(z)): 0.3795 Epoch: [2/20], Batch Num: [373/600] Discriminator Loss: 0.6296, Generator Loss: 1.0572 D(x): 0.8826, D(G(z)): 0.3737 Epoch: [2/20], Batch Num: [374/600] Discriminator Loss: 0.6678, Generator Loss: 1.1358 D(x): 0.8362, D(G(z)): 0.3513 Epoch: [2/20], Batch Num: [375/600] Discriminator Loss: 0.6253, Generator Loss: 1.1354 D(x): 0.8385, D(G(z)): 0.3307 Epoch: [2/20], Batch Num: [376/600] Discriminator Loss: 0.7209, Generator Loss: 1.1617 D(x): 0.8244, D(G(z)): 0.3658 Epoch: [2/20], Batch Num: [377/600] Discriminator Loss: 0.5981, Generator Loss: 1.2127 D(x): 0.8807, D(G(z)): 0.3517 Epoch: [2/20], Batch Num: [378/600] Discriminator Loss: 0.6075, Generator Loss: 1.2220 D(x): 0.8774, D(G(z)): 0.3481 Epoch: [2/20], Batch Num: [379/600] Discriminator Loss: 0.5462, Generator Loss: 1.2448 D(x): 0.8802, D(G(z)): 0.3063 Epoch: [2/20], Batch Num: [380/600] Discriminator Loss: 0.5356, Generator Loss: 1.3867 D(x): 0.8767, D(G(z)): 0.2931 Epoch: [2/20], Batch Num: [381/600] Discriminator Loss: 0.5490, Generator Loss: 1.4367 D(x): 0.8640, D(G(z)): 0.2971 Epoch: [2/20], Batch Num: [382/600] Discriminator Loss: 0.4586, Generator Loss: 1.4474 D(x): 0.8909, D(G(z)): 0.2580 Epoch: [2/20], Batch Num: [383/600] Discriminator Loss: 0.3824, Generator Loss: 1.6278 D(x): 0.9246, D(G(z)): 0.2452 Epoch: [2/20], Batch Num: [384/600] Discriminator Loss: 0.4622, Generator Loss: 1.8171 D(x): 0.8632, D(G(z)): 0.2318 Epoch: [2/20], Batch Num: [385/600] Discriminator Loss: 0.4306, Generator Loss: 1.8673 D(x): 0.8653, D(G(z)): 0.2060 Epoch: [2/20], Batch Num: [386/600] Discriminator Loss: 0.3572, Generator Loss: 1.9743 D(x): 0.9100, D(G(z)): 0.1856 Epoch: [2/20], Batch Num: [387/600] Discriminator Loss: 0.4001, Generator Loss: 1.9962 D(x): 0.8764, D(G(z)): 0.1846 Epoch: [2/20], Batch Num: [388/600] Discriminator Loss: 0.3196, Generator Loss: 2.3083 D(x): 0.9191, D(G(z)): 0.1705 Epoch: [2/20], Batch Num: [389/600] Discriminator Loss: 0.3370, Generator Loss: 2.2812 D(x): 0.9028, D(G(z)): 0.1718 Epoch: [2/20], Batch Num: [390/600] Discriminator Loss: 0.3373, Generator Loss: 2.4044 D(x): 0.9012, D(G(z)): 0.1634 Epoch: [2/20], Batch Num: [391/600] Discriminator Loss: 0.3272, Generator Loss: 2.4166 D(x): 0.8974, D(G(z)): 0.1556 Epoch: [2/20], Batch Num: [392/600] Discriminator Loss: 0.2181, Generator Loss: 2.6426 D(x): 0.9557, D(G(z)): 0.1411 Epoch: [2/20], Batch Num: [393/600] Discriminator Loss: 0.3140, Generator Loss: 2.8693 D(x): 0.8984, D(G(z)): 0.1491 Epoch: [2/20], Batch Num: [394/600] Discriminator Loss: 0.2852, Generator Loss: 2.9645 D(x): 0.8946, D(G(z)): 0.1160 Epoch: [2/20], Batch Num: [395/600] Discriminator Loss: 0.2875, Generator Loss: 2.7555 D(x): 0.9335, D(G(z)): 0.1660 Epoch: [2/20], Batch Num: [396/600] Discriminator Loss: 0.2950, Generator Loss: 2.8652 D(x): 0.9125, D(G(z)): 0.1421 Epoch: [2/20], Batch Num: [397/600] Discriminator Loss: 0.2988, Generator Loss: 2.9173 D(x): 0.8995, D(G(z)): 0.1202 Epoch: [2/20], Batch Num: [398/600] Discriminator Loss: 0.2143, Generator Loss: 3.3528 D(x): 0.9378, D(G(z)): 0.1145 Epoch: [2/20], Batch Num: [399/600] Discriminator Loss: 0.2044, Generator Loss: 3.2585 D(x): 0.9410, D(G(z)): 0.1053 Epoch: 2, Batch Num: [400/600]
Epoch: [2/20], Batch Num: [400/600] Discriminator Loss: 0.2745, Generator Loss: 3.5276 D(x): 0.9114, D(G(z)): 0.1193 Epoch: [2/20], Batch Num: [401/600] Discriminator Loss: 0.3204, Generator Loss: 3.3905 D(x): 0.8996, D(G(z)): 0.1278 Epoch: [2/20], Batch Num: [402/600] Discriminator Loss: 0.5178, Generator Loss: 3.0527 D(x): 0.8512, D(G(z)): 0.1514 Epoch: [2/20], Batch Num: [403/600] Discriminator Loss: 0.4249, Generator Loss: 2.3728 D(x): 0.9021, D(G(z)): 0.1925 Epoch: [2/20], Batch Num: [404/600] Discriminator Loss: 0.4569, Generator Loss: 2.5446 D(x): 0.8873, D(G(z)): 0.1860 Epoch: [2/20], Batch Num: [405/600] Discriminator Loss: 0.6384, Generator Loss: 2.4561 D(x): 0.8484, D(G(z)): 0.2373 Epoch: [2/20], Batch Num: [406/600] Discriminator Loss: 0.6375, Generator Loss: 2.2214 D(x): 0.8245, D(G(z)): 0.2323 Epoch: [2/20], Batch Num: [407/600] Discriminator Loss: 0.6409, Generator Loss: 2.0040 D(x): 0.8564, D(G(z)): 0.2882 Epoch: [2/20], Batch Num: [408/600] Discriminator Loss: 0.6918, Generator Loss: 2.0669 D(x): 0.8337, D(G(z)): 0.2745 Epoch: [2/20], Batch Num: [409/600] Discriminator Loss: 0.6648, Generator Loss: 2.3280 D(x): 0.8685, D(G(z)): 0.2900 Epoch: [2/20], Batch Num: [410/600] Discriminator Loss: 0.7923, Generator Loss: 2.3476 D(x): 0.7935, D(G(z)): 0.2406 Epoch: [2/20], Batch Num: [411/600] Discriminator Loss: 0.9216, Generator Loss: 2.0103 D(x): 0.7820, D(G(z)): 0.2554 Epoch: [2/20], Batch Num: [412/600] Discriminator Loss: 0.8199, Generator Loss: 1.8127 D(x): 0.8170, D(G(z)): 0.2865 Epoch: [2/20], Batch Num: [413/600] Discriminator Loss: 1.0407, Generator Loss: 1.7485 D(x): 0.7791, D(G(z)): 0.3252 Epoch: [2/20], Batch Num: [414/600] Discriminator Loss: 0.9186, Generator Loss: 1.5330 D(x): 0.8246, D(G(z)): 0.3607 Epoch: [2/20], Batch Num: [415/600] Discriminator Loss: 0.9723, Generator Loss: 1.6970 D(x): 0.7384, D(G(z)): 0.3086 Epoch: [2/20], Batch Num: [416/600] Discriminator Loss: 0.7287, Generator Loss: 1.7410 D(x): 0.8131, D(G(z)): 0.2981 Epoch: [2/20], Batch Num: [417/600] Discriminator Loss: 0.7887, Generator Loss: 1.7738 D(x): 0.8092, D(G(z)): 0.3046 Epoch: [2/20], Batch Num: [418/600] Discriminator Loss: 0.8488, Generator Loss: 1.7216 D(x): 0.7780, D(G(z)): 0.2843 Epoch: [2/20], Batch Num: [419/600] Discriminator Loss: 0.7017, Generator Loss: 1.7921 D(x): 0.8047, D(G(z)): 0.2528 Epoch: [2/20], Batch Num: [420/600] Discriminator Loss: 0.5650, Generator Loss: 1.9751 D(x): 0.8710, D(G(z)): 0.2500 Epoch: [2/20], Batch Num: [421/600] Discriminator Loss: 0.5945, Generator Loss: 2.0637 D(x): 0.8311, D(G(z)): 0.2327 Epoch: [2/20], Batch Num: [422/600] Discriminator Loss: 0.4296, Generator Loss: 2.4088 D(x): 0.8818, D(G(z)): 0.1592 Epoch: [2/20], Batch Num: [423/600] Discriminator Loss: 0.4433, Generator Loss: 2.7440 D(x): 0.8392, D(G(z)): 0.1475 Epoch: [2/20], Batch Num: [424/600] Discriminator Loss: 0.4738, Generator Loss: 2.5962 D(x): 0.8459, D(G(z)): 0.1379 Epoch: [2/20], Batch Num: [425/600] Discriminator Loss: 0.3484, Generator Loss: 2.7546 D(x): 0.8766, D(G(z)): 0.1364 Epoch: [2/20], Batch Num: [426/600] Discriminator Loss: 0.2977, Generator Loss: 2.9396 D(x): 0.9047, D(G(z)): 0.1257 Epoch: [2/20], Batch Num: [427/600] Discriminator Loss: 0.3023, Generator Loss: 3.0054 D(x): 0.9082, D(G(z)): 0.1496 Epoch: [2/20], Batch Num: [428/600] Discriminator Loss: 0.2720, Generator Loss: 3.7799 D(x): 0.9286, D(G(z)): 0.1323 Epoch: [2/20], Batch Num: [429/600] Discriminator Loss: 0.2703, Generator Loss: 3.6790 D(x): 0.9007, D(G(z)): 0.0817 Epoch: [2/20], Batch Num: [430/600] Discriminator Loss: 0.2057, Generator Loss: 4.0350 D(x): 0.9219, D(G(z)): 0.0736 Epoch: [2/20], Batch Num: [431/600] Discriminator Loss: 0.2822, Generator Loss: 4.1748 D(x): 0.8906, D(G(z)): 0.0686 Epoch: [2/20], Batch Num: [432/600] Discriminator Loss: 0.2269, Generator Loss: 4.1825 D(x): 0.9138, D(G(z)): 0.0791 Epoch: [2/20], Batch Num: [433/600] Discriminator Loss: 0.2580, Generator Loss: 4.1548 D(x): 0.9043, D(G(z)): 0.0723 Epoch: [2/20], Batch Num: [434/600] Discriminator Loss: 0.3496, Generator Loss: 3.7479 D(x): 0.8884, D(G(z)): 0.1083 Epoch: [2/20], Batch Num: [435/600] Discriminator Loss: 0.3469, Generator Loss: 3.8927 D(x): 0.9149, D(G(z)): 0.1508 Epoch: [2/20], Batch Num: [436/600] Discriminator Loss: 0.4160, Generator Loss: 4.0126 D(x): 0.8647, D(G(z)): 0.1251 Epoch: [2/20], Batch Num: [437/600] Discriminator Loss: 0.3025, Generator Loss: 3.6865 D(x): 0.8940, D(G(z)): 0.1053 Epoch: [2/20], Batch Num: [438/600] Discriminator Loss: 0.3433, Generator Loss: 3.4583 D(x): 0.9144, D(G(z)): 0.1415 Epoch: [2/20], Batch Num: [439/600] Discriminator Loss: 0.3446, Generator Loss: 3.4902 D(x): 0.8940, D(G(z)): 0.1186 Epoch: [2/20], Batch Num: [440/600] Discriminator Loss: 0.3959, Generator Loss: 3.3043 D(x): 0.8891, D(G(z)): 0.1329 Epoch: [2/20], Batch Num: [441/600] Discriminator Loss: 0.5427, Generator Loss: 2.6103 D(x): 0.8331, D(G(z)): 0.1192 Epoch: [2/20], Batch Num: [442/600] Discriminator Loss: 0.5560, Generator Loss: 2.4245 D(x): 0.8886, D(G(z)): 0.2203 Epoch: [2/20], Batch Num: [443/600] Discriminator Loss: 0.5837, Generator Loss: 2.5715 D(x): 0.8712, D(G(z)): 0.2275 Epoch: [2/20], Batch Num: [444/600] Discriminator Loss: 0.5535, Generator Loss: 2.8628 D(x): 0.8755, D(G(z)): 0.2157 Epoch: [2/20], Batch Num: [445/600] Discriminator Loss: 0.6432, Generator Loss: 3.0493 D(x): 0.8263, D(G(z)): 0.1648 Epoch: [2/20], Batch Num: [446/600] Discriminator Loss: 0.6699, Generator Loss: 2.9913 D(x): 0.8142, D(G(z)): 0.1734 Epoch: [2/20], Batch Num: [447/600] Discriminator Loss: 0.6816, Generator Loss: 2.5295 D(x): 0.8253, D(G(z)): 0.1597 Epoch: [2/20], Batch Num: [448/600] Discriminator Loss: 0.7495, Generator Loss: 2.2059 D(x): 0.8122, D(G(z)): 0.2041 Epoch: [2/20], Batch Num: [449/600] Discriminator Loss: 0.8510, Generator Loss: 2.0334 D(x): 0.8064, D(G(z)): 0.2469 Epoch: [2/20], Batch Num: [450/600] Discriminator Loss: 0.7071, Generator Loss: 1.9393 D(x): 0.8582, D(G(z)): 0.2745 Epoch: [2/20], Batch Num: [451/600] Discriminator Loss: 0.7735, Generator Loss: 1.9692 D(x): 0.8555, D(G(z)): 0.2940 Epoch: [2/20], Batch Num: [452/600] Discriminator Loss: 0.6170, Generator Loss: 2.3472 D(x): 0.8742, D(G(z)): 0.2587 Epoch: [2/20], Batch Num: [453/600] Discriminator Loss: 0.5012, Generator Loss: 2.6153 D(x): 0.8634, D(G(z)): 0.1874 Epoch: [2/20], Batch Num: [454/600] Discriminator Loss: 0.5718, Generator Loss: 2.6426 D(x): 0.8519, D(G(z)): 0.1927 Epoch: [2/20], Batch Num: [455/600] Discriminator Loss: 0.5511, Generator Loss: 2.6613 D(x): 0.8834, D(G(z)): 0.1863 Epoch: [2/20], Batch Num: [456/600] Discriminator Loss: 0.5833, Generator Loss: 2.3886 D(x): 0.8274, D(G(z)): 0.1661 Epoch: [2/20], Batch Num: [457/600] Discriminator Loss: 0.4469, Generator Loss: 2.4618 D(x): 0.8855, D(G(z)): 0.1778 Epoch: [2/20], Batch Num: [458/600] Discriminator Loss: 0.5454, Generator Loss: 2.3137 D(x): 0.8564, D(G(z)): 0.2063 Epoch: [2/20], Batch Num: [459/600] Discriminator Loss: 0.5122, Generator Loss: 2.2254 D(x): 0.8612, D(G(z)): 0.1866 Epoch: [2/20], Batch Num: [460/600] Discriminator Loss: 0.4347, Generator Loss: 2.3141 D(x): 0.8822, D(G(z)): 0.1798 Epoch: [2/20], Batch Num: [461/600] Discriminator Loss: 0.3830, Generator Loss: 2.5606 D(x): 0.9346, D(G(z)): 0.2049 Epoch: [2/20], Batch Num: [462/600] Discriminator Loss: 0.4267, Generator Loss: 2.6615 D(x): 0.9052, D(G(z)): 0.1967 Epoch: [2/20], Batch Num: [463/600] Discriminator Loss: 0.3264, Generator Loss: 2.8548 D(x): 0.9294, D(G(z)): 0.1532 Epoch: [2/20], Batch Num: [464/600] Discriminator Loss: 0.2993, Generator Loss: 3.2597 D(x): 0.9203, D(G(z)): 0.1194 Epoch: [2/20], Batch Num: [465/600] Discriminator Loss: 0.2845, Generator Loss: 3.4642 D(x): 0.9149, D(G(z)): 0.1258 Epoch: [2/20], Batch Num: [466/600] Discriminator Loss: 0.2151, Generator Loss: 3.4062 D(x): 0.9168, D(G(z)): 0.0707 Epoch: [2/20], Batch Num: [467/600] Discriminator Loss: 0.1855, Generator Loss: 3.3853 D(x): 0.9397, D(G(z)): 0.0831 Epoch: [2/20], Batch Num: [468/600] Discriminator Loss: 0.2478, Generator Loss: 3.2761 D(x): 0.9154, D(G(z)): 0.0882 Epoch: [2/20], Batch Num: [469/600] Discriminator Loss: 0.2758, Generator Loss: 3.0355 D(x): 0.9170, D(G(z)): 0.1078 Epoch: [2/20], Batch Num: [470/600] Discriminator Loss: 0.2224, Generator Loss: 2.7507 D(x): 0.9489, D(G(z)): 0.1262 Epoch: [2/20], Batch Num: [471/600] Discriminator Loss: 0.2787, Generator Loss: 3.2291 D(x): 0.9649, D(G(z)): 0.1545 Epoch: [2/20], Batch Num: [472/600] Discriminator Loss: 0.1915, Generator Loss: 3.6381 D(x): 0.9532, D(G(z)): 0.0963 Epoch: [2/20], Batch Num: [473/600] Discriminator Loss: 0.2094, Generator Loss: 3.9777 D(x): 0.9381, D(G(z)): 0.0972 Epoch: [2/20], Batch Num: [474/600] Discriminator Loss: 0.3151, Generator Loss: 4.3085 D(x): 0.9149, D(G(z)): 0.0855 Epoch: [2/20], Batch Num: [475/600] Discriminator Loss: 0.3432, Generator Loss: 3.8488 D(x): 0.8808, D(G(z)): 0.0834 Epoch: [2/20], Batch Num: [476/600] Discriminator Loss: 0.2883, Generator Loss: 3.8112 D(x): 0.9173, D(G(z)): 0.0977 Epoch: [2/20], Batch Num: [477/600] Discriminator Loss: 0.2179, Generator Loss: 3.8800 D(x): 0.9454, D(G(z)): 0.1125 Epoch: [2/20], Batch Num: [478/600] Discriminator Loss: 0.2282, Generator Loss: 4.1743 D(x): 0.9507, D(G(z)): 0.1280 Epoch: [2/20], Batch Num: [479/600] Discriminator Loss: 0.3396, Generator Loss: 4.0208 D(x): 0.9042, D(G(z)): 0.1220 Epoch: [2/20], Batch Num: [480/600] Discriminator Loss: 0.3884, Generator Loss: 4.3220 D(x): 0.8866, D(G(z)): 0.1051 Epoch: [2/20], Batch Num: [481/600] Discriminator Loss: 0.4102, Generator Loss: 4.0973 D(x): 0.8899, D(G(z)): 0.0890 Epoch: [2/20], Batch Num: [482/600] Discriminator Loss: 0.3637, Generator Loss: 3.3373 D(x): 0.8786, D(G(z)): 0.0859 Epoch: [2/20], Batch Num: [483/600] Discriminator Loss: 0.4131, Generator Loss: 3.4526 D(x): 0.9027, D(G(z)): 0.1551 Epoch: [2/20], Batch Num: [484/600] Discriminator Loss: 0.4898, Generator Loss: 3.1357 D(x): 0.8961, D(G(z)): 0.1636 Epoch: [2/20], Batch Num: [485/600] Discriminator Loss: 0.5895, Generator Loss: 3.5426 D(x): 0.8688, D(G(z)): 0.1843 Epoch: [2/20], Batch Num: [486/600] Discriminator Loss: 0.4594, Generator Loss: 3.5920 D(x): 0.8806, D(G(z)): 0.1480 Epoch: [2/20], Batch Num: [487/600] Discriminator Loss: 0.5292, Generator Loss: 3.5926 D(x): 0.8749, D(G(z)): 0.1484 Epoch: [2/20], Batch Num: [488/600] Discriminator Loss: 0.6545, Generator Loss: 2.9984 D(x): 0.8305, D(G(z)): 0.1365 Epoch: [2/20], Batch Num: [489/600] Discriminator Loss: 0.6481, Generator Loss: 2.7483 D(x): 0.8374, D(G(z)): 0.1723 Epoch: [2/20], Batch Num: [490/600] Discriminator Loss: 0.6186, Generator Loss: 1.9750 D(x): 0.8096, D(G(z)): 0.1556 Epoch: [2/20], Batch Num: [491/600] Discriminator Loss: 0.6628, Generator Loss: 2.0127 D(x): 0.8571, D(G(z)): 0.2682 Epoch: [2/20], Batch Num: [492/600] Discriminator Loss: 0.5876, Generator Loss: 2.3191 D(x): 0.8893, D(G(z)): 0.2564 Epoch: [2/20], Batch Num: [493/600] Discriminator Loss: 0.5343, Generator Loss: 2.9938 D(x): 0.8580, D(G(z)): 0.2106 Epoch: [2/20], Batch Num: [494/600] Discriminator Loss: 0.7279, Generator Loss: 2.8302 D(x): 0.8069, D(G(z)): 0.1431 Epoch: [2/20], Batch Num: [495/600] Discriminator Loss: 0.4339, Generator Loss: 3.0634 D(x): 0.8484, D(G(z)): 0.1268 Epoch: [2/20], Batch Num: [496/600] Discriminator Loss: 0.5605, Generator Loss: 2.9121 D(x): 0.8513, D(G(z)): 0.1616 Epoch: [2/20], Batch Num: [497/600] Discriminator Loss: 0.4371, Generator Loss: 2.5759 D(x): 0.8708, D(G(z)): 0.1573 Epoch: [2/20], Batch Num: [498/600] Discriminator Loss: 0.3628, Generator Loss: 2.8961 D(x): 0.9287, D(G(z)): 0.1851 Epoch: [2/20], Batch Num: [499/600] Discriminator Loss: 0.3382, Generator Loss: 3.3413 D(x): 0.9136, D(G(z)): 0.1471 Epoch: 2, Batch Num: [500/600]
Epoch: [2/20], Batch Num: [500/600] Discriminator Loss: 0.3280, Generator Loss: 3.5138 D(x): 0.8833, D(G(z)): 0.1026 Epoch: [2/20], Batch Num: [501/600] Discriminator Loss: 0.3592, Generator Loss: 3.5746 D(x): 0.8742, D(G(z)): 0.0982 Epoch: [2/20], Batch Num: [502/600] Discriminator Loss: 0.2388, Generator Loss: 3.3838 D(x): 0.9162, D(G(z)): 0.0946 Epoch: [2/20], Batch Num: [503/600] Discriminator Loss: 0.2142, Generator Loss: 3.8340 D(x): 0.9285, D(G(z)): 0.0847 Epoch: [2/20], Batch Num: [504/600] Discriminator Loss: 0.5557, Generator Loss: 3.3326 D(x): 0.8176, D(G(z)): 0.0983 Epoch: [2/20], Batch Num: [505/600] Discriminator Loss: 0.3239, Generator Loss: 2.9646 D(x): 0.9270, D(G(z)): 0.1338 Epoch: [2/20], Batch Num: [506/600] Discriminator Loss: 0.4123, Generator Loss: 3.1733 D(x): 0.9252, D(G(z)): 0.1817 Epoch: [2/20], Batch Num: [507/600] Discriminator Loss: 0.3160, Generator Loss: 3.6987 D(x): 0.9045, D(G(z)): 0.1223 Epoch: [2/20], Batch Num: [508/600] Discriminator Loss: 0.3227, Generator Loss: 3.5277 D(x): 0.8840, D(G(z)): 0.1054 Epoch: [2/20], Batch Num: [509/600] Discriminator Loss: 0.2917, Generator Loss: 3.5108 D(x): 0.8957, D(G(z)): 0.0996 Epoch: [2/20], Batch Num: [510/600] Discriminator Loss: 0.4209, Generator Loss: 3.7104 D(x): 0.8800, D(G(z)): 0.0975 Epoch: [2/20], Batch Num: [511/600] Discriminator Loss: 0.3071, Generator Loss: 3.4448 D(x): 0.8773, D(G(z)): 0.0921 Epoch: [2/20], Batch Num: [512/600] Discriminator Loss: 0.3566, Generator Loss: 2.8003 D(x): 0.9086, D(G(z)): 0.1400 Epoch: [2/20], Batch Num: [513/600] Discriminator Loss: 0.3298, Generator Loss: 3.3152 D(x): 0.9238, D(G(z)): 0.1657 Epoch: [2/20], Batch Num: [514/600] Discriminator Loss: 0.3061, Generator Loss: 3.0098 D(x): 0.9119, D(G(z)): 0.1314 Epoch: [2/20], Batch Num: [515/600] Discriminator Loss: 0.3857, Generator Loss: 3.4769 D(x): 0.8724, D(G(z)): 0.1262 Epoch: [2/20], Batch Num: [516/600] Discriminator Loss: 0.3805, Generator Loss: 3.2151 D(x): 0.8817, D(G(z)): 0.1168 Epoch: [2/20], Batch Num: [517/600] Discriminator Loss: 0.2494, Generator Loss: 3.2816 D(x): 0.9254, D(G(z)): 0.1093 Epoch: [2/20], Batch Num: [518/600] Discriminator Loss: 0.3378, Generator Loss: 3.4366 D(x): 0.9104, D(G(z)): 0.1427 Epoch: [2/20], Batch Num: [519/600] Discriminator Loss: 0.3430, Generator Loss: 3.2848 D(x): 0.8943, D(G(z)): 0.1266 Epoch: [2/20], Batch Num: [520/600] Discriminator Loss: 0.3168, Generator Loss: 3.9282 D(x): 0.9189, D(G(z)): 0.1051 Epoch: [2/20], Batch Num: [521/600] Discriminator Loss: 0.3326, Generator Loss: 4.0431 D(x): 0.9157, D(G(z)): 0.1112 Epoch: [2/20], Batch Num: [522/600] Discriminator Loss: 0.5829, Generator Loss: 3.2619 D(x): 0.8144, D(G(z)): 0.0955 Epoch: [2/20], Batch Num: [523/600] Discriminator Loss: 0.5408, Generator Loss: 3.2318 D(x): 0.8455, D(G(z)): 0.1323 Epoch: [2/20], Batch Num: [524/600] Discriminator Loss: 0.4846, Generator Loss: 2.5967 D(x): 0.8480, D(G(z)): 0.1365 Epoch: [2/20], Batch Num: [525/600] Discriminator Loss: 0.4630, Generator Loss: 2.7210 D(x): 0.9284, D(G(z)): 0.2446 Epoch: [2/20], Batch Num: [526/600] Discriminator Loss: 0.4760, Generator Loss: 3.4729 D(x): 0.8852, D(G(z)): 0.1866 Epoch: [2/20], Batch Num: [527/600] Discriminator Loss: 0.3595, Generator Loss: 3.7769 D(x): 0.8745, D(G(z)): 0.1102 Epoch: [2/20], Batch Num: [528/600] Discriminator Loss: 0.6139, Generator Loss: 2.9864 D(x): 0.7943, D(G(z)): 0.1106 Epoch: [2/20], Batch Num: [529/600] Discriminator Loss: 0.5276, Generator Loss: 2.6043 D(x): 0.8398, D(G(z)): 0.1444 Epoch: [2/20], Batch Num: [530/600] Discriminator Loss: 0.4922, Generator Loss: 1.9068 D(x): 0.8877, D(G(z)): 0.2195 Epoch: [2/20], Batch Num: [531/600] Discriminator Loss: 0.4768, Generator Loss: 2.1967 D(x): 0.9059, D(G(z)): 0.2310 Epoch: [2/20], Batch Num: [532/600] Discriminator Loss: 0.6611, Generator Loss: 2.6549 D(x): 0.8219, D(G(z)): 0.2043 Epoch: [2/20], Batch Num: [533/600] Discriminator Loss: 0.4753, Generator Loss: 2.7623 D(x): 0.8868, D(G(z)): 0.1713 Epoch: 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Generator Loss: 2.2791 D(x): 0.8404, D(G(z)): 0.1854 Epoch: [2/20], Batch Num: [543/600] Discriminator Loss: 0.6087, Generator Loss: 2.5898 D(x): 0.8369, D(G(z)): 0.1873 Epoch: [2/20], Batch Num: [544/600] Discriminator Loss: 0.6396, Generator Loss: 2.3771 D(x): 0.8585, D(G(z)): 0.2372 Epoch: [2/20], Batch Num: [545/600] Discriminator Loss: 0.5555, Generator Loss: 2.7561 D(x): 0.8592, D(G(z)): 0.2024 Epoch: [2/20], Batch Num: [546/600] Discriminator Loss: 0.5553, Generator Loss: 2.9965 D(x): 0.8929, D(G(z)): 0.2307 Epoch: [2/20], Batch Num: [547/600] Discriminator Loss: 0.5368, Generator Loss: 3.3924 D(x): 0.8460, D(G(z)): 0.1711 Epoch: [2/20], Batch Num: [548/600] Discriminator Loss: 0.6376, Generator Loss: 3.0162 D(x): 0.7824, D(G(z)): 0.1350 Epoch: [2/20], Batch Num: [549/600] Discriminator Loss: 0.5440, Generator Loss: 3.0545 D(x): 0.8086, D(G(z)): 0.0958 Epoch: [2/20], Batch Num: [550/600] Discriminator Loss: 0.4516, Generator Loss: 2.5883 D(x): 0.8426, D(G(z)): 0.1250 Epoch: [2/20], Batch Num: [551/600] Discriminator Loss: 0.4362, Generator Loss: 2.2810 D(x): 0.8881, D(G(z)): 0.1830 Epoch: [2/20], Batch Num: [552/600] Discriminator Loss: 0.4817, Generator Loss: 2.2490 D(x): 0.8478, D(G(z)): 0.1525 Epoch: [2/20], Batch Num: [553/600] Discriminator Loss: 0.4413, Generator Loss: 2.2241 D(x): 0.9011, D(G(z)): 0.2069 Epoch: [2/20], Batch Num: [554/600] Discriminator Loss: 0.5147, Generator Loss: 2.0441 D(x): 0.8487, D(G(z)): 0.1875 Epoch: [2/20], Batch Num: [555/600] Discriminator Loss: 0.5012, Generator Loss: 1.9946 D(x): 0.8450, D(G(z)): 0.2105 Epoch: [2/20], Batch Num: [556/600] Discriminator Loss: 0.5286, Generator Loss: 2.0413 D(x): 0.8613, D(G(z)): 0.2114 Epoch: [2/20], Batch Num: [557/600] Discriminator Loss: 0.4146, Generator Loss: 2.5323 D(x): 0.8805, D(G(z)): 0.1837 Epoch: [2/20], Batch Num: [558/600] Discriminator Loss: 0.5613, Generator Loss: 2.4303 D(x): 0.8454, D(G(z)): 0.2051 Epoch: [2/20], Batch Num: [559/600] Discriminator Loss: 0.4537, Generator Loss: 2.4315 D(x): 0.8343, D(G(z)): 0.1549 Epoch: [2/20], Batch Num: [560/600] Discriminator Loss: 0.5165, Generator Loss: 2.2935 D(x): 0.8397, D(G(z)): 0.1524 Epoch: [2/20], Batch Num: [561/600] Discriminator Loss: 0.4419, Generator Loss: 2.3356 D(x): 0.8601, D(G(z)): 0.1703 Epoch: [2/20], Batch Num: [562/600] Discriminator Loss: 0.4702, Generator Loss: 2.1895 D(x): 0.8549, D(G(z)): 0.1849 Epoch: [2/20], Batch Num: [563/600] Discriminator Loss: 0.4353, Generator Loss: 2.3790 D(x): 0.8705, D(G(z)): 0.1904 Epoch: [2/20], Batch Num: [564/600] Discriminator Loss: 0.6189, Generator Loss: 2.4683 D(x): 0.7985, D(G(z)): 0.1925 Epoch: [2/20], Batch Num: [565/600] Discriminator Loss: 0.6424, Generator Loss: 2.2647 D(x): 0.8378, D(G(z)): 0.2264 Epoch: [2/20], Batch Num: [566/600] Discriminator Loss: 0.5839, Generator Loss: 2.3010 D(x): 0.8535, D(G(z)): 0.2016 Epoch: [2/20], Batch Num: [567/600] Discriminator Loss: 0.6366, Generator Loss: 2.2389 D(x): 0.7911, D(G(z)): 0.1904 Epoch: [2/20], Batch Num: [568/600] Discriminator Loss: 0.5808, Generator Loss: 2.3189 D(x): 0.8298, D(G(z)): 0.2223 Epoch: [2/20], Batch Num: [569/600] Discriminator Loss: 0.6957, Generator Loss: 2.4354 D(x): 0.7666, D(G(z)): 0.2139 Epoch: [2/20], Batch Num: [570/600] Discriminator Loss: 0.5452, Generator Loss: 2.6839 D(x): 0.8782, D(G(z)): 0.2317 Epoch: [2/20], Batch Num: [571/600] Discriminator Loss: 0.6377, Generator Loss: 2.9216 D(x): 0.8109, D(G(z)): 0.1684 Epoch: [2/20], Batch Num: [572/600] Discriminator Loss: 0.5700, Generator Loss: 2.7795 D(x): 0.8136, D(G(z)): 0.1421 Epoch: [2/20], Batch Num: [573/600] Discriminator Loss: 0.6299, Generator Loss: 2.5451 D(x): 0.7773, D(G(z)): 0.1606 Epoch: [2/20], Batch Num: [574/600] Discriminator Loss: 0.7134, Generator Loss: 2.3985 D(x): 0.8287, D(G(z)): 0.1901 Epoch: [2/20], Batch Num: [575/600] Discriminator Loss: 0.8514, Generator Loss: 2.3777 D(x): 0.8184, D(G(z)): 0.2653 Epoch: [2/20], Batch Num: [576/600] Discriminator Loss: 0.7823, Generator Loss: 2.5128 D(x): 0.8205, D(G(z)): 0.2516 Epoch: [2/20], Batch Num: [577/600] Discriminator Loss: 0.6397, Generator Loss: 3.0134 D(x): 0.8297, D(G(z)): 0.1860 Epoch: [2/20], Batch Num: [578/600] Discriminator Loss: 0.5856, Generator Loss: 3.1050 D(x): 0.8485, D(G(z)): 0.1838 Epoch: [2/20], Batch Num: [579/600] Discriminator Loss: 0.7295, Generator Loss: 3.1830 D(x): 0.7949, D(G(z)): 0.1851 Epoch: [2/20], Batch Num: [580/600] Discriminator Loss: 0.5727, Generator Loss: 2.8754 D(x): 0.8319, D(G(z)): 0.1845 Epoch: [2/20], Batch Num: [581/600] Discriminator Loss: 0.6010, Generator Loss: 2.9561 D(x): 0.8483, D(G(z)): 0.1687 Epoch: [2/20], Batch Num: [582/600] Discriminator Loss: 0.6010, Generator Loss: 3.3821 D(x): 0.8452, D(G(z)): 0.1799 Epoch: [2/20], Batch Num: [583/600] Discriminator Loss: 0.4814, Generator Loss: 3.8126 D(x): 0.8789, D(G(z)): 0.1532 Epoch: [2/20], Batch Num: [584/600] Discriminator Loss: 0.4436, Generator Loss: 3.8514 D(x): 0.8659, D(G(z)): 0.1289 Epoch: [2/20], Batch Num: [585/600] Discriminator Loss: 0.3764, Generator Loss: 4.1429 D(x): 0.8784, D(G(z)): 0.0898 Epoch: [2/20], Batch Num: [586/600] Discriminator Loss: 0.3435, Generator Loss: 3.6664 D(x): 0.8634, D(G(z)): 0.0809 Epoch: [2/20], Batch Num: [587/600] Discriminator Loss: 0.2946, Generator Loss: 3.3778 D(x): 0.9128, D(G(z)): 0.1116 Epoch: [2/20], Batch Num: [588/600] Discriminator Loss: 0.3056, Generator Loss: 3.8511 D(x): 0.9287, D(G(z)): 0.1496 Epoch: [2/20], Batch Num: [589/600] Discriminator Loss: 0.2916, Generator Loss: 4.5541 D(x): 0.9632, D(G(z)): 0.1417 Epoch: [2/20], Batch Num: [590/600] Discriminator Loss: 0.2233, Generator Loss: 5.6558 D(x): 0.9111, D(G(z)): 0.0685 Epoch: [2/20], Batch Num: [591/600] Discriminator Loss: 0.2316, Generator Loss: 6.0755 D(x): 0.9092, D(G(z)): 0.0528 Epoch: [2/20], Batch Num: [592/600] Discriminator Loss: 0.3043, Generator Loss: 5.7643 D(x): 0.9000, D(G(z)): 0.0589 Epoch: [2/20], Batch Num: [593/600] Discriminator Loss: 0.1839, Generator Loss: 5.5321 D(x): 0.9292, D(G(z)): 0.0517 Epoch: [2/20], Batch Num: [594/600] Discriminator Loss: 0.3034, Generator Loss: 4.8261 D(x): 0.8907, D(G(z)): 0.0544 Epoch: [2/20], Batch Num: [595/600] Discriminator Loss: 0.1963, Generator Loss: 4.3770 D(x): 0.9441, D(G(z)): 0.0900 Epoch: [2/20], Batch Num: [596/600] Discriminator Loss: 0.3671, Generator Loss: 4.7107 D(x): 0.9218, D(G(z)): 0.1598 Epoch: [2/20], Batch Num: [597/600] Discriminator Loss: 0.2457, Generator Loss: 5.1484 D(x): 0.9385, D(G(z)): 0.1212 Epoch: [2/20], Batch Num: [598/600] Discriminator Loss: 0.3434, Generator Loss: 4.9325 D(x): 0.9160, D(G(z)): 0.1101 Epoch: [2/20], Batch Num: [599/600] Discriminator Loss: 0.3542, Generator Loss: 5.1652 D(x): 0.8854, D(G(z)): 0.0799 Epoch: 3, Batch Num: [0/600]
Epoch: [3/20], Batch Num: [0/600] Discriminator Loss: 0.2803, Generator Loss: 4.6414 D(x): 0.8805, D(G(z)): 0.0668 Epoch: [3/20], Batch Num: [1/600] Discriminator Loss: 0.5252, Generator Loss: 3.4366 D(x): 0.8180, D(G(z)): 0.1060 Epoch: [3/20], Batch Num: [2/600] Discriminator Loss: 0.4270, Generator Loss: 2.7340 D(x): 0.8839, D(G(z)): 0.1439 Epoch: [3/20], Batch Num: [3/600] Discriminator Loss: 0.5769, Generator Loss: 2.5804 D(x): 0.8975, D(G(z)): 0.2647 Epoch: [3/20], Batch Num: [4/600] Discriminator Loss: 0.7294, Generator Loss: 3.2428 D(x): 0.8473, D(G(z)): 0.2689 Epoch: [3/20], Batch Num: [5/600] Discriminator Loss: 0.5935, Generator Loss: 3.3538 D(x): 0.8202, D(G(z)): 0.1132 Epoch: [3/20], Batch Num: [6/600] Discriminator Loss: 0.6471, Generator Loss: 3.1355 D(x): 0.7632, D(G(z)): 0.1024 Epoch: [3/20], Batch Num: [7/600] Discriminator Loss: 0.5833, Generator Loss: 2.5193 D(x): 0.8336, D(G(z)): 0.1623 Epoch: [3/20], Batch Num: [8/600] Discriminator Loss: 0.7906, Generator Loss: 2.2499 D(x): 0.7884, D(G(z)): 0.1979 Epoch: [3/20], Batch Num: [9/600] Discriminator Loss: 0.7086, Generator Loss: 2.1264 D(x): 0.8079, D(G(z)): 0.2020 Epoch: [3/20], Batch Num: [10/600] Discriminator Loss: 0.6892, Generator Loss: 2.0445 D(x): 0.7967, D(G(z)): 0.2079 Epoch: [3/20], Batch Num: [11/600] Discriminator Loss: 0.6036, Generator Loss: 1.9682 D(x): 0.8458, D(G(z)): 0.2311 Epoch: [3/20], Batch Num: [12/600] Discriminator Loss: 0.5267, Generator Loss: 2.1940 D(x): 0.8711, D(G(z)): 0.2322 Epoch: [3/20], Batch Num: [13/600] Discriminator Loss: 0.5221, Generator Loss: 2.3340 D(x): 0.8212, D(G(z)): 0.1687 Epoch: [3/20], Batch Num: [14/600] Discriminator Loss: 0.6611, Generator Loss: 2.1094 D(x): 0.7706, D(G(z)): 0.1662 Epoch: [3/20], Batch Num: [15/600] Discriminator Loss: 0.5351, Generator Loss: 2.3262 D(x): 0.8346, D(G(z)): 0.1940 Epoch: [3/20], Batch Num: [16/600] Discriminator Loss: 0.4095, Generator Loss: 2.1235 D(x): 0.8817, D(G(z)): 0.1786 Epoch: [3/20], Batch Num: [17/600] Discriminator Loss: 0.5104, Generator Loss: 2.0567 D(x): 0.8444, D(G(z)): 0.1936 Epoch: [3/20], Batch Num: [18/600] Discriminator Loss: 0.5000, Generator Loss: 2.1121 D(x): 0.8345, D(G(z)): 0.1721 Epoch: [3/20], Batch Num: [19/600] Discriminator Loss: 0.3622, Generator Loss: 2.4158 D(x): 0.9006, D(G(z)): 0.1783 Epoch: [3/20], Batch Num: [20/600] Discriminator Loss: 0.4071, Generator Loss: 2.4992 D(x): 0.8647, D(G(z)): 0.1451 Epoch: [3/20], Batch Num: [21/600] Discriminator Loss: 0.4324, Generator Loss: 2.6467 D(x): 0.8727, D(G(z)): 0.1808 Epoch: [3/20], Batch Num: [22/600] Discriminator Loss: 0.3289, Generator Loss: 2.6265 D(x): 0.8949, D(G(z)): 0.1438 Epoch: [3/20], Batch Num: [23/600] Discriminator Loss: 0.3931, Generator Loss: 2.8208 D(x): 0.8764, D(G(z)): 0.1473 Epoch: [3/20], Batch Num: [24/600] Discriminator Loss: 0.3953, Generator Loss: 2.7778 D(x): 0.8661, D(G(z)): 0.1398 Epoch: [3/20], Batch Num: [25/600] Discriminator Loss: 0.4617, Generator Loss: 2.7617 D(x): 0.8773, D(G(z)): 0.1833 Epoch: [3/20], Batch Num: [26/600] Discriminator Loss: 0.3485, Generator Loss: 3.0695 D(x): 0.8841, D(G(z)): 0.1348 Epoch: [3/20], Batch Num: [27/600] Discriminator Loss: 0.4292, Generator Loss: 2.4642 D(x): 0.8560, D(G(z)): 0.1404 Epoch: [3/20], Batch Num: [28/600] Discriminator Loss: 0.3944, Generator Loss: 2.4170 D(x): 0.8791, D(G(z)): 0.1576 Epoch: [3/20], Batch Num: [29/600] Discriminator Loss: 0.4000, Generator Loss: 2.5855 D(x): 0.8966, D(G(z)): 0.1709 Epoch: [3/20], Batch Num: [30/600] Discriminator Loss: 0.4017, Generator Loss: 2.3666 D(x): 0.8876, D(G(z)): 0.1631 Epoch: [3/20], Batch Num: [31/600] Discriminator Loss: 0.3921, Generator Loss: 2.8048 D(x): 0.8751, D(G(z)): 0.1447 Epoch: [3/20], Batch Num: [32/600] Discriminator Loss: 0.3727, Generator Loss: 2.6628 D(x): 0.8917, D(G(z)): 0.1721 Epoch: [3/20], Batch Num: [33/600] Discriminator Loss: 0.3934, Generator Loss: 2.4001 D(x): 0.8708, D(G(z)): 0.1221 Epoch: [3/20], Batch Num: [34/600] Discriminator Loss: 0.3839, Generator Loss: 2.5294 D(x): 0.8921, D(G(z)): 0.1818 Epoch: [3/20], Batch Num: [35/600] Discriminator Loss: 0.4118, Generator Loss: 2.3544 D(x): 0.8780, D(G(z)): 0.1725 Epoch: [3/20], Batch Num: [36/600] Discriminator Loss: 0.5539, Generator Loss: 2.7144 D(x): 0.8452, D(G(z)): 0.1939 Epoch: [3/20], Batch Num: [37/600] Discriminator Loss: 0.4037, Generator Loss: 2.6046 D(x): 0.8819, D(G(z)): 0.1620 Epoch: [3/20], Batch Num: [38/600] Discriminator Loss: 0.4329, Generator Loss: 2.6971 D(x): 0.8806, D(G(z)): 0.1683 Epoch: [3/20], Batch Num: [39/600] Discriminator Loss: 0.4165, Generator Loss: 2.6403 D(x): 0.8641, D(G(z)): 0.1504 Epoch: [3/20], Batch Num: [40/600] Discriminator Loss: 0.4019, Generator Loss: 2.3765 D(x): 0.8845, D(G(z)): 0.1494 Epoch: [3/20], Batch Num: [41/600] Discriminator Loss: 0.3654, Generator Loss: 2.3894 D(x): 0.9150, D(G(z)): 0.1674 Epoch: [3/20], Batch Num: [42/600] Discriminator Loss: 0.2733, Generator Loss: 2.2136 D(x): 0.9281, D(G(z)): 0.1376 Epoch: [3/20], Batch Num: [43/600] Discriminator Loss: 0.3727, Generator Loss: 2.6807 D(x): 0.8942, D(G(z)): 0.1473 Epoch: [3/20], Batch Num: [44/600] Discriminator Loss: 0.3038, Generator Loss: 2.9641 D(x): 0.9176, D(G(z)): 0.1407 Epoch: [3/20], Batch Num: [45/600] Discriminator Loss: 0.3362, Generator Loss: 2.7564 D(x): 0.8751, D(G(z)): 0.1133 Epoch: [3/20], Batch Num: [46/600] Discriminator Loss: 0.3941, Generator Loss: 2.4695 D(x): 0.9085, D(G(z)): 0.1681 Epoch: [3/20], Batch Num: [47/600] Discriminator Loss: 0.3034, Generator Loss: 2.7549 D(x): 0.9308, D(G(z)): 0.1421 Epoch: [3/20], Batch Num: [48/600] Discriminator Loss: 0.2814, Generator Loss: 3.2147 D(x): 0.9405, D(G(z)): 0.1379 Epoch: [3/20], Batch Num: [49/600] Discriminator Loss: 0.3089, Generator Loss: 3.5201 D(x): 0.8737, D(G(z)): 0.0900 Epoch: [3/20], Batch Num: [50/600] Discriminator Loss: 0.2846, Generator Loss: 3.1533 D(x): 0.8900, D(G(z)): 0.0838 Epoch: [3/20], Batch Num: [51/600] Discriminator Loss: 0.3889, Generator Loss: 3.0328 D(x): 0.8697, D(G(z)): 0.1133 Epoch: [3/20], Batch Num: [52/600] Discriminator Loss: 0.3940, Generator Loss: 2.8693 D(x): 0.9481, D(G(z)): 0.1933 Epoch: [3/20], Batch Num: [53/600] Discriminator Loss: 0.3384, Generator Loss: 3.8388 D(x): 0.9549, D(G(z)): 0.1683 Epoch: [3/20], Batch Num: [54/600] Discriminator Loss: 0.2292, Generator Loss: 4.4998 D(x): 0.9481, D(G(z)): 0.1091 Epoch: [3/20], Batch Num: [55/600] Discriminator Loss: 0.2422, Generator Loss: 4.5663 D(x): 0.9304, D(G(z)): 0.0957 Epoch: [3/20], Batch Num: [56/600] Discriminator Loss: 0.1823, Generator Loss: 4.5039 D(x): 0.9295, D(G(z)): 0.0559 Epoch: [3/20], Batch Num: [57/600] Discriminator Loss: 0.4241, Generator Loss: 4.0898 D(x): 0.8496, D(G(z)): 0.0591 Epoch: [3/20], Batch Num: [58/600] Discriminator Loss: 0.1871, Generator Loss: 3.4584 D(x): 0.9390, D(G(z)): 0.0713 Epoch: [3/20], Batch Num: [59/600] Discriminator Loss: 0.4928, Generator Loss: 2.8912 D(x): 0.9260, D(G(z)): 0.1713 Epoch: [3/20], Batch Num: [60/600] Discriminator Loss: 0.3439, Generator Loss: 3.1216 D(x): 0.9721, D(G(z)): 0.1683 Epoch: [3/20], Batch Num: [61/600] Discriminator Loss: 0.4135, Generator Loss: 3.8479 D(x): 0.8973, D(G(z)): 0.1350 Epoch: [3/20], Batch Num: [62/600] Discriminator Loss: 0.4387, Generator Loss: 3.8701 D(x): 0.8818, D(G(z)): 0.1277 Epoch: [3/20], Batch Num: [63/600] Discriminator Loss: 0.4445, Generator Loss: 3.2708 D(x): 0.8562, D(G(z)): 0.1207 Epoch: [3/20], Batch Num: [64/600] Discriminator Loss: 0.4182, Generator Loss: 3.1337 D(x): 0.9082, D(G(z)): 0.1624 Epoch: [3/20], Batch Num: [65/600] Discriminator Loss: 0.3115, Generator Loss: 3.0938 D(x): 0.9496, D(G(z)): 0.1714 Epoch: [3/20], Batch Num: [66/600] Discriminator Loss: 0.3526, Generator Loss: 3.3179 D(x): 0.9106, D(G(z)): 0.1533 Epoch: [3/20], Batch Num: [67/600] Discriminator Loss: 0.3377, Generator Loss: 3.6486 D(x): 0.9228, D(G(z)): 0.1474 Epoch: [3/20], Batch Num: [68/600] Discriminator Loss: 0.4394, Generator Loss: 3.5376 D(x): 0.9028, D(G(z)): 0.1394 Epoch: [3/20], Batch Num: [69/600] Discriminator Loss: 0.3886, Generator Loss: 3.0050 D(x): 0.8993, D(G(z)): 0.1294 Epoch: [3/20], Batch Num: [70/600] Discriminator Loss: 0.5216, Generator Loss: 3.1296 D(x): 0.8907, D(G(z)): 0.1889 Epoch: [3/20], Batch Num: [71/600] Discriminator Loss: 0.4368, Generator Loss: 3.5754 D(x): 0.9191, D(G(z)): 0.1881 Epoch: [3/20], Batch Num: [72/600] Discriminator Loss: 0.5471, Generator Loss: 3.6357 D(x): 0.8844, D(G(z)): 0.1788 Epoch: [3/20], Batch Num: [73/600] Discriminator Loss: 0.4143, Generator Loss: 3.4073 D(x): 0.8806, D(G(z)): 0.1271 Epoch: [3/20], Batch Num: [74/600] Discriminator Loss: 0.4043, Generator Loss: 2.9204 D(x): 0.9292, D(G(z)): 0.1486 Epoch: [3/20], Batch Num: [75/600] Discriminator Loss: 0.3772, Generator Loss: 3.4457 D(x): 0.9166, D(G(z)): 0.1734 Epoch: [3/20], Batch Num: [76/600] Discriminator Loss: 0.7441, Generator Loss: 3.8181 D(x): 0.8819, D(G(z)): 0.2531 Epoch: [3/20], Batch Num: [77/600] Discriminator Loss: 0.5182, Generator Loss: 3.4760 D(x): 0.8666, D(G(z)): 0.1375 Epoch: [3/20], Batch Num: [78/600] Discriminator Loss: 0.3911, Generator Loss: 3.7167 D(x): 0.8821, D(G(z)): 0.1438 Epoch: [3/20], Batch Num: [79/600] Discriminator Loss: 0.6571, Generator Loss: 3.4813 D(x): 0.8004, D(G(z)): 0.1424 Epoch: [3/20], Batch Num: [80/600] Discriminator Loss: 0.5561, Generator Loss: 2.9209 D(x): 0.8411, D(G(z)): 0.1456 Epoch: [3/20], Batch Num: [81/600] Discriminator Loss: 0.6611, Generator Loss: 2.4294 D(x): 0.8710, D(G(z)): 0.2487 Epoch: [3/20], Batch Num: [82/600] Discriminator Loss: 0.8119, Generator Loss: 3.2956 D(x): 0.8909, D(G(z)): 0.3511 Epoch: [3/20], Batch Num: [83/600] Discriminator Loss: 0.6165, Generator Loss: 3.4864 D(x): 0.8185, D(G(z)): 0.1656 Epoch: [3/20], Batch Num: [84/600] Discriminator Loss: 0.6442, Generator Loss: 3.8543 D(x): 0.8097, D(G(z)): 0.1531 Epoch: [3/20], Batch Num: [85/600] Discriminator Loss: 0.8026, Generator Loss: 3.7600 D(x): 0.7806, D(G(z)): 0.1783 Epoch: [3/20], Batch Num: [86/600] Discriminator Loss: 0.7045, Generator Loss: 3.5956 D(x): 0.8259, D(G(z)): 0.1713 Epoch: [3/20], Batch Num: [87/600] Discriminator Loss: 0.8003, Generator Loss: 3.2359 D(x): 0.8252, D(G(z)): 0.2092 Epoch: [3/20], Batch Num: [88/600] Discriminator Loss: 0.5251, Generator Loss: 3.3844 D(x): 0.8896, D(G(z)): 0.2391 Epoch: [3/20], Batch Num: [89/600] Discriminator Loss: 0.7595, Generator Loss: 4.1413 D(x): 0.8268, D(G(z)): 0.2401 Epoch: [3/20], Batch Num: [90/600] Discriminator Loss: 0.5190, Generator Loss: 5.1972 D(x): 0.8390, D(G(z)): 0.1702 Epoch: [3/20], Batch Num: [91/600] Discriminator Loss: 0.3513, Generator Loss: 4.9275 D(x): 0.8672, D(G(z)): 0.1095 Epoch: [3/20], Batch Num: [92/600] Discriminator Loss: 0.8190, Generator Loss: 3.9976 D(x): 0.7384, D(G(z)): 0.0978 Epoch: [3/20], Batch Num: [93/600] Discriminator Loss: 0.5710, Generator Loss: 4.0246 D(x): 0.8503, D(G(z)): 0.1810 Epoch: [3/20], Batch Num: [94/600] Discriminator Loss: 0.4590, Generator Loss: 4.6406 D(x): 0.9437, D(G(z)): 0.2552 Epoch: [3/20], Batch Num: [95/600] Discriminator Loss: 0.7565, Generator Loss: 4.8868 D(x): 0.8351, D(G(z)): 0.2109 Epoch: [3/20], Batch Num: [96/600] Discriminator Loss: 0.5329, Generator Loss: 5.9336 D(x): 0.8568, D(G(z)): 0.1723 Epoch: [3/20], Batch Num: [97/600] Discriminator Loss: 0.4561, Generator Loss: 5.6637 D(x): 0.8258, D(G(z)): 0.0913 Epoch: [3/20], Batch Num: [98/600] Discriminator Loss: 0.4467, Generator Loss: 5.0490 D(x): 0.8087, D(G(z)): 0.0822 Epoch: [3/20], Batch Num: [99/600] Discriminator Loss: 0.4941, Generator Loss: 4.1341 D(x): 0.8442, D(G(z)): 0.1427 Epoch: 3, Batch Num: [100/600]
Epoch: [3/20], Batch Num: [100/600] Discriminator Loss: 0.6674, Generator Loss: 4.4320 D(x): 0.8805, D(G(z)): 0.2533 Epoch: [3/20], Batch Num: [101/600] Discriminator Loss: 0.5297, Generator Loss: 4.9036 D(x): 0.8862, D(G(z)): 0.2220 Epoch: [3/20], Batch Num: [102/600] Discriminator Loss: 0.4371, Generator Loss: 5.8543 D(x): 0.8681, D(G(z)): 0.1415 Epoch: [3/20], Batch Num: [103/600] Discriminator Loss: 0.4149, Generator Loss: 5.7760 D(x): 0.8237, D(G(z)): 0.0932 Epoch: [3/20], Batch Num: [104/600] Discriminator Loss: 0.5407, Generator Loss: 5.1412 D(x): 0.7944, D(G(z)): 0.1151 Epoch: [3/20], Batch Num: [105/600] Discriminator Loss: 0.6781, Generator Loss: 4.2498 D(x): 0.8103, D(G(z)): 0.1850 Epoch: [3/20], Batch Num: [106/600] Discriminator Loss: 0.6758, Generator Loss: 4.3425 D(x): 0.8725, D(G(z)): 0.2503 Epoch: [3/20], Batch Num: [107/600] Discriminator Loss: 0.7079, Generator Loss: 4.5658 D(x): 0.7682, D(G(z)): 0.1623 Epoch: [3/20], Batch Num: [108/600] Discriminator Loss: 1.1526, Generator Loss: 4.4287 D(x): 0.7017, D(G(z)): 0.2734 Epoch: [3/20], Batch Num: [109/600] Discriminator Loss: 0.6600, Generator Loss: 3.4691 D(x): 0.7653, D(G(z)): 0.1331 Epoch: [3/20], Batch Num: [110/600] Discriminator Loss: 0.9111, Generator Loss: 2.6549 D(x): 0.7399, D(G(z)): 0.2423 Epoch: [3/20], Batch Num: [111/600] Discriminator Loss: 0.9113, Generator Loss: 2.7409 D(x): 0.7970, D(G(z)): 0.3192 Epoch: [3/20], Batch Num: [112/600] Discriminator Loss: 0.7449, Generator Loss: 3.7599 D(x): 0.8067, D(G(z)): 0.2427 Epoch: [3/20], Batch Num: [113/600] Discriminator Loss: 0.7515, Generator Loss: 3.5121 D(x): 0.7247, D(G(z)): 0.1397 Epoch: [3/20], Batch Num: [114/600] Discriminator Loss: 0.8410, Generator Loss: 3.4111 D(x): 0.7310, D(G(z)): 0.2317 Epoch: [3/20], Batch Num: [115/600] Discriminator Loss: 0.5877, Generator Loss: 3.2018 D(x): 0.7874, D(G(z)): 0.1828 Epoch: [3/20], Batch Num: [116/600] Discriminator Loss: 0.5716, Generator Loss: 3.4074 D(x): 0.8138, D(G(z)): 0.1882 Epoch: [3/20], Batch Num: [117/600] Discriminator Loss: 0.4919, Generator Loss: 3.3198 D(x): 0.8370, D(G(z)): 0.1943 Epoch: [3/20], Batch Num: [118/600] Discriminator Loss: 0.4838, Generator Loss: 3.2900 D(x): 0.8413, D(G(z)): 0.1861 Epoch: [3/20], Batch Num: [119/600] Discriminator Loss: 0.5038, Generator Loss: 4.0856 D(x): 0.8341, D(G(z)): 0.1891 Epoch: [3/20], Batch Num: [120/600] Discriminator Loss: 0.4993, Generator Loss: 4.0682 D(x): 0.7912, D(G(z)): 0.1355 Epoch: [3/20], Batch Num: [121/600] Discriminator Loss: 0.5505, Generator Loss: 3.3495 D(x): 0.7644, D(G(z)): 0.1294 Epoch: [3/20], Batch Num: [122/600] Discriminator Loss: 0.5929, Generator Loss: 3.1933 D(x): 0.8253, D(G(z)): 0.2019 Epoch: [3/20], Batch Num: [123/600] Discriminator Loss: 0.5031, Generator Loss: 3.6100 D(x): 0.8553, D(G(z)): 0.1934 Epoch: [3/20], Batch Num: [124/600] Discriminator Loss: 0.5466, Generator Loss: 4.0162 D(x): 0.8423, D(G(z)): 0.1985 Epoch: [3/20], Batch Num: [125/600] Discriminator Loss: 0.5621, Generator Loss: 4.7576 D(x): 0.8526, D(G(z)): 0.2052 Epoch: [3/20], Batch Num: [126/600] Discriminator Loss: 0.6189, Generator Loss: 3.9730 D(x): 0.7423, D(G(z)): 0.0961 Epoch: [3/20], Batch Num: [127/600] Discriminator Loss: 0.6416, Generator Loss: 3.8987 D(x): 0.7698, D(G(z)): 0.1378 Epoch: [3/20], Batch Num: [128/600] Discriminator Loss: 0.6512, Generator Loss: 2.8783 D(x): 0.8310, D(G(z)): 0.2297 Epoch: [3/20], Batch Num: [129/600] Discriminator Loss: 0.6681, Generator Loss: 2.7682 D(x): 0.8468, D(G(z)): 0.2443 Epoch: [3/20], Batch Num: [130/600] Discriminator Loss: 0.8001, Generator Loss: 4.0513 D(x): 0.8020, D(G(z)): 0.2689 Epoch: [3/20], Batch Num: [131/600] Discriminator Loss: 0.6123, Generator Loss: 4.1237 D(x): 0.7802, D(G(z)): 0.1466 Epoch: [3/20], Batch Num: [132/600] Discriminator Loss: 0.6949, Generator Loss: 3.2453 D(x): 0.7543, D(G(z)): 0.1088 Epoch: [3/20], Batch Num: [133/600] Discriminator Loss: 0.6335, Generator Loss: 3.0134 D(x): 0.8314, D(G(z)): 0.2143 Epoch: [3/20], Batch Num: [134/600] Discriminator Loss: 0.5985, Generator Loss: 3.2447 D(x): 0.8828, D(G(z)): 0.2552 Epoch: [3/20], Batch Num: [135/600] Discriminator Loss: 0.6188, Generator Loss: 3.4409 D(x): 0.8360, D(G(z)): 0.1973 Epoch: [3/20], Batch Num: [136/600] Discriminator Loss: 0.5079, Generator Loss: 3.5692 D(x): 0.8478, D(G(z)): 0.1547 Epoch: [3/20], Batch Num: [137/600] Discriminator Loss: 0.6636, Generator Loss: 3.4818 D(x): 0.7702, D(G(z)): 0.1060 Epoch: [3/20], Batch Num: [138/600] Discriminator Loss: 0.6928, Generator Loss: 2.8284 D(x): 0.7595, D(G(z)): 0.1395 Epoch: [3/20], Batch Num: [139/600] Discriminator Loss: 0.6536, Generator Loss: 2.3380 D(x): 0.8039, D(G(z)): 0.1841 Epoch: [3/20], Batch Num: [140/600] Discriminator Loss: 0.6263, Generator Loss: 2.4725 D(x): 0.8788, D(G(z)): 0.2782 Epoch: [3/20], Batch Num: [141/600] Discriminator Loss: 0.4969, Generator Loss: 2.5413 D(x): 0.8755, D(G(z)): 0.2214 Epoch: [3/20], Batch Num: [142/600] Discriminator Loss: 0.4076, Generator Loss: 3.5683 D(x): 0.9012, D(G(z)): 0.1892 Epoch: [3/20], Batch Num: [143/600] Discriminator Loss: 0.3525, Generator Loss: 4.4821 D(x): 0.9007, D(G(z)): 0.1506 Epoch: [3/20], Batch Num: [144/600] Discriminator Loss: 0.3438, Generator Loss: 4.4414 D(x): 0.8800, D(G(z)): 0.1016 Epoch: [3/20], Batch Num: [145/600] Discriminator Loss: 0.4972, Generator Loss: 4.3704 D(x): 0.8090, D(G(z)): 0.0817 Epoch: [3/20], Batch Num: [146/600] Discriminator Loss: 0.5154, Generator Loss: 3.8427 D(x): 0.8126, D(G(z)): 0.0734 Epoch: [3/20], Batch Num: [147/600] Discriminator Loss: 0.5522, Generator Loss: 2.7418 D(x): 0.8126, D(G(z)): 0.1184 Epoch: [3/20], Batch Num: [148/600] Discriminator Loss: 0.4377, Generator Loss: 2.1112 D(x): 0.9215, D(G(z)): 0.2286 Epoch: [3/20], Batch Num: [149/600] Discriminator Loss: 0.5200, Generator Loss: 2.0913 D(x): 0.9196, D(G(z)): 0.2770 Epoch: [3/20], Batch Num: [150/600] Discriminator Loss: 0.4515, Generator Loss: 2.6459 D(x): 0.9335, D(G(z)): 0.2522 Epoch: [3/20], Batch Num: [151/600] Discriminator Loss: 0.3479, Generator Loss: 3.1265 D(x): 0.9210, D(G(z)): 0.1858 Epoch: [3/20], Batch Num: [152/600] Discriminator Loss: 0.4069, Generator Loss: 2.8907 D(x): 0.8894, D(G(z)): 0.1696 Epoch: [3/20], Batch Num: [153/600] Discriminator Loss: 0.2421, Generator Loss: 3.2735 D(x): 0.9378, D(G(z)): 0.1355 Epoch: [3/20], Batch Num: [154/600] Discriminator Loss: 0.4977, Generator Loss: 3.4185 D(x): 0.8161, D(G(z)): 0.1078 Epoch: [3/20], Batch Num: [155/600] Discriminator Loss: 0.4338, Generator Loss: 2.8930 D(x): 0.8374, D(G(z)): 0.1024 Epoch: [3/20], Batch Num: [156/600] Discriminator Loss: 0.3173, Generator Loss: 2.4036 D(x): 0.8994, D(G(z)): 0.1269 Epoch: [3/20], Batch Num: [157/600] Discriminator Loss: 0.3522, Generator Loss: 2.3934 D(x): 0.9177, D(G(z)): 0.1607 Epoch: [3/20], Batch Num: [158/600] Discriminator Loss: 0.3169, Generator Loss: 2.1136 D(x): 0.9347, D(G(z)): 0.1767 Epoch: [3/20], Batch Num: [159/600] Discriminator Loss: 0.3711, Generator Loss: 2.0274 D(x): 0.9411, D(G(z)): 0.2120 Epoch: [3/20], Batch Num: [160/600] Discriminator Loss: 0.3992, Generator Loss: 2.2576 D(x): 0.9319, D(G(z)): 0.2314 Epoch: [3/20], Batch Num: [161/600] Discriminator Loss: 0.3221, Generator Loss: 2.1921 D(x): 0.9319, D(G(z)): 0.1667 Epoch: [3/20], Batch Num: [162/600] Discriminator Loss: 0.3348, Generator Loss: 2.5532 D(x): 0.9271, D(G(z)): 0.1529 Epoch: [3/20], Batch Num: [163/600] Discriminator Loss: 0.3316, Generator Loss: 2.6082 D(x): 0.9055, D(G(z)): 0.1496 Epoch: [3/20], Batch Num: [164/600] Discriminator Loss: 0.3558, Generator Loss: 2.5630 D(x): 0.8824, D(G(z)): 0.1368 Epoch: [3/20], Batch Num: [165/600] Discriminator Loss: 0.3772, Generator Loss: 2.2649 D(x): 0.8783, D(G(z)): 0.1409 Epoch: [3/20], Batch Num: [166/600] Discriminator Loss: 0.3189, Generator Loss: 2.1891 D(x): 0.9271, D(G(z)): 0.1763 Epoch: [3/20], Batch Num: [167/600] Discriminator Loss: 0.2982, Generator Loss: 1.9847 D(x): 0.9288, D(G(z)): 0.1539 Epoch: [3/20], Batch Num: [168/600] Discriminator Loss: 0.3022, Generator Loss: 2.0354 D(x): 0.9517, D(G(z)): 0.1895 Epoch: [3/20], Batch Num: [169/600] Discriminator Loss: 0.3094, Generator Loss: 1.9416 D(x): 0.9565, D(G(z)): 0.2017 Epoch: [3/20], Batch Num: [170/600] Discriminator Loss: 0.4033, Generator Loss: 2.2200 D(x): 0.9294, D(G(z)): 0.1836 Epoch: [3/20], Batch Num: [171/600] Discriminator Loss: 0.3608, Generator Loss: 2.3482 D(x): 0.9251, D(G(z)): 0.1770 Epoch: [3/20], Batch Num: [172/600] Discriminator Loss: 0.4392, Generator Loss: 2.3070 D(x): 0.8819, D(G(z)): 0.1710 Epoch: [3/20], Batch Num: [173/600] Discriminator Loss: 0.3923, Generator Loss: 2.2878 D(x): 0.8995, D(G(z)): 0.1547 Epoch: [3/20], Batch Num: [174/600] Discriminator Loss: 0.2603, Generator Loss: 2.2627 D(x): 0.9524, D(G(z)): 0.1582 Epoch: [3/20], Batch Num: [175/600] Discriminator Loss: 0.4151, Generator Loss: 2.0882 D(x): 0.9049, D(G(z)): 0.1918 Epoch: [3/20], Batch Num: [176/600] Discriminator Loss: 0.4639, Generator Loss: 2.0359 D(x): 0.8942, D(G(z)): 0.1762 Epoch: [3/20], Batch Num: [177/600] Discriminator Loss: 0.3791, Generator Loss: 2.0015 D(x): 0.9435, D(G(z)): 0.2020 Epoch: [3/20], Batch Num: [178/600] Discriminator Loss: 0.3972, Generator Loss: 1.9219 D(x): 0.9325, D(G(z)): 0.2233 Epoch: [3/20], Batch Num: [179/600] Discriminator Loss: 0.6440, Generator Loss: 2.0223 D(x): 0.8775, D(G(z)): 0.2214 Epoch: [3/20], Batch Num: [180/600] Discriminator Loss: 0.4678, Generator Loss: 2.2763 D(x): 0.9212, D(G(z)): 0.2162 Epoch: [3/20], Batch Num: [181/600] Discriminator Loss: 0.4992, Generator Loss: 2.2462 D(x): 0.8896, D(G(z)): 0.1516 Epoch: [3/20], Batch Num: [182/600] Discriminator Loss: 0.6291, Generator Loss: 2.2669 D(x): 0.8429, D(G(z)): 0.1539 Epoch: [3/20], Batch Num: [183/600] Discriminator Loss: 0.4135, Generator Loss: 2.0453 D(x): 0.8943, D(G(z)): 0.1593 Epoch: [3/20], Batch Num: [184/600] Discriminator Loss: 0.4195, Generator Loss: 2.0278 D(x): 0.9168, D(G(z)): 0.1901 Epoch: [3/20], Batch Num: [185/600] Discriminator Loss: 0.3995, Generator Loss: 2.0500 D(x): 0.9252, D(G(z)): 0.1923 Epoch: [3/20], Batch Num: [186/600] Discriminator Loss: 0.5066, Generator Loss: 2.1401 D(x): 0.9206, D(G(z)): 0.2061 Epoch: [3/20], Batch Num: [187/600] Discriminator Loss: 0.5017, Generator Loss: 2.3928 D(x): 0.8982, D(G(z)): 0.1768 Epoch: [3/20], Batch Num: [188/600] Discriminator Loss: 0.3446, Generator Loss: 2.3754 D(x): 0.9336, D(G(z)): 0.1447 Epoch: [3/20], Batch Num: [189/600] Discriminator Loss: 0.4219, Generator Loss: 2.7620 D(x): 0.9152, D(G(z)): 0.1314 Epoch: [3/20], Batch Num: [190/600] Discriminator Loss: 0.3107, Generator Loss: 2.9532 D(x): 0.9399, D(G(z)): 0.1366 Epoch: [3/20], Batch Num: [191/600] Discriminator Loss: 0.1951, Generator Loss: 3.1432 D(x): 0.9644, D(G(z)): 0.0835 Epoch: [3/20], Batch Num: [192/600] Discriminator Loss: 0.1682, Generator Loss: 3.4565 D(x): 0.9659, D(G(z)): 0.0595 Epoch: [3/20], Batch Num: [193/600] Discriminator Loss: 0.3232, Generator Loss: 3.5414 D(x): 0.9131, D(G(z)): 0.0590 Epoch: [3/20], Batch Num: [194/600] Discriminator Loss: 0.2394, Generator Loss: 3.5277 D(x): 0.9329, D(G(z)): 0.0644 Epoch: [3/20], Batch Num: [195/600] Discriminator Loss: 0.2493, Generator Loss: 3.5817 D(x): 0.9328, D(G(z)): 0.0589 Epoch: [3/20], Batch Num: [196/600] Discriminator Loss: 0.3338, Generator Loss: 3.6358 D(x): 0.9354, D(G(z)): 0.0705 Epoch: [3/20], Batch Num: [197/600] Discriminator Loss: 0.2047, Generator Loss: 3.6545 D(x): 0.9564, D(G(z)): 0.0979 Epoch: [3/20], Batch Num: [198/600] Discriminator Loss: 0.1988, Generator Loss: 3.4510 D(x): 0.9570, D(G(z)): 0.0919 Epoch: [3/20], Batch Num: [199/600] Discriminator Loss: 0.2534, Generator Loss: 4.1448 D(x): 0.9699, D(G(z)): 0.1030 Epoch: 3, Batch Num: [200/600]
Epoch: [3/20], Batch Num: [200/600] Discriminator Loss: 0.3682, Generator Loss: 4.1992 D(x): 0.9406, D(G(z)): 0.0870 Epoch: [3/20], Batch Num: [201/600] Discriminator Loss: 0.2618, Generator Loss: 4.3445 D(x): 0.9370, D(G(z)): 0.0493 Epoch: [3/20], Batch Num: [202/600] Discriminator Loss: 0.3363, Generator Loss: 4.5155 D(x): 0.9078, D(G(z)): 0.0611 Epoch: [3/20], Batch Num: [203/600] Discriminator Loss: 0.2005, Generator Loss: 4.3117 D(x): 0.9273, D(G(z)): 0.0359 Epoch: [3/20], Batch Num: [204/600] Discriminator Loss: 0.2979, Generator Loss: 4.1807 D(x): 0.9285, D(G(z)): 0.0587 Epoch: [3/20], Batch Num: [205/600] Discriminator Loss: 0.2636, Generator Loss: 3.9513 D(x): 0.9480, D(G(z)): 0.0937 Epoch: [3/20], Batch Num: [206/600] Discriminator Loss: 0.1965, Generator Loss: 3.7793 D(x): 0.9726, D(G(z)): 0.0796 Epoch: [3/20], Batch Num: [207/600] Discriminator Loss: 0.2491, Generator Loss: 4.1090 D(x): 0.9519, D(G(z)): 0.0912 Epoch: [3/20], Batch Num: [208/600] Discriminator Loss: 0.1731, Generator Loss: 4.2706 D(x): 0.9694, D(G(z)): 0.0769 Epoch: [3/20], Batch Num: [209/600] Discriminator Loss: 0.2682, Generator Loss: 4.4743 D(x): 0.9607, D(G(z)): 0.0891 Epoch: [3/20], Batch Num: [210/600] Discriminator Loss: 0.4188, Generator Loss: 4.5707 D(x): 0.9206, D(G(z)): 0.0734 Epoch: [3/20], Batch Num: [211/600] Discriminator Loss: 0.0757, Generator Loss: 4.3377 D(x): 0.9796, D(G(z)): 0.0439 Epoch: [3/20], Batch Num: [212/600] Discriminator Loss: 0.2518, Generator Loss: 4.3431 D(x): 0.9459, D(G(z)): 0.0589 Epoch: [3/20], Batch Num: [213/600] Discriminator Loss: 0.2213, Generator Loss: 3.9368 D(x): 0.9508, D(G(z)): 0.0681 Epoch: [3/20], Batch Num: [214/600] Discriminator Loss: 0.4077, Generator Loss: 3.8235 D(x): 0.9233, D(G(z)): 0.0664 Epoch: [3/20], Batch Num: [215/600] Discriminator Loss: 0.3139, Generator Loss: 3.3027 D(x): 0.9532, D(G(z)): 0.1318 Epoch: [3/20], Batch Num: [216/600] Discriminator Loss: 0.3134, Generator Loss: 3.7103 D(x): 0.9513, D(G(z)): 0.1180 Epoch: [3/20], Batch Num: [217/600] Discriminator Loss: 0.6114, Generator Loss: 3.5533 D(x): 0.8608, D(G(z)): 0.1359 Epoch: [3/20], Batch Num: [218/600] Discriminator Loss: 0.7391, Generator Loss: 3.0347 D(x): 0.8855, D(G(z)): 0.2216 Epoch: [3/20], Batch Num: [219/600] Discriminator Loss: 0.5394, Generator Loss: 3.0010 D(x): 0.8993, D(G(z)): 0.1672 Epoch: [3/20], Batch Num: [220/600] Discriminator Loss: 0.6753, Generator Loss: 2.9527 D(x): 0.8626, D(G(z)): 0.1792 Epoch: [3/20], Batch Num: [221/600] Discriminator Loss: 0.8414, Generator Loss: 2.7139 D(x): 0.8703, D(G(z)): 0.2272 Epoch: [3/20], Batch Num: [222/600] Discriminator Loss: 0.5302, Generator Loss: 2.9246 D(x): 0.8934, D(G(z)): 0.1702 Epoch: [3/20], Batch Num: [223/600] Discriminator Loss: 0.6151, Generator Loss: 2.9639 D(x): 0.8673, D(G(z)): 0.1827 Epoch: [3/20], Batch Num: [224/600] Discriminator Loss: 1.0494, Generator Loss: 2.4187 D(x): 0.7514, D(G(z)): 0.1440 Epoch: [3/20], Batch Num: [225/600] Discriminator Loss: 0.8444, Generator Loss: 2.1595 D(x): 0.8090, D(G(z)): 0.2275 Epoch: [3/20], Batch Num: [226/600] Discriminator Loss: 0.8341, Generator Loss: 2.0799 D(x): 0.8639, D(G(z)): 0.2917 Epoch: [3/20], Batch Num: [227/600] Discriminator Loss: 0.7945, Generator Loss: 1.8836 D(x): 0.8550, D(G(z)): 0.2669 Epoch: [3/20], Batch Num: [228/600] Discriminator Loss: 0.8124, Generator Loss: 1.9553 D(x): 0.8598, D(G(z)): 0.3144 Epoch: [3/20], Batch Num: [229/600] Discriminator Loss: 0.6760, Generator Loss: 2.0186 D(x): 0.8649, D(G(z)): 0.2842 Epoch: [3/20], Batch Num: [230/600] Discriminator Loss: 0.8087, Generator Loss: 1.9637 D(x): 0.7908, D(G(z)): 0.2563 Epoch: [3/20], Batch Num: [231/600] Discriminator Loss: 0.7375, Generator Loss: 2.0488 D(x): 0.7940, D(G(z)): 0.2302 Epoch: [3/20], Batch Num: [232/600] Discriminator Loss: 0.6432, Generator Loss: 1.9478 D(x): 0.8186, D(G(z)): 0.2265 Epoch: [3/20], Batch Num: [233/600] Discriminator Loss: 0.4844, Generator Loss: 2.1048 D(x): 0.8781, D(G(z)): 0.2313 Epoch: [3/20], Batch Num: [234/600] Discriminator Loss: 0.5834, Generator Loss: 2.1665 D(x): 0.9146, D(G(z)): 0.2842 Epoch: [3/20], Batch Num: [235/600] Discriminator Loss: 0.4563, Generator Loss: 2.4299 D(x): 0.8880, D(G(z)): 0.2070 Epoch: [3/20], Batch Num: [236/600] Discriminator Loss: 0.4782, Generator Loss: 2.6078 D(x): 0.8789, D(G(z)): 0.1852 Epoch: [3/20], Batch Num: [237/600] Discriminator Loss: 0.4779, Generator Loss: 2.3691 D(x): 0.8421, D(G(z)): 0.1826 Epoch: [3/20], Batch Num: [238/600] Discriminator Loss: 0.4021, Generator Loss: 2.8311 D(x): 0.8511, D(G(z)): 0.1599 Epoch: [3/20], Batch Num: [239/600] Discriminator Loss: 0.4235, Generator Loss: 2.7076 D(x): 0.8537, D(G(z)): 0.1729 Epoch: [3/20], Batch Num: [240/600] Discriminator Loss: 0.3935, Generator Loss: 2.4688 D(x): 0.8650, D(G(z)): 0.1466 Epoch: [3/20], Batch Num: [241/600] Discriminator Loss: 0.3167, Generator Loss: 2.4936 D(x): 0.8846, D(G(z)): 0.1412 Epoch: [3/20], Batch Num: [242/600] Discriminator Loss: 0.4250, Generator Loss: 2.8054 D(x): 0.9175, D(G(z)): 0.2188 Epoch: [3/20], Batch Num: [243/600] Discriminator Loss: 0.3209, Generator Loss: 2.8669 D(x): 0.9088, D(G(z)): 0.1586 Epoch: [3/20], Batch Num: [244/600] Discriminator Loss: 0.3712, Generator Loss: 2.9539 D(x): 0.8868, D(G(z)): 0.1436 Epoch: [3/20], Batch Num: [245/600] Discriminator Loss: 0.3073, Generator Loss: 3.1538 D(x): 0.9117, D(G(z)): 0.1499 Epoch: [3/20], Batch Num: [246/600] Discriminator Loss: 0.3337, Generator Loss: 3.1807 D(x): 0.8484, D(G(z)): 0.0916 Epoch: [3/20], Batch Num: [247/600] Discriminator Loss: 0.2827, Generator Loss: 3.0274 D(x): 0.8961, D(G(z)): 0.1096 Epoch: [3/20], Batch Num: [248/600] Discriminator Loss: 0.2817, Generator Loss: 3.2895 D(x): 0.8859, D(G(z)): 0.1059 Epoch: [3/20], Batch Num: [249/600] Discriminator Loss: 0.2857, Generator Loss: 3.3615 D(x): 0.9016, D(G(z)): 0.1036 Epoch: [3/20], Batch Num: [250/600] Discriminator Loss: 0.2503, Generator Loss: 3.0705 D(x): 0.9111, D(G(z)): 0.1141 Epoch: [3/20], Batch Num: [251/600] Discriminator Loss: 0.2295, Generator Loss: 3.1986 D(x): 0.9385, D(G(z)): 0.1251 Epoch: [3/20], Batch Num: [252/600] Discriminator Loss: 0.2275, Generator Loss: 3.3584 D(x): 0.9339, D(G(z)): 0.1146 Epoch: [3/20], Batch Num: [253/600] Discriminator Loss: 0.2064, Generator Loss: 4.0934 D(x): 0.9445, D(G(z)): 0.1129 Epoch: [3/20], Batch Num: [254/600] Discriminator Loss: 0.2289, Generator Loss: 4.1113 D(x): 0.9110, D(G(z)): 0.0763 Epoch: [3/20], Batch Num: [255/600] Discriminator Loss: 0.3106, Generator Loss: 3.8347 D(x): 0.8638, D(G(z)): 0.0670 Epoch: [3/20], Batch Num: [256/600] Discriminator Loss: 0.2665, Generator Loss: 3.6830 D(x): 0.8870, D(G(z)): 0.0727 Epoch: [3/20], Batch Num: [257/600] Discriminator Loss: 0.2369, Generator Loss: 3.6001 D(x): 0.9376, D(G(z)): 0.1165 Epoch: [3/20], Batch Num: [258/600] Discriminator Loss: 0.2372, Generator Loss: 3.9783 D(x): 0.9514, D(G(z)): 0.1233 Epoch: [3/20], Batch Num: [259/600] Discriminator Loss: 0.2775, Generator Loss: 3.9384 D(x): 0.9256, D(G(z)): 0.1132 Epoch: [3/20], Batch Num: [260/600] Discriminator Loss: 0.2067, Generator Loss: 4.3169 D(x): 0.9289, D(G(z)): 0.0772 Epoch: [3/20], Batch Num: [261/600] Discriminator Loss: 0.3375, Generator Loss: 4.4638 D(x): 0.8826, D(G(z)): 0.0873 Epoch: [3/20], Batch Num: [262/600] Discriminator Loss: 0.2344, Generator Loss: 3.7392 D(x): 0.9241, D(G(z)): 0.0794 Epoch: [3/20], Batch Num: [263/600] Discriminator Loss: 0.2496, Generator Loss: 3.6510 D(x): 0.9141, D(G(z)): 0.0679 Epoch: [3/20], Batch Num: [264/600] Discriminator Loss: 0.2397, Generator Loss: 3.7980 D(x): 0.9222, D(G(z)): 0.1043 Epoch: [3/20], Batch Num: [265/600] Discriminator Loss: 0.2350, Generator Loss: 3.9991 D(x): 0.9513, D(G(z)): 0.1218 Epoch: [3/20], Batch Num: [266/600] Discriminator Loss: 0.2518, Generator Loss: 4.0131 D(x): 0.9311, D(G(z)): 0.1171 Epoch: [3/20], Batch Num: [267/600] Discriminator Loss: 0.3378, Generator Loss: 4.8104 D(x): 0.8704, D(G(z)): 0.0796 Epoch: [3/20], Batch Num: [268/600] Discriminator Loss: 0.4235, Generator Loss: 3.9279 D(x): 0.8481, D(G(z)): 0.0712 Epoch: [3/20], Batch Num: [269/600] Discriminator Loss: 0.3808, Generator Loss: 3.2301 D(x): 0.8765, D(G(z)): 0.1142 Epoch: [3/20], Batch Num: [270/600] Discriminator Loss: 0.4624, Generator Loss: 3.4756 D(x): 0.9144, D(G(z)): 0.1959 Epoch: [3/20], Batch Num: [271/600] Discriminator Loss: 0.5265, Generator Loss: 3.1931 D(x): 0.8498, D(G(z)): 0.1310 Epoch: [3/20], Batch Num: [272/600] Discriminator Loss: 0.4695, Generator Loss: 3.2906 D(x): 0.9128, D(G(z)): 0.1818 Epoch: [3/20], Batch Num: [273/600] Discriminator Loss: 0.5285, Generator Loss: 3.7930 D(x): 0.8365, D(G(z)): 0.1530 Epoch: [3/20], Batch Num: [274/600] Discriminator Loss: 0.7807, Generator Loss: 3.3141 D(x): 0.8114, D(G(z)): 0.1590 Epoch: [3/20], Batch Num: [275/600] Discriminator Loss: 0.5088, Generator Loss: 2.8634 D(x): 0.8455, D(G(z)): 0.1251 Epoch: [3/20], Batch Num: [276/600] Discriminator Loss: 0.4601, Generator Loss: 2.5341 D(x): 0.8900, D(G(z)): 0.1972 Epoch: [3/20], Batch Num: [277/600] Discriminator Loss: 0.6381, Generator Loss: 3.0845 D(x): 0.8623, D(G(z)): 0.2312 Epoch: [3/20], Batch Num: [278/600] Discriminator Loss: 0.6475, Generator Loss: 3.1509 D(x): 0.8248, D(G(z)): 0.1812 Epoch: [3/20], Batch Num: [279/600] Discriminator Loss: 0.5615, Generator Loss: 3.2070 D(x): 0.8077, D(G(z)): 0.1160 Epoch: [3/20], Batch Num: [280/600] Discriminator Loss: 0.7641, Generator Loss: 2.1285 D(x): 0.7566, D(G(z)): 0.1420 Epoch: [3/20], Batch Num: [281/600] Discriminator Loss: 0.7718, Generator Loss: 2.0824 D(x): 0.8740, D(G(z)): 0.2682 Epoch: [3/20], Batch Num: [282/600] Discriminator Loss: 0.6160, Generator Loss: 2.3453 D(x): 0.8850, D(G(z)): 0.2611 Epoch: [3/20], Batch Num: [283/600] Discriminator Loss: 0.7997, Generator Loss: 2.8753 D(x): 0.8451, D(G(z)): 0.2645 Epoch: [3/20], Batch Num: [284/600] Discriminator Loss: 0.7366, Generator Loss: 3.0771 D(x): 0.7775, D(G(z)): 0.1324 Epoch: [3/20], Batch Num: [285/600] Discriminator Loss: 0.8453, Generator Loss: 2.4100 D(x): 0.7652, D(G(z)): 0.1429 Epoch: [3/20], Batch Num: [286/600] Discriminator Loss: 0.6422, Generator Loss: 2.2009 D(x): 0.8624, D(G(z)): 0.2541 Epoch: [3/20], Batch Num: [287/600] Discriminator Loss: 0.5445, Generator Loss: 2.5719 D(x): 0.8384, D(G(z)): 0.1873 Epoch: [3/20], Batch Num: [288/600] Discriminator Loss: 0.6729, Generator Loss: 2.2512 D(x): 0.7635, D(G(z)): 0.1735 Epoch: [3/20], Batch Num: [289/600] Discriminator Loss: 0.6609, Generator Loss: 2.1726 D(x): 0.8008, D(G(z)): 0.2081 Epoch: [3/20], Batch Num: [290/600] Discriminator Loss: 0.5207, Generator Loss: 2.3847 D(x): 0.8759, D(G(z)): 0.2047 Epoch: [3/20], Batch Num: [291/600] Discriminator Loss: 0.5316, Generator Loss: 2.7051 D(x): 0.8618, D(G(z)): 0.1981 Epoch: [3/20], Batch Num: [292/600] Discriminator Loss: 0.5979, Generator Loss: 2.9160 D(x): 0.7799, D(G(z)): 0.1455 Epoch: [3/20], Batch Num: [293/600] Discriminator Loss: 0.4026, Generator Loss: 2.6699 D(x): 0.8726, D(G(z)): 0.1525 Epoch: [3/20], Batch Num: [294/600] Discriminator Loss: 0.4921, Generator Loss: 3.0639 D(x): 0.8685, D(G(z)): 0.1537 Epoch: [3/20], Batch Num: [295/600] Discriminator Loss: 0.4233, Generator Loss: 2.8994 D(x): 0.8487, D(G(z)): 0.1089 Epoch: [3/20], Batch Num: [296/600] Discriminator Loss: 0.4141, Generator Loss: 3.0089 D(x): 0.8856, D(G(z)): 0.1376 Epoch: [3/20], Batch Num: [297/600] Discriminator Loss: 0.3459, Generator Loss: 2.8101 D(x): 0.8913, D(G(z)): 0.1165 Epoch: [3/20], Batch Num: [298/600] Discriminator Loss: 0.4371, Generator Loss: 2.5771 D(x): 0.8667, D(G(z)): 0.1212 Epoch: [3/20], Batch Num: [299/600] Discriminator Loss: 0.2833, Generator Loss: 3.0718 D(x): 0.9292, D(G(z)): 0.1312 Epoch: 3, Batch Num: [300/600]
Epoch: [3/20], Batch Num: [300/600] Discriminator Loss: 0.2482, Generator Loss: 3.5954 D(x): 0.9362, D(G(z)): 0.1135 Epoch: [3/20], Batch Num: [301/600] Discriminator Loss: 0.3034, Generator Loss: 4.0051 D(x): 0.8816, D(G(z)): 0.0946 Epoch: [3/20], Batch Num: [302/600] Discriminator Loss: 0.1851, Generator Loss: 3.9329 D(x): 0.9337, D(G(z)): 0.0694 Epoch: [3/20], Batch Num: [303/600] Discriminator Loss: 0.3318, Generator Loss: 3.5036 D(x): 0.8605, D(G(z)): 0.0653 Epoch: [3/20], Batch Num: [304/600] Discriminator Loss: 0.3689, Generator Loss: 2.6976 D(x): 0.8728, D(G(z)): 0.1048 Epoch: [3/20], Batch Num: [305/600] Discriminator Loss: 0.3058, Generator Loss: 2.9521 D(x): 0.9565, D(G(z)): 0.1664 Epoch: [3/20], Batch Num: [306/600] Discriminator Loss: 0.1900, Generator Loss: 3.5290 D(x): 0.9422, D(G(z)): 0.0934 Epoch: [3/20], Batch Num: [307/600] Discriminator Loss: 0.2221, Generator Loss: 4.0833 D(x): 0.9303, D(G(z)): 0.0901 Epoch: [3/20], Batch Num: [308/600] Discriminator Loss: 0.2980, Generator Loss: 4.0587 D(x): 0.8880, D(G(z)): 0.0723 Epoch: [3/20], Batch Num: [309/600] Discriminator Loss: 0.2506, Generator Loss: 4.0598 D(x): 0.8903, D(G(z)): 0.0517 Epoch: [3/20], Batch Num: [310/600] Discriminator Loss: 0.2151, Generator Loss: 3.1264 D(x): 0.9139, D(G(z)): 0.0686 Epoch: [3/20], Batch Num: [311/600] Discriminator Loss: 0.2976, Generator Loss: 2.8178 D(x): 0.9196, D(G(z)): 0.1191 Epoch: [3/20], Batch Num: [312/600] Discriminator Loss: 0.4020, Generator Loss: 3.2205 D(x): 0.8987, D(G(z)): 0.1505 Epoch: [3/20], Batch Num: [313/600] Discriminator Loss: 0.2520, Generator Loss: 3.7606 D(x): 0.9577, D(G(z)): 0.1429 Epoch: [3/20], Batch Num: [314/600] Discriminator Loss: 0.3087, Generator Loss: 4.4027 D(x): 0.8839, D(G(z)): 0.0781 Epoch: [3/20], Batch Num: [315/600] Discriminator Loss: 0.2253, Generator Loss: 4.2632 D(x): 0.8925, D(G(z)): 0.0488 Epoch: [3/20], Batch Num: [316/600] Discriminator Loss: 0.4547, Generator Loss: 3.2022 D(x): 0.8231, D(G(z)): 0.0595 Epoch: [3/20], Batch Num: [317/600] Discriminator Loss: 0.3507, Generator Loss: 2.7669 D(x): 0.9200, D(G(z)): 0.1767 Epoch: [3/20], Batch Num: [318/600] Discriminator Loss: 0.3907, Generator Loss: 3.5272 D(x): 0.9213, D(G(z)): 0.1778 Epoch: [3/20], Batch Num: [319/600] Discriminator Loss: 0.2557, Generator Loss: 4.1384 D(x): 0.9138, D(G(z)): 0.1044 Epoch: [3/20], Batch Num: [320/600] Discriminator Loss: 0.3363, Generator Loss: 3.8535 D(x): 0.8801, D(G(z)): 0.0634 Epoch: [3/20], Batch Num: [321/600] Discriminator Loss: 0.2782, Generator Loss: 4.1872 D(x): 0.9056, D(G(z)): 0.0875 Epoch: [3/20], Batch Num: [322/600] Discriminator Loss: 0.3440, Generator Loss: 3.3955 D(x): 0.8727, D(G(z)): 0.0618 Epoch: [3/20], Batch Num: [323/600] Discriminator Loss: 0.4467, Generator Loss: 2.8212 D(x): 0.8479, D(G(z)): 0.1083 Epoch: [3/20], Batch Num: [324/600] Discriminator Loss: 0.4607, Generator Loss: 3.7155 D(x): 0.9431, D(G(z)): 0.2225 Epoch: [3/20], Batch Num: [325/600] Discriminator Loss: 0.4951, Generator Loss: 4.4152 D(x): 0.8760, D(G(z)): 0.1293 Epoch: [3/20], Batch Num: [326/600] Discriminator Loss: 0.6056, Generator Loss: 3.8299 D(x): 0.7983, D(G(z)): 0.0872 Epoch: [3/20], Batch Num: [327/600] Discriminator Loss: 0.3761, Generator Loss: 3.3839 D(x): 0.8689, D(G(z)): 0.0706 Epoch: [3/20], Batch Num: [328/600] Discriminator Loss: 0.6198, Generator Loss: 2.8917 D(x): 0.8454, D(G(z)): 0.1988 Epoch: [3/20], Batch Num: [329/600] Discriminator Loss: 0.5658, Generator Loss: 3.1835 D(x): 0.8531, D(G(z)): 0.2029 Epoch: [3/20], Batch Num: [330/600] Discriminator Loss: 0.6184, Generator Loss: 3.2958 D(x): 0.8577, D(G(z)): 0.1745 Epoch: [3/20], Batch Num: [331/600] Discriminator Loss: 0.6803, Generator Loss: 2.9718 D(x): 0.7829, D(G(z)): 0.1267 Epoch: [3/20], Batch Num: [332/600] Discriminator Loss: 0.4778, Generator Loss: 2.9699 D(x): 0.8617, D(G(z)): 0.1547 Epoch: [3/20], Batch Num: [333/600] Discriminator Loss: 0.5861, Generator Loss: 2.6847 D(x): 0.8473, D(G(z)): 0.1886 Epoch: [3/20], Batch Num: [334/600] Discriminator Loss: 0.5853, Generator Loss: 2.5978 D(x): 0.8211, D(G(z)): 0.1691 Epoch: [3/20], Batch Num: [335/600] Discriminator Loss: 0.5192, Generator Loss: 2.5373 D(x): 0.8504, D(G(z)): 0.1910 Epoch: [3/20], Batch Num: [336/600] Discriminator Loss: 0.4621, Generator Loss: 2.4559 D(x): 0.8531, D(G(z)): 0.1682 Epoch: [3/20], Batch Num: [337/600] Discriminator Loss: 0.5826, Generator Loss: 2.3659 D(x): 0.8387, D(G(z)): 0.1966 Epoch: [3/20], Batch Num: [338/600] Discriminator Loss: 0.5796, Generator Loss: 2.5562 D(x): 0.8090, D(G(z)): 0.1561 Epoch: [3/20], Batch Num: [339/600] Discriminator Loss: 0.5109, Generator Loss: 2.2722 D(x): 0.8136, D(G(z)): 0.1291 Epoch: [3/20], Batch Num: [340/600] Discriminator Loss: 0.5457, Generator Loss: 2.3883 D(x): 0.8533, D(G(z)): 0.1800 Epoch: [3/20], Batch Num: [341/600] Discriminator Loss: 0.5950, Generator Loss: 2.5169 D(x): 0.8417, D(G(z)): 0.1771 Epoch: [3/20], Batch Num: [342/600] Discriminator Loss: 0.4344, Generator Loss: 2.8085 D(x): 0.8576, D(G(z)): 0.1367 Epoch: [3/20], Batch Num: [343/600] Discriminator Loss: 0.4556, Generator Loss: 2.6756 D(x): 0.8645, D(G(z)): 0.1602 Epoch: [3/20], Batch Num: [344/600] Discriminator Loss: 0.4987, Generator Loss: 2.6721 D(x): 0.8445, D(G(z)): 0.1358 Epoch: [3/20], Batch Num: [345/600] Discriminator Loss: 0.4302, Generator Loss: 2.4499 D(x): 0.8612, D(G(z)): 0.1254 Epoch: [3/20], Batch Num: [346/600] Discriminator Loss: 0.3931, Generator Loss: 2.4644 D(x): 0.8676, D(G(z)): 0.1200 Epoch: [3/20], Batch Num: [347/600] Discriminator Loss: 0.4362, Generator Loss: 2.3760 D(x): 0.8936, D(G(z)): 0.1999 Epoch: [3/20], Batch Num: [348/600] Discriminator Loss: 0.3400, Generator Loss: 2.8074 D(x): 0.9169, D(G(z)): 0.1619 Epoch: [3/20], Batch Num: [349/600] Discriminator Loss: 0.4131, Generator Loss: 3.0643 D(x): 0.8398, D(G(z)): 0.1133 Epoch: [3/20], Batch Num: [350/600] Discriminator Loss: 0.4471, Generator Loss: 2.9457 D(x): 0.8425, D(G(z)): 0.1202 Epoch: [3/20], Batch Num: [351/600] Discriminator Loss: 0.3858, Generator Loss: 2.7053 D(x): 0.8900, D(G(z)): 0.1077 Epoch: [3/20], Batch Num: [352/600] Discriminator Loss: 0.2421, Generator Loss: 2.8034 D(x): 0.9629, D(G(z)): 0.1455 Epoch: [3/20], Batch Num: [353/600] Discriminator Loss: 0.2251, Generator Loss: 3.2224 D(x): 0.9493, D(G(z)): 0.1063 Epoch: [3/20], Batch Num: [354/600] Discriminator Loss: 0.3887, Generator Loss: 3.5876 D(x): 0.8783, D(G(z)): 0.0740 Epoch: [3/20], Batch Num: [355/600] Discriminator Loss: 0.2023, Generator Loss: 3.5868 D(x): 0.9167, D(G(z)): 0.0549 Epoch: [3/20], Batch Num: [356/600] Discriminator Loss: 0.2597, Generator Loss: 3.4964 D(x): 0.8977, D(G(z)): 0.0596 Epoch: [3/20], Batch Num: [357/600] Discriminator Loss: 0.2002, Generator Loss: 3.3760 D(x): 0.9428, D(G(z)): 0.0928 Epoch: [3/20], Batch Num: [358/600] Discriminator Loss: 0.2736, Generator Loss: 3.4936 D(x): 0.9170, D(G(z)): 0.0936 Epoch: [3/20], Batch Num: [359/600] Discriminator Loss: 0.2256, Generator Loss: 3.5832 D(x): 0.9295, D(G(z)): 0.0885 Epoch: [3/20], Batch Num: [360/600] Discriminator Loss: 0.2572, Generator Loss: 3.5325 D(x): 0.9199, D(G(z)): 0.0862 Epoch: [3/20], Batch Num: [361/600] Discriminator Loss: 0.2190, Generator Loss: 3.8812 D(x): 0.9546, D(G(z)): 0.1039 Epoch: [3/20], Batch Num: [362/600] Discriminator Loss: 0.1612, Generator Loss: 4.3084 D(x): 0.9455, D(G(z)): 0.0462 Epoch: [3/20], Batch Num: [363/600] Discriminator Loss: 0.1210, Generator Loss: 4.2972 D(x): 0.9531, D(G(z)): 0.0389 Epoch: [3/20], Batch Num: [364/600] Discriminator Loss: 0.1593, Generator Loss: 4.4312 D(x): 0.9402, D(G(z)): 0.0477 Epoch: [3/20], Batch Num: [365/600] Discriminator Loss: 0.1561, Generator Loss: 4.4121 D(x): 0.9377, D(G(z)): 0.0440 Epoch: [3/20], Batch Num: [366/600] Discriminator Loss: 0.2134, Generator Loss: 4.2684 D(x): 0.9480, D(G(z)): 0.0826 Epoch: [3/20], Batch Num: [367/600] Discriminator Loss: 0.2050, Generator Loss: 4.2254 D(x): 0.9466, D(G(z)): 0.0791 Epoch: [3/20], Batch Num: [368/600] Discriminator Loss: 0.2038, Generator Loss: 4.1590 D(x): 0.9478, D(G(z)): 0.0712 Epoch: [3/20], Batch Num: [369/600] Discriminator Loss: 0.1506, Generator Loss: 4.4684 D(x): 0.9483, D(G(z)): 0.0447 Epoch: [3/20], Batch Num: [370/600] Discriminator Loss: 0.2122, Generator Loss: 4.1785 D(x): 0.9381, D(G(z)): 0.0613 Epoch: [3/20], Batch Num: [371/600] Discriminator Loss: 0.2218, Generator Loss: 4.0705 D(x): 0.9491, D(G(z)): 0.0882 Epoch: [3/20], Batch Num: [372/600] Discriminator Loss: 0.1926, Generator Loss: 4.4691 D(x): 0.9453, D(G(z)): 0.0762 Epoch: [3/20], Batch Num: [373/600] Discriminator Loss: 0.2459, Generator Loss: 4.1535 D(x): 0.9196, D(G(z)): 0.0617 Epoch: [3/20], Batch Num: [374/600] Discriminator Loss: 0.1982, Generator Loss: 3.8078 D(x): 0.9299, D(G(z)): 0.0673 Epoch: [3/20], Batch Num: [375/600] Discriminator Loss: 0.5162, Generator Loss: 3.0894 D(x): 0.8460, D(G(z)): 0.0774 Epoch: [3/20], Batch Num: [376/600] Discriminator Loss: 0.4443, Generator Loss: 2.4999 D(x): 0.8978, D(G(z)): 0.1315 Epoch: [3/20], Batch Num: [377/600] Discriminator Loss: 0.6040, Generator Loss: 3.3890 D(x): 0.9241, D(G(z)): 0.2528 Epoch: [3/20], Batch Num: [378/600] Discriminator Loss: 0.6489, Generator Loss: 3.8030 D(x): 0.8511, D(G(z)): 0.1361 Epoch: [3/20], Batch Num: [379/600] Discriminator Loss: 0.7951, Generator Loss: 3.8652 D(x): 0.7919, D(G(z)): 0.0622 Epoch: [3/20], Batch Num: [380/600] Discriminator Loss: 0.7358, Generator Loss: 3.2918 D(x): 0.7788, D(G(z)): 0.0751 Epoch: [3/20], Batch Num: [381/600] Discriminator Loss: 0.5072, Generator Loss: 2.5926 D(x): 0.8536, D(G(z)): 0.1237 Epoch: [3/20], Batch Num: [382/600] Discriminator Loss: 0.5013, Generator Loss: 2.2255 D(x): 0.9086, D(G(z)): 0.2019 Epoch: [3/20], Batch Num: [383/600] Discriminator Loss: 0.3833, Generator Loss: 2.6761 D(x): 0.9504, D(G(z)): 0.2175 Epoch: [3/20], Batch Num: [384/600] Discriminator Loss: 0.3492, Generator Loss: 3.4918 D(x): 0.9415, D(G(z)): 0.1533 Epoch: [3/20], Batch Num: [385/600] Discriminator Loss: 0.3076, Generator Loss: 4.1630 D(x): 0.9354, D(G(z)): 0.1158 Epoch: [3/20], Batch Num: [386/600] Discriminator Loss: 0.2764, Generator Loss: 4.6366 D(x): 0.9395, D(G(z)): 0.0818 Epoch: [3/20], Batch Num: [387/600] Discriminator Loss: 0.3107, Generator Loss: 4.6286 D(x): 0.9001, D(G(z)): 0.0747 Epoch: [3/20], Batch Num: [388/600] Discriminator Loss: 0.2414, Generator Loss: 5.0615 D(x): 0.9088, D(G(z)): 0.0631 Epoch: [3/20], Batch Num: [389/600] Discriminator Loss: 0.2569, Generator Loss: 4.2267 D(x): 0.9123, D(G(z)): 0.0677 Epoch: [3/20], Batch Num: [390/600] Discriminator Loss: 0.1798, Generator Loss: 3.9529 D(x): 0.9252, D(G(z)): 0.0604 Epoch: [3/20], Batch Num: [391/600] Discriminator Loss: 0.1757, Generator Loss: 3.6140 D(x): 0.9561, D(G(z)): 0.0925 Epoch: [3/20], Batch Num: [392/600] Discriminator Loss: 0.2530, Generator Loss: 3.5236 D(x): 0.9431, D(G(z)): 0.1201 Epoch: [3/20], Batch Num: [393/600] Discriminator Loss: 0.3618, Generator Loss: 3.7532 D(x): 0.9416, D(G(z)): 0.1565 Epoch: [3/20], Batch Num: [394/600] Discriminator Loss: 0.2169, Generator Loss: 4.1160 D(x): 0.9570, D(G(z)): 0.1160 Epoch: [3/20], Batch Num: [395/600] Discriminator Loss: 0.2753, Generator Loss: 4.3822 D(x): 0.9227, D(G(z)): 0.1210 Epoch: [3/20], Batch Num: [396/600] Discriminator Loss: 0.3409, Generator Loss: 5.0604 D(x): 0.8959, D(G(z)): 0.1266 Epoch: [3/20], Batch Num: [397/600] Discriminator Loss: 0.3628, Generator Loss: 4.8191 D(x): 0.8977, D(G(z)): 0.1286 Epoch: [3/20], Batch Num: [398/600] Discriminator Loss: 0.3966, Generator Loss: 4.9587 D(x): 0.8742, D(G(z)): 0.1098 Epoch: [3/20], Batch Num: [399/600] Discriminator Loss: 0.4308, Generator Loss: 4.5817 D(x): 0.8652, D(G(z)): 0.1104 Epoch: 3, Batch Num: [400/600]
Epoch: [3/20], Batch Num: [400/600] Discriminator Loss: 0.6885, Generator Loss: 3.9052 D(x): 0.8004, D(G(z)): 0.1465 Epoch: [3/20], Batch Num: [401/600] Discriminator Loss: 0.5118, Generator Loss: 2.7711 D(x): 0.8761, D(G(z)): 0.1827 Epoch: [3/20], Batch Num: [402/600] Discriminator Loss: 0.3831, Generator Loss: 2.9700 D(x): 0.9070, D(G(z)): 0.1670 Epoch: [3/20], Batch Num: [403/600] Discriminator Loss: 0.7117, Generator Loss: 3.0673 D(x): 0.8617, D(G(z)): 0.2646 Epoch: [3/20], Batch Num: [404/600] Discriminator Loss: 0.5370, Generator Loss: 3.1071 D(x): 0.8263, D(G(z)): 0.1533 Epoch: [3/20], Batch Num: [405/600] Discriminator Loss: 0.6949, Generator Loss: 2.6662 D(x): 0.7923, D(G(z)): 0.1451 Epoch: [3/20], Batch Num: [406/600] Discriminator Loss: 0.5157, Generator Loss: 2.5191 D(x): 0.8353, D(G(z)): 0.1572 Epoch: [3/20], Batch Num: [407/600] Discriminator Loss: 0.6304, Generator Loss: 2.0944 D(x): 0.8493, D(G(z)): 0.2317 Epoch: [3/20], Batch Num: [408/600] Discriminator Loss: 0.6762, Generator Loss: 2.0679 D(x): 0.8270, D(G(z)): 0.2473 Epoch: [3/20], Batch Num: [409/600] Discriminator Loss: 0.5669, Generator Loss: 2.1122 D(x): 0.8561, D(G(z)): 0.2296 Epoch: [3/20], Batch Num: [410/600] Discriminator Loss: 0.4502, Generator Loss: 2.8230 D(x): 0.9143, D(G(z)): 0.2211 Epoch: [3/20], Batch Num: [411/600] Discriminator Loss: 0.4512, Generator Loss: 2.9681 D(x): 0.8745, D(G(z)): 0.1869 Epoch: [3/20], Batch Num: [412/600] Discriminator Loss: 0.5145, Generator Loss: 3.6297 D(x): 0.8447, D(G(z)): 0.1380 Epoch: [3/20], Batch Num: [413/600] Discriminator Loss: 0.5525, Generator Loss: 3.5937 D(x): 0.8077, D(G(z)): 0.1318 Epoch: [3/20], Batch Num: [414/600] Discriminator Loss: 0.5862, Generator Loss: 2.8294 D(x): 0.7943, D(G(z)): 0.1351 Epoch: [3/20], Batch Num: [415/600] Discriminator Loss: 0.6125, Generator Loss: 2.2825 D(x): 0.8429, D(G(z)): 0.1532 Epoch: [3/20], Batch Num: [416/600] Discriminator Loss: 0.5908, Generator Loss: 2.4679 D(x): 0.8695, D(G(z)): 0.2567 Epoch: [3/20], Batch Num: [417/600] Discriminator Loss: 0.4611, Generator Loss: 2.8970 D(x): 0.8737, D(G(z)): 0.1908 Epoch: [3/20], Batch Num: [418/600] Discriminator Loss: 0.4712, Generator Loss: 3.1956 D(x): 0.9014, D(G(z)): 0.2022 Epoch: [3/20], Batch Num: [419/600] Discriminator Loss: 0.4612, Generator Loss: 3.4397 D(x): 0.8340, D(G(z)): 0.1110 Epoch: [3/20], Batch Num: [420/600] Discriminator Loss: 0.6738, Generator Loss: 3.3245 D(x): 0.7148, D(G(z)): 0.0792 Epoch: [3/20], Batch Num: [421/600] Discriminator Loss: 0.6222, Generator Loss: 2.3634 D(x): 0.7734, D(G(z)): 0.1256 Epoch: [3/20], Batch Num: [422/600] Discriminator Loss: 0.4871, Generator Loss: 2.1942 D(x): 0.8523, D(G(z)): 0.1879 Epoch: [3/20], Batch Num: [423/600] Discriminator Loss: 0.5486, Generator Loss: 2.4527 D(x): 0.8590, D(G(z)): 0.2260 Epoch: [3/20], Batch Num: [424/600] Discriminator Loss: 0.3669, Generator Loss: 3.2299 D(x): 0.9191, D(G(z)): 0.1989 Epoch: [3/20], Batch Num: [425/600] Discriminator Loss: 0.3053, Generator Loss: 4.2166 D(x): 0.9003, D(G(z)): 0.1110 Epoch: [3/20], Batch Num: [426/600] Discriminator Loss: 0.2630, Generator Loss: 4.3988 D(x): 0.8743, D(G(z)): 0.0530 Epoch: [3/20], Batch Num: [427/600] Discriminator Loss: 0.4531, Generator Loss: 4.4710 D(x): 0.7954, D(G(z)): 0.0474 Epoch: [3/20], Batch Num: [428/600] Discriminator Loss: 0.3130, Generator Loss: 3.5288 D(x): 0.8702, D(G(z)): 0.0534 Epoch: [3/20], Batch Num: [429/600] Discriminator Loss: 0.2412, Generator Loss: 3.1840 D(x): 0.9119, D(G(z)): 0.0917 Epoch: [3/20], Batch Num: [430/600] Discriminator Loss: 0.3057, Generator Loss: 2.5692 D(x): 0.8796, D(G(z)): 0.1047 Epoch: [3/20], Batch Num: [431/600] Discriminator Loss: 0.4951, Generator Loss: 3.5045 D(x): 0.9329, D(G(z)): 0.2251 Epoch: [3/20], Batch Num: [432/600] Discriminator Loss: 0.3215, Generator Loss: 4.4333 D(x): 0.9202, D(G(z)): 0.1381 Epoch: [3/20], Batch Num: [433/600] Discriminator Loss: 0.2524, Generator Loss: 5.6012 D(x): 0.9117, D(G(z)): 0.0841 Epoch: [3/20], Batch Num: [434/600] Discriminator Loss: 0.3146, Generator Loss: 6.1546 D(x): 0.8635, D(G(z)): 0.0312 Epoch: [3/20], Batch Num: [435/600] Discriminator Loss: 0.2841, Generator Loss: 5.7144 D(x): 0.8898, D(G(z)): 0.0380 Epoch: [3/20], Batch Num: [436/600] Discriminator Loss: 0.1843, Generator Loss: 5.2747 D(x): 0.9128, D(G(z)): 0.0263 Epoch: [3/20], Batch Num: [437/600] Discriminator Loss: 0.1840, Generator Loss: 4.8654 D(x): 0.9128, D(G(z)): 0.0248 Epoch: [3/20], Batch Num: [438/600] Discriminator Loss: 0.2340, Generator Loss: 4.1283 D(x): 0.9069, D(G(z)): 0.0583 Epoch: [3/20], Batch Num: [439/600] Discriminator Loss: 0.1905, Generator Loss: 3.2262 D(x): 0.9422, D(G(z)): 0.0948 Epoch: [3/20], Batch Num: [440/600] Discriminator Loss: 0.2340, Generator Loss: 3.4210 D(x): 0.9398, D(G(z)): 0.1150 Epoch: [3/20], Batch Num: [441/600] Discriminator Loss: 0.3092, Generator Loss: 3.4077 D(x): 0.9155, D(G(z)): 0.1229 Epoch: [3/20], Batch Num: [442/600] Discriminator Loss: 0.2654, Generator Loss: 3.8080 D(x): 0.9226, D(G(z)): 0.1234 Epoch: [3/20], Batch Num: [443/600] Discriminator Loss: 0.1783, Generator Loss: 4.0517 D(x): 0.9525, D(G(z)): 0.0925 Epoch: [3/20], Batch Num: [444/600] Discriminator Loss: 0.2152, Generator Loss: 4.7074 D(x): 0.9234, D(G(z)): 0.0493 Epoch: [3/20], Batch Num: [445/600] Discriminator Loss: 0.3228, Generator Loss: 4.4494 D(x): 0.8830, D(G(z)): 0.0429 Epoch: [3/20], Batch Num: [446/600] Discriminator Loss: 0.3038, Generator Loss: 3.6569 D(x): 0.8730, D(G(z)): 0.0699 Epoch: [3/20], Batch Num: [447/600] Discriminator Loss: 0.3139, Generator Loss: 2.8133 D(x): 0.9008, D(G(z)): 0.1054 Epoch: [3/20], Batch Num: [448/600] Discriminator Loss: 0.3328, Generator Loss: 2.9956 D(x): 0.9662, D(G(z)): 0.1887 Epoch: [3/20], Batch Num: [449/600] Discriminator Loss: 0.2752, Generator Loss: 4.2083 D(x): 0.9404, D(G(z)): 0.1363 Epoch: [3/20], Batch Num: [450/600] Discriminator Loss: 0.3059, Generator Loss: 4.4291 D(x): 0.8930, D(G(z)): 0.0731 Epoch: [3/20], Batch Num: [451/600] Discriminator Loss: 0.4213, Generator Loss: 4.0178 D(x): 0.8534, D(G(z)): 0.0792 Epoch: [3/20], Batch Num: [452/600] Discriminator Loss: 0.5312, Generator Loss: 2.6982 D(x): 0.8228, D(G(z)): 0.0836 Epoch: [3/20], Batch Num: [453/600] Discriminator Loss: 0.3421, Generator Loss: 2.5202 D(x): 0.9390, D(G(z)): 0.1692 Epoch: [3/20], Batch Num: [454/600] Discriminator Loss: 0.4454, Generator Loss: 3.1258 D(x): 0.9341, D(G(z)): 0.2070 Epoch: [3/20], Batch Num: [455/600] Discriminator Loss: 0.4178, Generator Loss: 3.2947 D(x): 0.9106, D(G(z)): 0.1658 Epoch: [3/20], Batch Num: [456/600] Discriminator Loss: 0.2606, Generator Loss: 4.2180 D(x): 0.9347, D(G(z)): 0.1112 Epoch: [3/20], Batch Num: [457/600] Discriminator Loss: 0.3864, Generator Loss: 3.9907 D(x): 0.8689, D(G(z)): 0.0712 Epoch: [3/20], Batch Num: [458/600] Discriminator Loss: 0.2956, Generator Loss: 3.6176 D(x): 0.8912, D(G(z)): 0.0565 Epoch: [3/20], Batch Num: [459/600] Discriminator Loss: 0.3377, Generator Loss: 3.1029 D(x): 0.8835, D(G(z)): 0.0788 Epoch: [3/20], Batch Num: [460/600] Discriminator Loss: 0.3536, Generator Loss: 2.7251 D(x): 0.9097, D(G(z)): 0.1236 Epoch: [3/20], Batch Num: [461/600] Discriminator Loss: 0.4412, Generator Loss: 2.9507 D(x): 0.9249, D(G(z)): 0.2016 Epoch: [3/20], Batch Num: [462/600] Discriminator Loss: 0.4935, Generator Loss: 3.4354 D(x): 0.8807, D(G(z)): 0.1556 Epoch: [3/20], Batch Num: [463/600] Discriminator Loss: 0.3191, Generator Loss: 3.5513 D(x): 0.8928, D(G(z)): 0.0857 Epoch: [3/20], Batch Num: [464/600] Discriminator Loss: 0.2270, Generator Loss: 3.4992 D(x): 0.9426, D(G(z)): 0.0867 Epoch: [3/20], Batch Num: [465/600] Discriminator Loss: 0.3006, Generator Loss: 3.9763 D(x): 0.9173, D(G(z)): 0.0845 Epoch: [3/20], Batch Num: [466/600] Discriminator Loss: 0.2745, Generator Loss: 4.0558 D(x): 0.9212, D(G(z)): 0.0376 Epoch: [3/20], Batch Num: [467/600] Discriminator Loss: 0.1979, Generator Loss: 3.8710 D(x): 0.9317, D(G(z)): 0.0472 Epoch: [3/20], Batch Num: [468/600] Discriminator Loss: 0.2348, Generator Loss: 3.4363 D(x): 0.9328, D(G(z)): 0.0889 Epoch: [3/20], Batch Num: [469/600] Discriminator Loss: 0.1926, Generator Loss: 3.7265 D(x): 0.9551, D(G(z)): 0.0923 Epoch: [3/20], Batch Num: [470/600] Discriminator Loss: 0.1332, Generator Loss: 4.1708 D(x): 0.9686, D(G(z)): 0.0673 Epoch: [3/20], Batch Num: [471/600] Discriminator Loss: 0.1699, Generator Loss: 4.2745 D(x): 0.9720, D(G(z)): 0.0902 Epoch: [3/20], Batch Num: [472/600] Discriminator Loss: 0.1737, Generator Loss: 4.3588 D(x): 0.9546, D(G(z)): 0.0564 Epoch: [3/20], Batch Num: [473/600] Discriminator Loss: 0.1710, Generator Loss: 4.9999 D(x): 0.9406, D(G(z)): 0.0442 Epoch: [3/20], Batch Num: [474/600] Discriminator Loss: 0.1373, Generator Loss: 5.1099 D(x): 0.9728, D(G(z)): 0.0513 Epoch: [3/20], Batch Num: [475/600] Discriminator Loss: 0.1558, Generator Loss: 5.4643 D(x): 0.9466, D(G(z)): 0.0349 Epoch: [3/20], Batch Num: [476/600] Discriminator Loss: 0.0882, Generator Loss: 5.4138 D(x): 0.9818, D(G(z)): 0.0424 Epoch: [3/20], Batch Num: [477/600] Discriminator Loss: 0.1815, Generator Loss: 5.3728 D(x): 0.9570, D(G(z)): 0.0596 Epoch: [3/20], Batch Num: [478/600] Discriminator Loss: 0.1636, Generator Loss: 5.0299 D(x): 0.9423, D(G(z)): 0.0453 Epoch: [3/20], Batch Num: [479/600] Discriminator Loss: 0.1497, Generator Loss: 4.8217 D(x): 0.9784, D(G(z)): 0.0455 Epoch: [3/20], Batch Num: [480/600] Discriminator Loss: 0.2459, Generator Loss: 4.5272 D(x): 0.9254, D(G(z)): 0.0689 Epoch: [3/20], Batch Num: [481/600] Discriminator Loss: 0.2223, Generator Loss: 4.5785 D(x): 0.9677, D(G(z)): 0.1229 Epoch: [3/20], Batch Num: [482/600] Discriminator Loss: 0.3008, Generator Loss: 4.3807 D(x): 0.9180, D(G(z)): 0.0797 Epoch: [3/20], Batch Num: [483/600] Discriminator Loss: 0.3144, Generator Loss: 4.4334 D(x): 0.9347, D(G(z)): 0.1072 Epoch: [3/20], Batch Num: [484/600] Discriminator Loss: 0.2514, Generator Loss: 4.7252 D(x): 0.9522, D(G(z)): 0.1130 Epoch: [3/20], Batch Num: [485/600] Discriminator Loss: 0.2514, Generator Loss: 3.9578 D(x): 0.9211, D(G(z)): 0.0763 Epoch: [3/20], Batch Num: [486/600] Discriminator Loss: 0.2672, Generator Loss: 4.2760 D(x): 0.9352, D(G(z)): 0.1107 Epoch: [3/20], Batch Num: [487/600] Discriminator Loss: 0.2670, Generator Loss: 4.1592 D(x): 0.9450, D(G(z)): 0.1024 Epoch: [3/20], Batch Num: [488/600] Discriminator Loss: 0.4903, Generator Loss: 3.7715 D(x): 0.9044, D(G(z)): 0.1217 Epoch: [3/20], Batch Num: [489/600] Discriminator Loss: 0.5324, Generator Loss: 3.7273 D(x): 0.8836, D(G(z)): 0.1267 Epoch: [3/20], Batch Num: [490/600] Discriminator Loss: 0.4036, Generator Loss: 3.3756 D(x): 0.8869, D(G(z)): 0.1006 Epoch: [3/20], Batch Num: [491/600] Discriminator Loss: 0.5240, Generator Loss: 3.3643 D(x): 0.8870, D(G(z)): 0.1646 Epoch: [3/20], Batch Num: [492/600] Discriminator Loss: 0.2577, Generator Loss: 3.4623 D(x): 0.9388, D(G(z)): 0.1131 Epoch: [3/20], Batch Num: [493/600] Discriminator Loss: 0.3257, Generator Loss: 3.6265 D(x): 0.9449, D(G(z)): 0.1313 Epoch: [3/20], Batch Num: [494/600] Discriminator Loss: 0.2999, Generator Loss: 4.2397 D(x): 0.9448, D(G(z)): 0.1332 Epoch: [3/20], Batch Num: [495/600] Discriminator Loss: 0.2494, Generator Loss: 4.6255 D(x): 0.9319, D(G(z)): 0.0820 Epoch: [3/20], Batch Num: [496/600] Discriminator Loss: 0.2074, Generator Loss: 4.7900 D(x): 0.9478, D(G(z)): 0.0529 Epoch: [3/20], Batch Num: [497/600] Discriminator Loss: 0.3672, Generator Loss: 4.8335 D(x): 0.8873, D(G(z)): 0.0596 Epoch: [3/20], Batch Num: [498/600] Discriminator Loss: 0.2239, Generator Loss: 5.0160 D(x): 0.9386, D(G(z)): 0.0439 Epoch: [3/20], Batch Num: [499/600] Discriminator Loss: 0.1579, Generator Loss: 4.8238 D(x): 0.9642, D(G(z)): 0.0681 Epoch: 3, Batch Num: [500/600]
Epoch: [3/20], Batch Num: [500/600] Discriminator Loss: 0.1309, Generator Loss: 4.6292 D(x): 0.9663, D(G(z)): 0.0688 Epoch: [3/20], Batch Num: [501/600] Discriminator Loss: 0.2291, Generator Loss: 4.7240 D(x): 0.9528, D(G(z)): 0.0853 Epoch: [3/20], Batch Num: [502/600] Discriminator Loss: 0.2264, Generator Loss: 4.6318 D(x): 0.9342, D(G(z)): 0.0718 Epoch: [3/20], Batch Num: [503/600] Discriminator Loss: 0.1661, Generator Loss: 5.2279 D(x): 0.9592, D(G(z)): 0.0742 Epoch: [3/20], Batch Num: [504/600] Discriminator Loss: 0.1088, Generator Loss: 5.6378 D(x): 0.9685, D(G(z)): 0.0431 Epoch: [3/20], Batch Num: [505/600] Discriminator Loss: 0.1229, Generator Loss: 5.6673 D(x): 0.9623, D(G(z)): 0.0412 Epoch: [3/20], Batch Num: [506/600] Discriminator Loss: 0.1554, Generator Loss: 5.9345 D(x): 0.9651, D(G(z)): 0.0668 Epoch: [3/20], Batch Num: [507/600] Discriminator Loss: 0.1734, Generator Loss: 6.2592 D(x): 0.9486, D(G(z)): 0.0558 Epoch: [3/20], Batch Num: [508/600] Discriminator Loss: 0.1245, Generator Loss: 6.2123 D(x): 0.9560, D(G(z)): 0.0459 Epoch: [3/20], Batch Num: [509/600] Discriminator Loss: 0.1665, Generator Loss: 5.9161 D(x): 0.9481, D(G(z)): 0.0330 Epoch: [3/20], Batch Num: [510/600] Discriminator Loss: 0.2047, Generator Loss: 5.7138 D(x): 0.9389, D(G(z)): 0.0569 Epoch: [3/20], Batch Num: [511/600] Discriminator Loss: 0.2350, Generator Loss: 5.9417 D(x): 0.9399, D(G(z)): 0.0665 Epoch: [3/20], Batch Num: [512/600] Discriminator Loss: 0.1867, Generator Loss: 5.8507 D(x): 0.9409, D(G(z)): 0.0394 Epoch: [3/20], Batch Num: [513/600] Discriminator Loss: 0.3378, Generator Loss: 6.1427 D(x): 0.9428, D(G(z)): 0.1249 Epoch: [3/20], Batch Num: [514/600] Discriminator Loss: 0.2694, Generator Loss: 6.6355 D(x): 0.9103, D(G(z)): 0.0550 Epoch: [3/20], Batch Num: [515/600] Discriminator Loss: 0.6078, Generator Loss: 6.2718 D(x): 0.8155, D(G(z)): 0.0573 Epoch: [3/20], Batch Num: [516/600] Discriminator Loss: 0.3861, Generator Loss: 4.9492 D(x): 0.8826, D(G(z)): 0.0422 Epoch: [3/20], Batch Num: [517/600] Discriminator Loss: 0.3019, Generator Loss: 4.3938 D(x): 0.9498, D(G(z)): 0.1167 Epoch: [3/20], Batch Num: [518/600] Discriminator Loss: 0.3563, Generator Loss: 5.1080 D(x): 0.9269, D(G(z)): 0.1356 Epoch: [3/20], Batch Num: [519/600] Discriminator Loss: 0.2156, Generator Loss: 5.8741 D(x): 0.9357, D(G(z)): 0.0704 Epoch: [3/20], Batch Num: [520/600] Discriminator Loss: 0.1781, Generator Loss: 5.7746 D(x): 0.9275, D(G(z)): 0.0346 Epoch: [3/20], Batch Num: [521/600] Discriminator Loss: 0.1041, Generator Loss: 6.2661 D(x): 0.9703, D(G(z)): 0.0400 Epoch: [3/20], Batch Num: [522/600] Discriminator Loss: 0.2121, Generator Loss: 5.7395 D(x): 0.9234, D(G(z)): 0.0467 Epoch: [3/20], Batch Num: [523/600] Discriminator Loss: 0.2534, Generator Loss: 5.4451 D(x): 0.9118, D(G(z)): 0.0427 Epoch: [3/20], Batch Num: [524/600] Discriminator Loss: 0.1430, Generator Loss: 5.4305 D(x): 0.9693, D(G(z)): 0.0651 Epoch: [3/20], Batch Num: [525/600] Discriminator Loss: 0.2568, Generator Loss: 5.8840 D(x): 0.9379, D(G(z)): 0.0651 Epoch: [3/20], Batch Num: [526/600] Discriminator Loss: 0.1881, Generator Loss: 5.4781 D(x): 0.9326, D(G(z)): 0.0455 Epoch: [3/20], Batch Num: [527/600] Discriminator Loss: 0.2350, Generator Loss: 5.3307 D(x): 0.9308, D(G(z)): 0.0538 Epoch: [3/20], Batch Num: [528/600] Discriminator Loss: 0.2348, Generator Loss: 5.8115 D(x): 0.9503, D(G(z)): 0.0768 Epoch: [3/20], Batch Num: [529/600] Discriminator Loss: 0.1780, Generator Loss: 5.7098 D(x): 0.9354, D(G(z)): 0.0366 Epoch: [3/20], Batch Num: [530/600] Discriminator Loss: 0.0969, Generator Loss: 5.4568 D(x): 0.9707, D(G(z)): 0.0343 Epoch: [3/20], Batch Num: [531/600] Discriminator Loss: 0.1927, Generator Loss: 5.1947 D(x): 0.9778, D(G(z)): 0.0840 Epoch: [3/20], Batch Num: [532/600] Discriminator Loss: 0.1912, Generator Loss: 6.0581 D(x): 0.9591, D(G(z)): 0.0808 Epoch: [3/20], Batch Num: [533/600] Discriminator Loss: 0.3643, Generator Loss: 5.5317 D(x): 0.9003, D(G(z)): 0.0594 Epoch: [3/20], Batch Num: [534/600] Discriminator Loss: 0.3533, Generator Loss: 4.8534 D(x): 0.9024, D(G(z)): 0.0595 Epoch: [3/20], Batch Num: [535/600] Discriminator Loss: 0.3124, Generator Loss: 4.8646 D(x): 0.9265, D(G(z)): 0.1110 Epoch: [3/20], Batch Num: [536/600] Discriminator Loss: 0.4381, Generator Loss: 4.5017 D(x): 0.8852, D(G(z)): 0.0927 Epoch: [3/20], Batch Num: [537/600] Discriminator Loss: 0.2347, Generator Loss: 4.3872 D(x): 0.9324, D(G(z)): 0.0724 Epoch: [3/20], Batch Num: [538/600] Discriminator Loss: 0.4702, Generator Loss: 4.1829 D(x): 0.8822, D(G(z)): 0.1035 Epoch: [3/20], Batch Num: [539/600] Discriminator Loss: 0.3688, Generator Loss: 3.5669 D(x): 0.8672, D(G(z)): 0.0611 Epoch: [3/20], Batch Num: [540/600] Discriminator Loss: 0.2207, Generator Loss: 3.3427 D(x): 0.9432, D(G(z)): 0.0975 Epoch: [3/20], Batch Num: [541/600] Discriminator Loss: 0.2174, Generator Loss: 3.8222 D(x): 0.9571, D(G(z)): 0.1122 Epoch: [3/20], Batch Num: [542/600] Discriminator Loss: 0.3254, Generator Loss: 3.8087 D(x): 0.9206, D(G(z)): 0.0689 Epoch: [3/20], Batch Num: [543/600] Discriminator Loss: 0.2279, Generator Loss: 3.4177 D(x): 0.8968, D(G(z)): 0.0342 Epoch: [3/20], Batch Num: [544/600] Discriminator Loss: 0.2444, Generator Loss: 3.3644 D(x): 0.9274, D(G(z)): 0.0615 Epoch: [3/20], Batch Num: [545/600] Discriminator Loss: 0.2382, Generator Loss: 3.0686 D(x): 0.9254, D(G(z)): 0.0907 Epoch: [3/20], Batch Num: [546/600] Discriminator Loss: 0.1678, Generator Loss: 3.3768 D(x): 0.9743, D(G(z)): 0.1090 Epoch: [3/20], Batch Num: [547/600] Discriminator Loss: 0.1798, Generator Loss: 3.9192 D(x): 0.9692, D(G(z)): 0.0851 Epoch: [3/20], Batch Num: [548/600] Discriminator Loss: 0.1698, Generator Loss: 4.3908 D(x): 0.9543, D(G(z)): 0.0723 Epoch: [3/20], Batch Num: [549/600] Discriminator Loss: 0.1401, Generator Loss: 4.4507 D(x): 0.9573, D(G(z)): 0.0486 Epoch: [3/20], Batch Num: [550/600] Discriminator Loss: 0.2610, Generator Loss: 4.3889 D(x): 0.8958, D(G(z)): 0.0338 Epoch: [3/20], Batch Num: [551/600] Discriminator Loss: 0.2651, Generator Loss: 3.8920 D(x): 0.9065, D(G(z)): 0.0374 Epoch: [3/20], Batch Num: [552/600] Discriminator Loss: 0.2355, Generator Loss: 3.8112 D(x): 0.9530, D(G(z)): 0.1088 Epoch: [3/20], Batch Num: [553/600] Discriminator Loss: 0.3297, Generator Loss: 3.6284 D(x): 0.9010, D(G(z)): 0.0833 Epoch: [3/20], Batch Num: [554/600] Discriminator Loss: 0.1521, Generator Loss: 4.0353 D(x): 0.9520, D(G(z)): 0.0666 Epoch: [3/20], Batch Num: [555/600] Discriminator Loss: 0.1374, Generator Loss: 4.1843 D(x): 0.9655, D(G(z)): 0.0641 Epoch: [3/20], Batch Num: [556/600] Discriminator Loss: 0.2079, Generator Loss: 4.4706 D(x): 0.9295, D(G(z)): 0.0569 Epoch: [3/20], Batch Num: [557/600] Discriminator Loss: 0.1521, Generator Loss: 4.7666 D(x): 0.9444, D(G(z)): 0.0451 Epoch: [3/20], Batch Num: [558/600] Discriminator Loss: 0.2069, Generator Loss: 4.3717 D(x): 0.9218, D(G(z)): 0.0397 Epoch: [3/20], Batch Num: [559/600] Discriminator Loss: 0.1747, Generator Loss: 4.2676 D(x): 0.9398, D(G(z)): 0.0521 Epoch: [3/20], Batch Num: [560/600] Discriminator Loss: 0.2300, Generator Loss: 4.0344 D(x): 0.9316, D(G(z)): 0.0836 Epoch: [3/20], Batch Num: [561/600] Discriminator Loss: 0.1361, Generator Loss: 4.5009 D(x): 0.9494, D(G(z)): 0.0396 Epoch: [3/20], Batch Num: [562/600] Discriminator Loss: 0.1444, Generator Loss: 4.0155 D(x): 0.9512, D(G(z)): 0.0461 Epoch: [3/20], Batch Num: [563/600] Discriminator Loss: 0.1600, Generator Loss: 4.2971 D(x): 0.9381, D(G(z)): 0.0418 Epoch: [3/20], Batch Num: [564/600] Discriminator Loss: 0.1594, Generator Loss: 4.3969 D(x): 0.9512, D(G(z)): 0.0556 Epoch: [3/20], Batch Num: [565/600] Discriminator Loss: 0.0758, Generator Loss: 4.3297 D(x): 0.9725, D(G(z)): 0.0358 Epoch: [3/20], Batch Num: [566/600] Discriminator Loss: 0.0617, Generator Loss: 4.4837 D(x): 0.9756, D(G(z)): 0.0281 Epoch: [3/20], Batch Num: [567/600] Discriminator Loss: 0.0574, Generator Loss: 4.7388 D(x): 0.9842, D(G(z)): 0.0330 Epoch: [3/20], Batch Num: [568/600] Discriminator Loss: 0.0639, Generator Loss: 4.8575 D(x): 0.9894, D(G(z)): 0.0398 Epoch: [3/20], Batch Num: [569/600] Discriminator Loss: 0.1346, Generator Loss: 5.8641 D(x): 0.9617, D(G(z)): 0.0223 Epoch: [3/20], Batch Num: [570/600] Discriminator Loss: 0.0260, Generator Loss: 5.9045 D(x): 0.9891, D(G(z)): 0.0124 Epoch: [3/20], Batch Num: [571/600] Discriminator Loss: 0.0369, Generator Loss: 5.9373 D(x): 0.9861, D(G(z)): 0.0153 Epoch: [3/20], Batch Num: [572/600] Discriminator Loss: 0.0165, Generator Loss: 5.9538 D(x): 0.9935, D(G(z)): 0.0092 Epoch: [3/20], Batch Num: [573/600] Discriminator Loss: 0.0353, Generator Loss: 6.2329 D(x): 0.9829, D(G(z)): 0.0103 Epoch: [3/20], Batch Num: [574/600] Discriminator Loss: 0.0140, Generator Loss: 6.1992 D(x): 0.9972, D(G(z)): 0.0107 Epoch: [3/20], Batch Num: [575/600] Discriminator Loss: 0.0208, Generator Loss: 6.0994 D(x): 0.9954, D(G(z)): 0.0147 Epoch: [3/20], Batch Num: [576/600] Discriminator Loss: 0.0228, Generator Loss: 6.2537 D(x): 0.9972, D(G(z)): 0.0190 Epoch: [3/20], Batch Num: [577/600] Discriminator Loss: 0.0375, Generator Loss: 6.3344 D(x): 0.9857, D(G(z)): 0.0171 Epoch: [3/20], Batch Num: [578/600] Discriminator Loss: 0.0441, Generator Loss: 6.5684 D(x): 0.9850, D(G(z)): 0.0177 Epoch: [3/20], Batch Num: [579/600] Discriminator Loss: 0.0605, Generator Loss: 7.0896 D(x): 0.9821, D(G(z)): 0.0155 Epoch: [3/20], Batch Num: [580/600] Discriminator Loss: 0.0102, Generator Loss: 7.5751 D(x): 0.9981, D(G(z)): 0.0079 Epoch: [3/20], Batch Num: [581/600] Discriminator Loss: 0.0142, Generator Loss: 8.1902 D(x): 0.9925, D(G(z)): 0.0040 Epoch: [3/20], Batch Num: [582/600] Discriminator Loss: 0.0174, Generator Loss: 7.9958 D(x): 0.9899, D(G(z)): 0.0044 Epoch: [3/20], Batch Num: [583/600] Discriminator Loss: 0.0663, Generator Loss: 7.3812 D(x): 0.9731, D(G(z)): 0.0056 Epoch: [3/20], Batch Num: [584/600] Discriminator Loss: 0.0495, Generator Loss: 6.8249 D(x): 0.9847, D(G(z)): 0.0029 Epoch: [3/20], Batch Num: [585/600] Discriminator Loss: 0.0136, Generator Loss: 6.3376 D(x): 0.9950, D(G(z)): 0.0079 Epoch: [3/20], Batch Num: [586/600] Discriminator Loss: 0.0115, Generator Loss: 5.8652 D(x): 0.9970, D(G(z)): 0.0082 Epoch: [3/20], Batch Num: [587/600] Discriminator Loss: 0.0552, Generator Loss: 5.8886 D(x): 0.9919, D(G(z)): 0.0296 Epoch: [3/20], Batch Num: [588/600] Discriminator Loss: 0.0163, Generator Loss: 6.5976 D(x): 0.9983, D(G(z)): 0.0140 Epoch: [3/20], Batch Num: [589/600] Discriminator Loss: 0.0119, Generator Loss: 6.6057 D(x): 0.9990, D(G(z)): 0.0106 Epoch: [3/20], Batch Num: [590/600] Discriminator Loss: 0.0171, Generator Loss: 6.8599 D(x): 0.9967, D(G(z)): 0.0131 Epoch: [3/20], Batch Num: [591/600] Discriminator Loss: 0.0570, Generator Loss: 7.0975 D(x): 0.9832, D(G(z)): 0.0103 Epoch: [3/20], Batch Num: [592/600] Discriminator Loss: 0.0197, Generator Loss: 7.0059 D(x): 0.9963, D(G(z)): 0.0151 Epoch: [3/20], Batch Num: [593/600] Discriminator Loss: 0.0283, Generator Loss: 6.8731 D(x): 0.9869, D(G(z)): 0.0123 Epoch: [3/20], Batch Num: [594/600] Discriminator Loss: 0.0473, Generator Loss: 7.2553 D(x): 0.9963, D(G(z)): 0.0344 Epoch: [3/20], Batch Num: [595/600] Discriminator Loss: 0.0728, Generator Loss: 7.1238 D(x): 0.9707, D(G(z)): 0.0155 Epoch: [3/20], Batch Num: [596/600] Discriminator Loss: 0.0517, Generator Loss: 7.9196 D(x): 0.9833, D(G(z)): 0.0204 Epoch: [3/20], Batch Num: [597/600] Discriminator Loss: 0.0932, Generator Loss: 7.7314 D(x): 0.9661, D(G(z)): 0.0068 Epoch: [3/20], Batch Num: [598/600] Discriminator Loss: 0.0437, Generator Loss: 7.1023 D(x): 0.9788, D(G(z)): 0.0105 Epoch: [3/20], Batch Num: [599/600] Discriminator Loss: 0.1029, Generator Loss: 6.0554 D(x): 0.9644, D(G(z)): 0.0309 Epoch: 4, Batch Num: [0/600]
Epoch: [4/20], Batch Num: [0/600] Discriminator Loss: 0.1072, Generator Loss: 6.4802 D(x): 0.9893, D(G(z)): 0.0516 Epoch: [4/20], Batch Num: [1/600] Discriminator Loss: 0.2384, Generator Loss: 7.0929 D(x): 0.9590, D(G(z)): 0.0587 Epoch: [4/20], Batch Num: [2/600] Discriminator Loss: 0.1146, Generator Loss: 6.8372 D(x): 0.9577, D(G(z)): 0.0357 Epoch: [4/20], Batch Num: [3/600] Discriminator Loss: 0.2100, Generator Loss: 7.5124 D(x): 0.9640, D(G(z)): 0.0470 Epoch: [4/20], Batch Num: [4/600] Discriminator Loss: 0.1122, Generator Loss: 7.8918 D(x): 0.9611, D(G(z)): 0.0294 Epoch: [4/20], Batch Num: [5/600] Discriminator Loss: 0.1658, Generator Loss: 8.2442 D(x): 0.9617, D(G(z)): 0.0516 Epoch: [4/20], Batch Num: [6/600] Discriminator Loss: 0.1128, Generator Loss: 7.6133 D(x): 0.9757, D(G(z)): 0.0313 Epoch: [4/20], Batch Num: [7/600] Discriminator Loss: 0.1500, Generator Loss: 7.9525 D(x): 0.9612, D(G(z)): 0.0263 Epoch: [4/20], Batch Num: [8/600] Discriminator Loss: 0.2084, Generator Loss: 7.2970 D(x): 0.9441, D(G(z)): 0.0201 Epoch: [4/20], Batch Num: [9/600] Discriminator Loss: 0.1397, Generator Loss: 6.8978 D(x): 0.9676, D(G(z)): 0.0340 Epoch: [4/20], Batch Num: [10/600] Discriminator Loss: 0.1795, Generator Loss: 6.9386 D(x): 0.9765, D(G(z)): 0.0701 Epoch: [4/20], Batch Num: [11/600] Discriminator Loss: 0.1461, Generator Loss: 7.1556 D(x): 0.9810, D(G(z)): 0.0618 Epoch: [4/20], Batch Num: [12/600] Discriminator Loss: 0.1566, Generator Loss: 8.1084 D(x): 0.9570, D(G(z)): 0.0471 Epoch: [4/20], Batch Num: [13/600] Discriminator Loss: 0.1410, Generator Loss: 8.1784 D(x): 0.9578, D(G(z)): 0.0265 Epoch: [4/20], Batch Num: [14/600] Discriminator Loss: 0.2130, Generator Loss: 8.2614 D(x): 0.9488, D(G(z)): 0.0339 Epoch: [4/20], Batch Num: [15/600] Discriminator Loss: 0.2605, Generator Loss: 7.9845 D(x): 0.9615, D(G(z)): 0.0738 Epoch: [4/20], Batch Num: [16/600] Discriminator Loss: 0.1266, Generator Loss: 7.5464 D(x): 0.9615, D(G(z)): 0.0529 Epoch: [4/20], Batch Num: [17/600] Discriminator Loss: 0.2311, Generator Loss: 6.4602 D(x): 0.9393, D(G(z)): 0.0625 Epoch: [4/20], Batch Num: [18/600] Discriminator Loss: 0.4871, Generator Loss: 7.7528 D(x): 0.9818, D(G(z)): 0.2078 Epoch: [4/20], Batch Num: [19/600] Discriminator Loss: 0.4107, Generator Loss: 7.2438 D(x): 0.9386, D(G(z)): 0.1386 Epoch: [4/20], Batch Num: [20/600] Discriminator Loss: 0.4113, Generator Loss: 8.7295 D(x): 0.9311, D(G(z)): 0.1182 Epoch: [4/20], Batch Num: [21/600] Discriminator Loss: 0.3691, Generator Loss: 7.6844 D(x): 0.9060, D(G(z)): 0.0984 Epoch: [4/20], Batch Num: [22/600] Discriminator Loss: 0.5503, Generator Loss: 5.8229 D(x): 0.8842, D(G(z)): 0.0930 Epoch: [4/20], Batch Num: [23/600] Discriminator Loss: 0.5905, Generator Loss: 5.0161 D(x): 0.8121, D(G(z)): 0.1053 Epoch: [4/20], Batch Num: [24/600] Discriminator Loss: 0.7162, Generator Loss: 3.8389 D(x): 0.8771, D(G(z)): 0.2174 Epoch: [4/20], Batch Num: [25/600] Discriminator Loss: 0.7803, Generator Loss: 3.4952 D(x): 0.9366, D(G(z)): 0.3239 Epoch: [4/20], Batch Num: [26/600] Discriminator Loss: 0.8598, Generator Loss: 4.9928 D(x): 0.8842, D(G(z)): 0.3226 Epoch: [4/20], Batch Num: [27/600] Discriminator Loss: 0.5163, Generator Loss: 5.5884 D(x): 0.8832, D(G(z)): 0.1401 Epoch: [4/20], Batch Num: [28/600] Discriminator Loss: 0.4471, Generator Loss: 6.5239 D(x): 0.8533, D(G(z)): 0.0857 Epoch: [4/20], Batch Num: [29/600] Discriminator Loss: 0.5204, Generator Loss: 5.4166 D(x): 0.7994, D(G(z)): 0.0446 Epoch: [4/20], Batch Num: [30/600] Discriminator Loss: 0.5045, Generator Loss: 4.8988 D(x): 0.8173, D(G(z)): 0.0573 Epoch: [4/20], Batch Num: [31/600] Discriminator Loss: 0.5455, Generator Loss: 3.9772 D(x): 0.8591, D(G(z)): 0.1349 Epoch: [4/20], Batch Num: [32/600] Discriminator Loss: 0.4631, Generator Loss: 3.4366 D(x): 0.9172, D(G(z)): 0.1856 Epoch: [4/20], Batch Num: [33/600] Discriminator Loss: 0.5931, Generator Loss: 3.8007 D(x): 0.9241, D(G(z)): 0.2536 Epoch: [4/20], Batch Num: [34/600] Discriminator Loss: 0.4734, Generator Loss: 5.2266 D(x): 0.9008, D(G(z)): 0.2055 Epoch: [4/20], Batch Num: [35/600] Discriminator Loss: 0.5733, Generator Loss: 5.6980 D(x): 0.8841, D(G(z)): 0.1516 Epoch: [4/20], Batch Num: [36/600] Discriminator Loss: 0.5564, Generator Loss: 6.5305 D(x): 0.7913, D(G(z)): 0.0655 Epoch: [4/20], Batch Num: [37/600] Discriminator Loss: 0.5502, Generator Loss: 5.3786 D(x): 0.8018, D(G(z)): 0.0724 Epoch: [4/20], Batch Num: [38/600] Discriminator Loss: 0.7638, Generator Loss: 4.5821 D(x): 0.7734, D(G(z)): 0.1036 Epoch: [4/20], Batch Num: [39/600] Discriminator Loss: 0.8969, Generator Loss: 2.7162 D(x): 0.8201, D(G(z)): 0.2084 Epoch: [4/20], Batch Num: [40/600] Discriminator Loss: 0.6947, Generator Loss: 2.7197 D(x): 0.8936, D(G(z)): 0.3007 Epoch: [4/20], Batch Num: [41/600] Discriminator Loss: 0.6508, Generator Loss: 3.0914 D(x): 0.9099, D(G(z)): 0.2995 Epoch: [4/20], Batch Num: [42/600] Discriminator Loss: 0.7317, Generator Loss: 3.8575 D(x): 0.8583, D(G(z)): 0.2413 Epoch: [4/20], Batch Num: [43/600] Discriminator Loss: 0.6819, Generator Loss: 4.0843 D(x): 0.7591, D(G(z)): 0.1006 Epoch: [4/20], Batch Num: [44/600] Discriminator Loss: 0.7278, Generator Loss: 4.1200 D(x): 0.7354, D(G(z)): 0.1211 Epoch: [4/20], Batch Num: [45/600] Discriminator Loss: 1.0406, Generator Loss: 2.4562 D(x): 0.6568, D(G(z)): 0.1601 Epoch: [4/20], Batch Num: [46/600] Discriminator Loss: 0.9040, Generator Loss: 1.9330 D(x): 0.7895, D(G(z)): 0.2800 Epoch: [4/20], Batch Num: [47/600] Discriminator Loss: 0.9516, Generator Loss: 1.9214 D(x): 0.8246, D(G(z)): 0.3872 Epoch: [4/20], Batch Num: [48/600] Discriminator Loss: 1.2891, Generator Loss: 2.3484 D(x): 0.6834, D(G(z)): 0.3549 Epoch: [4/20], Batch Num: [49/600] Discriminator Loss: 1.1486, Generator Loss: 1.9339 D(x): 0.6483, D(G(z)): 0.2658 Epoch: [4/20], Batch Num: [50/600] Discriminator Loss: 0.9530, Generator Loss: 2.0866 D(x): 0.7000, D(G(z)): 0.2466 Epoch: [4/20], Batch Num: [51/600] Discriminator Loss: 1.1771, Generator Loss: 1.7437 D(x): 0.6149, D(G(z)): 0.2247 Epoch: [4/20], Batch Num: [52/600] Discriminator Loss: 1.2130, Generator Loss: 1.5488 D(x): 0.6342, D(G(z)): 0.3022 Epoch: [4/20], Batch Num: [53/600] Discriminator Loss: 0.9065, Generator Loss: 1.2553 D(x): 0.7117, D(G(z)): 0.2960 Epoch: [4/20], Batch Num: [54/600] Discriminator Loss: 0.9645, Generator Loss: 1.4204 D(x): 0.7151, D(G(z)): 0.3247 Epoch: [4/20], Batch Num: [55/600] Discriminator Loss: 1.0618, Generator Loss: 1.3763 D(x): 0.6508, D(G(z)): 0.3261 Epoch: [4/20], Batch Num: [56/600] Discriminator Loss: 0.8111, Generator Loss: 1.4239 D(x): 0.7404, D(G(z)): 0.3009 Epoch: [4/20], Batch Num: [57/600] Discriminator Loss: 0.8510, Generator Loss: 1.4571 D(x): 0.7298, D(G(z)): 0.3100 Epoch: [4/20], Batch Num: [58/600] Discriminator Loss: 0.7297, Generator Loss: 1.5942 D(x): 0.7496, D(G(z)): 0.2647 Epoch: [4/20], Batch Num: [59/600] Discriminator Loss: 0.6840, Generator Loss: 1.6504 D(x): 0.7703, D(G(z)): 0.2662 Epoch: [4/20], Batch Num: [60/600] Discriminator Loss: 0.5871, Generator Loss: 1.8016 D(x): 0.8359, D(G(z)): 0.2485 Epoch: [4/20], Batch Num: [61/600] Discriminator Loss: 0.5975, Generator Loss: 2.0051 D(x): 0.7880, D(G(z)): 0.2235 Epoch: [4/20], Batch Num: [62/600] Discriminator Loss: 0.5758, Generator Loss: 1.8989 D(x): 0.7889, D(G(z)): 0.2117 Epoch: [4/20], Batch Num: [63/600] Discriminator Loss: 0.4016, Generator Loss: 2.0366 D(x): 0.8536, D(G(z)): 0.1683 Epoch: [4/20], Batch Num: [64/600] Discriminator Loss: 0.4661, Generator Loss: 1.8159 D(x): 0.8283, D(G(z)): 0.1768 Epoch: [4/20], Batch Num: [65/600] Discriminator Loss: 0.4975, Generator Loss: 1.9863 D(x): 0.8534, D(G(z)): 0.2212 Epoch: [4/20], Batch Num: [66/600] Discriminator Loss: 0.4247, Generator Loss: 2.0772 D(x): 0.8815, D(G(z)): 0.1886 Epoch: [4/20], Batch Num: [67/600] Discriminator Loss: 0.3915, Generator Loss: 2.4297 D(x): 0.8785, D(G(z)): 0.1506 Epoch: [4/20], Batch Num: [68/600] Discriminator Loss: 0.4209, Generator Loss: 2.4177 D(x): 0.8687, D(G(z)): 0.1646 Epoch: [4/20], Batch Num: [69/600] Discriminator Loss: 0.3875, Generator Loss: 2.8695 D(x): 0.9158, D(G(z)): 0.1719 Epoch: [4/20], Batch Num: [70/600] Discriminator Loss: 0.4407, Generator Loss: 3.2973 D(x): 0.8335, D(G(z)): 0.1178 Epoch: [4/20], Batch Num: [71/600] Discriminator Loss: 0.3862, Generator Loss: 3.0066 D(x): 0.8743, D(G(z)): 0.1017 Epoch: [4/20], Batch Num: [72/600] Discriminator Loss: 0.3438, Generator Loss: 2.7135 D(x): 0.8731, D(G(z)): 0.0882 Epoch: [4/20], Batch Num: [73/600] Discriminator Loss: 0.2417, Generator Loss: 2.5084 D(x): 0.9248, D(G(z)): 0.1060 Epoch: [4/20], Batch Num: [74/600] Discriminator Loss: 0.3436, Generator Loss: 2.3467 D(x): 0.9246, D(G(z)): 0.1646 Epoch: [4/20], Batch Num: [75/600] Discriminator Loss: 0.3305, Generator Loss: 2.9540 D(x): 0.9420, D(G(z)): 0.1725 Epoch: [4/20], Batch Num: [76/600] Discriminator Loss: 0.2830, Generator Loss: 3.6131 D(x): 0.9336, D(G(z)): 0.1297 Epoch: [4/20], Batch Num: [77/600] Discriminator Loss: 0.2395, Generator Loss: 4.1846 D(x): 0.9084, D(G(z)): 0.0698 Epoch: [4/20], Batch Num: [78/600] Discriminator Loss: 0.3204, Generator Loss: 4.0181 D(x): 0.8755, D(G(z)): 0.0451 Epoch: [4/20], Batch Num: [79/600] Discriminator Loss: 0.2987, Generator Loss: 2.9825 D(x): 0.8742, D(G(z)): 0.0432 Epoch: [4/20], Batch Num: [80/600] Discriminator Loss: 0.2630, Generator Loss: 2.2442 D(x): 0.9240, D(G(z)): 0.1016 Epoch: [4/20], Batch Num: [81/600] Discriminator Loss: 0.3758, Generator Loss: 2.9371 D(x): 0.9673, D(G(z)): 0.2210 Epoch: [4/20], Batch Num: [82/600] Discriminator Loss: 0.3227, Generator Loss: 3.2633 D(x): 0.9177, D(G(z)): 0.1345 Epoch: [4/20], Batch Num: [83/600] Discriminator Loss: 0.3247, Generator Loss: 3.3779 D(x): 0.9017, D(G(z)): 0.0918 Epoch: [4/20], Batch Num: [84/600] Discriminator Loss: 0.2675, Generator Loss: 3.5780 D(x): 0.9299, D(G(z)): 0.0808 Epoch: [4/20], Batch Num: [85/600] Discriminator Loss: 0.2475, Generator Loss: 3.3650 D(x): 0.9206, D(G(z)): 0.0647 Epoch: [4/20], Batch Num: [86/600] Discriminator Loss: 0.3723, Generator Loss: 2.9614 D(x): 0.8775, D(G(z)): 0.0892 Epoch: [4/20], Batch Num: [87/600] Discriminator Loss: 0.4657, Generator Loss: 2.6730 D(x): 0.9153, D(G(z)): 0.1485 Epoch: [4/20], Batch Num: [88/600] Discriminator Loss: 0.3679, Generator Loss: 2.9919 D(x): 0.9104, D(G(z)): 0.1515 Epoch: [4/20], Batch Num: [89/600] Discriminator Loss: 0.1865, Generator Loss: 3.7474 D(x): 0.9729, D(G(z)): 0.1257 Epoch: [4/20], Batch Num: [90/600] Discriminator Loss: 0.3649, Generator Loss: 3.7715 D(x): 0.8746, D(G(z)): 0.0831 Epoch: [4/20], Batch Num: [91/600] Discriminator Loss: 0.4219, Generator Loss: 3.3962 D(x): 0.8752, D(G(z)): 0.0984 Epoch: [4/20], Batch Num: [92/600] Discriminator Loss: 0.4060, Generator Loss: 3.2735 D(x): 0.8814, D(G(z)): 0.0829 Epoch: [4/20], Batch Num: [93/600] Discriminator Loss: 0.3052, Generator Loss: 2.8474 D(x): 0.9560, D(G(z)): 0.1463 Epoch: [4/20], Batch Num: [94/600] Discriminator Loss: 0.2495, Generator Loss: 3.3790 D(x): 0.9389, D(G(z)): 0.1215 Epoch: [4/20], Batch Num: [95/600] Discriminator Loss: 0.1912, Generator Loss: 4.0309 D(x): 0.9604, D(G(z)): 0.0956 Epoch: [4/20], Batch Num: [96/600] Discriminator Loss: 0.3116, Generator Loss: 3.8398 D(x): 0.9002, D(G(z)): 0.0770 Epoch: [4/20], Batch Num: [97/600] Discriminator Loss: 0.3890, Generator Loss: 3.3222 D(x): 0.8594, D(G(z)): 0.0561 Epoch: [4/20], Batch Num: [98/600] Discriminator Loss: 0.2836, Generator Loss: 3.2248 D(x): 0.9347, D(G(z)): 0.1180 Epoch: [4/20], Batch Num: [99/600] Discriminator Loss: 0.3335, Generator Loss: 3.0520 D(x): 0.9099, D(G(z)): 0.0995 Epoch: 4, Batch Num: [100/600]
Epoch: [4/20], Batch Num: [100/600] Discriminator Loss: 0.2645, Generator Loss: 2.9137 D(x): 0.9379, D(G(z)): 0.1221 Epoch: [4/20], Batch Num: [101/600] Discriminator Loss: 0.2310, Generator Loss: 3.7265 D(x): 0.9673, D(G(z)): 0.1154 Epoch: [4/20], Batch Num: [102/600] Discriminator Loss: 0.1261, Generator Loss: 3.8735 D(x): 0.9777, D(G(z)): 0.0779 Epoch: [4/20], Batch Num: [103/600] Discriminator Loss: 0.1285, Generator Loss: 4.6112 D(x): 0.9525, D(G(z)): 0.0548 Epoch: [4/20], Batch Num: [104/600] Discriminator Loss: 0.2581, Generator Loss: 4.6940 D(x): 0.8951, D(G(z)): 0.0427 Epoch: [4/20], Batch Num: [105/600] Discriminator Loss: 0.1936, Generator Loss: 4.5081 D(x): 0.9230, D(G(z)): 0.0491 Epoch: [4/20], Batch Num: [106/600] Discriminator Loss: 0.2277, Generator Loss: 4.3372 D(x): 0.9463, D(G(z)): 0.0587 Epoch: [4/20], Batch Num: [107/600] Discriminator Loss: 0.1309, Generator Loss: 4.2961 D(x): 0.9631, D(G(z)): 0.0558 Epoch: [4/20], Batch Num: [108/600] Discriminator Loss: 0.1104, Generator Loss: 3.8932 D(x): 0.9741, D(G(z)): 0.0596 Epoch: [4/20], Batch Num: [109/600] Discriminator Loss: 0.1101, Generator Loss: 4.0616 D(x): 0.9723, D(G(z)): 0.0587 Epoch: [4/20], Batch Num: [110/600] Discriminator Loss: 0.2671, Generator Loss: 4.6487 D(x): 0.9639, D(G(z)): 0.1140 Epoch: [4/20], Batch Num: [111/600] Discriminator Loss: 0.1174, Generator Loss: 4.9186 D(x): 0.9777, D(G(z)): 0.0617 Epoch: [4/20], Batch Num: [112/600] Discriminator Loss: 0.2567, Generator Loss: 5.0285 D(x): 0.9355, D(G(z)): 0.0623 Epoch: [4/20], Batch Num: [113/600] Discriminator Loss: 0.1537, Generator Loss: 5.1422 D(x): 0.9463, D(G(z)): 0.0340 Epoch: [4/20], Batch Num: [114/600] Discriminator Loss: 0.1270, Generator Loss: 5.0178 D(x): 0.9582, D(G(z)): 0.0513 Epoch: [4/20], Batch Num: [115/600] Discriminator Loss: 0.1265, Generator Loss: 4.8586 D(x): 0.9495, D(G(z)): 0.0484 Epoch: [4/20], Batch Num: [116/600] Discriminator Loss: 0.2284, Generator Loss: 4.8060 D(x): 0.9142, D(G(z)): 0.0357 Epoch: [4/20], Batch Num: [117/600] Discriminator Loss: 0.1013, Generator Loss: 3.9753 D(x): 0.9894, D(G(z)): 0.0722 Epoch: [4/20], Batch Num: [118/600] Discriminator Loss: 0.2563, Generator Loss: 3.6055 D(x): 0.9467, D(G(z)): 0.1080 Epoch: [4/20], Batch Num: [119/600] Discriminator Loss: 0.1555, Generator Loss: 4.0020 D(x): 0.9878, D(G(z)): 0.1015 Epoch: [4/20], Batch Num: [120/600] Discriminator Loss: 0.1473, Generator Loss: 4.4720 D(x): 0.9670, D(G(z)): 0.0776 Epoch: [4/20], Batch Num: [121/600] Discriminator Loss: 0.2098, Generator Loss: 4.5879 D(x): 0.9561, D(G(z)): 0.0907 Epoch: [4/20], Batch Num: [122/600] Discriminator Loss: 0.2847, Generator Loss: 4.8071 D(x): 0.9023, D(G(z)): 0.0449 Epoch: [4/20], Batch Num: [123/600] Discriminator Loss: 0.2272, Generator Loss: 4.0179 D(x): 0.9327, D(G(z)): 0.0565 Epoch: [4/20], Batch Num: [124/600] Discriminator Loss: 0.2536, Generator Loss: 3.2823 D(x): 0.9019, D(G(z)): 0.0682 Epoch: [4/20], Batch Num: [125/600] Discriminator Loss: 0.4135, Generator Loss: 2.8633 D(x): 0.9231, D(G(z)): 0.1537 Epoch: [4/20], Batch Num: [126/600] Discriminator Loss: 0.2918, Generator Loss: 3.1692 D(x): 0.9728, D(G(z)): 0.1644 Epoch: [4/20], Batch Num: [127/600] Discriminator Loss: 0.3166, Generator Loss: 3.3580 D(x): 0.9442, D(G(z)): 0.1497 Epoch: [4/20], Batch Num: [128/600] Discriminator Loss: 0.3298, Generator Loss: 3.9556 D(x): 0.9084, D(G(z)): 0.0990 Epoch: [4/20], Batch Num: [129/600] Discriminator Loss: 0.3275, Generator Loss: 4.1296 D(x): 0.9206, D(G(z)): 0.0778 Epoch: [4/20], Batch Num: [130/600] Discriminator Loss: 0.2099, Generator Loss: 3.6871 D(x): 0.9443, D(G(z)): 0.0749 Epoch: [4/20], Batch Num: [131/600] Discriminator Loss: 0.3041, Generator Loss: 3.1473 D(x): 0.9028, D(G(z)): 0.0779 Epoch: [4/20], Batch Num: [132/600] Discriminator Loss: 0.3687, Generator Loss: 2.5818 D(x): 0.9059, D(G(z)): 0.1109 Epoch: [4/20], Batch Num: [133/600] Discriminator Loss: 0.3263, Generator Loss: 2.4848 D(x): 0.9305, D(G(z)): 0.1357 Epoch: [4/20], Batch Num: [134/600] Discriminator Loss: 0.4183, Generator Loss: 2.6917 D(x): 0.9670, D(G(z)): 0.2142 Epoch: [4/20], Batch Num: [135/600] Discriminator Loss: 0.3273, Generator Loss: 3.2109 D(x): 0.9439, D(G(z)): 0.1641 Epoch: [4/20], Batch Num: [136/600] Discriminator Loss: 0.2224, Generator Loss: 3.5060 D(x): 0.9476, D(G(z)): 0.1027 Epoch: [4/20], Batch Num: [137/600] Discriminator Loss: 0.3561, Generator Loss: 3.3047 D(x): 0.8776, D(G(z)): 0.0583 Epoch: [4/20], Batch Num: [138/600] Discriminator Loss: 0.3901, Generator Loss: 3.3500 D(x): 0.8780, D(G(z)): 0.0705 Epoch: [4/20], Batch Num: [139/600] Discriminator Loss: 0.2930, Generator Loss: 2.6436 D(x): 0.9043, D(G(z)): 0.0682 Epoch: [4/20], Batch Num: [140/600] Discriminator Loss: 0.2273, Generator Loss: 2.3871 D(x): 0.9558, D(G(z)): 0.1232 Epoch: [4/20], Batch Num: [141/600] Discriminator Loss: 0.2969, Generator Loss: 2.3676 D(x): 0.9592, D(G(z)): 0.1875 Epoch: [4/20], Batch Num: [142/600] Discriminator Loss: 0.2448, Generator Loss: 2.1769 D(x): 0.9622, D(G(z)): 0.1617 Epoch: [4/20], Batch Num: [143/600] Discriminator Loss: 0.3250, Generator Loss: 2.3573 D(x): 0.9559, D(G(z)): 0.1934 Epoch: [4/20], Batch Num: [144/600] Discriminator Loss: 0.2625, Generator Loss: 2.4979 D(x): 0.9375, D(G(z)): 0.1433 Epoch: [4/20], Batch Num: [145/600] Discriminator Loss: 0.3794, Generator Loss: 2.4836 D(x): 0.9019, D(G(z)): 0.1443 Epoch: [4/20], Batch Num: [146/600] Discriminator Loss: 0.3475, Generator Loss: 2.7190 D(x): 0.9498, D(G(z)): 0.1802 Epoch: [4/20], Batch Num: [147/600] Discriminator Loss: 0.3553, Generator Loss: 2.7119 D(x): 0.9195, D(G(z)): 0.1262 Epoch: [4/20], Batch Num: [148/600] Discriminator Loss: 0.3529, Generator Loss: 2.6155 D(x): 0.9400, D(G(z)): 0.1456 Epoch: [4/20], Batch Num: [149/600] Discriminator Loss: 0.4500, Generator Loss: 2.4114 D(x): 0.8919, D(G(z)): 0.1443 Epoch: [4/20], Batch Num: [150/600] Discriminator Loss: 0.3934, Generator Loss: 2.1045 D(x): 0.9110, D(G(z)): 0.1448 Epoch: [4/20], Batch Num: [151/600] Discriminator Loss: 0.3782, Generator Loss: 2.0095 D(x): 0.9441, D(G(z)): 0.2002 Epoch: [4/20], Batch Num: [152/600] Discriminator Loss: 0.5119, Generator Loss: 2.0659 D(x): 0.9547, D(G(z)): 0.2313 Epoch: [4/20], Batch Num: [153/600] Discriminator Loss: 0.5723, Generator Loss: 2.2646 D(x): 0.9124, D(G(z)): 0.2387 Epoch: [4/20], Batch Num: [154/600] Discriminator Loss: 0.4838, Generator Loss: 2.4584 D(x): 0.9316, D(G(z)): 0.2241 Epoch: [4/20], Batch Num: [155/600] Discriminator Loss: 0.5291, Generator Loss: 2.5226 D(x): 0.9474, D(G(z)): 0.2138 Epoch: [4/20], Batch Num: [156/600] Discriminator Loss: 0.5073, Generator Loss: 2.7312 D(x): 0.8652, D(G(z)): 0.1448 Epoch: [4/20], Batch Num: [157/600] Discriminator Loss: 0.6420, Generator Loss: 2.7070 D(x): 0.8523, D(G(z)): 0.1532 Epoch: [4/20], Batch Num: [158/600] Discriminator Loss: 0.3318, Generator Loss: 2.4594 D(x): 0.9110, D(G(z)): 0.1448 Epoch: [4/20], Batch Num: [159/600] Discriminator Loss: 0.5579, Generator Loss: 2.5251 D(x): 0.8948, D(G(z)): 0.1973 Epoch: [4/20], Batch Num: [160/600] Discriminator Loss: 0.3905, Generator Loss: 2.5179 D(x): 0.9602, D(G(z)): 0.2089 Epoch: [4/20], Batch Num: [161/600] Discriminator Loss: 0.3516, Generator Loss: 2.6561 D(x): 0.9555, D(G(z)): 0.2021 Epoch: [4/20], Batch Num: [162/600] Discriminator Loss: 0.3343, Generator Loss: 3.0472 D(x): 0.9680, D(G(z)): 0.1707 Epoch: [4/20], Batch Num: [163/600] Discriminator Loss: 0.2501, Generator Loss: 3.4776 D(x): 0.9414, D(G(z)): 0.1176 Epoch: [4/20], Batch Num: [164/600] Discriminator Loss: 0.2666, Generator Loss: 3.7752 D(x): 0.9074, D(G(z)): 0.0850 Epoch: [4/20], Batch Num: [165/600] Discriminator Loss: 0.2136, Generator Loss: 3.7241 D(x): 0.9326, D(G(z)): 0.0749 Epoch: [4/20], Batch Num: [166/600] Discriminator Loss: 0.2790, Generator Loss: 3.9004 D(x): 0.9086, D(G(z)): 0.0711 Epoch: [4/20], Batch Num: [167/600] Discriminator Loss: 0.2381, Generator Loss: 3.8098 D(x): 0.9201, D(G(z)): 0.0656 Epoch: [4/20], Batch Num: [168/600] Discriminator Loss: 0.1691, Generator Loss: 3.5468 D(x): 0.9584, D(G(z)): 0.0809 Epoch: [4/20], Batch Num: [169/600] Discriminator Loss: 0.2286, Generator Loss: 3.3353 D(x): 0.9698, D(G(z)): 0.1225 Epoch: [4/20], Batch Num: [170/600] Discriminator Loss: 0.3044, Generator Loss: 3.5961 D(x): 0.9505, D(G(z)): 0.1300 Epoch: [4/20], Batch Num: [171/600] Discriminator Loss: 0.1703, Generator Loss: 3.7342 D(x): 0.9594, D(G(z)): 0.0755 Epoch: [4/20], Batch Num: [172/600] Discriminator Loss: 0.1954, Generator Loss: 3.9897 D(x): 0.9478, D(G(z)): 0.0651 Epoch: [4/20], Batch Num: [173/600] Discriminator Loss: 0.1459, Generator Loss: 4.3666 D(x): 0.9522, D(G(z)): 0.0677 Epoch: [4/20], Batch Num: [174/600] Discriminator Loss: 0.2646, Generator Loss: 4.4789 D(x): 0.9028, D(G(z)): 0.0501 Epoch: [4/20], Batch Num: [175/600] Discriminator Loss: 0.1836, Generator Loss: 4.3579 D(x): 0.9222, D(G(z)): 0.0477 Epoch: [4/20], Batch Num: [176/600] Discriminator Loss: 0.2838, Generator Loss: 4.1035 D(x): 0.9240, D(G(z)): 0.0673 Epoch: [4/20], Batch Num: [177/600] Discriminator Loss: 0.3681, Generator Loss: 3.6322 D(x): 0.9079, D(G(z)): 0.1238 Epoch: [4/20], Batch Num: [178/600] Discriminator Loss: 0.3030, Generator Loss: 3.7420 D(x): 0.9277, D(G(z)): 0.0967 Epoch: [4/20], Batch Num: [179/600] Discriminator Loss: 0.3937, Generator Loss: 4.1868 D(x): 0.9167, D(G(z)): 0.1180 Epoch: [4/20], Batch Num: [180/600] Discriminator Loss: 0.2426, Generator Loss: 4.5777 D(x): 0.9236, D(G(z)): 0.0699 Epoch: [4/20], Batch Num: [181/600] Discriminator Loss: 0.1915, Generator Loss: 4.9134 D(x): 0.9499, D(G(z)): 0.0767 Epoch: [4/20], Batch Num: [182/600] Discriminator Loss: 0.2676, Generator Loss: 4.6297 D(x): 0.9189, D(G(z)): 0.0455 Epoch: [4/20], Batch Num: [183/600] Discriminator Loss: 0.2055, Generator Loss: 4.5154 D(x): 0.9359, D(G(z)): 0.0478 Epoch: [4/20], Batch Num: [184/600] Discriminator Loss: 0.1705, Generator Loss: 4.2805 D(x): 0.9620, D(G(z)): 0.0763 Epoch: [4/20], Batch Num: [185/600] Discriminator Loss: 0.2089, Generator Loss: 4.5304 D(x): 0.9431, D(G(z)): 0.0808 Epoch: [4/20], Batch Num: [186/600] Discriminator Loss: 0.0812, Generator Loss: 4.7930 D(x): 0.9748, D(G(z)): 0.0437 Epoch: [4/20], Batch Num: [187/600] Discriminator Loss: 0.2151, Generator Loss: 4.4958 D(x): 0.9378, D(G(z)): 0.0742 Epoch: [4/20], Batch Num: [188/600] Discriminator Loss: 0.2380, Generator Loss: 4.7407 D(x): 0.9449, D(G(z)): 0.0573 Epoch: [4/20], Batch Num: [189/600] Discriminator Loss: 0.2869, Generator Loss: 4.1497 D(x): 0.9018, D(G(z)): 0.0559 Epoch: [4/20], Batch Num: [190/600] Discriminator Loss: 0.4008, Generator Loss: 3.7108 D(x): 0.9196, D(G(z)): 0.1239 Epoch: [4/20], Batch Num: [191/600] Discriminator Loss: 0.3598, Generator Loss: 4.2803 D(x): 0.9552, D(G(z)): 0.1655 Epoch: [4/20], Batch Num: [192/600] Discriminator Loss: 0.4252, Generator Loss: 4.3834 D(x): 0.8933, D(G(z)): 0.0845 Epoch: [4/20], Batch Num: [193/600] Discriminator Loss: 0.6188, Generator Loss: 3.4054 D(x): 0.8668, D(G(z)): 0.1046 Epoch: [4/20], Batch Num: [194/600] Discriminator Loss: 0.5420, Generator Loss: 2.9810 D(x): 0.8844, D(G(z)): 0.1411 Epoch: [4/20], Batch Num: [195/600] Discriminator Loss: 0.6476, Generator Loss: 3.1129 D(x): 0.9059, D(G(z)): 0.2067 Epoch: [4/20], Batch Num: [196/600] Discriminator Loss: 0.5747, Generator Loss: 3.2459 D(x): 0.8692, D(G(z)): 0.1609 Epoch: [4/20], Batch Num: [197/600] Discriminator Loss: 0.5679, Generator Loss: 3.1749 D(x): 0.8776, D(G(z)): 0.1517 Epoch: [4/20], Batch Num: [198/600] Discriminator Loss: 0.3698, Generator Loss: 3.5293 D(x): 0.9089, D(G(z)): 0.1304 Epoch: [4/20], Batch Num: [199/600] Discriminator Loss: 0.4594, Generator Loss: 3.0087 D(x): 0.8877, D(G(z)): 0.1248 Epoch: 4, Batch Num: [200/600]
Epoch: [4/20], Batch Num: [200/600] Discriminator Loss: 0.4166, Generator Loss: 3.0356 D(x): 0.8990, D(G(z)): 0.1227 Epoch: [4/20], Batch Num: [201/600] Discriminator Loss: 0.3838, Generator Loss: 3.1462 D(x): 0.9203, D(G(z)): 0.1374 Epoch: [4/20], Batch Num: [202/600] Discriminator Loss: 0.4935, Generator Loss: 2.9721 D(x): 0.8704, D(G(z)): 0.1235 Epoch: [4/20], Batch Num: [203/600] Discriminator Loss: 0.3762, Generator Loss: 2.7346 D(x): 0.9029, D(G(z)): 0.1485 Epoch: [4/20], Batch Num: [204/600] Discriminator Loss: 0.3764, Generator Loss: 2.7901 D(x): 0.9310, D(G(z)): 0.1632 Epoch: [4/20], Batch Num: [205/600] Discriminator Loss: 0.3029, Generator Loss: 3.0026 D(x): 0.9474, D(G(z)): 0.1471 Epoch: [4/20], Batch Num: [206/600] Discriminator Loss: 0.2662, Generator Loss: 3.6819 D(x): 0.9270, D(G(z)): 0.0981 Epoch: [4/20], Batch Num: [207/600] Discriminator Loss: 0.2250, Generator Loss: 3.5871 D(x): 0.9310, D(G(z)): 0.0837 Epoch: [4/20], Batch Num: [208/600] Discriminator Loss: 0.2181, Generator Loss: 3.9734 D(x): 0.9354, D(G(z)): 0.0653 Epoch: [4/20], Batch Num: [209/600] Discriminator Loss: 0.2660, Generator Loss: 3.7994 D(x): 0.8977, D(G(z)): 0.0613 Epoch: [4/20], Batch Num: [210/600] Discriminator Loss: 0.2692, Generator Loss: 3.7337 D(x): 0.9198, D(G(z)): 0.0868 Epoch: [4/20], Batch Num: [211/600] Discriminator Loss: 0.2234, Generator Loss: 3.1305 D(x): 0.9193, D(G(z)): 0.0720 Epoch: [4/20], Batch Num: [212/600] Discriminator Loss: 0.3438, Generator Loss: 3.4313 D(x): 0.9327, D(G(z)): 0.1360 Epoch: [4/20], Batch Num: [213/600] Discriminator Loss: 0.2154, Generator Loss: 3.5311 D(x): 0.9501, D(G(z)): 0.1109 Epoch: [4/20], Batch Num: [214/600] Discriminator Loss: 0.3836, Generator Loss: 3.6669 D(x): 0.8928, D(G(z)): 0.1166 Epoch: [4/20], Batch Num: [215/600] Discriminator Loss: 0.3744, Generator Loss: 3.7060 D(x): 0.9043, D(G(z)): 0.1151 Epoch: [4/20], Batch Num: [216/600] Discriminator Loss: 0.2416, Generator Loss: 4.2348 D(x): 0.9452, D(G(z)): 0.0888 Epoch: [4/20], Batch Num: [217/600] Discriminator Loss: 0.3707, Generator Loss: 3.6854 D(x): 0.8745, D(G(z)): 0.0660 Epoch: [4/20], Batch Num: [218/600] Discriminator Loss: 0.5033, Generator Loss: 3.2937 D(x): 0.8435, D(G(z)): 0.0741 Epoch: [4/20], Batch Num: [219/600] Discriminator Loss: 0.3175, Generator Loss: 3.0348 D(x): 0.9152, D(G(z)): 0.1206 Epoch: [4/20], Batch Num: [220/600] Discriminator Loss: 0.4876, Generator Loss: 3.1146 D(x): 0.8857, D(G(z)): 0.1795 Epoch: [4/20], Batch Num: [221/600] Discriminator Loss: 0.4939, Generator Loss: 3.7188 D(x): 0.9210, D(G(z)): 0.1758 Epoch: [4/20], Batch Num: [222/600] Discriminator Loss: 0.6843, Generator Loss: 3.6128 D(x): 0.8045, D(G(z)): 0.1052 Epoch: [4/20], Batch Num: [223/600] Discriminator Loss: 0.5476, Generator Loss: 3.3267 D(x): 0.8504, D(G(z)): 0.1100 Epoch: [4/20], Batch Num: [224/600] Discriminator Loss: 0.5003, Generator Loss: 2.9497 D(x): 0.8297, D(G(z)): 0.1198 Epoch: [4/20], Batch Num: [225/600] Discriminator Loss: 0.5096, Generator Loss: 2.7785 D(x): 0.8703, D(G(z)): 0.1551 Epoch: [4/20], Batch Num: [226/600] Discriminator Loss: 0.3784, Generator Loss: 3.0216 D(x): 0.9369, D(G(z)): 0.1866 Epoch: [4/20], Batch Num: [227/600] Discriminator Loss: 0.6164, Generator Loss: 3.5360 D(x): 0.8334, D(G(z)): 0.1316 Epoch: [4/20], Batch Num: [228/600] Discriminator Loss: 0.4795, Generator Loss: 3.2060 D(x): 0.8448, D(G(z)): 0.1034 Epoch: [4/20], Batch Num: [229/600] Discriminator Loss: 0.4328, Generator Loss: 2.9559 D(x): 0.8489, D(G(z)): 0.1089 Epoch: [4/20], Batch Num: [230/600] Discriminator Loss: 0.3478, Generator Loss: 2.7962 D(x): 0.8768, D(G(z)): 0.1022 Epoch: [4/20], Batch Num: [231/600] Discriminator Loss: 0.3997, Generator Loss: 2.6788 D(x): 0.9075, D(G(z)): 0.1625 Epoch: [4/20], Batch Num: [232/600] Discriminator Loss: 0.5176, Generator Loss: 2.6039 D(x): 0.8561, D(G(z)): 0.1497 Epoch: [4/20], Batch Num: [233/600] Discriminator Loss: 0.3155, Generator Loss: 3.0601 D(x): 0.9175, D(G(z)): 0.1382 Epoch: [4/20], Batch Num: [234/600] Discriminator Loss: 0.3688, Generator Loss: 3.1849 D(x): 0.8634, D(G(z)): 0.1008 Epoch: [4/20], Batch Num: [235/600] Discriminator Loss: 0.3365, Generator Loss: 3.3929 D(x): 0.9107, D(G(z)): 0.1235 Epoch: [4/20], Batch Num: [236/600] Discriminator Loss: 0.2946, Generator Loss: 3.3857 D(x): 0.8946, D(G(z)): 0.0825 Epoch: [4/20], Batch Num: [237/600] Discriminator Loss: 0.3482, Generator Loss: 3.7244 D(x): 0.8838, D(G(z)): 0.0883 Epoch: [4/20], Batch Num: [238/600] Discriminator Loss: 0.2576, Generator Loss: 4.0544 D(x): 0.9441, D(G(z)): 0.1111 Epoch: [4/20], Batch Num: [239/600] Discriminator Loss: 0.2527, Generator Loss: 4.2542 D(x): 0.9073, D(G(z)): 0.0621 Epoch: [4/20], Batch Num: [240/600] Discriminator Loss: 0.3374, Generator Loss: 4.5206 D(x): 0.9028, D(G(z)): 0.0637 Epoch: [4/20], Batch Num: [241/600] Discriminator Loss: 0.3808, Generator Loss: 4.2332 D(x): 0.8977, D(G(z)): 0.0828 Epoch: [4/20], Batch Num: [242/600] Discriminator Loss: 0.1588, Generator Loss: 4.4285 D(x): 0.9410, D(G(z)): 0.0679 Epoch: [4/20], Batch Num: [243/600] Discriminator Loss: 0.3111, Generator Loss: 3.6124 D(x): 0.8868, D(G(z)): 0.0619 Epoch: [4/20], Batch Num: [244/600] Discriminator Loss: 0.2727, Generator Loss: 3.4255 D(x): 0.9433, D(G(z)): 0.0846 Epoch: [4/20], Batch Num: [245/600] Discriminator Loss: 0.2370, Generator Loss: 3.5152 D(x): 0.9470, D(G(z)): 0.1173 Epoch: [4/20], Batch Num: [246/600] Discriminator Loss: 0.3192, Generator Loss: 3.6820 D(x): 0.9186, D(G(z)): 0.0934 Epoch: [4/20], Batch Num: [247/600] Discriminator Loss: 0.2981, Generator Loss: 4.3883 D(x): 0.9433, D(G(z)): 0.1066 Epoch: [4/20], Batch Num: [248/600] Discriminator Loss: 0.2021, Generator Loss: 4.4554 D(x): 0.9245, D(G(z)): 0.0648 Epoch: [4/20], Batch Num: [249/600] Discriminator Loss: 0.2791, Generator Loss: 4.3082 D(x): 0.8875, D(G(z)): 0.0460 Epoch: [4/20], Batch Num: [250/600] Discriminator Loss: 0.2778, Generator Loss: 3.6879 D(x): 0.8713, D(G(z)): 0.0464 Epoch: [4/20], Batch Num: [251/600] Discriminator Loss: 0.2204, Generator Loss: 3.6089 D(x): 0.9412, D(G(z)): 0.0933 Epoch: [4/20], Batch Num: [252/600] Discriminator Loss: 0.3422, Generator Loss: 3.7693 D(x): 0.9205, D(G(z)): 0.1396 Epoch: [4/20], Batch Num: [253/600] Discriminator Loss: 0.2963, Generator Loss: 4.3126 D(x): 0.9117, D(G(z)): 0.1041 Epoch: [4/20], Batch Num: [254/600] Discriminator Loss: 0.2012, Generator Loss: 4.5464 D(x): 0.9448, D(G(z)): 0.0870 Epoch: [4/20], Batch Num: [255/600] Discriminator Loss: 0.2515, Generator Loss: 4.8797 D(x): 0.9172, D(G(z)): 0.0510 Epoch: [4/20], Batch Num: [256/600] Discriminator Loss: 0.2090, Generator Loss: 4.8233 D(x): 0.9184, D(G(z)): 0.0378 Epoch: [4/20], Batch Num: [257/600] Discriminator Loss: 0.2196, Generator Loss: 4.5407 D(x): 0.9166, D(G(z)): 0.0505 Epoch: [4/20], Batch Num: [258/600] Discriminator Loss: 0.2021, Generator Loss: 3.9074 D(x): 0.9324, D(G(z)): 0.0509 Epoch: [4/20], Batch Num: [259/600] Discriminator Loss: 0.3206, Generator Loss: 3.6092 D(x): 0.9180, D(G(z)): 0.1143 Epoch: [4/20], Batch Num: [260/600] Discriminator Loss: 0.4022, Generator Loss: 3.7126 D(x): 0.9206, D(G(z)): 0.1537 Epoch: [4/20], Batch Num: [261/600] Discriminator Loss: 0.3855, Generator Loss: 4.1152 D(x): 0.8835, D(G(z)): 0.0958 Epoch: [4/20], Batch Num: [262/600] Discriminator Loss: 0.6275, Generator Loss: 3.6369 D(x): 0.8376, D(G(z)): 0.1060 Epoch: [4/20], Batch Num: [263/600] Discriminator Loss: 0.4580, Generator Loss: 3.4731 D(x): 0.9077, D(G(z)): 0.1205 Epoch: [4/20], Batch Num: [264/600] Discriminator Loss: 0.4700, Generator Loss: 3.1238 D(x): 0.8963, D(G(z)): 0.1530 Epoch: [4/20], Batch Num: [265/600] Discriminator Loss: 0.5053, Generator Loss: 2.8452 D(x): 0.8748, D(G(z)): 0.1296 Epoch: [4/20], Batch Num: [266/600] Discriminator Loss: 0.5661, Generator Loss: 3.2579 D(x): 0.8683, D(G(z)): 0.1751 Epoch: [4/20], Batch Num: [267/600] Discriminator Loss: 0.6176, Generator Loss: 2.7720 D(x): 0.8112, D(G(z)): 0.1204 Epoch: [4/20], Batch Num: [268/600] Discriminator Loss: 0.8373, Generator Loss: 2.4217 D(x): 0.7791, D(G(z)): 0.1780 Epoch: [4/20], Batch Num: [269/600] Discriminator Loss: 0.6296, Generator Loss: 2.2065 D(x): 0.8515, D(G(z)): 0.1884 Epoch: [4/20], Batch Num: [270/600] Discriminator Loss: 0.6328, Generator Loss: 2.4673 D(x): 0.8725, D(G(z)): 0.2331 Epoch: [4/20], Batch Num: [271/600] Discriminator Loss: 0.5197, Generator Loss: 2.8974 D(x): 0.8673, D(G(z)): 0.1699 Epoch: [4/20], Batch Num: [272/600] Discriminator Loss: 0.5769, Generator Loss: 2.8238 D(x): 0.8165, D(G(z)): 0.1423 Epoch: [4/20], Batch Num: [273/600] Discriminator Loss: 0.6322, Generator Loss: 2.7777 D(x): 0.8121, D(G(z)): 0.1145 Epoch: [4/20], Batch Num: [274/600] Discriminator Loss: 0.3770, Generator Loss: 2.3838 D(x): 0.8817, D(G(z)): 0.1107 Epoch: [4/20], Batch Num: [275/600] Discriminator Loss: 0.6418, Generator Loss: 2.0637 D(x): 0.8150, D(G(z)): 0.1447 Epoch: [4/20], Batch Num: [276/600] Discriminator Loss: 0.5368, Generator Loss: 1.8031 D(x): 0.8735, D(G(z)): 0.2054 Epoch: [4/20], Batch Num: [277/600] Discriminator Loss: 0.5055, Generator Loss: 2.1526 D(x): 0.8891, D(G(z)): 0.2343 Epoch: [4/20], Batch Num: [278/600] Discriminator Loss: 0.5010, Generator Loss: 2.2883 D(x): 0.8892, D(G(z)): 0.2019 Epoch: [4/20], Batch Num: [279/600] Discriminator Loss: 0.3727, Generator Loss: 2.6668 D(x): 0.8683, D(G(z)): 0.1286 Epoch: [4/20], Batch Num: [280/600] Discriminator Loss: 0.4113, Generator Loss: 2.6640 D(x): 0.8616, D(G(z)): 0.1263 Epoch: [4/20], Batch Num: [281/600] Discriminator Loss: 0.3389, Generator Loss: 2.7015 D(x): 0.8799, D(G(z)): 0.1026 Epoch: [4/20], Batch Num: [282/600] Discriminator Loss: 0.3246, Generator Loss: 2.6630 D(x): 0.8792, D(G(z)): 0.0939 Epoch: [4/20], Batch Num: [283/600] Discriminator Loss: 0.2940, Generator Loss: 2.6549 D(x): 0.8938, D(G(z)): 0.1116 Epoch: [4/20], Batch Num: [284/600] Discriminator Loss: 0.4271, Generator Loss: 2.3837 D(x): 0.8729, D(G(z)): 0.1248 Epoch: [4/20], Batch Num: [285/600] Discriminator Loss: 0.3765, Generator Loss: 2.4338 D(x): 0.9036, D(G(z)): 0.1452 Epoch: [4/20], Batch Num: [286/600] Discriminator Loss: 0.3231, Generator Loss: 2.7480 D(x): 0.9580, D(G(z)): 0.1793 Epoch: [4/20], Batch Num: [287/600] Discriminator Loss: 0.2966, Generator Loss: 3.2309 D(x): 0.9338, D(G(z)): 0.1363 Epoch: [4/20], Batch Num: [288/600] Discriminator Loss: 0.2901, Generator Loss: 3.4097 D(x): 0.8985, D(G(z)): 0.0798 Epoch: [4/20], Batch Num: [289/600] Discriminator Loss: 0.2834, Generator Loss: 3.4746 D(x): 0.8902, D(G(z)): 0.0526 Epoch: [4/20], Batch Num: [290/600] Discriminator Loss: 0.3032, Generator Loss: 3.3728 D(x): 0.8884, D(G(z)): 0.0532 Epoch: [4/20], Batch Num: [291/600] Discriminator Loss: 0.2553, Generator Loss: 3.3609 D(x): 0.9129, D(G(z)): 0.0731 Epoch: [4/20], Batch Num: [292/600] Discriminator Loss: 0.2932, Generator Loss: 3.1376 D(x): 0.8895, D(G(z)): 0.0757 Epoch: [4/20], Batch Num: [293/600] Discriminator Loss: 0.2014, Generator Loss: 3.0585 D(x): 0.9581, D(G(z)): 0.1019 Epoch: [4/20], Batch Num: [294/600] Discriminator Loss: 0.1891, Generator Loss: 3.1658 D(x): 0.9556, D(G(z)): 0.0889 Epoch: [4/20], Batch Num: [295/600] Discriminator Loss: 0.2246, Generator Loss: 3.1243 D(x): 0.9297, D(G(z)): 0.0800 Epoch: [4/20], Batch Num: [296/600] Discriminator Loss: 0.2310, Generator Loss: 3.2236 D(x): 0.9434, D(G(z)): 0.0995 Epoch: [4/20], Batch Num: [297/600] Discriminator Loss: 0.1527, Generator Loss: 3.8842 D(x): 0.9595, D(G(z)): 0.0718 Epoch: [4/20], Batch Num: [298/600] Discriminator Loss: 0.2365, Generator Loss: 4.0881 D(x): 0.9150, D(G(z)): 0.0586 Epoch: [4/20], Batch Num: [299/600] Discriminator Loss: 0.2442, Generator Loss: 3.9506 D(x): 0.9113, D(G(z)): 0.0507 Epoch: 4, Batch Num: [300/600]
Epoch: [4/20], Batch Num: [300/600] Discriminator Loss: 0.2819, Generator Loss: 3.6702 D(x): 0.9113, D(G(z)): 0.0528 Epoch: [4/20], Batch Num: [301/600] Discriminator Loss: 0.2203, Generator Loss: 3.5173 D(x): 0.9344, D(G(z)): 0.0668 Epoch: [4/20], Batch Num: [302/600] Discriminator Loss: 0.1333, Generator Loss: 3.5199 D(x): 0.9631, D(G(z)): 0.0519 Epoch: [4/20], Batch Num: [303/600] Discriminator Loss: 0.1362, Generator Loss: 3.6099 D(x): 0.9692, D(G(z)): 0.0565 Epoch: [4/20], Batch Num: [304/600] Discriminator Loss: 0.2287, Generator Loss: 3.6629 D(x): 0.9485, D(G(z)): 0.0808 Epoch: [4/20], Batch Num: [305/600] Discriminator Loss: 0.1650, Generator Loss: 3.4217 D(x): 0.9633, D(G(z)): 0.0836 Epoch: [4/20], Batch Num: [306/600] Discriminator Loss: 0.1643, Generator Loss: 3.9484 D(x): 0.9601, D(G(z)): 0.0674 Epoch: [4/20], Batch Num: [307/600] Discriminator Loss: 0.1515, Generator Loss: 3.7423 D(x): 0.9396, D(G(z)): 0.0465 Epoch: [4/20], Batch Num: [308/600] Discriminator Loss: 0.2210, Generator Loss: 3.7722 D(x): 0.9236, D(G(z)): 0.0360 Epoch: [4/20], Batch Num: [309/600] Discriminator Loss: 0.2638, Generator Loss: 3.6172 D(x): 0.9188, D(G(z)): 0.0483 Epoch: [4/20], Batch Num: [310/600] Discriminator Loss: 0.2644, Generator Loss: 3.2595 D(x): 0.9128, D(G(z)): 0.0740 Epoch: [4/20], Batch Num: [311/600] Discriminator Loss: 0.1516, Generator Loss: 3.3743 D(x): 0.9755, D(G(z)): 0.0920 Epoch: [4/20], Batch Num: [312/600] Discriminator Loss: 0.1132, Generator Loss: 3.3118 D(x): 0.9880, D(G(z)): 0.0808 Epoch: [4/20], Batch Num: [313/600] Discriminator Loss: 0.1868, Generator Loss: 3.5908 D(x): 0.9497, D(G(z)): 0.0785 Epoch: [4/20], Batch Num: [314/600] Discriminator Loss: 0.2758, Generator Loss: 3.5439 D(x): 0.9424, D(G(z)): 0.0826 Epoch: [4/20], Batch Num: [315/600] Discriminator Loss: 0.3071, Generator Loss: 3.8540 D(x): 0.9272, D(G(z)): 0.0981 Epoch: [4/20], Batch Num: [316/600] Discriminator Loss: 0.2943, Generator Loss: 3.8308 D(x): 0.9098, D(G(z)): 0.0537 Epoch: [4/20], Batch Num: [317/600] Discriminator Loss: 0.2718, Generator Loss: 3.7211 D(x): 0.9239, D(G(z)): 0.0522 Epoch: [4/20], Batch Num: [318/600] Discriminator Loss: 0.2510, Generator Loss: 3.4117 D(x): 0.9375, D(G(z)): 0.0726 Epoch: [4/20], Batch Num: [319/600] Discriminator Loss: 0.2364, Generator Loss: 3.5937 D(x): 0.9632, D(G(z)): 0.1001 Epoch: [4/20], Batch Num: [320/600] Discriminator Loss: 0.3478, Generator Loss: 3.8081 D(x): 0.9405, D(G(z)): 0.0951 Epoch: [4/20], Batch Num: [321/600] Discriminator Loss: 0.3555, Generator Loss: 3.5333 D(x): 0.9024, D(G(z)): 0.0884 Epoch: [4/20], Batch Num: [322/600] Discriminator Loss: 0.3421, Generator Loss: 3.5075 D(x): 0.9030, D(G(z)): 0.0927 Epoch: [4/20], Batch Num: [323/600] Discriminator Loss: 0.2272, Generator Loss: 3.0599 D(x): 0.9414, D(G(z)): 0.0819 Epoch: [4/20], Batch Num: [324/600] Discriminator Loss: 0.3079, Generator Loss: 3.1635 D(x): 0.9136, D(G(z)): 0.0892 Epoch: [4/20], Batch Num: [325/600] Discriminator Loss: 0.2755, Generator Loss: 3.3414 D(x): 0.9426, D(G(z)): 0.1144 Epoch: [4/20], Batch Num: [326/600] Discriminator Loss: 0.2767, Generator Loss: 3.3294 D(x): 0.9319, D(G(z)): 0.1051 Epoch: [4/20], Batch Num: [327/600] Discriminator Loss: 0.2838, Generator Loss: 3.6436 D(x): 0.9403, D(G(z)): 0.1066 Epoch: [4/20], Batch Num: [328/600] Discriminator Loss: 0.3184, Generator Loss: 4.2502 D(x): 0.9000, D(G(z)): 0.0613 Epoch: [4/20], Batch Num: [329/600] Discriminator Loss: 0.2865, Generator Loss: 4.0895 D(x): 0.9216, D(G(z)): 0.0454 Epoch: [4/20], Batch Num: [330/600] Discriminator Loss: 0.2590, Generator Loss: 3.9197 D(x): 0.9129, D(G(z)): 0.0602 Epoch: [4/20], Batch Num: [331/600] Discriminator Loss: 0.1975, Generator Loss: 4.0609 D(x): 0.9364, D(G(z)): 0.0478 Epoch: [4/20], Batch Num: [332/600] Discriminator Loss: 0.1664, Generator Loss: 3.7700 D(x): 0.9719, D(G(z)): 0.0806 Epoch: [4/20], Batch Num: [333/600] Discriminator Loss: 0.1511, Generator Loss: 4.0684 D(x): 0.9663, D(G(z)): 0.0781 Epoch: [4/20], Batch Num: [334/600] Discriminator Loss: 0.1137, Generator Loss: 4.1665 D(x): 0.9750, D(G(z)): 0.0548 Epoch: [4/20], Batch Num: [335/600] Discriminator Loss: 0.2483, Generator Loss: 4.0181 D(x): 0.9031, D(G(z)): 0.0548 Epoch: [4/20], Batch Num: [336/600] Discriminator Loss: 0.1950, Generator Loss: 4.1593 D(x): 0.9555, D(G(z)): 0.0818 Epoch: [4/20], Batch Num: [337/600] Discriminator Loss: 0.2326, Generator Loss: 4.3918 D(x): 0.9624, D(G(z)): 0.1018 Epoch: [4/20], Batch Num: [338/600] Discriminator Loss: 0.2190, Generator Loss: 5.1063 D(x): 0.9390, D(G(z)): 0.0653 Epoch: [4/20], Batch Num: [339/600] Discriminator Loss: 0.3823, Generator Loss: 4.3140 D(x): 0.8912, D(G(z)): 0.0528 Epoch: [4/20], Batch Num: [340/600] Discriminator Loss: 0.2898, Generator Loss: 4.2955 D(x): 0.9062, D(G(z)): 0.0404 Epoch: [4/20], Batch Num: [341/600] Discriminator Loss: 0.1800, Generator Loss: 3.8015 D(x): 0.9648, D(G(z)): 0.0809 Epoch: [4/20], Batch Num: [342/600] Discriminator Loss: 0.3101, Generator Loss: 3.4312 D(x): 0.9177, D(G(z)): 0.0916 Epoch: [4/20], Batch Num: [343/600] Discriminator Loss: 0.3105, Generator Loss: 3.5614 D(x): 0.9435, D(G(z)): 0.1390 Epoch: [4/20], Batch Num: [344/600] Discriminator Loss: 0.4293, Generator Loss: 3.8946 D(x): 0.8970, D(G(z)): 0.1392 Epoch: [4/20], Batch Num: [345/600] Discriminator Loss: 0.3228, Generator Loss: 4.1591 D(x): 0.8942, D(G(z)): 0.0904 Epoch: [4/20], Batch Num: [346/600] Discriminator Loss: 0.2450, Generator Loss: 3.9771 D(x): 0.9155, D(G(z)): 0.0742 Epoch: [4/20], Batch Num: [347/600] Discriminator Loss: 0.4045, Generator Loss: 3.9577 D(x): 0.8773, D(G(z)): 0.0781 Epoch: [4/20], Batch Num: [348/600] Discriminator Loss: 0.5542, Generator Loss: 3.2233 D(x): 0.8416, D(G(z)): 0.1098 Epoch: [4/20], Batch Num: [349/600] Discriminator Loss: 0.2994, Generator Loss: 2.9320 D(x): 0.9233, D(G(z)): 0.1270 Epoch: [4/20], Batch Num: [350/600] Discriminator Loss: 0.3129, Generator Loss: 2.9719 D(x): 0.9193, D(G(z)): 0.1416 Epoch: [4/20], Batch Num: [351/600] Discriminator Loss: 0.4761, Generator Loss: 3.5930 D(x): 0.8770, D(G(z)): 0.1749 Epoch: [4/20], Batch Num: [352/600] Discriminator Loss: 0.2566, Generator Loss: 3.4952 D(x): 0.9426, D(G(z)): 0.1206 Epoch: [4/20], Batch Num: [353/600] Discriminator Loss: 0.4624, Generator Loss: 3.1995 D(x): 0.8245, D(G(z)): 0.0935 Epoch: [4/20], Batch Num: [354/600] Discriminator Loss: 0.3895, Generator Loss: 2.5460 D(x): 0.8659, D(G(z)): 0.1126 Epoch: [4/20], Batch Num: [355/600] Discriminator Loss: 0.5955, Generator Loss: 2.8827 D(x): 0.8818, D(G(z)): 0.2294 Epoch: [4/20], Batch Num: [356/600] Discriminator Loss: 0.4001, Generator Loss: 2.8870 D(x): 0.8925, D(G(z)): 0.1442 Epoch: [4/20], Batch Num: [357/600] Discriminator Loss: 0.5592, Generator Loss: 3.0866 D(x): 0.8479, D(G(z)): 0.1639 Epoch: [4/20], Batch Num: [358/600] Discriminator Loss: 0.4188, Generator Loss: 2.9214 D(x): 0.8708, D(G(z)): 0.1181 Epoch: [4/20], Batch Num: [359/600] Discriminator Loss: 0.6128, Generator Loss: 2.6878 D(x): 0.8076, D(G(z)): 0.1220 Epoch: [4/20], Batch Num: [360/600] Discriminator Loss: 0.5984, Generator Loss: 2.6043 D(x): 0.8494, D(G(z)): 0.1966 Epoch: [4/20], Batch Num: [361/600] Discriminator Loss: 0.4751, Generator Loss: 2.9873 D(x): 0.8738, D(G(z)): 0.1631 Epoch: [4/20], Batch Num: [362/600] Discriminator Loss: 0.4982, Generator Loss: 3.1015 D(x): 0.8864, D(G(z)): 0.1434 Epoch: [4/20], Batch Num: [363/600] Discriminator Loss: 0.4224, Generator Loss: 2.9661 D(x): 0.8530, D(G(z)): 0.0956 Epoch: [4/20], Batch Num: [364/600] Discriminator Loss: 0.4279, Generator Loss: 2.4817 D(x): 0.8390, D(G(z)): 0.1039 Epoch: [4/20], Batch Num: [365/600] Discriminator Loss: 0.3846, Generator Loss: 2.4134 D(x): 0.8975, D(G(z)): 0.1214 Epoch: [4/20], Batch Num: [366/600] Discriminator Loss: 0.3032, Generator Loss: 2.6005 D(x): 0.9266, D(G(z)): 0.1538 Epoch: [4/20], Batch Num: [367/600] Discriminator Loss: 0.3222, Generator Loss: 3.0002 D(x): 0.9113, D(G(z)): 0.1344 Epoch: [4/20], Batch Num: [368/600] Discriminator Loss: 0.3111, Generator Loss: 3.3578 D(x): 0.9083, D(G(z)): 0.0905 Epoch: [4/20], Batch Num: [369/600] Discriminator Loss: 0.2100, Generator Loss: 3.7698 D(x): 0.9336, D(G(z)): 0.0843 Epoch: [4/20], Batch Num: [370/600] Discriminator Loss: 0.3587, Generator Loss: 3.4364 D(x): 0.8465, D(G(z)): 0.0593 Epoch: [4/20], Batch Num: [371/600] Discriminator Loss: 0.2583, Generator Loss: 2.8867 D(x): 0.9166, D(G(z)): 0.0822 Epoch: [4/20], Batch Num: [372/600] Discriminator Loss: 0.3445, Generator Loss: 2.9281 D(x): 0.9108, D(G(z)): 0.1181 Epoch: [4/20], Batch Num: [373/600] Discriminator Loss: 0.3664, Generator Loss: 3.0303 D(x): 0.9188, D(G(z)): 0.1509 Epoch: [4/20], Batch Num: [374/600] Discriminator Loss: 0.3043, Generator Loss: 3.6977 D(x): 0.9164, D(G(z)): 0.1096 Epoch: [4/20], Batch Num: [375/600] Discriminator Loss: 0.4537, Generator Loss: 3.5195 D(x): 0.8305, D(G(z)): 0.0744 Epoch: [4/20], Batch Num: [376/600] Discriminator Loss: 0.3078, Generator Loss: 3.0186 D(x): 0.8817, D(G(z)): 0.0649 Epoch: [4/20], Batch Num: [377/600] Discriminator Loss: 0.2849, Generator Loss: 2.9700 D(x): 0.9105, D(G(z)): 0.1013 Epoch: [4/20], Batch Num: [378/600] Discriminator Loss: 0.1767, Generator Loss: 3.2260 D(x): 0.9627, D(G(z)): 0.0899 Epoch: [4/20], Batch Num: [379/600] Discriminator Loss: 0.2371, Generator Loss: 3.4844 D(x): 0.9433, D(G(z)): 0.1041 Epoch: [4/20], Batch Num: [380/600] Discriminator Loss: 0.2372, Generator Loss: 3.4688 D(x): 0.9335, D(G(z)): 0.0780 Epoch: [4/20], Batch Num: [381/600] Discriminator Loss: 0.3889, Generator Loss: 3.3723 D(x): 0.8680, D(G(z)): 0.0514 Epoch: [4/20], Batch Num: [382/600] Discriminator Loss: 0.1910, Generator Loss: 3.2622 D(x): 0.9577, D(G(z)): 0.0961 Epoch: [4/20], Batch Num: [383/600] Discriminator Loss: 0.3093, Generator Loss: 3.5567 D(x): 0.9103, D(G(z)): 0.1004 Epoch: [4/20], Batch Num: [384/600] Discriminator Loss: 0.1949, Generator Loss: 3.6000 D(x): 0.9466, D(G(z)): 0.0679 Epoch: [4/20], Batch Num: [385/600] Discriminator Loss: 0.2972, Generator Loss: 3.6592 D(x): 0.8950, D(G(z)): 0.0892 Epoch: [4/20], Batch Num: [386/600] Discriminator Loss: 0.2660, Generator Loss: 3.4897 D(x): 0.9186, D(G(z)): 0.0711 Epoch: [4/20], Batch Num: [387/600] Discriminator Loss: 0.3634, Generator Loss: 3.3967 D(x): 0.8785, D(G(z)): 0.0833 Epoch: [4/20], Batch Num: [388/600] Discriminator Loss: 0.2809, Generator Loss: 3.2963 D(x): 0.9386, D(G(z)): 0.1027 Epoch: [4/20], Batch Num: [389/600] Discriminator Loss: 0.2869, Generator Loss: 2.9591 D(x): 0.9423, D(G(z)): 0.1087 Epoch: [4/20], Batch Num: [390/600] Discriminator Loss: 0.2733, Generator Loss: 3.4913 D(x): 0.9289, D(G(z)): 0.1028 Epoch: [4/20], Batch Num: [391/600] Discriminator Loss: 0.3223, Generator Loss: 3.5650 D(x): 0.9063, D(G(z)): 0.0768 Epoch: [4/20], Batch Num: [392/600] Discriminator Loss: 0.2851, Generator Loss: 3.6685 D(x): 0.8937, D(G(z)): 0.0571 Epoch: [4/20], Batch Num: [393/600] Discriminator Loss: 0.2781, Generator Loss: 3.1081 D(x): 0.9365, D(G(z)): 0.0909 Epoch: [4/20], Batch Num: [394/600] Discriminator Loss: 0.3424, Generator Loss: 3.0541 D(x): 0.8999, D(G(z)): 0.0831 Epoch: [4/20], Batch Num: [395/600] Discriminator Loss: 0.3154, Generator Loss: 3.1268 D(x): 0.9213, D(G(z)): 0.1067 Epoch: [4/20], Batch Num: [396/600] Discriminator Loss: 0.3521, Generator Loss: 3.1112 D(x): 0.9040, D(G(z)): 0.1040 Epoch: [4/20], Batch Num: [397/600] Discriminator Loss: 0.3406, Generator Loss: 2.7848 D(x): 0.8972, D(G(z)): 0.1117 Epoch: [4/20], Batch Num: [398/600] Discriminator Loss: 0.2505, Generator Loss: 3.4447 D(x): 0.9541, D(G(z)): 0.1313 Epoch: [4/20], Batch Num: [399/600] Discriminator Loss: 0.2358, Generator Loss: 3.5665 D(x): 0.9305, D(G(z)): 0.0791 Epoch: 4, Batch Num: [400/600]
Epoch: [4/20], Batch Num: [400/600] Discriminator Loss: 0.1678, Generator Loss: 3.9275 D(x): 0.9418, D(G(z)): 0.0669 Epoch: [4/20], Batch Num: [401/600] Discriminator Loss: 0.4014, Generator Loss: 3.5121 D(x): 0.8684, D(G(z)): 0.0522 Epoch: [4/20], Batch Num: [402/600] Discriminator Loss: 0.2752, Generator Loss: 2.8550 D(x): 0.8968, D(G(z)): 0.0645 Epoch: [4/20], Batch Num: [403/600] Discriminator Loss: 0.2603, Generator Loss: 2.5281 D(x): 0.9410, D(G(z)): 0.1063 Epoch: [4/20], Batch Num: [404/600] Discriminator Loss: 0.2478, Generator Loss: 3.1680 D(x): 0.9547, D(G(z)): 0.1343 Epoch: [4/20], Batch Num: [405/600] Discriminator Loss: 0.3030, Generator Loss: 3.9050 D(x): 0.9196, D(G(z)): 0.1097 Epoch: [4/20], Batch Num: [406/600] Discriminator Loss: 0.3422, Generator Loss: 3.4719 D(x): 0.8957, D(G(z)): 0.0664 Epoch: [4/20], Batch Num: [407/600] Discriminator Loss: 0.3256, Generator Loss: 3.8147 D(x): 0.9090, D(G(z)): 0.0716 Epoch: [4/20], Batch Num: [408/600] Discriminator Loss: 0.2649, Generator Loss: 3.6919 D(x): 0.9396, D(G(z)): 0.0837 Epoch: [4/20], Batch Num: [409/600] Discriminator Loss: 0.2326, Generator Loss: 3.4104 D(x): 0.9156, D(G(z)): 0.0522 Epoch: [4/20], Batch Num: [410/600] Discriminator Loss: 0.3130, Generator Loss: 3.2969 D(x): 0.9267, D(G(z)): 0.0889 Epoch: [4/20], Batch Num: [411/600] Discriminator Loss: 0.2431, Generator Loss: 2.9933 D(x): 0.9285, D(G(z)): 0.0969 Epoch: [4/20], Batch Num: [412/600] Discriminator Loss: 0.1700, Generator Loss: 3.5515 D(x): 0.9611, D(G(z)): 0.0985 Epoch: [4/20], Batch Num: [413/600] Discriminator Loss: 0.1879, Generator Loss: 3.6452 D(x): 0.9401, D(G(z)): 0.0752 Epoch: [4/20], Batch Num: [414/600] Discriminator Loss: 0.2552, Generator Loss: 3.8989 D(x): 0.9258, D(G(z)): 0.0730 Epoch: [4/20], Batch Num: [415/600] Discriminator Loss: 0.2197, Generator Loss: 3.7370 D(x): 0.9423, D(G(z)): 0.0614 Epoch: [4/20], Batch Num: [416/600] Discriminator Loss: 0.1849, Generator Loss: 4.0072 D(x): 0.9510, D(G(z)): 0.0607 Epoch: [4/20], Batch Num: [417/600] Discriminator Loss: 0.2366, Generator Loss: 3.7698 D(x): 0.9274, D(G(z)): 0.0619 Epoch: [4/20], Batch Num: [418/600] Discriminator Loss: 0.2463, Generator Loss: 3.4446 D(x): 0.9228, D(G(z)): 0.0716 Epoch: [4/20], Batch Num: [419/600] Discriminator Loss: 0.1207, Generator Loss: 3.3765 D(x): 0.9711, D(G(z)): 0.0712 Epoch: [4/20], Batch Num: [420/600] Discriminator Loss: 0.2123, Generator Loss: 4.1573 D(x): 0.9793, D(G(z)): 0.1255 Epoch: [4/20], Batch Num: [421/600] Discriminator Loss: 0.2512, Generator Loss: 5.2640 D(x): 0.9526, D(G(z)): 0.0832 Epoch: [4/20], Batch Num: [422/600] Discriminator Loss: 0.2390, Generator Loss: 5.3065 D(x): 0.9147, D(G(z)): 0.0248 Epoch: [4/20], Batch Num: [423/600] Discriminator Loss: 0.2604, Generator Loss: 4.5698 D(x): 0.9004, D(G(z)): 0.0175 Epoch: [4/20], Batch Num: [424/600] Discriminator Loss: 0.2292, Generator Loss: 3.5911 D(x): 0.9211, D(G(z)): 0.0342 Epoch: [4/20], Batch Num: [425/600] Discriminator Loss: 0.3132, Generator Loss: 3.0457 D(x): 0.9625, D(G(z)): 0.1132 Epoch: [4/20], Batch Num: [426/600] Discriminator Loss: 0.2149, Generator Loss: 3.8392 D(x): 0.9678, D(G(z)): 0.1162 Epoch: [4/20], Batch Num: [427/600] Discriminator Loss: 0.3970, Generator Loss: 3.9775 D(x): 0.9005, D(G(z)): 0.0854 Epoch: [4/20], Batch Num: [428/600] Discriminator Loss: 0.3891, Generator Loss: 4.6277 D(x): 0.9055, D(G(z)): 0.0682 Epoch: [4/20], Batch Num: [429/600] Discriminator Loss: 0.5148, Generator Loss: 3.9176 D(x): 0.8946, D(G(z)): 0.0828 Epoch: [4/20], Batch Num: [430/600] Discriminator Loss: 0.1484, Generator Loss: 3.7260 D(x): 0.9416, D(G(z)): 0.0343 Epoch: [4/20], Batch Num: [431/600] Discriminator Loss: 0.2411, Generator Loss: 3.6691 D(x): 0.9475, D(G(z)): 0.1005 Epoch: [4/20], Batch Num: [432/600] Discriminator Loss: 0.4496, Generator Loss: 3.7269 D(x): 0.9125, D(G(z)): 0.1119 Epoch: [4/20], Batch Num: [433/600] Discriminator Loss: 0.2890, Generator Loss: 3.8615 D(x): 0.9214, D(G(z)): 0.0866 Epoch: [4/20], Batch Num: [434/600] Discriminator Loss: 0.3468, Generator Loss: 3.6633 D(x): 0.9249, D(G(z)): 0.0826 Epoch: [4/20], Batch Num: [435/600] Discriminator Loss: 0.3628, Generator Loss: 3.8557 D(x): 0.9155, D(G(z)): 0.1080 Epoch: [4/20], Batch Num: [436/600] Discriminator Loss: 0.2373, Generator Loss: 3.7656 D(x): 0.9223, D(G(z)): 0.0547 Epoch: [4/20], Batch Num: [437/600] Discriminator Loss: 0.2613, Generator Loss: 3.6853 D(x): 0.9093, D(G(z)): 0.0505 Epoch: [4/20], Batch Num: [438/600] Discriminator Loss: 0.2278, Generator Loss: 3.5100 D(x): 0.9552, D(G(z)): 0.1001 Epoch: [4/20], Batch Num: [439/600] Discriminator Loss: 0.3463, Generator Loss: 3.2940 D(x): 0.9020, D(G(z)): 0.0725 Epoch: [4/20], Batch Num: [440/600] Discriminator Loss: 0.2454, Generator Loss: 3.4489 D(x): 0.9442, D(G(z)): 0.1203 Epoch: [4/20], Batch Num: [441/600] Discriminator Loss: 0.2524, Generator Loss: 3.5176 D(x): 0.9307, D(G(z)): 0.0860 Epoch: [4/20], Batch Num: [442/600] Discriminator Loss: 0.4181, Generator Loss: 3.2988 D(x): 0.8987, D(G(z)): 0.1085 Epoch: [4/20], Batch Num: [443/600] Discriminator Loss: 0.3755, Generator Loss: 3.0853 D(x): 0.8813, D(G(z)): 0.0791 Epoch: [4/20], Batch Num: [444/600] Discriminator Loss: 0.3029, Generator Loss: 3.1614 D(x): 0.9549, D(G(z)): 0.1210 Epoch: [4/20], Batch Num: [445/600] Discriminator Loss: 0.2766, Generator Loss: 3.0360 D(x): 0.9358, D(G(z)): 0.1114 Epoch: [4/20], Batch Num: [446/600] Discriminator Loss: 0.4092, Generator Loss: 3.0196 D(x): 0.9262, D(G(z)): 0.1210 Epoch: [4/20], Batch Num: [447/600] Discriminator Loss: 0.1965, Generator Loss: 2.9220 D(x): 0.9479, D(G(z)): 0.0846 Epoch: [4/20], Batch Num: [448/600] Discriminator Loss: 0.1526, Generator Loss: 3.3968 D(x): 0.9576, D(G(z)): 0.0776 Epoch: [4/20], Batch Num: [449/600] Discriminator Loss: 0.1909, Generator Loss: 3.7358 D(x): 0.9602, D(G(z)): 0.0852 Epoch: [4/20], Batch Num: [450/600] Discriminator Loss: 0.2524, Generator Loss: 3.4736 D(x): 0.9134, D(G(z)): 0.0616 Epoch: [4/20], Batch Num: [451/600] Discriminator Loss: 0.2383, Generator Loss: 3.3788 D(x): 0.9278, D(G(z)): 0.0832 Epoch: [4/20], Batch Num: [452/600] Discriminator Loss: 0.3674, Generator Loss: 3.6239 D(x): 0.9000, D(G(z)): 0.0904 Epoch: [4/20], Batch Num: [453/600] Discriminator Loss: 0.2895, Generator Loss: 3.4087 D(x): 0.9304, D(G(z)): 0.0883 Epoch: [4/20], Batch Num: [454/600] Discriminator Loss: 0.3642, Generator Loss: 3.4860 D(x): 0.9243, D(G(z)): 0.1109 Epoch: [4/20], Batch Num: [455/600] Discriminator Loss: 0.2661, Generator Loss: 3.7450 D(x): 0.9245, D(G(z)): 0.1124 Epoch: [4/20], Batch Num: [456/600] Discriminator Loss: 0.2872, Generator Loss: 3.6526 D(x): 0.9088, D(G(z)): 0.0882 Epoch: [4/20], Batch Num: [457/600] Discriminator Loss: 0.3063, Generator Loss: 3.8181 D(x): 0.9115, D(G(z)): 0.0945 Epoch: [4/20], Batch Num: [458/600] Discriminator Loss: 0.2851, Generator Loss: 3.6461 D(x): 0.9027, D(G(z)): 0.0693 Epoch: [4/20], Batch Num: [459/600] Discriminator Loss: 0.3639, Generator Loss: 3.3132 D(x): 0.9044, D(G(z)): 0.1060 Epoch: [4/20], Batch Num: [460/600] Discriminator Loss: 0.4853, Generator Loss: 3.0389 D(x): 0.8531, D(G(z)): 0.1192 Epoch: [4/20], Batch Num: [461/600] Discriminator Loss: 0.3587, Generator Loss: 3.0995 D(x): 0.9163, D(G(z)): 0.1499 Epoch: [4/20], Batch Num: [462/600] Discriminator Loss: 0.3716, Generator Loss: 3.5178 D(x): 0.9303, D(G(z)): 0.1395 Epoch: [4/20], Batch Num: [463/600] Discriminator Loss: 0.2337, Generator Loss: 3.4638 D(x): 0.9191, D(G(z)): 0.0759 Epoch: [4/20], Batch Num: [464/600] Discriminator Loss: 0.1800, Generator Loss: 3.9761 D(x): 0.9356, D(G(z)): 0.0699 Epoch: [4/20], Batch Num: [465/600] Discriminator Loss: 0.5380, Generator Loss: 3.9270 D(x): 0.8373, D(G(z)): 0.0779 Epoch: [4/20], Batch Num: [466/600] Discriminator Loss: 0.3238, Generator Loss: 3.7654 D(x): 0.9278, D(G(z)): 0.1036 Epoch: [4/20], Batch Num: [467/600] Discriminator Loss: 0.3020, Generator Loss: 4.0099 D(x): 0.8998, D(G(z)): 0.0794 Epoch: [4/20], Batch Num: [468/600] Discriminator Loss: 0.3946, Generator Loss: 3.8163 D(x): 0.8901, D(G(z)): 0.1038 Epoch: [4/20], Batch Num: [469/600] Discriminator Loss: 0.2200, Generator Loss: 4.2495 D(x): 0.9556, D(G(z)): 0.0917 Epoch: [4/20], Batch Num: [470/600] Discriminator Loss: 0.2882, Generator Loss: 4.0551 D(x): 0.9069, D(G(z)): 0.0699 Epoch: [4/20], Batch Num: [471/600] Discriminator Loss: 0.5540, Generator Loss: 3.3303 D(x): 0.8441, D(G(z)): 0.0679 Epoch: [4/20], Batch Num: [472/600] Discriminator Loss: 0.4108, Generator Loss: 3.0798 D(x): 0.8889, D(G(z)): 0.1219 Epoch: [4/20], Batch Num: [473/600] Discriminator Loss: 0.5504, Generator Loss: 3.5292 D(x): 0.8882, D(G(z)): 0.1923 Epoch: [4/20], Batch Num: [474/600] Discriminator Loss: 0.3564, Generator Loss: 3.9194 D(x): 0.8931, D(G(z)): 0.0909 Epoch: [4/20], Batch Num: [475/600] Discriminator Loss: 0.3299, Generator Loss: 4.3900 D(x): 0.8955, D(G(z)): 0.0890 Epoch: [4/20], Batch Num: [476/600] Discriminator Loss: 0.2652, Generator Loss: 4.2379 D(x): 0.8966, D(G(z)): 0.0445 Epoch: [4/20], Batch Num: [477/600] Discriminator Loss: 0.3620, Generator Loss: 3.5153 D(x): 0.8814, D(G(z)): 0.0564 Epoch: [4/20], Batch Num: [478/600] Discriminator Loss: 0.2583, Generator Loss: 3.1504 D(x): 0.9442, D(G(z)): 0.1019 Epoch: [4/20], Batch Num: [479/600] Discriminator Loss: 0.3253, Generator Loss: 3.7081 D(x): 0.9307, D(G(z)): 0.1223 Epoch: [4/20], Batch Num: [480/600] Discriminator Loss: 0.3691, Generator Loss: 3.8857 D(x): 0.9044, D(G(z)): 0.1046 Epoch: [4/20], Batch Num: [481/600] Discriminator Loss: 0.2636, Generator Loss: 3.9980 D(x): 0.9106, D(G(z)): 0.0545 Epoch: [4/20], Batch Num: [482/600] Discriminator Loss: 0.3020, Generator Loss: 3.6445 D(x): 0.9083, D(G(z)): 0.0636 Epoch: [4/20], Batch Num: [483/600] Discriminator Loss: 0.2891, Generator Loss: 3.9766 D(x): 0.9298, D(G(z)): 0.1003 Epoch: [4/20], Batch Num: [484/600] Discriminator Loss: 0.2821, Generator Loss: 4.4669 D(x): 0.9260, D(G(z)): 0.1000 Epoch: [4/20], Batch Num: [485/600] Discriminator Loss: 0.1520, Generator Loss: 4.5709 D(x): 0.9322, D(G(z)): 0.0318 Epoch: [4/20], Batch Num: [486/600] Discriminator Loss: 0.2296, Generator Loss: 4.1357 D(x): 0.9147, D(G(z)): 0.0457 Epoch: [4/20], Batch Num: [487/600] Discriminator Loss: 0.3578, Generator Loss: 3.4486 D(x): 0.8959, D(G(z)): 0.0517 Epoch: [4/20], Batch Num: [488/600] Discriminator Loss: 0.3051, Generator Loss: 3.5298 D(x): 0.9342, D(G(z)): 0.1217 Epoch: [4/20], Batch Num: [489/600] Discriminator Loss: 0.3080, Generator Loss: 3.9110 D(x): 0.9491, D(G(z)): 0.1226 Epoch: [4/20], Batch Num: [490/600] Discriminator Loss: 0.3051, Generator Loss: 4.4026 D(x): 0.9102, D(G(z)): 0.0734 Epoch: [4/20], Batch Num: [491/600] Discriminator Loss: 0.2988, Generator Loss: 4.4776 D(x): 0.8753, D(G(z)): 0.0351 Epoch: [4/20], Batch Num: [492/600] Discriminator Loss: 0.3679, Generator Loss: 3.7268 D(x): 0.8587, D(G(z)): 0.0486 Epoch: [4/20], Batch Num: [493/600] Discriminator Loss: 0.3439, Generator Loss: 2.8347 D(x): 0.9079, D(G(z)): 0.1029 Epoch: [4/20], Batch Num: [494/600] Discriminator Loss: 0.4547, Generator Loss: 3.7941 D(x): 0.9808, D(G(z)): 0.2378 Epoch: [4/20], Batch Num: [495/600] Discriminator Loss: 0.3401, Generator Loss: 4.5330 D(x): 0.9025, D(G(z)): 0.0805 Epoch: [4/20], Batch Num: [496/600] Discriminator Loss: 0.5866, Generator Loss: 4.6822 D(x): 0.8129, D(G(z)): 0.0366 Epoch: [4/20], Batch Num: [497/600] Discriminator Loss: 0.3912, Generator Loss: 4.0006 D(x): 0.8645, D(G(z)): 0.0518 Epoch: [4/20], Batch Num: [498/600] Discriminator Loss: 0.4952, Generator Loss: 2.8718 D(x): 0.8575, D(G(z)): 0.1009 Epoch: [4/20], Batch Num: [499/600] Discriminator Loss: 0.7026, Generator Loss: 2.4998 D(x): 0.8550, D(G(z)): 0.1756 Epoch: 4, Batch Num: [500/600]
Epoch: [4/20], Batch Num: [500/600] Discriminator Loss: 0.5606, Generator Loss: 3.0590 D(x): 0.8958, D(G(z)): 0.2208 Epoch: [4/20], Batch Num: [501/600] Discriminator Loss: 0.5744, Generator Loss: 3.1576 D(x): 0.8494, D(G(z)): 0.1441 Epoch: [4/20], Batch Num: [502/600] Discriminator Loss: 0.2738, Generator Loss: 3.3390 D(x): 0.9108, D(G(z)): 0.0947 Epoch: [4/20], Batch Num: [503/600] Discriminator Loss: 0.3006, Generator Loss: 3.3366 D(x): 0.8850, D(G(z)): 0.0700 Epoch: [4/20], Batch Num: [504/600] Discriminator Loss: 0.4224, Generator Loss: 3.1869 D(x): 0.8744, D(G(z)): 0.1218 Epoch: [4/20], Batch Num: [505/600] Discriminator Loss: 0.4941, Generator Loss: 2.9791 D(x): 0.8631, D(G(z)): 0.1297 Epoch: [4/20], Batch Num: [506/600] Discriminator Loss: 0.5751, Generator Loss: 2.7349 D(x): 0.8399, D(G(z)): 0.1409 Epoch: [4/20], Batch Num: [507/600] Discriminator Loss: 0.6095, Generator Loss: 2.4477 D(x): 0.7822, D(G(z)): 0.1306 Epoch: [4/20], Batch Num: [508/600] Discriminator Loss: 0.2963, Generator Loss: 2.4082 D(x): 0.9534, D(G(z)): 0.1644 Epoch: [4/20], Batch Num: [509/600] Discriminator Loss: 0.3330, Generator Loss: 2.7215 D(x): 0.9313, D(G(z)): 0.1675 Epoch: [4/20], Batch Num: [510/600] Discriminator Loss: 0.3497, Generator Loss: 2.9984 D(x): 0.9378, D(G(z)): 0.1567 Epoch: [4/20], Batch Num: [511/600] Discriminator Loss: 0.2814, Generator Loss: 3.7085 D(x): 0.9452, D(G(z)): 0.1393 Epoch: [4/20], Batch Num: [512/600] Discriminator Loss: 0.2396, Generator Loss: 3.7944 D(x): 0.9047, D(G(z)): 0.0729 Epoch: [4/20], Batch Num: [513/600] Discriminator Loss: 0.3568, Generator Loss: 3.8890 D(x): 0.8499, D(G(z)): 0.0307 Epoch: [4/20], Batch Num: [514/600] Discriminator Loss: 0.3630, Generator Loss: 3.7524 D(x): 0.8805, D(G(z)): 0.0831 Epoch: [4/20], Batch Num: [515/600] Discriminator Loss: 0.2813, Generator Loss: 3.2629 D(x): 0.8751, D(G(z)): 0.0617 Epoch: [4/20], Batch Num: [516/600] Discriminator Loss: 0.2887, Generator Loss: 2.7523 D(x): 0.9056, D(G(z)): 0.0915 Epoch: [4/20], Batch Num: [517/600] Discriminator Loss: 0.2532, Generator Loss: 3.0460 D(x): 0.9600, D(G(z)): 0.1303 Epoch: [4/20], Batch Num: [518/600] Discriminator Loss: 0.2263, Generator Loss: 2.9437 D(x): 0.9685, D(G(z)): 0.1514 Epoch: [4/20], Batch Num: [519/600] Discriminator Loss: 0.4523, Generator Loss: 3.3047 D(x): 0.9032, D(G(z)): 0.1807 Epoch: [4/20], Batch Num: [520/600] Discriminator Loss: 0.2241, Generator Loss: 3.7329 D(x): 0.9533, D(G(z)): 0.1283 Epoch: [4/20], Batch Num: [521/600] Discriminator Loss: 0.3756, Generator Loss: 4.5789 D(x): 0.8674, D(G(z)): 0.1042 Epoch: [4/20], Batch Num: [522/600] Discriminator Loss: 0.2825, Generator Loss: 3.9369 D(x): 0.8956, D(G(z)): 0.0696 Epoch: [4/20], Batch Num: [523/600] Discriminator Loss: 0.3095, Generator Loss: 3.8691 D(x): 0.8639, D(G(z)): 0.0736 Epoch: [4/20], Batch Num: [524/600] Discriminator Loss: 0.3892, Generator Loss: 3.2020 D(x): 0.8863, D(G(z)): 0.1209 Epoch: [4/20], Batch Num: [525/600] Discriminator Loss: 0.4784, Generator Loss: 3.5256 D(x): 0.9137, D(G(z)): 0.1877 Epoch: [4/20], Batch Num: [526/600] Discriminator Loss: 0.4668, Generator Loss: 4.1775 D(x): 0.9399, D(G(z)): 0.2000 Epoch: [4/20], Batch Num: [527/600] Discriminator Loss: 0.2829, Generator Loss: 4.4378 D(x): 0.8794, D(G(z)): 0.0732 Epoch: [4/20], Batch Num: [528/600] Discriminator Loss: 0.5054, Generator Loss: 4.3047 D(x): 0.8197, D(G(z)): 0.0712 Epoch: [4/20], Batch Num: [529/600] Discriminator Loss: 0.3752, Generator Loss: 3.6055 D(x): 0.8687, D(G(z)): 0.0504 Epoch: [4/20], Batch Num: [530/600] Discriminator Loss: 0.3193, Generator Loss: 3.2387 D(x): 0.9271, D(G(z)): 0.1242 Epoch: [4/20], Batch Num: [531/600] Discriminator Loss: 0.3854, Generator Loss: 2.5445 D(x): 0.9116, D(G(z)): 0.1400 Epoch: [4/20], Batch Num: [532/600] Discriminator Loss: 0.3508, Generator Loss: 3.3654 D(x): 0.9487, D(G(z)): 0.1725 Epoch: [4/20], Batch Num: [533/600] Discriminator Loss: 0.3173, Generator Loss: 4.5937 D(x): 0.9161, D(G(z)): 0.1199 Epoch: [4/20], Batch Num: [534/600] Discriminator Loss: 0.2908, Generator Loss: 4.4907 D(x): 0.8893, D(G(z)): 0.0653 Epoch: [4/20], Batch Num: [535/600] Discriminator Loss: 0.3378, Generator Loss: 4.4458 D(x): 0.8569, D(G(z)): 0.0436 Epoch: [4/20], Batch Num: [536/600] Discriminator Loss: 0.4162, Generator Loss: 3.8442 D(x): 0.8669, D(G(z)): 0.0765 Epoch: [4/20], Batch Num: [537/600] Discriminator Loss: 0.3314, Generator Loss: 2.8671 D(x): 0.8936, D(G(z)): 0.0952 Epoch: [4/20], Batch Num: [538/600] Discriminator Loss: 0.2444, Generator Loss: 2.7351 D(x): 0.9598, D(G(z)): 0.1535 Epoch: [4/20], Batch Num: [539/600] Discriminator Loss: 0.3682, Generator Loss: 3.3111 D(x): 0.9431, D(G(z)): 0.1732 Epoch: [4/20], Batch Num: [540/600] Discriminator Loss: 0.2643, Generator Loss: 4.4522 D(x): 0.9282, D(G(z)): 0.0993 Epoch: [4/20], Batch Num: [541/600] Discriminator Loss: 0.3940, Generator Loss: 4.1538 D(x): 0.8778, D(G(z)): 0.0677 Epoch: [4/20], Batch Num: [542/600] Discriminator Loss: 0.3047, Generator Loss: 3.7221 D(x): 0.8753, D(G(z)): 0.0497 Epoch: [4/20], Batch Num: [543/600] Discriminator Loss: 0.4002, Generator Loss: 3.3409 D(x): 0.9090, D(G(z)): 0.1344 Epoch: [4/20], Batch Num: [544/600] Discriminator Loss: 0.2489, Generator Loss: 3.3631 D(x): 0.9286, D(G(z)): 0.0990 Epoch: [4/20], Batch Num: [545/600] Discriminator Loss: 0.3260, Generator Loss: 3.4647 D(x): 0.9346, D(G(z)): 0.1256 Epoch: [4/20], Batch Num: [546/600] Discriminator Loss: 0.4551, Generator Loss: 4.2684 D(x): 0.9094, D(G(z)): 0.1353 Epoch: [4/20], Batch Num: [547/600] Discriminator Loss: 0.4155, Generator Loss: 4.8090 D(x): 0.8822, D(G(z)): 0.0715 Epoch: [4/20], Batch Num: [548/600] Discriminator Loss: 0.4980, Generator Loss: 3.9054 D(x): 0.8425, D(G(z)): 0.0639 Epoch: [4/20], Batch Num: [549/600] Discriminator Loss: 0.2567, Generator Loss: 3.1799 D(x): 0.8971, D(G(z)): 0.0582 Epoch: [4/20], Batch Num: [550/600] Discriminator Loss: 0.3745, Generator Loss: 2.7333 D(x): 0.9249, D(G(z)): 0.1496 Epoch: [4/20], Batch Num: [551/600] Discriminator Loss: 0.4099, Generator Loss: 3.4283 D(x): 0.9408, D(G(z)): 0.1861 Epoch: [4/20], Batch Num: [552/600] Discriminator Loss: 0.3107, Generator Loss: 4.1872 D(x): 0.9164, D(G(z)): 0.0989 Epoch: [4/20], Batch Num: [553/600] Discriminator Loss: 0.3051, Generator Loss: 4.3131 D(x): 0.8879, D(G(z)): 0.0658 Epoch: [4/20], Batch Num: [554/600] Discriminator Loss: 0.4125, Generator Loss: 3.6517 D(x): 0.8470, D(G(z)): 0.0501 Epoch: [4/20], Batch Num: [555/600] Discriminator Loss: 0.3573, Generator Loss: 2.9716 D(x): 0.8686, D(G(z)): 0.0711 Epoch: [4/20], Batch Num: [556/600] Discriminator Loss: 0.4788, Generator Loss: 2.6411 D(x): 0.9199, D(G(z)): 0.2178 Epoch: [4/20], Batch Num: [557/600] Discriminator Loss: 0.3285, Generator Loss: 2.9458 D(x): 0.9332, D(G(z)): 0.1387 Epoch: [4/20], Batch Num: [558/600] Discriminator Loss: 0.3749, Generator Loss: 3.4872 D(x): 0.8974, D(G(z)): 0.1165 Epoch: [4/20], Batch Num: [559/600] Discriminator Loss: 0.5014, Generator Loss: 3.5593 D(x): 0.8527, D(G(z)): 0.0916 Epoch: [4/20], Batch Num: [560/600] Discriminator Loss: 0.5690, Generator Loss: 3.1080 D(x): 0.8441, D(G(z)): 0.0879 Epoch: [4/20], Batch Num: [561/600] Discriminator Loss: 0.3757, Generator Loss: 2.7368 D(x): 0.9088, D(G(z)): 0.1281 Epoch: [4/20], Batch Num: [562/600] Discriminator Loss: 0.4195, Generator Loss: 2.8376 D(x): 0.8991, D(G(z)): 0.1601 Epoch: [4/20], Batch Num: [563/600] Discriminator Loss: 0.3454, Generator Loss: 3.0818 D(x): 0.9159, D(G(z)): 0.1328 Epoch: [4/20], Batch Num: [564/600] Discriminator Loss: 0.3491, Generator Loss: 3.2180 D(x): 0.8757, D(G(z)): 0.0813 Epoch: [4/20], Batch Num: [565/600] Discriminator Loss: 0.2503, Generator Loss: 3.0089 D(x): 0.8970, D(G(z)): 0.0681 Epoch: [4/20], Batch Num: [566/600] Discriminator Loss: 0.3933, Generator Loss: 2.6330 D(x): 0.8676, D(G(z)): 0.1043 Epoch: [4/20], Batch Num: [567/600] Discriminator Loss: 0.3100, Generator Loss: 2.6039 D(x): 0.9099, D(G(z)): 0.1083 Epoch: [4/20], Batch Num: [568/600] Discriminator Loss: 0.3540, Generator Loss: 2.9489 D(x): 0.9285, D(G(z)): 0.1644 Epoch: [4/20], Batch Num: [569/600] Discriminator Loss: 0.3444, Generator Loss: 3.1927 D(x): 0.9346, D(G(z)): 0.1216 Epoch: [4/20], Batch Num: [570/600] Discriminator Loss: 0.3125, Generator Loss: 3.5997 D(x): 0.8860, D(G(z)): 0.0670 Epoch: [4/20], Batch Num: [571/600] Discriminator Loss: 0.3171, Generator Loss: 3.8898 D(x): 0.9053, D(G(z)): 0.0844 Epoch: [4/20], Batch Num: [572/600] Discriminator Loss: 0.3211, Generator Loss: 3.3073 D(x): 0.9001, D(G(z)): 0.0670 Epoch: [4/20], Batch Num: [573/600] Discriminator Loss: 0.2048, Generator Loss: 3.1513 D(x): 0.9173, D(G(z)): 0.0563 Epoch: [4/20], Batch Num: [574/600] Discriminator Loss: 0.2456, Generator Loss: 2.6759 D(x): 0.9223, D(G(z)): 0.0862 Epoch: [4/20], Batch Num: [575/600] Discriminator Loss: 0.2623, Generator Loss: 3.2600 D(x): 0.9736, D(G(z)): 0.1647 Epoch: [4/20], Batch Num: [576/600] Discriminator Loss: 0.2940, Generator Loss: 4.1266 D(x): 0.9305, D(G(z)): 0.0919 Epoch: [4/20], Batch Num: [577/600] Discriminator Loss: 0.2400, Generator Loss: 4.1856 D(x): 0.8994, D(G(z)): 0.0452 Epoch: [4/20], Batch Num: [578/600] Discriminator Loss: 0.2588, Generator Loss: 4.3341 D(x): 0.9067, D(G(z)): 0.0477 Epoch: [4/20], Batch Num: [579/600] Discriminator Loss: 0.2535, Generator Loss: 3.9753 D(x): 0.9126, D(G(z)): 0.0384 Epoch: [4/20], Batch Num: [580/600] Discriminator Loss: 0.2565, Generator Loss: 3.4272 D(x): 0.9163, D(G(z)): 0.0518 Epoch: [4/20], Batch Num: [581/600] Discriminator Loss: 0.2962, Generator Loss: 3.5031 D(x): 0.9225, D(G(z)): 0.1024 Epoch: [4/20], Batch Num: [582/600] Discriminator Loss: 0.3017, Generator Loss: 3.2705 D(x): 0.9128, D(G(z)): 0.0844 Epoch: [4/20], Batch Num: [583/600] Discriminator Loss: 0.2998, Generator Loss: 3.3506 D(x): 0.9460, D(G(z)): 0.1155 Epoch: [4/20], Batch Num: [584/600] Discriminator Loss: 0.2045, Generator Loss: 4.1631 D(x): 0.9603, D(G(z)): 0.0970 Epoch: [4/20], Batch Num: [585/600] Discriminator Loss: 0.1324, Generator Loss: 4.6403 D(x): 0.9657, D(G(z)): 0.0567 Epoch: [4/20], Batch Num: [586/600] Discriminator Loss: 0.2618, Generator Loss: 4.8177 D(x): 0.9095, D(G(z)): 0.0637 Epoch: [4/20], Batch Num: [587/600] Discriminator Loss: 0.3262, Generator Loss: 4.5827 D(x): 0.8823, D(G(z)): 0.0270 Epoch: [4/20], Batch Num: [588/600] Discriminator Loss: 0.3271, Generator Loss: 3.7843 D(x): 0.8799, D(G(z)): 0.0305 Epoch: [4/20], Batch Num: [589/600] Discriminator Loss: 0.2557, Generator Loss: 3.2072 D(x): 0.9260, D(G(z)): 0.0762 Epoch: [4/20], Batch Num: [590/600] Discriminator Loss: 0.2773, Generator Loss: 3.7999 D(x): 0.9587, D(G(z)): 0.1217 Epoch: [4/20], Batch Num: [591/600] Discriminator Loss: 0.2309, Generator Loss: 3.4608 D(x): 0.9387, D(G(z)): 0.0724 Epoch: [4/20], Batch Num: [592/600] Discriminator Loss: 0.2739, Generator Loss: 3.9530 D(x): 0.9213, D(G(z)): 0.0795 Epoch: [4/20], Batch Num: [593/600] Discriminator Loss: 0.1834, Generator Loss: 3.9317 D(x): 0.9449, D(G(z)): 0.0819 Epoch: [4/20], Batch Num: [594/600] Discriminator Loss: 0.1674, Generator Loss: 4.2305 D(x): 0.9594, D(G(z)): 0.0638 Epoch: [4/20], Batch Num: [595/600] Discriminator Loss: 0.0926, Generator Loss: 4.1689 D(x): 0.9664, D(G(z)): 0.0398 Epoch: [4/20], Batch Num: [596/600] Discriminator Loss: 0.0693, Generator Loss: 4.3632 D(x): 0.9790, D(G(z)): 0.0384 Epoch: [4/20], Batch Num: [597/600] Discriminator Loss: 0.3033, Generator Loss: 4.1278 D(x): 0.9075, D(G(z)): 0.0238 Epoch: [4/20], Batch Num: [598/600] Discriminator Loss: 0.1370, Generator Loss: 4.0671 D(x): 0.9519, D(G(z)): 0.0326 Epoch: [4/20], Batch Num: [599/600] Discriminator Loss: 0.1377, Generator Loss: 3.8009 D(x): 0.9727, D(G(z)): 0.0516 Epoch: 5, Batch Num: [0/600]
Epoch: [5/20], Batch Num: [0/600] Discriminator Loss: 0.3212, Generator Loss: 3.4082 D(x): 0.9103, D(G(z)): 0.0642 Epoch: [5/20], Batch Num: [1/600] Discriminator Loss: 0.1953, Generator Loss: 3.4145 D(x): 0.9613, D(G(z)): 0.1024 Epoch: [5/20], Batch Num: [2/600] Discriminator Loss: 0.2166, Generator Loss: 3.5712 D(x): 0.9660, D(G(z)): 0.0949 Epoch: [5/20], Batch Num: [3/600] Discriminator Loss: 0.2406, Generator Loss: 3.9384 D(x): 0.9409, D(G(z)): 0.0865 Epoch: [5/20], Batch Num: [4/600] Discriminator Loss: 0.2343, Generator Loss: 3.9536 D(x): 0.9225, D(G(z)): 0.0433 Epoch: [5/20], Batch Num: [5/600] Discriminator Loss: 0.2505, Generator Loss: 3.5946 D(x): 0.9095, D(G(z)): 0.0460 Epoch: [5/20], Batch Num: [6/600] Discriminator Loss: 0.2312, Generator Loss: 3.3356 D(x): 0.9142, D(G(z)): 0.0510 Epoch: [5/20], Batch Num: [7/600] Discriminator Loss: 0.2041, Generator Loss: 3.0601 D(x): 0.9433, D(G(z)): 0.0803 Epoch: [5/20], Batch Num: [8/600] Discriminator Loss: 0.2884, Generator Loss: 3.6390 D(x): 0.9846, D(G(z)): 0.1498 Epoch: [5/20], Batch Num: [9/600] Discriminator Loss: 0.2191, Generator Loss: 4.0197 D(x): 0.9575, D(G(z)): 0.0955 Epoch: [5/20], Batch Num: [10/600] Discriminator Loss: 0.2114, Generator Loss: 4.4582 D(x): 0.9265, D(G(z)): 0.0458 Epoch: [5/20], Batch Num: [11/600] Discriminator Loss: 0.2220, Generator Loss: 4.2163 D(x): 0.9118, D(G(z)): 0.0248 Epoch: [5/20], Batch Num: [12/600] Discriminator Loss: 0.3913, Generator Loss: 3.5734 D(x): 0.8396, D(G(z)): 0.0234 Epoch: [5/20], Batch Num: [13/600] Discriminator Loss: 0.2489, Generator Loss: 2.5417 D(x): 0.9214, D(G(z)): 0.0858 Epoch: [5/20], Batch Num: [14/600] Discriminator Loss: 0.4178, Generator Loss: 2.9253 D(x): 0.9650, D(G(z)): 0.2309 Epoch: [5/20], Batch Num: [15/600] Discriminator Loss: 0.2162, Generator Loss: 3.5234 D(x): 0.9731, D(G(z)): 0.1183 Epoch: [5/20], Batch Num: [16/600] Discriminator Loss: 0.2392, Generator Loss: 3.9572 D(x): 0.9289, D(G(z)): 0.0560 Epoch: [5/20], Batch Num: [17/600] Discriminator Loss: 0.1794, Generator Loss: 4.3472 D(x): 0.9308, D(G(z)): 0.0416 Epoch: [5/20], Batch Num: [18/600] Discriminator Loss: 0.4257, Generator Loss: 4.0126 D(x): 0.8566, D(G(z)): 0.0339 Epoch: [5/20], Batch Num: [19/600] Discriminator Loss: 0.1777, Generator Loss: 3.4106 D(x): 0.9308, D(G(z)): 0.0406 Epoch: [5/20], Batch Num: [20/600] Discriminator Loss: 0.2155, Generator Loss: 3.0587 D(x): 0.9415, D(G(z)): 0.0614 Epoch: [5/20], Batch Num: [21/600] Discriminator Loss: 0.2829, Generator Loss: 3.0382 D(x): 0.9484, D(G(z)): 0.1372 Epoch: [5/20], Batch Num: [22/600] Discriminator Loss: 0.1913, Generator Loss: 3.0858 D(x): 0.9571, D(G(z)): 0.0912 Epoch: [5/20], Batch Num: [23/600] Discriminator Loss: 0.2073, Generator Loss: 3.5729 D(x): 0.9488, D(G(z)): 0.0918 Epoch: [5/20], Batch Num: [24/600] Discriminator Loss: 0.2273, Generator Loss: 4.0500 D(x): 0.9329, D(G(z)): 0.0628 Epoch: [5/20], Batch Num: [25/600] Discriminator Loss: 0.3302, Generator Loss: 3.5655 D(x): 0.8857, D(G(z)): 0.0440 Epoch: [5/20], Batch Num: [26/600] Discriminator Loss: 0.1901, Generator Loss: 3.7856 D(x): 0.9422, D(G(z)): 0.0531 Epoch: [5/20], Batch Num: [27/600] Discriminator Loss: 0.1640, Generator Loss: 3.3957 D(x): 0.9496, D(G(z)): 0.0562 Epoch: [5/20], Batch Num: [28/600] Discriminator Loss: 0.1632, Generator Loss: 3.3958 D(x): 0.9557, D(G(z)): 0.0730 Epoch: [5/20], Batch Num: [29/600] Discriminator Loss: 0.1625, Generator Loss: 3.4757 D(x): 0.9600, D(G(z)): 0.0803 Epoch: [5/20], Batch Num: [30/600] Discriminator Loss: 0.1727, Generator Loss: 3.7224 D(x): 0.9631, D(G(z)): 0.0854 Epoch: [5/20], Batch Num: [31/600] Discriminator Loss: 0.2704, Generator Loss: 3.7350 D(x): 0.9164, D(G(z)): 0.0582 Epoch: [5/20], Batch Num: [32/600] Discriminator Loss: 0.2080, Generator Loss: 3.7916 D(x): 0.9432, D(G(z)): 0.0633 Epoch: [5/20], Batch Num: [33/600] Discriminator Loss: 0.2908, Generator Loss: 3.4949 D(x): 0.8996, D(G(z)): 0.0464 Epoch: [5/20], Batch Num: [34/600] Discriminator Loss: 0.2194, Generator Loss: 3.5329 D(x): 0.9544, D(G(z)): 0.1016 Epoch: [5/20], Batch Num: [35/600] Discriminator Loss: 0.2642, Generator Loss: 3.2079 D(x): 0.9401, D(G(z)): 0.0766 Epoch: [5/20], Batch Num: [36/600] Discriminator Loss: 0.2298, Generator Loss: 3.7943 D(x): 0.9514, D(G(z)): 0.1096 Epoch: [5/20], Batch Num: [37/600] Discriminator Loss: 0.2420, Generator Loss: 4.4665 D(x): 0.9638, D(G(z)): 0.0869 Epoch: [5/20], Batch Num: [38/600] Discriminator Loss: 0.1836, Generator Loss: 4.8152 D(x): 0.9378, D(G(z)): 0.0242 Epoch: [5/20], Batch Num: [39/600] Discriminator Loss: 0.2576, Generator Loss: 4.6420 D(x): 0.8988, D(G(z)): 0.0273 Epoch: [5/20], Batch Num: [40/600] Discriminator Loss: 0.4327, Generator Loss: 3.8146 D(x): 0.8779, D(G(z)): 0.0356 Epoch: [5/20], Batch Num: [41/600] Discriminator Loss: 0.2071, Generator Loss: 3.4284 D(x): 0.9432, D(G(z)): 0.0710 Epoch: [5/20], Batch Num: [42/600] Discriminator Loss: 0.2670, Generator Loss: 3.3093 D(x): 0.9424, D(G(z)): 0.1042 Epoch: [5/20], Batch Num: [43/600] Discriminator Loss: 0.1992, Generator Loss: 3.3550 D(x): 0.9745, D(G(z)): 0.1224 Epoch: [5/20], Batch Num: [44/600] Discriminator Loss: 0.2931, Generator Loss: 3.8135 D(x): 0.9362, D(G(z)): 0.0906 Epoch: [5/20], Batch Num: [45/600] Discriminator Loss: 0.2371, Generator Loss: 4.2820 D(x): 0.9390, D(G(z)): 0.0741 Epoch: [5/20], Batch Num: [46/600] Discriminator Loss: 0.3057, Generator Loss: 4.1235 D(x): 0.9062, D(G(z)): 0.0380 Epoch: [5/20], Batch Num: [47/600] Discriminator Loss: 0.3133, Generator Loss: 4.0462 D(x): 0.9067, D(G(z)): 0.0570 Epoch: [5/20], Batch Num: [48/600] Discriminator Loss: 0.2832, Generator Loss: 3.6340 D(x): 0.9195, D(G(z)): 0.0643 Epoch: [5/20], Batch Num: [49/600] Discriminator Loss: 0.3003, Generator Loss: 3.5192 D(x): 0.9510, D(G(z)): 0.1039 Epoch: [5/20], Batch Num: [50/600] Discriminator Loss: 0.2451, Generator Loss: 3.6213 D(x): 0.9316, D(G(z)): 0.0918 Epoch: [5/20], Batch Num: [51/600] Discriminator Loss: 0.3123, Generator Loss: 3.6720 D(x): 0.9366, D(G(z)): 0.0953 Epoch: [5/20], Batch Num: [52/600] Discriminator Loss: 0.3290, Generator Loss: 3.6650 D(x): 0.9040, D(G(z)): 0.0749 Epoch: [5/20], Batch Num: [53/600] Discriminator Loss: 0.2705, Generator Loss: 3.5752 D(x): 0.9204, D(G(z)): 0.0852 Epoch: [5/20], Batch Num: [54/600] Discriminator Loss: 0.2360, Generator Loss: 3.6223 D(x): 0.9518, D(G(z)): 0.0904 Epoch: [5/20], Batch Num: [55/600] Discriminator Loss: 0.2385, Generator Loss: 3.4290 D(x): 0.9245, D(G(z)): 0.0761 Epoch: [5/20], Batch Num: [56/600] Discriminator Loss: 0.1964, Generator Loss: 3.7530 D(x): 0.9422, D(G(z)): 0.0877 Epoch: [5/20], Batch Num: [57/600] Discriminator Loss: 0.3906, Generator Loss: 3.3035 D(x): 0.8700, D(G(z)): 0.0461 Epoch: [5/20], Batch Num: [58/600] Discriminator Loss: 0.2578, Generator Loss: 2.9168 D(x): 0.9346, D(G(z)): 0.1009 Epoch: [5/20], Batch Num: [59/600] Discriminator Loss: 0.2351, Generator Loss: 3.3972 D(x): 0.9486, D(G(z)): 0.1191 Epoch: [5/20], Batch Num: [60/600] Discriminator Loss: 0.2145, Generator Loss: 3.6716 D(x): 0.9261, D(G(z)): 0.0719 Epoch: [5/20], Batch Num: [61/600] Discriminator Loss: 0.1800, Generator Loss: 3.9390 D(x): 0.9331, D(G(z)): 0.0676 Epoch: [5/20], Batch Num: [62/600] Discriminator Loss: 0.2928, Generator Loss: 3.4753 D(x): 0.9090, D(G(z)): 0.0749 Epoch: [5/20], Batch Num: [63/600] Discriminator Loss: 0.2095, Generator Loss: 3.4364 D(x): 0.9458, D(G(z)): 0.0739 Epoch: [5/20], Batch Num: [64/600] Discriminator Loss: 0.1825, Generator Loss: 3.9069 D(x): 0.9365, D(G(z)): 0.0564 Epoch: [5/20], Batch Num: [65/600] Discriminator Loss: 0.1956, Generator Loss: 3.4644 D(x): 0.9688, D(G(z)): 0.0864 Epoch: [5/20], Batch Num: [66/600] Discriminator Loss: 0.2188, Generator Loss: 3.8315 D(x): 0.9572, D(G(z)): 0.0734 Epoch: [5/20], Batch Num: [67/600] Discriminator Loss: 0.1357, Generator Loss: 4.0402 D(x): 0.9572, D(G(z)): 0.0535 Epoch: [5/20], Batch Num: [68/600] Discriminator Loss: 0.2173, Generator Loss: 4.1823 D(x): 0.9382, D(G(z)): 0.0600 Epoch: [5/20], Batch Num: [69/600] Discriminator Loss: 0.1986, Generator Loss: 3.9880 D(x): 0.9296, D(G(z)): 0.0445 Epoch: [5/20], Batch Num: [70/600] Discriminator Loss: 0.1480, Generator Loss: 3.4460 D(x): 0.9395, D(G(z)): 0.0366 Epoch: [5/20], Batch Num: [71/600] Discriminator Loss: 0.1830, Generator Loss: 3.7724 D(x): 0.9712, D(G(z)): 0.0759 Epoch: [5/20], Batch Num: [72/600] Discriminator Loss: 0.2023, Generator Loss: 3.9296 D(x): 0.9786, D(G(z)): 0.0961 Epoch: [5/20], Batch Num: [73/600] Discriminator Loss: 0.2281, Generator Loss: 4.1926 D(x): 0.9458, D(G(z)): 0.0508 Epoch: [5/20], Batch Num: [74/600] Discriminator Loss: 0.1336, Generator Loss: 4.3585 D(x): 0.9460, D(G(z)): 0.0395 Epoch: [5/20], Batch Num: [75/600] Discriminator Loss: 0.1231, Generator Loss: 4.1052 D(x): 0.9596, D(G(z)): 0.0483 Epoch: [5/20], Batch Num: [76/600] Discriminator Loss: 0.1342, Generator Loss: 4.1463 D(x): 0.9633, D(G(z)): 0.0504 Epoch: [5/20], Batch Num: [77/600] Discriminator Loss: 0.1791, Generator Loss: 4.1438 D(x): 0.9565, D(G(z)): 0.0734 Epoch: [5/20], Batch Num: [78/600] Discriminator Loss: 0.2119, Generator Loss: 4.0706 D(x): 0.9408, D(G(z)): 0.0714 Epoch: [5/20], Batch Num: [79/600] Discriminator Loss: 0.2124, Generator Loss: 4.2292 D(x): 0.9396, D(G(z)): 0.0662 Epoch: [5/20], Batch Num: [80/600] Discriminator Loss: 0.2282, Generator Loss: 4.0968 D(x): 0.9477, D(G(z)): 0.0822 Epoch: [5/20], Batch Num: [81/600] Discriminator Loss: 0.2107, Generator Loss: 4.4508 D(x): 0.9426, D(G(z)): 0.0735 Epoch: [5/20], Batch Num: [82/600] Discriminator Loss: 0.3505, Generator Loss: 3.9590 D(x): 0.9270, D(G(z)): 0.0773 Epoch: [5/20], Batch Num: [83/600] Discriminator Loss: 0.2899, Generator Loss: 3.7785 D(x): 0.9148, D(G(z)): 0.0742 Epoch: [5/20], Batch Num: [84/600] Discriminator Loss: 0.3611, Generator Loss: 3.6005 D(x): 0.9368, D(G(z)): 0.0992 Epoch: [5/20], Batch Num: [85/600] Discriminator Loss: 0.2446, Generator Loss: 3.1346 D(x): 0.9343, D(G(z)): 0.0736 Epoch: [5/20], Batch Num: [86/600] Discriminator Loss: 0.4480, Generator Loss: 3.5594 D(x): 0.9461, D(G(z)): 0.1587 Epoch: [5/20], Batch Num: [87/600] Discriminator Loss: 0.2741, Generator Loss: 4.3481 D(x): 0.9489, D(G(z)): 0.1100 Epoch: [5/20], Batch Num: [88/600] Discriminator Loss: 0.3868, Generator Loss: 4.2666 D(x): 0.8724, D(G(z)): 0.0458 Epoch: [5/20], Batch Num: [89/600] Discriminator Loss: 0.5124, Generator Loss: 3.5610 D(x): 0.8551, D(G(z)): 0.0608 Epoch: [5/20], Batch Num: [90/600] Discriminator Loss: 0.3789, Generator Loss: 2.4774 D(x): 0.8881, D(G(z)): 0.0816 Epoch: [5/20], Batch Num: [91/600] Discriminator Loss: 0.6544, Generator Loss: 3.5061 D(x): 0.9334, D(G(z)): 0.2557 Epoch: [5/20], Batch Num: [92/600] Discriminator Loss: 0.4515, Generator Loss: 4.1078 D(x): 0.8933, D(G(z)): 0.0846 Epoch: [5/20], Batch Num: [93/600] Discriminator Loss: 0.4126, Generator Loss: 4.2083 D(x): 0.8557, D(G(z)): 0.0502 Epoch: [5/20], Batch Num: [94/600] Discriminator Loss: 0.3174, Generator Loss: 3.9274 D(x): 0.8922, D(G(z)): 0.0607 Epoch: [5/20], Batch Num: [95/600] Discriminator Loss: 0.3185, Generator Loss: 3.5335 D(x): 0.8820, D(G(z)): 0.0752 Epoch: [5/20], Batch Num: [96/600] Discriminator Loss: 0.2679, Generator Loss: 3.1256 D(x): 0.9015, D(G(z)): 0.0566 Epoch: [5/20], Batch Num: [97/600] Discriminator Loss: 0.3906, Generator Loss: 2.7738 D(x): 0.8893, D(G(z)): 0.1219 Epoch: [5/20], Batch Num: [98/600] Discriminator Loss: 0.3642, Generator Loss: 2.6140 D(x): 0.9406, D(G(z)): 0.1658 Epoch: [5/20], Batch Num: [99/600] Discriminator Loss: 0.2730, Generator Loss: 3.0651 D(x): 0.9569, D(G(z)): 0.1301 Epoch: 5, Batch Num: [100/600]
Epoch: [5/20], Batch Num: [100/600] Discriminator Loss: 0.1770, Generator Loss: 3.4383 D(x): 0.9637, D(G(z)): 0.0933 Epoch: [5/20], Batch Num: [101/600] Discriminator Loss: 0.2982, Generator Loss: 3.5569 D(x): 0.9179, D(G(z)): 0.0653 Epoch: [5/20], Batch Num: [102/600] Discriminator Loss: 0.3353, Generator Loss: 3.9805 D(x): 0.8739, D(G(z)): 0.0682 Epoch: [5/20], Batch Num: [103/600] Discriminator Loss: 0.2426, Generator Loss: 3.6342 D(x): 0.8874, D(G(z)): 0.0482 Epoch: [5/20], Batch Num: [104/600] Discriminator Loss: 0.1931, Generator Loss: 3.1433 D(x): 0.9330, D(G(z)): 0.0726 Epoch: [5/20], Batch Num: [105/600] Discriminator Loss: 0.2532, Generator Loss: 3.1092 D(x): 0.9546, D(G(z)): 0.1264 Epoch: [5/20], Batch Num: [106/600] Discriminator Loss: 0.2179, Generator Loss: 3.4084 D(x): 0.9531, D(G(z)): 0.0882 Epoch: [5/20], Batch Num: [107/600] Discriminator Loss: 0.2052, Generator Loss: 3.7419 D(x): 0.9242, D(G(z)): 0.0634 Epoch: [5/20], Batch Num: [108/600] Discriminator Loss: 0.1821, Generator Loss: 3.9074 D(x): 0.9418, D(G(z)): 0.0632 Epoch: [5/20], Batch Num: [109/600] Discriminator Loss: 0.1630, Generator Loss: 3.9424 D(x): 0.9326, D(G(z)): 0.0371 Epoch: [5/20], Batch Num: [110/600] Discriminator Loss: 0.1942, Generator Loss: 3.7707 D(x): 0.9348, D(G(z)): 0.0501 Epoch: [5/20], Batch Num: [111/600] Discriminator Loss: 0.0828, Generator Loss: 3.6595 D(x): 0.9715, D(G(z)): 0.0443 Epoch: [5/20], Batch Num: [112/600] Discriminator Loss: 0.1906, Generator Loss: 3.9420 D(x): 0.9481, D(G(z)): 0.0704 Epoch: [5/20], Batch Num: [113/600] Discriminator Loss: 0.2450, Generator Loss: 4.4690 D(x): 0.9376, D(G(z)): 0.0872 Epoch: [5/20], Batch Num: [114/600] Discriminator Loss: 0.1418, Generator Loss: 4.8457 D(x): 0.9580, D(G(z)): 0.0507 Epoch: [5/20], Batch Num: [115/600] Discriminator Loss: 0.2495, Generator Loss: 4.0857 D(x): 0.9039, D(G(z)): 0.0364 Epoch: [5/20], Batch Num: [116/600] Discriminator Loss: 0.2181, Generator Loss: 4.0483 D(x): 0.9139, D(G(z)): 0.0449 Epoch: [5/20], Batch Num: [117/600] Discriminator Loss: 0.1251, Generator Loss: 3.9614 D(x): 0.9604, D(G(z)): 0.0426 Epoch: [5/20], Batch Num: [118/600] Discriminator Loss: 0.2468, Generator Loss: 3.6358 D(x): 0.9547, D(G(z)): 0.0896 Epoch: [5/20], Batch Num: [119/600] Discriminator Loss: 0.1749, Generator Loss: 4.0035 D(x): 0.9577, D(G(z)): 0.0670 Epoch: [5/20], Batch Num: [120/600] Discriminator Loss: 0.1047, Generator Loss: 4.8121 D(x): 0.9730, D(G(z)): 0.0602 Epoch: [5/20], Batch Num: [121/600] Discriminator Loss: 0.1417, Generator Loss: 4.8563 D(x): 0.9733, D(G(z)): 0.0621 Epoch: [5/20], Batch Num: [122/600] Discriminator Loss: 0.0912, Generator Loss: 5.0482 D(x): 0.9636, D(G(z)): 0.0228 Epoch: [5/20], Batch Num: [123/600] Discriminator Loss: 0.1314, Generator Loss: 4.8895 D(x): 0.9344, D(G(z)): 0.0143 Epoch: [5/20], Batch Num: [124/600] Discriminator Loss: 0.1914, Generator Loss: 4.2943 D(x): 0.9191, D(G(z)): 0.0339 Epoch: [5/20], Batch Num: [125/600] Discriminator Loss: 0.1715, Generator Loss: 3.4205 D(x): 0.9566, D(G(z)): 0.0560 Epoch: [5/20], Batch Num: [126/600] Discriminator Loss: 0.1168, Generator Loss: 3.6350 D(x): 0.9737, D(G(z)): 0.0692 Epoch: [5/20], Batch Num: [127/600] Discriminator Loss: 0.2521, Generator Loss: 4.7921 D(x): 0.9611, D(G(z)): 0.1219 Epoch: [5/20], Batch Num: [128/600] Discriminator Loss: 0.2274, Generator Loss: 4.5584 D(x): 0.9263, D(G(z)): 0.0642 Epoch: [5/20], Batch Num: [129/600] Discriminator Loss: 0.1283, Generator Loss: 4.4926 D(x): 0.9459, D(G(z)): 0.0326 Epoch: [5/20], Batch Num: [130/600] Discriminator Loss: 0.1862, Generator Loss: 4.0517 D(x): 0.9382, D(G(z)): 0.0395 Epoch: [5/20], Batch Num: [131/600] Discriminator Loss: 0.2059, Generator Loss: 4.1125 D(x): 0.9461, D(G(z)): 0.0687 Epoch: [5/20], Batch Num: [132/600] Discriminator Loss: 0.2028, Generator Loss: 3.9744 D(x): 0.9403, D(G(z)): 0.0681 Epoch: [5/20], Batch Num: [133/600] Discriminator Loss: 0.2689, Generator Loss: 3.6498 D(x): 0.9290, D(G(z)): 0.0802 Epoch: [5/20], Batch Num: [134/600] Discriminator Loss: 0.1993, Generator Loss: 3.9873 D(x): 0.9599, D(G(z)): 0.1064 Epoch: [5/20], Batch Num: [135/600] Discriminator Loss: 0.2721, Generator Loss: 4.0355 D(x): 0.9292, D(G(z)): 0.0798 Epoch: [5/20], Batch Num: [136/600] Discriminator Loss: 0.5245, Generator Loss: 3.8551 D(x): 0.8795, D(G(z)): 0.0874 Epoch: [5/20], Batch Num: [137/600] Discriminator Loss: 0.2641, Generator Loss: 3.5374 D(x): 0.9234, D(G(z)): 0.0794 Epoch: [5/20], Batch Num: [138/600] Discriminator Loss: 0.3485, Generator Loss: 3.8147 D(x): 0.9311, D(G(z)): 0.1344 Epoch: [5/20], Batch Num: [139/600] Discriminator Loss: 0.2460, Generator Loss: 3.7500 D(x): 0.9481, D(G(z)): 0.1108 Epoch: [5/20], Batch Num: [140/600] Discriminator Loss: 0.3542, Generator Loss: 3.6669 D(x): 0.8597, D(G(z)): 0.0435 Epoch: [5/20], Batch Num: [141/600] Discriminator Loss: 0.3269, Generator Loss: 3.2525 D(x): 0.9207, D(G(z)): 0.1008 Epoch: [5/20], Batch Num: [142/600] Discriminator Loss: 0.4946, Generator Loss: 3.0151 D(x): 0.8978, D(G(z)): 0.1621 Epoch: [5/20], Batch Num: [143/600] Discriminator Loss: 0.3729, Generator Loss: 3.5627 D(x): 0.9259, D(G(z)): 0.1545 Epoch: [5/20], Batch Num: [144/600] Discriminator Loss: 0.3533, Generator Loss: 4.0994 D(x): 0.9126, D(G(z)): 0.1139 Epoch: [5/20], Batch Num: [145/600] Discriminator Loss: 0.2868, Generator Loss: 3.8084 D(x): 0.8935, D(G(z)): 0.0568 Epoch: [5/20], Batch Num: [146/600] Discriminator Loss: 0.2408, Generator Loss: 3.8847 D(x): 0.9266, D(G(z)): 0.0803 Epoch: [5/20], Batch Num: [147/600] Discriminator Loss: 0.3091, Generator Loss: 3.8647 D(x): 0.9121, D(G(z)): 0.0825 Epoch: [5/20], Batch Num: [148/600] Discriminator Loss: 0.1803, Generator Loss: 3.6867 D(x): 0.9440, D(G(z)): 0.0723 Epoch: [5/20], Batch Num: [149/600] Discriminator Loss: 0.3137, Generator Loss: 3.4844 D(x): 0.9212, D(G(z)): 0.0966 Epoch: [5/20], Batch Num: [150/600] Discriminator Loss: 0.3394, Generator Loss: 4.6234 D(x): 0.9493, D(G(z)): 0.1588 Epoch: [5/20], Batch Num: [151/600] Discriminator Loss: 0.3872, Generator Loss: 4.7503 D(x): 0.8714, D(G(z)): 0.0700 Epoch: [5/20], Batch Num: [152/600] Discriminator Loss: 0.3090, Generator Loss: 4.2767 D(x): 0.9172, D(G(z)): 0.0854 Epoch: [5/20], Batch Num: [153/600] Discriminator Loss: 0.3348, Generator Loss: 4.5064 D(x): 0.9394, D(G(z)): 0.1068 Epoch: [5/20], Batch Num: [154/600] Discriminator Loss: 0.3533, Generator Loss: 4.4463 D(x): 0.9103, D(G(z)): 0.0749 Epoch: [5/20], Batch Num: [155/600] Discriminator Loss: 0.4161, Generator Loss: 4.0486 D(x): 0.8811, D(G(z)): 0.0589 Epoch: [5/20], Batch Num: [156/600] Discriminator Loss: 0.1606, Generator Loss: 3.1287 D(x): 0.9455, D(G(z)): 0.0563 Epoch: [5/20], Batch Num: [157/600] Discriminator Loss: 0.3162, Generator Loss: 3.5282 D(x): 0.9537, D(G(z)): 0.1358 Epoch: [5/20], Batch Num: [158/600] Discriminator Loss: 0.3201, Generator Loss: 4.3804 D(x): 0.9637, D(G(z)): 0.1467 Epoch: [5/20], Batch Num: [159/600] Discriminator Loss: 0.2679, Generator Loss: 5.3391 D(x): 0.8915, D(G(z)): 0.0537 Epoch: [5/20], Batch Num: [160/600] Discriminator Loss: 0.4341, Generator Loss: 4.4318 D(x): 0.8313, D(G(z)): 0.0487 Epoch: [5/20], Batch Num: [161/600] Discriminator Loss: 0.4558, Generator Loss: 3.5464 D(x): 0.8474, D(G(z)): 0.0738 Epoch: [5/20], Batch Num: [162/600] Discriminator Loss: 0.2471, Generator Loss: 3.1508 D(x): 0.9403, D(G(z)): 0.1022 Epoch: [5/20], Batch Num: [163/600] Discriminator Loss: 0.3423, Generator Loss: 3.4287 D(x): 0.9570, D(G(z)): 0.1793 Epoch: [5/20], Batch Num: [164/600] Discriminator Loss: 0.2672, Generator Loss: 4.3106 D(x): 0.9472, D(G(z)): 0.1106 Epoch: [5/20], Batch Num: [165/600] Discriminator Loss: 0.2901, Generator Loss: 5.2639 D(x): 0.9167, D(G(z)): 0.0710 Epoch: [5/20], Batch Num: [166/600] Discriminator Loss: 0.4520, Generator Loss: 4.4796 D(x): 0.8235, D(G(z)): 0.0169 Epoch: [5/20], Batch Num: [167/600] Discriminator Loss: 0.4424, Generator Loss: 3.5852 D(x): 0.8793, D(G(z)): 0.0686 Epoch: [5/20], Batch Num: [168/600] Discriminator Loss: 0.3113, Generator Loss: 3.2860 D(x): 0.9235, D(G(z)): 0.1260 Epoch: [5/20], Batch Num: [169/600] Discriminator Loss: 0.3343, Generator Loss: 3.3738 D(x): 0.9429, D(G(z)): 0.1353 Epoch: [5/20], Batch Num: [170/600] Discriminator Loss: 0.3459, Generator Loss: 4.5718 D(x): 0.9585, D(G(z)): 0.1772 Epoch: [5/20], Batch Num: [171/600] Discriminator Loss: 0.2291, Generator Loss: 5.4503 D(x): 0.8966, D(G(z)): 0.0383 Epoch: [5/20], Batch Num: [172/600] Discriminator Loss: 0.1717, Generator Loss: 5.1199 D(x): 0.9352, D(G(z)): 0.0315 Epoch: [5/20], Batch Num: [173/600] Discriminator Loss: 0.2915, Generator Loss: 4.9336 D(x): 0.8764, D(G(z)): 0.0179 Epoch: [5/20], Batch Num: [174/600] Discriminator Loss: 0.2216, Generator Loss: 3.8284 D(x): 0.9265, D(G(z)): 0.0510 Epoch: [5/20], Batch Num: [175/600] Discriminator Loss: 0.2887, Generator Loss: 3.3340 D(x): 0.9308, D(G(z)): 0.0959 Epoch: [5/20], Batch Num: [176/600] Discriminator Loss: 0.2780, Generator Loss: 3.9172 D(x): 0.9451, D(G(z)): 0.1374 Epoch: [5/20], Batch Num: [177/600] Discriminator Loss: 0.4029, Generator Loss: 4.9844 D(x): 0.9431, D(G(z)): 0.1663 Epoch: [5/20], Batch Num: [178/600] Discriminator Loss: 0.2144, Generator Loss: 5.4801 D(x): 0.9399, D(G(z)): 0.0631 Epoch: [5/20], Batch Num: [179/600] Discriminator Loss: 0.4367, Generator Loss: 5.6735 D(x): 0.8543, D(G(z)): 0.0461 Epoch: [5/20], Batch Num: [180/600] Discriminator Loss: 0.2249, Generator Loss: 5.2985 D(x): 0.9051, D(G(z)): 0.0408 Epoch: [5/20], Batch Num: [181/600] Discriminator Loss: 0.2964, Generator Loss: 4.6406 D(x): 0.8827, D(G(z)): 0.0365 Epoch: [5/20], Batch Num: [182/600] Discriminator Loss: 0.2765, Generator Loss: 3.7159 D(x): 0.9027, D(G(z)): 0.0663 Epoch: [5/20], Batch Num: [183/600] Discriminator Loss: 0.2684, Generator Loss: 4.1696 D(x): 0.9806, D(G(z)): 0.1790 Epoch: [5/20], Batch Num: [184/600] Discriminator Loss: 0.2767, Generator Loss: 4.8946 D(x): 0.9437, D(G(z)): 0.1115 Epoch: [5/20], Batch Num: [185/600] Discriminator Loss: 0.2559, Generator Loss: 6.1081 D(x): 0.9488, D(G(z)): 0.0895 Epoch: [5/20], Batch Num: [186/600] Discriminator Loss: 0.1534, Generator Loss: 6.3236 D(x): 0.9235, D(G(z)): 0.0266 Epoch: [5/20], Batch Num: [187/600] Discriminator Loss: 0.2903, Generator Loss: 5.1729 D(x): 0.8750, D(G(z)): 0.0205 Epoch: [5/20], Batch Num: [188/600] Discriminator Loss: 0.2378, Generator Loss: 5.0758 D(x): 0.9112, D(G(z)): 0.0449 Epoch: [5/20], Batch Num: [189/600] Discriminator Loss: 0.1919, Generator Loss: 3.9320 D(x): 0.9424, D(G(z)): 0.0740 Epoch: [5/20], Batch Num: [190/600] Discriminator Loss: 0.3496, Generator Loss: 5.1738 D(x): 0.9695, D(G(z)): 0.1748 Epoch: [5/20], Batch Num: [191/600] Discriminator Loss: 0.1179, Generator Loss: 6.2181 D(x): 0.9607, D(G(z)): 0.0408 Epoch: [5/20], Batch Num: [192/600] Discriminator Loss: 0.4104, Generator Loss: 5.8268 D(x): 0.8598, D(G(z)): 0.0469 Epoch: [5/20], Batch Num: [193/600] Discriminator Loss: 0.2396, Generator Loss: 5.9033 D(x): 0.9199, D(G(z)): 0.0443 Epoch: [5/20], Batch Num: [194/600] Discriminator Loss: 0.3085, Generator Loss: 5.0827 D(x): 0.9144, D(G(z)): 0.0686 Epoch: [5/20], Batch Num: [195/600] Discriminator Loss: 0.2896, Generator Loss: 4.4295 D(x): 0.9249, D(G(z)): 0.0825 Epoch: [5/20], Batch Num: [196/600] Discriminator Loss: 0.5220, Generator Loss: 4.8991 D(x): 0.9331, D(G(z)): 0.1545 Epoch: [5/20], Batch Num: [197/600] Discriminator Loss: 0.4387, Generator Loss: 5.2979 D(x): 0.8996, D(G(z)): 0.0918 Epoch: [5/20], Batch Num: [198/600] Discriminator Loss: 0.4704, Generator Loss: 5.5425 D(x): 0.9111, D(G(z)): 0.0830 Epoch: [5/20], Batch Num: [199/600] Discriminator Loss: 0.4469, Generator Loss: 5.9171 D(x): 0.9114, D(G(z)): 0.0828 Epoch: 5, Batch Num: [200/600]
Epoch: [5/20], Batch Num: [200/600] Discriminator Loss: 0.4472, Generator Loss: 5.0455 D(x): 0.8628, D(G(z)): 0.0903 Epoch: [5/20], Batch Num: [201/600] Discriminator Loss: 0.3336, Generator Loss: 4.5257 D(x): 0.9015, D(G(z)): 0.0862 Epoch: [5/20], Batch Num: [202/600] Discriminator Loss: 0.6331, Generator Loss: 4.0561 D(x): 0.8383, D(G(z)): 0.1106 Epoch: [5/20], Batch Num: [203/600] Discriminator Loss: 0.3364, Generator Loss: 4.7706 D(x): 0.9520, D(G(z)): 0.1435 Epoch: [5/20], Batch Num: [204/600] Discriminator Loss: 0.4398, Generator Loss: 4.3089 D(x): 0.8490, D(G(z)): 0.0746 Epoch: [5/20], Batch Num: [205/600] Discriminator Loss: 0.2647, Generator Loss: 4.1617 D(x): 0.8805, D(G(z)): 0.0535 Epoch: [5/20], Batch Num: [206/600] Discriminator Loss: 0.3241, Generator Loss: 3.4259 D(x): 0.9087, D(G(z)): 0.0871 Epoch: [5/20], Batch Num: [207/600] Discriminator Loss: 0.3251, Generator Loss: 4.1509 D(x): 0.9241, D(G(z)): 0.1265 Epoch: [5/20], Batch Num: [208/600] Discriminator Loss: 0.1904, Generator Loss: 4.8240 D(x): 0.9445, D(G(z)): 0.0764 Epoch: [5/20], Batch Num: [209/600] Discriminator Loss: 0.1962, Generator Loss: 4.8202 D(x): 0.9292, D(G(z)): 0.0560 Epoch: [5/20], Batch Num: [210/600] Discriminator Loss: 0.2745, Generator Loss: 5.3400 D(x): 0.9173, D(G(z)): 0.0663 Epoch: [5/20], Batch Num: [211/600] Discriminator Loss: 0.1929, Generator Loss: 5.3901 D(x): 0.9231, D(G(z)): 0.0543 Epoch: [5/20], Batch Num: [212/600] Discriminator Loss: 0.1330, Generator Loss: 5.1968 D(x): 0.9610, D(G(z)): 0.0457 Epoch: [5/20], Batch Num: [213/600] Discriminator Loss: 0.2864, Generator Loss: 4.8341 D(x): 0.9407, D(G(z)): 0.0868 Epoch: [5/20], Batch Num: [214/600] Discriminator Loss: 0.3025, Generator Loss: 4.9695 D(x): 0.9166, D(G(z)): 0.0704 Epoch: [5/20], Batch Num: [215/600] Discriminator Loss: 0.1645, Generator Loss: 4.7205 D(x): 0.9423, D(G(z)): 0.0555 Epoch: [5/20], Batch Num: [216/600] Discriminator Loss: 0.1875, Generator Loss: 5.2643 D(x): 0.9727, D(G(z)): 0.0909 Epoch: [5/20], Batch Num: [217/600] Discriminator Loss: 0.2640, Generator Loss: 5.8240 D(x): 0.9461, D(G(z)): 0.0539 Epoch: [5/20], Batch Num: [218/600] Discriminator Loss: 0.2794, Generator Loss: 5.6278 D(x): 0.9067, D(G(z)): 0.0304 Epoch: [5/20], Batch Num: [219/600] Discriminator Loss: 0.2421, Generator Loss: 4.8606 D(x): 0.9037, D(G(z)): 0.0326 Epoch: [5/20], Batch Num: [220/600] Discriminator Loss: 0.3846, Generator Loss: 4.8598 D(x): 0.9309, D(G(z)): 0.1248 Epoch: [5/20], Batch Num: [221/600] Discriminator Loss: 0.2649, Generator Loss: 4.4356 D(x): 0.9230, D(G(z)): 0.0690 Epoch: [5/20], Batch Num: [222/600] Discriminator Loss: 0.2395, Generator Loss: 4.9687 D(x): 0.9350, D(G(z)): 0.0843 Epoch: [5/20], Batch Num: [223/600] Discriminator Loss: 0.2508, Generator Loss: 5.2322 D(x): 0.9310, D(G(z)): 0.0769 Epoch: [5/20], Batch Num: [224/600] Discriminator Loss: 0.3396, Generator Loss: 5.2561 D(x): 0.8877, D(G(z)): 0.0761 Epoch: [5/20], Batch Num: [225/600] Discriminator Loss: 0.3309, Generator Loss: 4.8473 D(x): 0.8949, D(G(z)): 0.0940 Epoch: [5/20], Batch Num: [226/600] Discriminator Loss: 0.2386, Generator Loss: 4.6364 D(x): 0.9395, D(G(z)): 0.0944 Epoch: [5/20], Batch Num: [227/600] Discriminator Loss: 0.5052, Generator Loss: 4.3677 D(x): 0.8752, D(G(z)): 0.0954 Epoch: [5/20], Batch Num: [228/600] Discriminator Loss: 0.4089, Generator Loss: 3.9508 D(x): 0.9106, D(G(z)): 0.1414 Epoch: [5/20], Batch Num: [229/600] Discriminator Loss: 0.4664, Generator Loss: 3.7923 D(x): 0.8803, D(G(z)): 0.1111 Epoch: [5/20], Batch Num: [230/600] Discriminator Loss: 0.4148, Generator Loss: 4.4160 D(x): 0.9091, D(G(z)): 0.1126 Epoch: [5/20], Batch Num: [231/600] Discriminator Loss: 0.6454, Generator Loss: 4.2484 D(x): 0.8661, D(G(z)): 0.1375 Epoch: [5/20], Batch Num: [232/600] Discriminator Loss: 0.5241, Generator Loss: 3.4312 D(x): 0.8332, D(G(z)): 0.0920 Epoch: [5/20], Batch Num: [233/600] Discriminator Loss: 0.2926, Generator Loss: 3.2515 D(x): 0.9120, D(G(z)): 0.0934 Epoch: [5/20], Batch Num: [234/600] Discriminator Loss: 0.3608, Generator Loss: 2.9422 D(x): 0.9098, D(G(z)): 0.1389 Epoch: [5/20], Batch Num: [235/600] Discriminator Loss: 0.3382, Generator Loss: 2.9157 D(x): 0.9074, D(G(z)): 0.1273 Epoch: [5/20], Batch Num: [236/600] Discriminator Loss: 0.4136, Generator Loss: 2.7176 D(x): 0.9029, D(G(z)): 0.1297 Epoch: [5/20], Batch Num: [237/600] Discriminator Loss: 0.2354, Generator Loss: 3.2477 D(x): 0.9449, D(G(z)): 0.1165 Epoch: [5/20], Batch Num: [238/600] Discriminator Loss: 0.3805, Generator Loss: 3.4797 D(x): 0.8559, D(G(z)): 0.1048 Epoch: [5/20], Batch Num: [239/600] Discriminator Loss: 0.3948, Generator Loss: 3.3112 D(x): 0.8594, D(G(z)): 0.1089 Epoch: [5/20], Batch Num: [240/600] Discriminator Loss: 0.3750, Generator Loss: 2.9087 D(x): 0.8691, D(G(z)): 0.1086 Epoch: [5/20], Batch Num: [241/600] Discriminator Loss: 0.3550, Generator Loss: 2.5593 D(x): 0.9033, D(G(z)): 0.1528 Epoch: [5/20], Batch Num: [242/600] Discriminator Loss: 0.4346, Generator Loss: 2.8800 D(x): 0.9142, D(G(z)): 0.1829 Epoch: [5/20], Batch Num: [243/600] Discriminator Loss: 0.3392, Generator Loss: 3.5350 D(x): 0.9150, D(G(z)): 0.1413 Epoch: [5/20], Batch Num: [244/600] Discriminator Loss: 0.3931, Generator Loss: 3.3438 D(x): 0.8612, D(G(z)): 0.0767 Epoch: [5/20], Batch Num: [245/600] Discriminator Loss: 0.2277, Generator Loss: 3.1189 D(x): 0.8995, D(G(z)): 0.0767 Epoch: [5/20], Batch Num: [246/600] Discriminator Loss: 0.2758, Generator Loss: 3.0408 D(x): 0.9181, D(G(z)): 0.0959 Epoch: [5/20], Batch Num: [247/600] Discriminator Loss: 0.2894, Generator Loss: 2.9522 D(x): 0.9273, D(G(z)): 0.1090 Epoch: [5/20], Batch Num: [248/600] Discriminator Loss: 0.3125, Generator Loss: 3.1865 D(x): 0.9328, D(G(z)): 0.1355 Epoch: [5/20], Batch Num: [249/600] Discriminator Loss: 0.2415, Generator Loss: 3.4930 D(x): 0.9370, D(G(z)): 0.0933 Epoch: [5/20], Batch Num: [250/600] Discriminator Loss: 0.1614, Generator Loss: 3.6942 D(x): 0.9487, D(G(z)): 0.0585 Epoch: [5/20], Batch Num: [251/600] Discriminator Loss: 0.1825, Generator Loss: 3.6986 D(x): 0.9253, D(G(z)): 0.0516 Epoch: [5/20], Batch Num: [252/600] Discriminator Loss: 0.1647, Generator Loss: 3.5901 D(x): 0.9252, D(G(z)): 0.0445 Epoch: [5/20], Batch Num: [253/600] Discriminator Loss: 0.2408, Generator Loss: 3.3026 D(x): 0.9108, D(G(z)): 0.0640 Epoch: [5/20], Batch Num: [254/600] Discriminator Loss: 0.2313, Generator Loss: 3.2589 D(x): 0.9413, D(G(z)): 0.0932 Epoch: [5/20], Batch Num: [255/600] Discriminator Loss: 0.2706, Generator Loss: 3.3300 D(x): 0.9230, D(G(z)): 0.0964 Epoch: [5/20], Batch Num: [256/600] Discriminator Loss: 0.2146, Generator Loss: 3.2471 D(x): 0.9637, D(G(z)): 0.1157 Epoch: [5/20], Batch Num: [257/600] Discriminator Loss: 0.1290, Generator Loss: 4.3433 D(x): 0.9633, D(G(z)): 0.0624 Epoch: [5/20], Batch Num: [258/600] Discriminator Loss: 0.2393, Generator Loss: 4.0116 D(x): 0.9002, D(G(z)): 0.0341 Epoch: [5/20], Batch Num: [259/600] Discriminator Loss: 0.2001, Generator Loss: 3.9732 D(x): 0.9327, D(G(z)): 0.0671 Epoch: [5/20], Batch Num: [260/600] Discriminator Loss: 0.3313, Generator Loss: 3.4329 D(x): 0.9067, D(G(z)): 0.0743 Epoch: [5/20], Batch Num: [261/600] Discriminator Loss: 0.2153, Generator Loss: 3.2540 D(x): 0.9466, D(G(z)): 0.0790 Epoch: [5/20], Batch Num: [262/600] Discriminator Loss: 0.2524, Generator Loss: 3.4726 D(x): 0.9445, D(G(z)): 0.1160 Epoch: [5/20], Batch Num: [263/600] Discriminator Loss: 0.1469, Generator Loss: 3.9907 D(x): 0.9689, D(G(z)): 0.0829 Epoch: [5/20], Batch Num: [264/600] Discriminator Loss: 0.2159, Generator Loss: 4.1712 D(x): 0.9353, D(G(z)): 0.0603 Epoch: [5/20], Batch Num: [265/600] Discriminator Loss: 0.1746, Generator Loss: 4.6354 D(x): 0.9345, D(G(z)): 0.0413 Epoch: [5/20], Batch Num: [266/600] Discriminator Loss: 0.0909, Generator Loss: 4.7462 D(x): 0.9569, D(G(z)): 0.0345 Epoch: [5/20], Batch Num: [267/600] Discriminator Loss: 0.1839, Generator Loss: 4.6493 D(x): 0.9326, D(G(z)): 0.0488 Epoch: [5/20], Batch Num: [268/600] Discriminator Loss: 0.1901, Generator Loss: 4.3234 D(x): 0.9462, D(G(z)): 0.0490 Epoch: [5/20], Batch Num: [269/600] Discriminator Loss: 0.2231, Generator Loss: 3.4640 D(x): 0.9365, D(G(z)): 0.0525 Epoch: [5/20], Batch Num: [270/600] Discriminator Loss: 0.1794, Generator Loss: 3.7285 D(x): 0.9647, D(G(z)): 0.0823 Epoch: [5/20], Batch Num: [271/600] Discriminator Loss: 0.2098, Generator Loss: 4.0084 D(x): 0.9318, D(G(z)): 0.0786 Epoch: [5/20], Batch Num: [272/600] Discriminator Loss: 0.1670, Generator Loss: 4.3554 D(x): 0.9652, D(G(z)): 0.0673 Epoch: [5/20], Batch Num: [273/600] Discriminator Loss: 0.2462, Generator Loss: 4.7727 D(x): 0.9298, D(G(z)): 0.0507 Epoch: [5/20], Batch Num: [274/600] Discriminator Loss: 0.2487, Generator Loss: 4.5796 D(x): 0.9285, D(G(z)): 0.0439 Epoch: [5/20], Batch Num: [275/600] Discriminator Loss: 0.2873, Generator Loss: 3.9842 D(x): 0.9231, D(G(z)): 0.0407 Epoch: [5/20], Batch Num: [276/600] Discriminator Loss: 0.1767, Generator Loss: 4.1307 D(x): 0.9543, D(G(z)): 0.0661 Epoch: [5/20], Batch Num: [277/600] Discriminator Loss: 0.2994, Generator Loss: 4.0062 D(x): 0.9284, D(G(z)): 0.0817 Epoch: [5/20], Batch Num: [278/600] Discriminator Loss: 0.2289, Generator Loss: 4.2536 D(x): 0.9665, D(G(z)): 0.1048 Epoch: [5/20], Batch Num: [279/600] Discriminator Loss: 0.1857, Generator Loss: 4.3322 D(x): 0.9204, D(G(z)): 0.0397 Epoch: [5/20], Batch Num: [280/600] Discriminator Loss: 0.2944, Generator Loss: 3.9852 D(x): 0.9129, D(G(z)): 0.0555 Epoch: [5/20], Batch Num: [281/600] Discriminator Loss: 0.2325, Generator Loss: 4.4308 D(x): 0.9567, D(G(z)): 0.0816 Epoch: [5/20], Batch Num: [282/600] Discriminator Loss: 0.2405, Generator Loss: 4.3139 D(x): 0.9374, D(G(z)): 0.0787 Epoch: [5/20], Batch Num: [283/600] Discriminator Loss: 0.1736, Generator Loss: 4.2267 D(x): 0.9427, D(G(z)): 0.0461 Epoch: [5/20], Batch Num: [284/600] Discriminator Loss: 0.2907, Generator Loss: 3.7504 D(x): 0.8829, D(G(z)): 0.0452 Epoch: [5/20], Batch Num: [285/600] Discriminator Loss: 0.2354, Generator Loss: 3.4756 D(x): 0.9406, D(G(z)): 0.0813 Epoch: [5/20], Batch Num: [286/600] Discriminator Loss: 0.3256, Generator Loss: 3.7663 D(x): 0.9480, D(G(z)): 0.1237 Epoch: [5/20], Batch Num: [287/600] Discriminator Loss: 0.2676, Generator Loss: 3.8906 D(x): 0.9397, D(G(z)): 0.0947 Epoch: [5/20], Batch Num: [288/600] Discriminator Loss: 0.1686, Generator Loss: 3.9435 D(x): 0.9373, D(G(z)): 0.0549 Epoch: [5/20], Batch Num: [289/600] Discriminator Loss: 0.3760, Generator Loss: 4.0270 D(x): 0.8952, D(G(z)): 0.0824 Epoch: [5/20], Batch Num: [290/600] Discriminator Loss: 0.1480, Generator Loss: 4.3107 D(x): 0.9532, D(G(z)): 0.0397 Epoch: [5/20], Batch Num: [291/600] Discriminator Loss: 0.2899, Generator Loss: 3.8811 D(x): 0.9072, D(G(z)): 0.0391 Epoch: [5/20], Batch Num: [292/600] Discriminator Loss: 0.3098, Generator Loss: 3.3939 D(x): 0.9454, D(G(z)): 0.1080 Epoch: [5/20], Batch Num: [293/600] Discriminator Loss: 0.2866, Generator Loss: 4.3705 D(x): 0.9551, D(G(z)): 0.1251 Epoch: [5/20], Batch Num: [294/600] Discriminator Loss: 0.2845, Generator Loss: 3.9698 D(x): 0.9293, D(G(z)): 0.0793 Epoch: [5/20], Batch Num: [295/600] Discriminator Loss: 0.3200, Generator Loss: 4.2451 D(x): 0.9127, D(G(z)): 0.0543 Epoch: [5/20], Batch Num: [296/600] Discriminator Loss: 0.3175, Generator Loss: 4.1103 D(x): 0.9100, D(G(z)): 0.0503 Epoch: [5/20], Batch Num: [297/600] Discriminator Loss: 0.1954, Generator Loss: 3.9927 D(x): 0.9631, D(G(z)): 0.0873 Epoch: [5/20], Batch Num: [298/600] Discriminator Loss: 0.2271, Generator Loss: 3.5807 D(x): 0.9527, D(G(z)): 0.0706 Epoch: [5/20], Batch Num: [299/600] Discriminator Loss: 0.3130, Generator Loss: 3.6272 D(x): 0.8991, D(G(z)): 0.0608 Epoch: 5, Batch Num: [300/600]
Epoch: [5/20], Batch Num: [300/600] Discriminator Loss: 0.1913, Generator Loss: 3.4515 D(x): 0.9491, D(G(z)): 0.0904 Epoch: [5/20], Batch Num: [301/600] Discriminator Loss: 0.2186, Generator Loss: 3.6411 D(x): 0.9539, D(G(z)): 0.0908 Epoch: [5/20], Batch Num: [302/600] Discriminator Loss: 0.2661, Generator Loss: 4.2519 D(x): 0.9462, D(G(z)): 0.1097 Epoch: [5/20], Batch Num: [303/600] Discriminator Loss: 0.1918, Generator Loss: 4.2480 D(x): 0.9376, D(G(z)): 0.0710 Epoch: [5/20], Batch Num: [304/600] Discriminator Loss: 0.2573, Generator Loss: 4.1587 D(x): 0.9166, D(G(z)): 0.0600 Epoch: [5/20], Batch Num: [305/600] Discriminator Loss: 0.3393, Generator Loss: 4.4750 D(x): 0.8970, D(G(z)): 0.0736 Epoch: [5/20], Batch Num: [306/600] Discriminator Loss: 0.2696, Generator Loss: 4.1544 D(x): 0.9226, D(G(z)): 0.0615 Epoch: [5/20], Batch Num: [307/600] Discriminator Loss: 0.3449, Generator Loss: 3.7534 D(x): 0.9009, D(G(z)): 0.0644 Epoch: [5/20], Batch Num: [308/600] Discriminator Loss: 0.2879, Generator Loss: 3.7779 D(x): 0.9408, D(G(z)): 0.1038 Epoch: [5/20], Batch Num: [309/600] Discriminator Loss: 0.2662, Generator Loss: 3.8654 D(x): 0.9493, D(G(z)): 0.0875 Epoch: [5/20], Batch Num: [310/600] Discriminator Loss: 0.2512, Generator Loss: 3.9264 D(x): 0.9228, D(G(z)): 0.0805 Epoch: [5/20], Batch Num: [311/600] Discriminator Loss: 0.1931, Generator Loss: 4.2121 D(x): 0.9545, D(G(z)): 0.0968 Epoch: [5/20], Batch Num: [312/600] Discriminator Loss: 0.2434, Generator Loss: 4.2272 D(x): 0.9090, D(G(z)): 0.0613 Epoch: [5/20], Batch Num: [313/600] Discriminator Loss: 0.2426, Generator Loss: 3.9189 D(x): 0.9099, D(G(z)): 0.0495 Epoch: [5/20], Batch Num: [314/600] Discriminator Loss: 0.2046, Generator Loss: 3.6378 D(x): 0.9397, D(G(z)): 0.0834 Epoch: [5/20], Batch Num: [315/600] Discriminator Loss: 0.2401, Generator Loss: 3.5543 D(x): 0.9248, D(G(z)): 0.0655 Epoch: [5/20], Batch Num: [316/600] Discriminator Loss: 0.3762, Generator Loss: 3.9358 D(x): 0.9429, D(G(z)): 0.1478 Epoch: [5/20], Batch Num: [317/600] Discriminator Loss: 0.5054, Generator Loss: 3.7389 D(x): 0.8359, D(G(z)): 0.0807 Epoch: [5/20], Batch Num: [318/600] Discriminator Loss: 0.2240, Generator Loss: 3.7251 D(x): 0.9361, D(G(z)): 0.0661 Epoch: [5/20], Batch Num: [319/600] Discriminator Loss: 0.2438, Generator Loss: 4.3217 D(x): 0.9375, D(G(z)): 0.0867 Epoch: [5/20], Batch Num: [320/600] Discriminator Loss: 0.1944, Generator Loss: 4.1490 D(x): 0.9357, D(G(z)): 0.0782 Epoch: [5/20], Batch Num: [321/600] Discriminator Loss: 0.2703, Generator Loss: 4.2780 D(x): 0.8904, D(G(z)): 0.0580 Epoch: [5/20], Batch Num: [322/600] Discriminator Loss: 0.2468, Generator Loss: 3.7601 D(x): 0.9281, D(G(z)): 0.0666 Epoch: [5/20], Batch Num: [323/600] Discriminator Loss: 0.4332, Generator Loss: 3.1458 D(x): 0.8649, D(G(z)): 0.0624 Epoch: [5/20], Batch Num: [324/600] Discriminator Loss: 0.4239, Generator Loss: 2.7096 D(x): 0.8820, D(G(z)): 0.1130 Epoch: [5/20], Batch Num: [325/600] Discriminator Loss: 0.4575, Generator Loss: 3.3317 D(x): 0.9705, D(G(z)): 0.2308 Epoch: [5/20], Batch Num: [326/600] Discriminator Loss: 0.4142, Generator Loss: 4.3021 D(x): 0.9466, D(G(z)): 0.1673 Epoch: [5/20], Batch Num: [327/600] Discriminator Loss: 0.3349, Generator Loss: 4.2880 D(x): 0.8889, D(G(z)): 0.0521 Epoch: [5/20], Batch Num: [328/600] Discriminator Loss: 0.2959, Generator Loss: 4.1941 D(x): 0.8840, D(G(z)): 0.0317 Epoch: [5/20], Batch Num: [329/600] Discriminator Loss: 0.3868, Generator Loss: 3.5783 D(x): 0.8525, D(G(z)): 0.0383 Epoch: [5/20], Batch Num: [330/600] Discriminator Loss: 0.2778, Generator Loss: 3.0435 D(x): 0.8870, D(G(z)): 0.0564 Epoch: [5/20], Batch Num: [331/600] Discriminator Loss: 0.4525, Generator Loss: 2.8657 D(x): 0.9013, D(G(z)): 0.1536 Epoch: [5/20], Batch Num: [332/600] Discriminator Loss: 0.4201, Generator Loss: 3.1380 D(x): 0.9249, D(G(z)): 0.1820 Epoch: [5/20], Batch Num: [333/600] Discriminator Loss: 0.3331, Generator Loss: 3.3753 D(x): 0.9381, D(G(z)): 0.1228 Epoch: [5/20], Batch Num: [334/600] Discriminator Loss: 0.2275, Generator Loss: 4.0609 D(x): 0.9365, D(G(z)): 0.0837 Epoch: [5/20], Batch Num: [335/600] Discriminator Loss: 0.2072, Generator Loss: 4.4172 D(x): 0.9289, D(G(z)): 0.0594 Epoch: [5/20], Batch Num: [336/600] Discriminator Loss: 0.2757, Generator Loss: 4.5633 D(x): 0.8838, D(G(z)): 0.0343 Epoch: [5/20], Batch Num: [337/600] Discriminator Loss: 0.2103, Generator Loss: 3.7202 D(x): 0.9205, D(G(z)): 0.0426 Epoch: [5/20], Batch Num: [338/600] Discriminator Loss: 0.2191, Generator Loss: 3.6336 D(x): 0.9346, D(G(z)): 0.0788 Epoch: [5/20], Batch Num: [339/600] Discriminator Loss: 0.2342, Generator Loss: 3.3096 D(x): 0.9437, D(G(z)): 0.1143 Epoch: [5/20], Batch Num: [340/600] Discriminator Loss: 0.2273, Generator Loss: 3.7957 D(x): 0.9573, D(G(z)): 0.1183 Epoch: [5/20], Batch Num: [341/600] Discriminator Loss: 0.1872, Generator Loss: 4.1165 D(x): 0.9549, D(G(z)): 0.0666 Epoch: [5/20], Batch Num: [342/600] Discriminator Loss: 0.4054, Generator Loss: 4.1554 D(x): 0.8622, D(G(z)): 0.0431 Epoch: [5/20], Batch Num: [343/600] Discriminator Loss: 0.2141, Generator Loss: 4.0531 D(x): 0.9274, D(G(z)): 0.0639 Epoch: [5/20], Batch Num: [344/600] Discriminator Loss: 0.2367, Generator Loss: 4.2331 D(x): 0.9466, D(G(z)): 0.1036 Epoch: [5/20], Batch Num: [345/600] Discriminator Loss: 0.1712, Generator Loss: 3.9924 D(x): 0.9484, D(G(z)): 0.0716 Epoch: [5/20], Batch Num: [346/600] Discriminator Loss: 0.1548, Generator Loss: 3.7712 D(x): 0.9532, D(G(z)): 0.0738 Epoch: [5/20], Batch Num: [347/600] Discriminator Loss: 0.4567, Generator Loss: 4.0923 D(x): 0.8560, D(G(z)): 0.0720 Epoch: [5/20], Batch Num: [348/600] Discriminator Loss: 0.2179, Generator Loss: 3.8262 D(x): 0.9345, D(G(z)): 0.0637 Epoch: [5/20], Batch Num: [349/600] Discriminator Loss: 0.1267, Generator Loss: 3.7189 D(x): 0.9594, D(G(z)): 0.0550 Epoch: [5/20], Batch Num: [350/600] Discriminator Loss: 0.2237, Generator Loss: 3.7737 D(x): 0.9456, D(G(z)): 0.0893 Epoch: [5/20], Batch Num: [351/600] Discriminator Loss: 0.2580, Generator Loss: 3.8078 D(x): 0.9399, D(G(z)): 0.0756 Epoch: [5/20], Batch Num: [352/600] Discriminator Loss: 0.1393, Generator Loss: 4.1848 D(x): 0.9758, D(G(z)): 0.0837 Epoch: [5/20], Batch Num: [353/600] Discriminator Loss: 0.3011, Generator Loss: 4.2365 D(x): 0.8959, D(G(z)): 0.0427 Epoch: [5/20], Batch Num: [354/600] Discriminator Loss: 0.3372, Generator Loss: 4.2204 D(x): 0.8946, D(G(z)): 0.0565 Epoch: [5/20], Batch Num: [355/600] Discriminator Loss: 0.2061, Generator Loss: 3.8420 D(x): 0.9366, D(G(z)): 0.0619 Epoch: [5/20], Batch Num: [356/600] Discriminator Loss: 0.2086, Generator Loss: 3.9608 D(x): 0.9536, D(G(z)): 0.1023 Epoch: [5/20], Batch Num: [357/600] Discriminator Loss: 0.1931, Generator Loss: 4.2576 D(x): 0.9301, D(G(z)): 0.0677 Epoch: [5/20], Batch Num: [358/600] Discriminator Loss: 0.2600, Generator Loss: 4.0582 D(x): 0.9076, D(G(z)): 0.0519 Epoch: [5/20], Batch Num: [359/600] Discriminator Loss: 0.4290, Generator Loss: 3.6596 D(x): 0.8740, D(G(z)): 0.0726 Epoch: [5/20], Batch Num: [360/600] Discriminator Loss: 0.2535, Generator Loss: 3.5356 D(x): 0.9249, D(G(z)): 0.0972 Epoch: [5/20], Batch Num: [361/600] Discriminator Loss: 0.3840, Generator Loss: 3.8344 D(x): 0.9166, D(G(z)): 0.1154 Epoch: [5/20], Batch Num: [362/600] Discriminator Loss: 0.2386, Generator Loss: 4.4273 D(x): 0.9500, D(G(z)): 0.1012 Epoch: [5/20], Batch Num: [363/600] Discriminator Loss: 0.2570, Generator Loss: 4.3929 D(x): 0.8999, D(G(z)): 0.0502 Epoch: [5/20], Batch Num: [364/600] Discriminator Loss: 0.2735, Generator Loss: 4.1002 D(x): 0.8962, D(G(z)): 0.0435 Epoch: [5/20], Batch Num: [365/600] Discriminator Loss: 0.2170, Generator Loss: 4.1079 D(x): 0.9520, D(G(z)): 0.0533 Epoch: [5/20], Batch Num: [366/600] Discriminator Loss: 0.4456, Generator Loss: 3.9943 D(x): 0.8870, D(G(z)): 0.1152 Epoch: [5/20], Batch Num: [367/600] Discriminator Loss: 0.2434, Generator Loss: 4.0054 D(x): 0.9116, D(G(z)): 0.0673 Epoch: [5/20], Batch Num: [368/600] Discriminator Loss: 0.2481, Generator Loss: 3.5521 D(x): 0.9202, D(G(z)): 0.0729 Epoch: [5/20], Batch Num: [369/600] Discriminator Loss: 0.2845, Generator Loss: 4.4980 D(x): 0.9444, D(G(z)): 0.1153 Epoch: [5/20], Batch Num: [370/600] Discriminator Loss: 0.2871, Generator Loss: 4.6671 D(x): 0.9186, D(G(z)): 0.0631 Epoch: [5/20], Batch Num: [371/600] Discriminator Loss: 0.3777, Generator Loss: 4.7528 D(x): 0.8684, D(G(z)): 0.0331 Epoch: [5/20], Batch Num: [372/600] Discriminator Loss: 0.2841, Generator Loss: 4.1444 D(x): 0.9004, D(G(z)): 0.0478 Epoch: [5/20], Batch Num: [373/600] Discriminator Loss: 0.2955, Generator Loss: 3.3942 D(x): 0.8974, D(G(z)): 0.0645 Epoch: [5/20], Batch Num: [374/600] Discriminator Loss: 0.3716, Generator Loss: 4.1971 D(x): 0.9524, D(G(z)): 0.1891 Epoch: [5/20], Batch Num: [375/600] Discriminator Loss: 0.2282, Generator Loss: 4.5638 D(x): 0.9163, D(G(z)): 0.0398 Epoch: [5/20], Batch Num: [376/600] Discriminator Loss: 0.2050, Generator Loss: 4.3042 D(x): 0.9147, D(G(z)): 0.0289 Epoch: [5/20], Batch Num: [377/600] Discriminator Loss: 0.3954, Generator Loss: 3.5369 D(x): 0.8661, D(G(z)): 0.0466 Epoch: [5/20], Batch Num: [378/600] Discriminator Loss: 0.2594, Generator Loss: 3.4758 D(x): 0.9663, D(G(z)): 0.1297 Epoch: [5/20], Batch Num: [379/600] Discriminator Loss: 0.2520, Generator Loss: 4.0748 D(x): 0.9380, D(G(z)): 0.0944 Epoch: [5/20], Batch Num: [380/600] Discriminator Loss: 0.2775, Generator Loss: 4.7516 D(x): 0.9285, D(G(z)): 0.0845 Epoch: [5/20], Batch Num: [381/600] Discriminator Loss: 0.3852, Generator Loss: 4.6103 D(x): 0.8880, D(G(z)): 0.0672 Epoch: [5/20], Batch Num: [382/600] Discriminator Loss: 0.3680, Generator Loss: 4.0928 D(x): 0.8886, D(G(z)): 0.0491 Epoch: [5/20], Batch Num: [383/600] Discriminator Loss: 0.2423, Generator Loss: 3.3243 D(x): 0.9173, D(G(z)): 0.0451 Epoch: [5/20], Batch Num: [384/600] Discriminator Loss: 0.3328, Generator Loss: 3.3193 D(x): 0.9452, D(G(z)): 0.1143 Epoch: [5/20], Batch Num: [385/600] Discriminator Loss: 0.2267, Generator Loss: 3.6202 D(x): 0.9481, D(G(z)): 0.1034 Epoch: [5/20], Batch Num: [386/600] Discriminator Loss: 0.3183, Generator Loss: 4.4291 D(x): 0.9208, D(G(z)): 0.0808 Epoch: [5/20], Batch Num: [387/600] Discriminator Loss: 0.2608, Generator Loss: 3.9210 D(x): 0.8933, D(G(z)): 0.0322 Epoch: [5/20], Batch Num: [388/600] Discriminator Loss: 0.1669, Generator Loss: 3.3325 D(x): 0.9471, D(G(z)): 0.0553 Epoch: [5/20], Batch Num: [389/600] Discriminator Loss: 0.3524, Generator Loss: 3.2055 D(x): 0.8990, D(G(z)): 0.1064 Epoch: [5/20], Batch Num: [390/600] Discriminator Loss: 0.3245, Generator Loss: 4.1214 D(x): 0.9554, D(G(z)): 0.1593 Epoch: [5/20], Batch Num: [391/600] Discriminator Loss: 0.2088, Generator Loss: 4.6964 D(x): 0.9303, D(G(z)): 0.0596 Epoch: [5/20], Batch Num: [392/600] Discriminator Loss: 0.3265, Generator Loss: 4.3707 D(x): 0.8721, D(G(z)): 0.0357 Epoch: [5/20], Batch Num: [393/600] Discriminator Loss: 0.3145, Generator Loss: 3.7428 D(x): 0.8929, D(G(z)): 0.0296 Epoch: [5/20], Batch Num: [394/600] Discriminator Loss: 0.3757, Generator Loss: 3.0776 D(x): 0.8981, D(G(z)): 0.0876 Epoch: [5/20], Batch Num: [395/600] Discriminator Loss: 0.4361, Generator Loss: 3.0487 D(x): 0.9156, D(G(z)): 0.1575 Epoch: [5/20], Batch Num: [396/600] Discriminator Loss: 0.2071, Generator Loss: 3.7428 D(x): 0.9635, D(G(z)): 0.1115 Epoch: [5/20], Batch Num: [397/600] Discriminator Loss: 0.1201, Generator Loss: 4.2732 D(x): 0.9702, D(G(z)): 0.0705 Epoch: [5/20], Batch Num: [398/600] Discriminator Loss: 0.1667, Generator Loss: 4.5667 D(x): 0.9286, D(G(z)): 0.0367 Epoch: [5/20], Batch Num: [399/600] Discriminator Loss: 0.3319, Generator Loss: 4.6347 D(x): 0.8983, D(G(z)): 0.0202 Epoch: 5, Batch Num: [400/600]
Epoch: [5/20], Batch Num: [400/600] Discriminator Loss: 0.2517, Generator Loss: 4.2165 D(x): 0.9161, D(G(z)): 0.0348 Epoch: [5/20], Batch Num: [401/600] Discriminator Loss: 0.1940, Generator Loss: 3.6943 D(x): 0.9342, D(G(z)): 0.0391 Epoch: [5/20], Batch Num: [402/600] Discriminator Loss: 0.2257, Generator Loss: 3.3947 D(x): 0.9456, D(G(z)): 0.0900 Epoch: [5/20], Batch Num: [403/600] Discriminator Loss: 0.2359, Generator Loss: 3.4097 D(x): 0.9930, D(G(z)): 0.1291 Epoch: [5/20], Batch Num: [404/600] Discriminator Loss: 0.1777, Generator Loss: 4.3407 D(x): 0.9793, D(G(z)): 0.0939 Epoch: [5/20], Batch Num: [405/600] Discriminator Loss: 0.1774, Generator Loss: 5.0991 D(x): 0.9397, D(G(z)): 0.0402 Epoch: [5/20], Batch Num: [406/600] Discriminator Loss: 0.2044, Generator Loss: 5.2275 D(x): 0.9168, D(G(z)): 0.0160 Epoch: [5/20], Batch Num: [407/600] Discriminator Loss: 0.1708, Generator Loss: 4.8341 D(x): 0.9185, D(G(z)): 0.0211 Epoch: [5/20], Batch Num: [408/600] Discriminator Loss: 0.5296, Generator Loss: 4.1889 D(x): 0.8537, D(G(z)): 0.0541 Epoch: [5/20], Batch Num: [409/600] Discriminator Loss: 0.1681, Generator Loss: 3.1770 D(x): 0.9614, D(G(z)): 0.0738 Epoch: [5/20], Batch Num: [410/600] Discriminator Loss: 0.3470, Generator Loss: 4.4616 D(x): 0.9718, D(G(z)): 0.1648 Epoch: [5/20], Batch Num: [411/600] Discriminator Loss: 0.2069, Generator Loss: 4.9947 D(x): 0.9528, D(G(z)): 0.0696 Epoch: [5/20], Batch Num: [412/600] Discriminator Loss: 0.1184, Generator Loss: 5.2735 D(x): 0.9462, D(G(z)): 0.0293 Epoch: [5/20], Batch Num: [413/600] Discriminator Loss: 0.3493, Generator Loss: 4.7549 D(x): 0.8752, D(G(z)): 0.0150 Epoch: [5/20], Batch Num: [414/600] Discriminator Loss: 0.3528, Generator Loss: 4.5361 D(x): 0.9158, D(G(z)): 0.0550 Epoch: [5/20], Batch Num: [415/600] Discriminator Loss: 0.1588, Generator Loss: 3.8056 D(x): 0.9268, D(G(z)): 0.0424 Epoch: [5/20], Batch Num: [416/600] Discriminator Loss: 0.2233, Generator Loss: 3.4296 D(x): 0.9618, D(G(z)): 0.1069 Epoch: [5/20], Batch Num: [417/600] Discriminator Loss: 0.2572, Generator Loss: 3.5478 D(x): 0.9524, D(G(z)): 0.1130 Epoch: [5/20], Batch Num: [418/600] Discriminator Loss: 0.2374, Generator Loss: 3.8701 D(x): 0.9573, D(G(z)): 0.1106 Epoch: [5/20], Batch Num: [419/600] Discriminator Loss: 0.2945, Generator Loss: 4.4730 D(x): 0.9414, D(G(z)): 0.0982 Epoch: [5/20], Batch Num: [420/600] Discriminator Loss: 0.2305, Generator Loss: 4.8692 D(x): 0.9355, D(G(z)): 0.0354 Epoch: [5/20], Batch Num: [421/600] Discriminator Loss: 0.2906, Generator Loss: 4.9421 D(x): 0.9215, D(G(z)): 0.0623 Epoch: [5/20], Batch Num: [422/600] Discriminator Loss: 0.2197, Generator Loss: 4.3532 D(x): 0.9167, D(G(z)): 0.0332 Epoch: [5/20], Batch Num: [423/600] Discriminator Loss: 0.1652, Generator Loss: 3.7160 D(x): 0.9449, D(G(z)): 0.0500 Epoch: [5/20], Batch Num: [424/600] Discriminator Loss: 0.1650, Generator Loss: 3.7176 D(x): 0.9630, D(G(z)): 0.0795 Epoch: [5/20], Batch Num: [425/600] Discriminator Loss: 0.2883, Generator Loss: 3.6704 D(x): 0.9709, D(G(z)): 0.1403 Epoch: [5/20], Batch Num: [426/600] Discriminator Loss: 0.2508, Generator Loss: 3.8525 D(x): 0.9306, D(G(z)): 0.0888 Epoch: [5/20], Batch Num: [427/600] Discriminator Loss: 0.2430, Generator Loss: 3.9266 D(x): 0.9260, D(G(z)): 0.0621 Epoch: [5/20], Batch Num: [428/600] Discriminator Loss: 0.3583, Generator Loss: 3.8396 D(x): 0.9068, D(G(z)): 0.0826 Epoch: [5/20], Batch Num: [429/600] Discriminator Loss: 0.2724, Generator Loss: 3.6725 D(x): 0.9131, D(G(z)): 0.0627 Epoch: [5/20], Batch Num: [430/600] Discriminator Loss: 0.2648, Generator Loss: 3.5180 D(x): 0.9460, D(G(z)): 0.1197 Epoch: [5/20], Batch Num: [431/600] Discriminator Loss: 0.3612, Generator Loss: 3.5749 D(x): 0.8911, D(G(z)): 0.0985 Epoch: [5/20], Batch Num: [432/600] Discriminator Loss: 0.3650, Generator Loss: 3.5647 D(x): 0.8862, D(G(z)): 0.1144 Epoch: [5/20], Batch Num: [433/600] Discriminator Loss: 0.3080, Generator Loss: 3.8109 D(x): 0.9285, D(G(z)): 0.1176 Epoch: [5/20], Batch Num: [434/600] Discriminator Loss: 0.3428, Generator Loss: 3.4880 D(x): 0.9107, D(G(z)): 0.0967 Epoch: [5/20], Batch Num: [435/600] Discriminator Loss: 0.3707, Generator Loss: 3.5337 D(x): 0.9113, D(G(z)): 0.1180 Epoch: [5/20], Batch Num: [436/600] Discriminator Loss: 0.3193, Generator Loss: 3.3045 D(x): 0.8936, D(G(z)): 0.0856 Epoch: [5/20], Batch Num: [437/600] Discriminator Loss: 0.3551, Generator Loss: 3.1373 D(x): 0.8928, D(G(z)): 0.1115 Epoch: [5/20], Batch Num: [438/600] Discriminator Loss: 0.3601, Generator Loss: 3.3837 D(x): 0.9321, D(G(z)): 0.1532 Epoch: [5/20], Batch Num: [439/600] Discriminator Loss: 0.2803, Generator Loss: 3.7091 D(x): 0.9182, D(G(z)): 0.1083 Epoch: [5/20], Batch Num: [440/600] Discriminator Loss: 0.3304, Generator Loss: 3.5325 D(x): 0.8654, D(G(z)): 0.0658 Epoch: [5/20], Batch Num: [441/600] Discriminator Loss: 0.4890, Generator Loss: 2.9346 D(x): 0.8409, D(G(z)): 0.1015 Epoch: [5/20], Batch Num: [442/600] Discriminator Loss: 0.4254, Generator Loss: 3.0214 D(x): 0.9091, D(G(z)): 0.1833 Epoch: [5/20], Batch Num: [443/600] Discriminator Loss: 0.3796, Generator Loss: 3.1235 D(x): 0.8907, D(G(z)): 0.1344 Epoch: [5/20], Batch Num: [444/600] Discriminator Loss: 0.3083, Generator Loss: 3.7125 D(x): 0.9086, D(G(z)): 0.1091 Epoch: [5/20], Batch Num: [445/600] Discriminator Loss: 0.2350, Generator Loss: 3.6968 D(x): 0.9082, D(G(z)): 0.0695 Epoch: [5/20], Batch Num: [446/600] Discriminator Loss: 0.2557, Generator Loss: 3.5691 D(x): 0.9051, D(G(z)): 0.0671 Epoch: [5/20], Batch Num: [447/600] Discriminator Loss: 0.2653, Generator Loss: 3.4272 D(x): 0.8989, D(G(z)): 0.0847 Epoch: [5/20], Batch Num: [448/600] Discriminator Loss: 0.3676, Generator Loss: 2.9421 D(x): 0.8868, D(G(z)): 0.0962 Epoch: [5/20], Batch Num: [449/600] Discriminator Loss: 0.2910, Generator Loss: 3.2172 D(x): 0.9229, D(G(z)): 0.1148 Epoch: [5/20], Batch Num: [450/600] Discriminator Loss: 0.2049, Generator Loss: 3.6215 D(x): 0.9645, D(G(z)): 0.1111 Epoch: [5/20], Batch Num: [451/600] Discriminator Loss: 0.2567, Generator Loss: 3.9271 D(x): 0.9068, D(G(z)): 0.0557 Epoch: [5/20], Batch Num: [452/600] Discriminator Loss: 0.2215, Generator Loss: 4.0910 D(x): 0.9324, D(G(z)): 0.0906 Epoch: [5/20], Batch Num: [453/600] Discriminator Loss: 0.2339, Generator Loss: 4.0723 D(x): 0.9071, D(G(z)): 0.0508 Epoch: [5/20], Batch Num: [454/600] Discriminator Loss: 0.2266, Generator Loss: 3.6505 D(x): 0.9213, D(G(z)): 0.0650 Epoch: [5/20], Batch Num: [455/600] Discriminator Loss: 0.2655, Generator Loss: 3.3284 D(x): 0.9435, D(G(z)): 0.0891 Epoch: [5/20], Batch Num: [456/600] Discriminator Loss: 0.3416, Generator Loss: 3.6662 D(x): 0.9482, D(G(z)): 0.1471 Epoch: [5/20], Batch Num: [457/600] Discriminator Loss: 0.2048, Generator Loss: 4.4980 D(x): 0.9563, D(G(z)): 0.1004 Epoch: [5/20], Batch Num: [458/600] Discriminator Loss: 0.2188, Generator Loss: 5.0887 D(x): 0.9191, D(G(z)): 0.0419 Epoch: [5/20], Batch Num: [459/600] Discriminator Loss: 0.2712, Generator Loss: 4.5921 D(x): 0.8811, D(G(z)): 0.0335 Epoch: [5/20], Batch Num: [460/600] Discriminator Loss: 0.2324, Generator Loss: 3.9376 D(x): 0.9033, D(G(z)): 0.0540 Epoch: [5/20], Batch Num: [461/600] Discriminator Loss: 0.1703, Generator Loss: 3.2861 D(x): 0.9545, D(G(z)): 0.0841 Epoch: [5/20], Batch Num: [462/600] Discriminator Loss: 0.2575, Generator Loss: 4.2638 D(x): 0.9499, D(G(z)): 0.1352 Epoch: [5/20], Batch Num: [463/600] Discriminator Loss: 0.1454, Generator Loss: 4.5039 D(x): 0.9393, D(G(z)): 0.0465 Epoch: [5/20], Batch Num: [464/600] Discriminator Loss: 0.1137, Generator Loss: 4.7245 D(x): 0.9660, D(G(z)): 0.0374 Epoch: [5/20], Batch Num: [465/600] Discriminator Loss: 0.1616, Generator Loss: 4.5298 D(x): 0.9428, D(G(z)): 0.0457 Epoch: [5/20], Batch Num: [466/600] Discriminator Loss: 0.3039, Generator Loss: 4.3898 D(x): 0.9164, D(G(z)): 0.0406 Epoch: [5/20], Batch Num: [467/600] Discriminator Loss: 0.1269, Generator Loss: 3.9034 D(x): 0.9584, D(G(z)): 0.0509 Epoch: [5/20], Batch Num: [468/600] Discriminator Loss: 0.2068, Generator Loss: 4.3358 D(x): 0.9515, D(G(z)): 0.0903 Epoch: [5/20], Batch Num: [469/600] Discriminator Loss: 0.3213, Generator Loss: 4.7761 D(x): 0.9420, D(G(z)): 0.0915 Epoch: [5/20], Batch Num: [470/600] Discriminator Loss: 0.3144, Generator Loss: 4.2803 D(x): 0.8738, D(G(z)): 0.0561 Epoch: [5/20], Batch Num: [471/600] Discriminator Loss: 0.2445, Generator Loss: 4.1977 D(x): 0.9330, D(G(z)): 0.0706 Epoch: [5/20], Batch Num: [472/600] Discriminator Loss: 0.2579, Generator Loss: 4.3564 D(x): 0.9443, D(G(z)): 0.0909 Epoch: [5/20], Batch Num: [473/600] Discriminator Loss: 0.3097, Generator Loss: 4.6521 D(x): 0.9314, D(G(z)): 0.1088 Epoch: [5/20], Batch Num: [474/600] Discriminator Loss: 0.2670, Generator Loss: 4.5656 D(x): 0.9335, D(G(z)): 0.0814 Epoch: [5/20], Batch Num: [475/600] Discriminator Loss: 0.2406, Generator Loss: 4.5115 D(x): 0.9096, D(G(z)): 0.0529 Epoch: [5/20], Batch Num: [476/600] Discriminator Loss: 0.2728, Generator Loss: 3.8535 D(x): 0.9246, D(G(z)): 0.0492 Epoch: [5/20], Batch Num: [477/600] Discriminator Loss: 0.2212, Generator Loss: 3.9635 D(x): 0.9638, D(G(z)): 0.0929 Epoch: [5/20], Batch Num: [478/600] Discriminator Loss: 0.2509, Generator Loss: 4.8972 D(x): 0.9480, D(G(z)): 0.0960 Epoch: [5/20], Batch Num: [479/600] Discriminator Loss: 0.2651, Generator Loss: 4.7752 D(x): 0.9001, D(G(z)): 0.0334 Epoch: [5/20], Batch Num: [480/600] Discriminator Loss: 0.2821, Generator Loss: 4.2777 D(x): 0.9008, D(G(z)): 0.0318 Epoch: [5/20], Batch Num: [481/600] Discriminator Loss: 0.1764, Generator Loss: 3.7452 D(x): 0.9446, D(G(z)): 0.0559 Epoch: [5/20], Batch Num: [482/600] Discriminator Loss: 0.2822, Generator Loss: 3.7609 D(x): 0.9266, D(G(z)): 0.0884 Epoch: [5/20], Batch Num: [483/600] Discriminator Loss: 0.3205, Generator Loss: 4.0478 D(x): 0.9498, D(G(z)): 0.1287 Epoch: [5/20], Batch Num: [484/600] Discriminator Loss: 0.2481, Generator Loss: 4.6859 D(x): 0.9344, D(G(z)): 0.0852 Epoch: [5/20], Batch Num: [485/600] Discriminator Loss: 0.2669, Generator Loss: 4.5531 D(x): 0.9073, D(G(z)): 0.0407 Epoch: [5/20], Batch Num: [486/600] Discriminator Loss: 0.4069, Generator Loss: 4.1291 D(x): 0.8812, D(G(z)): 0.0514 Epoch: [5/20], Batch Num: [487/600] Discriminator Loss: 0.2385, Generator Loss: 3.9124 D(x): 0.9259, D(G(z)): 0.0581 Epoch: [5/20], Batch Num: [488/600] Discriminator Loss: 0.2789, Generator Loss: 4.0428 D(x): 0.9582, D(G(z)): 0.1095 Epoch: [5/20], Batch Num: [489/600] Discriminator Loss: 0.2563, Generator Loss: 4.8589 D(x): 0.9667, D(G(z)): 0.1080 Epoch: [5/20], Batch Num: [490/600] Discriminator Loss: 0.3261, Generator Loss: 5.5886 D(x): 0.9322, D(G(z)): 0.0840 Epoch: [5/20], Batch Num: [491/600] Discriminator Loss: 0.2816, Generator Loss: 5.7702 D(x): 0.9152, D(G(z)): 0.0329 Epoch: [5/20], Batch Num: [492/600] Discriminator Loss: 0.3356, Generator Loss: 5.1083 D(x): 0.8954, D(G(z)): 0.0155 Epoch: [5/20], Batch Num: [493/600] Discriminator Loss: 0.1480, Generator Loss: 4.5330 D(x): 0.9344, D(G(z)): 0.0254 Epoch: [5/20], Batch Num: [494/600] Discriminator Loss: 0.2988, Generator Loss: 4.2539 D(x): 0.9603, D(G(z)): 0.1072 Epoch: [5/20], Batch Num: [495/600] Discriminator Loss: 0.1235, Generator Loss: 4.4880 D(x): 0.9802, D(G(z)): 0.0688 Epoch: [5/20], Batch Num: [496/600] Discriminator Loss: 0.1166, Generator Loss: 4.6531 D(x): 0.9736, D(G(z)): 0.0618 Epoch: [5/20], Batch Num: [497/600] Discriminator Loss: 0.1888, Generator Loss: 5.2890 D(x): 0.9491, D(G(z)): 0.0584 Epoch: [5/20], Batch Num: [498/600] Discriminator Loss: 0.1753, Generator Loss: 5.5927 D(x): 0.9439, D(G(z)): 0.0301 Epoch: [5/20], Batch Num: [499/600] Discriminator Loss: 0.3690, Generator Loss: 5.0932 D(x): 0.8817, D(G(z)): 0.0400 Epoch: 5, Batch Num: [500/600]
Epoch: [5/20], Batch Num: [500/600] Discriminator Loss: 0.2546, Generator Loss: 5.0093 D(x): 0.9391, D(G(z)): 0.0439 Epoch: [5/20], Batch Num: [501/600] Discriminator Loss: 0.1924, Generator Loss: 3.7575 D(x): 0.9278, D(G(z)): 0.0410 Epoch: [5/20], Batch Num: [502/600] Discriminator Loss: 0.1869, Generator Loss: 4.5502 D(x): 0.9971, D(G(z)): 0.1207 Epoch: [5/20], Batch Num: [503/600] Discriminator Loss: 0.1413, Generator Loss: 5.2511 D(x): 0.9911, D(G(z)): 0.0772 Epoch: [5/20], Batch Num: [504/600] Discriminator Loss: 0.1681, Generator Loss: 5.9934 D(x): 0.9752, D(G(z)): 0.0735 Epoch: [5/20], Batch Num: [505/600] Discriminator Loss: 0.1305, Generator Loss: 6.7653 D(x): 0.9518, D(G(z)): 0.0176 Epoch: [5/20], Batch Num: [506/600] Discriminator Loss: 0.1679, Generator Loss: 6.4464 D(x): 0.9266, D(G(z)): 0.0123 Epoch: [5/20], Batch Num: [507/600] Discriminator Loss: 0.2133, Generator Loss: 6.0276 D(x): 0.9210, D(G(z)): 0.0194 Epoch: [5/20], Batch Num: [508/600] Discriminator Loss: 0.3138, Generator Loss: 5.1590 D(x): 0.9250, D(G(z)): 0.0525 Epoch: [5/20], Batch Num: [509/600] Discriminator Loss: 0.2078, Generator Loss: 4.0547 D(x): 0.9435, D(G(z)): 0.0624 Epoch: [5/20], Batch Num: [510/600] Discriminator Loss: 0.4631, Generator Loss: 3.8308 D(x): 0.9185, D(G(z)): 0.1763 Epoch: [5/20], Batch Num: [511/600] Discriminator Loss: 0.2137, Generator Loss: 4.2184 D(x): 0.9590, D(G(z)): 0.0943 Epoch: [5/20], Batch Num: [512/600] Discriminator Loss: 0.3263, Generator Loss: 4.5611 D(x): 0.9188, D(G(z)): 0.0871 Epoch: [5/20], Batch Num: [513/600] Discriminator Loss: 0.3225, Generator Loss: 4.0674 D(x): 0.8807, D(G(z)): 0.0611 Epoch: [5/20], Batch Num: [514/600] Discriminator Loss: 0.2752, Generator Loss: 4.2805 D(x): 0.9413, D(G(z)): 0.1045 Epoch: [5/20], Batch Num: [515/600] Discriminator Loss: 0.3671, Generator Loss: 4.5210 D(x): 0.9186, D(G(z)): 0.1215 Epoch: [5/20], Batch Num: [516/600] Discriminator Loss: 0.3530, Generator Loss: 4.6558 D(x): 0.9062, D(G(z)): 0.0754 Epoch: [5/20], Batch Num: [517/600] Discriminator Loss: 0.3257, Generator Loss: 4.1598 D(x): 0.8912, D(G(z)): 0.0503 Epoch: [5/20], Batch Num: [518/600] Discriminator Loss: 0.6417, Generator Loss: 4.1383 D(x): 0.8605, D(G(z)): 0.1015 Epoch: [5/20], Batch Num: [519/600] Discriminator Loss: 0.5545, Generator Loss: 4.5504 D(x): 0.9120, D(G(z)): 0.1522 Epoch: [5/20], Batch Num: [520/600] Discriminator Loss: 0.5519, Generator Loss: 4.4132 D(x): 0.8562, D(G(z)): 0.1095 Epoch: [5/20], Batch Num: [521/600] Discriminator Loss: 0.5217, Generator Loss: 4.1176 D(x): 0.8732, D(G(z)): 0.0885 Epoch: [5/20], Batch Num: [522/600] Discriminator Loss: 0.3495, Generator Loss: 3.7466 D(x): 0.8895, D(G(z)): 0.0636 Epoch: [5/20], Batch Num: [523/600] Discriminator Loss: 0.5140, Generator Loss: 3.2185 D(x): 0.8496, D(G(z)): 0.0826 Epoch: [5/20], Batch Num: [524/600] Discriminator Loss: 0.5378, Generator Loss: 2.4662 D(x): 0.8764, D(G(z)): 0.1551 Epoch: [5/20], Batch Num: [525/600] Discriminator Loss: 0.5459, Generator Loss: 2.9499 D(x): 0.8982, D(G(z)): 0.1976 Epoch: [5/20], Batch Num: [526/600] Discriminator Loss: 0.5107, Generator Loss: 3.4398 D(x): 0.9135, D(G(z)): 0.1768 Epoch: [5/20], Batch Num: [527/600] Discriminator Loss: 0.4488, Generator Loss: 3.9837 D(x): 0.8598, D(G(z)): 0.0978 Epoch: [5/20], Batch Num: [528/600] Discriminator Loss: 0.4607, Generator Loss: 3.9235 D(x): 0.8461, D(G(z)): 0.0596 Epoch: [5/20], Batch Num: [529/600] Discriminator Loss: 0.3226, Generator Loss: 3.4424 D(x): 0.8885, D(G(z)): 0.0570 Epoch: [5/20], Batch Num: [530/600] Discriminator Loss: 0.3688, Generator Loss: 3.2224 D(x): 0.8623, D(G(z)): 0.0744 Epoch: [5/20], Batch Num: [531/600] Discriminator Loss: 0.3161, Generator Loss: 2.6665 D(x): 0.9464, D(G(z)): 0.1408 Epoch: [5/20], Batch Num: [532/600] Discriminator Loss: 0.2447, Generator Loss: 2.8402 D(x): 0.9559, D(G(z)): 0.1403 Epoch: [5/20], Batch Num: [533/600] Discriminator Loss: 0.3434, Generator Loss: 3.3567 D(x): 0.9403, D(G(z)): 0.1457 Epoch: [5/20], Batch Num: [534/600] Discriminator Loss: 0.3220, Generator Loss: 3.7606 D(x): 0.8985, D(G(z)): 0.0978 Epoch: [5/20], Batch Num: [535/600] Discriminator Loss: 0.3341, Generator Loss: 3.5372 D(x): 0.8520, D(G(z)): 0.0522 Epoch: [5/20], Batch Num: [536/600] Discriminator Loss: 0.3473, Generator Loss: 3.5265 D(x): 0.8553, D(G(z)): 0.0705 Epoch: [5/20], Batch Num: [537/600] Discriminator Loss: 0.3077, Generator Loss: 2.7582 D(x): 0.9034, D(G(z)): 0.0929 Epoch: [5/20], Batch Num: [538/600] Discriminator Loss: 0.3088, Generator Loss: 2.8805 D(x): 0.9246, D(G(z)): 0.1384 Epoch: [5/20], Batch Num: [539/600] Discriminator Loss: 0.2707, Generator Loss: 2.8370 D(x): 0.9495, D(G(z)): 0.1510 Epoch: [5/20], Batch Num: [540/600] Discriminator Loss: 0.3069, Generator Loss: 3.3671 D(x): 0.9279, D(G(z)): 0.1222 Epoch: [5/20], Batch Num: [541/600] Discriminator Loss: 0.2957, Generator Loss: 3.3630 D(x): 0.8959, D(G(z)): 0.0856 Epoch: [5/20], Batch Num: [542/600] Discriminator Loss: 0.2411, Generator Loss: 3.6212 D(x): 0.9253, D(G(z)): 0.0836 Epoch: [5/20], Batch Num: [543/600] Discriminator Loss: 0.4519, Generator Loss: 3.5961 D(x): 0.8887, D(G(z)): 0.1280 Epoch: [5/20], Batch Num: [544/600] Discriminator Loss: 0.4068, Generator Loss: 3.2356 D(x): 0.8613, D(G(z)): 0.0849 Epoch: [5/20], Batch Num: [545/600] Discriminator Loss: 0.1797, Generator Loss: 3.2324 D(x): 0.9370, D(G(z)): 0.0735 Epoch: [5/20], Batch Num: [546/600] Discriminator Loss: 0.2182, Generator Loss: 3.0705 D(x): 0.9255, D(G(z)): 0.0886 Epoch: [5/20], Batch Num: [547/600] Discriminator Loss: 0.2005, Generator Loss: 3.0223 D(x): 0.9573, D(G(z)): 0.0959 Epoch: [5/20], Batch Num: [548/600] Discriminator Loss: 0.3124, Generator Loss: 3.0398 D(x): 0.9235, D(G(z)): 0.1315 Epoch: [5/20], Batch Num: [549/600] Discriminator Loss: 0.3376, Generator Loss: 3.7700 D(x): 0.9195, D(G(z)): 0.1460 Epoch: [5/20], Batch Num: [550/600] Discriminator Loss: 0.1873, Generator Loss: 4.1676 D(x): 0.9573, D(G(z)): 0.0854 Epoch: [5/20], Batch Num: [551/600] Discriminator Loss: 0.4164, Generator Loss: 4.2275 D(x): 0.8338, D(G(z)): 0.0486 Epoch: [5/20], Batch Num: [552/600] Discriminator Loss: 0.4105, Generator Loss: 3.8516 D(x): 0.8414, D(G(z)): 0.0382 Epoch: [5/20], Batch Num: [553/600] Discriminator Loss: 0.2799, Generator Loss: 3.0683 D(x): 0.8875, D(G(z)): 0.0518 Epoch: [5/20], Batch Num: [554/600] Discriminator Loss: 0.3255, Generator Loss: 2.6289 D(x): 0.9491, D(G(z)): 0.1515 Epoch: [5/20], Batch Num: [555/600] Discriminator Loss: 0.4262, Generator Loss: 3.1307 D(x): 0.9375, D(G(z)): 0.1948 Epoch: [5/20], Batch Num: [556/600] Discriminator Loss: 0.3015, Generator Loss: 3.9365 D(x): 0.9195, D(G(z)): 0.1071 Epoch: [5/20], Batch Num: [557/600] Discriminator Loss: 0.2425, Generator Loss: 3.7953 D(x): 0.8852, D(G(z)): 0.0508 Epoch: [5/20], Batch Num: [558/600] Discriminator Loss: 0.3398, Generator Loss: 3.7704 D(x): 0.8690, D(G(z)): 0.0662 Epoch: [5/20], Batch Num: [559/600] Discriminator Loss: 0.2712, Generator Loss: 3.2862 D(x): 0.8849, D(G(z)): 0.0717 Epoch: [5/20], Batch Num: [560/600] Discriminator Loss: 0.2347, Generator Loss: 2.8166 D(x): 0.9174, D(G(z)): 0.0661 Epoch: [5/20], Batch Num: [561/600] Discriminator Loss: 0.2988, Generator Loss: 3.2472 D(x): 0.9530, D(G(z)): 0.1559 Epoch: [5/20], Batch Num: [562/600] Discriminator Loss: 0.2477, Generator Loss: 3.4620 D(x): 0.9426, D(G(z)): 0.0975 Epoch: [5/20], Batch Num: [563/600] Discriminator Loss: 0.3083, Generator Loss: 3.9568 D(x): 0.8993, D(G(z)): 0.1002 Epoch: [5/20], Batch Num: [564/600] Discriminator Loss: 0.2517, Generator Loss: 4.1912 D(x): 0.8998, D(G(z)): 0.0498 Epoch: [5/20], Batch Num: [565/600] Discriminator Loss: 0.2776, Generator Loss: 3.8731 D(x): 0.8962, D(G(z)): 0.0688 Epoch: [5/20], Batch Num: [566/600] Discriminator Loss: 0.2183, Generator Loss: 3.4573 D(x): 0.9176, D(G(z)): 0.0598 Epoch: [5/20], Batch Num: [567/600] Discriminator Loss: 0.3374, Generator Loss: 3.5743 D(x): 0.9415, D(G(z)): 0.1571 Epoch: [5/20], Batch Num: [568/600] Discriminator Loss: 0.2600, Generator Loss: 4.0953 D(x): 0.9039, D(G(z)): 0.0886 Epoch: [5/20], Batch Num: [569/600] Discriminator Loss: 0.4100, Generator Loss: 3.6270 D(x): 0.8507, D(G(z)): 0.0668 Epoch: [5/20], Batch Num: [570/600] Discriminator Loss: 0.2053, Generator Loss: 3.4122 D(x): 0.9444, D(G(z)): 0.0554 Epoch: [5/20], Batch Num: [571/600] Discriminator Loss: 0.1916, Generator Loss: 3.4588 D(x): 0.9464, D(G(z)): 0.0733 Epoch: [5/20], Batch Num: [572/600] Discriminator Loss: 0.3364, Generator Loss: 3.5555 D(x): 0.9266, D(G(z)): 0.1226 Epoch: [5/20], Batch Num: [573/600] Discriminator Loss: 0.3339, Generator Loss: 4.0235 D(x): 0.9090, D(G(z)): 0.0937 Epoch: [5/20], Batch Num: [574/600] Discriminator Loss: 0.2629, Generator Loss: 3.7440 D(x): 0.9073, D(G(z)): 0.0672 Epoch: [5/20], Batch Num: [575/600] Discriminator Loss: 0.2747, Generator Loss: 3.9205 D(x): 0.8977, D(G(z)): 0.0840 Epoch: [5/20], Batch Num: [576/600] Discriminator Loss: 0.4156, Generator Loss: 3.1045 D(x): 0.8863, D(G(z)): 0.1099 Epoch: [5/20], Batch Num: [577/600] Discriminator Loss: 0.3577, Generator Loss: 3.6454 D(x): 0.9136, D(G(z)): 0.1308 Epoch: [5/20], Batch Num: [578/600] Discriminator Loss: 0.2455, Generator Loss: 3.6534 D(x): 0.9340, D(G(z)): 0.1067 Epoch: [5/20], Batch Num: [579/600] Discriminator Loss: 0.2217, Generator Loss: 3.9558 D(x): 0.9259, D(G(z)): 0.0784 Epoch: [5/20], Batch Num: [580/600] Discriminator Loss: 0.3157, Generator Loss: 3.7057 D(x): 0.8908, D(G(z)): 0.0692 Epoch: [5/20], Batch Num: [581/600] Discriminator Loss: 0.3338, Generator Loss: 3.7020 D(x): 0.9042, D(G(z)): 0.0919 Epoch: [5/20], Batch Num: [582/600] Discriminator Loss: 0.3366, Generator Loss: 3.4195 D(x): 0.9143, D(G(z)): 0.1180 Epoch: [5/20], Batch Num: [583/600] Discriminator Loss: 0.2070, Generator Loss: 3.7611 D(x): 0.9401, D(G(z)): 0.0885 Epoch: [5/20], Batch Num: [584/600] Discriminator Loss: 0.2973, Generator Loss: 3.3777 D(x): 0.9132, D(G(z)): 0.0726 Epoch: [5/20], Batch Num: [585/600] Discriminator Loss: 0.2384, Generator Loss: 3.5570 D(x): 0.9313, D(G(z)): 0.0785 Epoch: [5/20], Batch Num: [586/600] Discriminator Loss: 0.4098, Generator Loss: 3.4972 D(x): 0.8904, D(G(z)): 0.1084 Epoch: [5/20], Batch Num: [587/600] Discriminator Loss: 0.3961, Generator Loss: 3.5378 D(x): 0.9101, D(G(z)): 0.1333 Epoch: [5/20], Batch Num: [588/600] Discriminator Loss: 0.2847, Generator Loss: 4.1318 D(x): 0.9256, D(G(z)): 0.1015 Epoch: [5/20], Batch Num: [589/600] Discriminator Loss: 0.2944, Generator Loss: 3.7244 D(x): 0.8955, D(G(z)): 0.0622 Epoch: [5/20], Batch Num: [590/600] Discriminator Loss: 0.1852, Generator Loss: 3.5971 D(x): 0.9478, D(G(z)): 0.0669 Epoch: [5/20], Batch Num: [591/600] Discriminator Loss: 0.2548, Generator Loss: 3.2989 D(x): 0.8986, D(G(z)): 0.0736 Epoch: [5/20], Batch Num: [592/600] Discriminator Loss: 0.1907, Generator Loss: 3.8844 D(x): 0.9742, D(G(z)): 0.1219 Epoch: [5/20], Batch Num: [593/600] Discriminator Loss: 0.1728, Generator Loss: 4.2704 D(x): 0.9552, D(G(z)): 0.0681 Epoch: [5/20], Batch Num: [594/600] Discriminator Loss: 0.2118, Generator Loss: 4.2851 D(x): 0.9365, D(G(z)): 0.0478 Epoch: [5/20], Batch Num: [595/600] Discriminator Loss: 0.1446, Generator Loss: 4.3092 D(x): 0.9461, D(G(z)): 0.0343 Epoch: [5/20], Batch Num: [596/600] Discriminator Loss: 0.3022, Generator Loss: 4.6517 D(x): 0.9454, D(G(z)): 0.0981 Epoch: [5/20], Batch Num: [597/600] Discriminator Loss: 0.1504, Generator Loss: 4.6858 D(x): 0.9257, D(G(z)): 0.0331 Epoch: [5/20], Batch Num: [598/600] Discriminator Loss: 0.1375, Generator Loss: 4.2670 D(x): 0.9524, D(G(z)): 0.0277 Epoch: [5/20], Batch Num: [599/600] Discriminator Loss: 0.1745, Generator Loss: 3.7661 D(x): 0.9363, D(G(z)): 0.0446 Epoch: 6, Batch Num: [0/600]
Epoch: [6/20], Batch Num: [0/600] Discriminator Loss: 0.2797, Generator Loss: 3.6841 D(x): 0.9459, D(G(z)): 0.0988 Epoch: [6/20], Batch Num: [1/600] Discriminator Loss: 0.2495, Generator Loss: 4.1320 D(x): 0.9669, D(G(z)): 0.1014 Epoch: [6/20], Batch Num: [2/600] Discriminator Loss: 0.2155, Generator Loss: 5.2308 D(x): 0.9532, D(G(z)): 0.0730 Epoch: [6/20], Batch Num: [3/600] Discriminator Loss: 0.2818, Generator Loss: 5.8174 D(x): 0.9176, D(G(z)): 0.0455 Epoch: [6/20], Batch Num: [4/600] Discriminator Loss: 0.4154, Generator Loss: 5.0660 D(x): 0.8535, D(G(z)): 0.0405 Epoch: [6/20], Batch Num: [5/600] Discriminator Loss: 0.0904, Generator Loss: 4.5539 D(x): 0.9660, D(G(z)): 0.0381 Epoch: [6/20], Batch Num: [6/600] Discriminator Loss: 0.1906, Generator Loss: 4.4636 D(x): 0.9623, D(G(z)): 0.0807 Epoch: [6/20], Batch Num: [7/600] Discriminator Loss: 0.2037, Generator Loss: 4.4621 D(x): 0.9359, D(G(z)): 0.0516 Epoch: [6/20], Batch Num: [8/600] Discriminator Loss: 0.2019, Generator Loss: 4.6034 D(x): 0.9646, D(G(z)): 0.0845 Epoch: [6/20], Batch Num: [9/600] Discriminator Loss: 0.1271, Generator Loss: 4.8254 D(x): 0.9684, D(G(z)): 0.0519 Epoch: [6/20], Batch Num: [10/600] Discriminator Loss: 0.1654, Generator Loss: 5.1797 D(x): 0.9544, D(G(z)): 0.0513 Epoch: [6/20], Batch Num: [11/600] Discriminator Loss: 0.1974, Generator Loss: 5.4552 D(x): 0.9362, D(G(z)): 0.0414 Epoch: [6/20], Batch Num: [12/600] Discriminator Loss: 0.1720, Generator Loss: 4.3438 D(x): 0.9336, D(G(z)): 0.0319 Epoch: [6/20], Batch Num: [13/600] Discriminator Loss: 0.2591, Generator Loss: 4.5532 D(x): 0.9490, D(G(z)): 0.0896 Epoch: [6/20], Batch Num: [14/600] Discriminator Loss: 0.2757, Generator Loss: 4.9932 D(x): 0.9409, D(G(z)): 0.0971 Epoch: [6/20], Batch Num: [15/600] Discriminator Loss: 0.2759, Generator Loss: 4.5145 D(x): 0.9274, D(G(z)): 0.0685 Epoch: [6/20], Batch Num: [16/600] Discriminator Loss: 0.2527, Generator Loss: 4.2470 D(x): 0.9103, D(G(z)): 0.0450 Epoch: [6/20], Batch Num: [17/600] Discriminator Loss: 0.2008, Generator Loss: 3.5557 D(x): 0.9451, D(G(z)): 0.0610 Epoch: [6/20], Batch Num: [18/600] Discriminator Loss: 0.4311, Generator Loss: 3.7312 D(x): 0.9273, D(G(z)): 0.1539 Epoch: [6/20], Batch Num: [19/600] Discriminator Loss: 0.2253, Generator Loss: 4.5875 D(x): 0.9319, D(G(z)): 0.0770 Epoch: [6/20], Batch Num: [20/600] Discriminator Loss: 0.2908, Generator Loss: 4.2340 D(x): 0.8976, D(G(z)): 0.0531 Epoch: [6/20], Batch Num: [21/600] Discriminator Loss: 0.3437, Generator Loss: 4.0690 D(x): 0.8982, D(G(z)): 0.0705 Epoch: [6/20], Batch Num: [22/600] Discriminator Loss: 0.4352, Generator Loss: 2.9697 D(x): 0.8654, D(G(z)): 0.1000 Epoch: [6/20], Batch Num: [23/600] Discriminator Loss: 0.5243, Generator Loss: 4.0246 D(x): 0.9337, D(G(z)): 0.2050 Epoch: [6/20], Batch Num: [24/600] Discriminator Loss: 0.3214, Generator Loss: 4.3483 D(x): 0.9005, D(G(z)): 0.0846 Epoch: [6/20], Batch Num: [25/600] Discriminator Loss: 0.3051, Generator Loss: 3.7985 D(x): 0.8980, D(G(z)): 0.0486 Epoch: [6/20], Batch Num: [26/600] Discriminator Loss: 0.3422, Generator Loss: 3.0958 D(x): 0.8772, D(G(z)): 0.0608 Epoch: [6/20], Batch Num: [27/600] Discriminator Loss: 0.5128, Generator Loss: 2.8951 D(x): 0.8954, D(G(z)): 0.1578 Epoch: [6/20], Batch Num: [28/600] Discriminator Loss: 0.3213, Generator Loss: 3.5124 D(x): 0.9444, D(G(z)): 0.1626 Epoch: [6/20], Batch Num: [29/600] Discriminator Loss: 0.3017, Generator Loss: 4.2363 D(x): 0.9101, D(G(z)): 0.0943 Epoch: [6/20], Batch Num: [30/600] Discriminator Loss: 0.4309, Generator Loss: 3.7427 D(x): 0.8475, D(G(z)): 0.0389 Epoch: [6/20], Batch Num: [31/600] Discriminator Loss: 0.3898, Generator Loss: 2.8168 D(x): 0.8687, D(G(z)): 0.0639 Epoch: [6/20], Batch Num: [32/600] Discriminator Loss: 0.2823, Generator Loss: 2.7535 D(x): 0.9233, D(G(z)): 0.1186 Epoch: [6/20], Batch Num: [33/600] Discriminator Loss: 0.3623, Generator Loss: 3.4245 D(x): 0.9425, D(G(z)): 0.1826 Epoch: [6/20], Batch Num: [34/600] Discriminator Loss: 0.2283, Generator Loss: 3.9612 D(x): 0.9364, D(G(z)): 0.0881 Epoch: [6/20], Batch Num: [35/600] Discriminator Loss: 0.1744, Generator Loss: 4.0911 D(x): 0.9315, D(G(z)): 0.0354 Epoch: [6/20], Batch Num: [36/600] Discriminator Loss: 0.2662, Generator Loss: 3.9727 D(x): 0.9091, D(G(z)): 0.0360 Epoch: [6/20], Batch Num: [37/600] Discriminator Loss: 0.2441, Generator Loss: 3.6087 D(x): 0.8962, D(G(z)): 0.0423 Epoch: [6/20], Batch Num: [38/600] Discriminator Loss: 0.2419, Generator Loss: 3.0926 D(x): 0.9225, D(G(z)): 0.0604 Epoch: [6/20], Batch Num: [39/600] Discriminator Loss: 0.2622, Generator Loss: 2.8966 D(x): 0.9425, D(G(z)): 0.1277 Epoch: [6/20], Batch Num: [40/600] Discriminator Loss: 0.2852, Generator Loss: 3.5070 D(x): 0.9481, D(G(z)): 0.1305 Epoch: [6/20], Batch Num: [41/600] Discriminator Loss: 0.1459, Generator Loss: 4.2171 D(x): 0.9564, D(G(z)): 0.0682 Epoch: [6/20], Batch Num: [42/600] Discriminator Loss: 0.2632, Generator Loss: 4.2484 D(x): 0.9011, D(G(z)): 0.0515 Epoch: [6/20], Batch Num: [43/600] Discriminator Loss: 0.1779, Generator Loss: 4.0881 D(x): 0.9395, D(G(z)): 0.0347 Epoch: [6/20], Batch Num: [44/600] Discriminator Loss: 0.1983, Generator Loss: 3.5501 D(x): 0.9211, D(G(z)): 0.0435 Epoch: [6/20], Batch Num: [45/600] Discriminator Loss: 0.2279, Generator Loss: 3.5669 D(x): 0.9319, D(G(z)): 0.0881 Epoch: [6/20], Batch Num: [46/600] Discriminator Loss: 0.1481, Generator Loss: 3.6107 D(x): 0.9648, D(G(z)): 0.0851 Epoch: [6/20], Batch Num: [47/600] Discriminator Loss: 0.2513, Generator Loss: 4.2724 D(x): 0.9209, D(G(z)): 0.0721 Epoch: [6/20], Batch Num: [48/600] Discriminator Loss: 0.1459, Generator Loss: 3.9344 D(x): 0.9479, D(G(z)): 0.0567 Epoch: [6/20], Batch Num: [49/600] Discriminator Loss: 0.1434, Generator Loss: 3.9996 D(x): 0.9411, D(G(z)): 0.0492 Epoch: [6/20], Batch Num: [50/600] Discriminator Loss: 0.2820, Generator Loss: 3.6856 D(x): 0.9025, D(G(z)): 0.0673 Epoch: [6/20], Batch Num: [51/600] Discriminator Loss: 0.1332, Generator Loss: 3.7144 D(x): 0.9727, D(G(z)): 0.0775 Epoch: [6/20], Batch Num: [52/600] Discriminator Loss: 0.1485, Generator Loss: 3.9557 D(x): 0.9555, D(G(z)): 0.0515 Epoch: [6/20], Batch Num: [53/600] Discriminator Loss: 0.1929, Generator Loss: 3.8192 D(x): 0.9519, D(G(z)): 0.0601 Epoch: [6/20], Batch Num: [54/600] Discriminator Loss: 0.2020, Generator Loss: 3.8831 D(x): 0.9322, D(G(z)): 0.0584 Epoch: [6/20], Batch Num: [55/600] Discriminator Loss: 0.1570, Generator Loss: 4.3469 D(x): 0.9657, D(G(z)): 0.0677 Epoch: [6/20], Batch Num: [56/600] Discriminator Loss: 0.1883, Generator Loss: 4.4694 D(x): 0.9338, D(G(z)): 0.0449 Epoch: [6/20], Batch Num: [57/600] Discriminator Loss: 0.1121, Generator Loss: 4.1787 D(x): 0.9538, D(G(z)): 0.0328 Epoch: [6/20], Batch Num: [58/600] Discriminator Loss: 0.2112, Generator Loss: 3.7055 D(x): 0.9239, D(G(z)): 0.0553 Epoch: [6/20], Batch Num: [59/600] Discriminator Loss: 0.2780, Generator Loss: 3.6187 D(x): 0.9120, D(G(z)): 0.0538 Epoch: [6/20], Batch Num: [60/600] Discriminator Loss: 0.2025, Generator Loss: 3.4358 D(x): 0.9608, D(G(z)): 0.1017 Epoch: [6/20], Batch Num: [61/600] Discriminator Loss: 0.1573, Generator Loss: 4.3092 D(x): 0.9761, D(G(z)): 0.0937 Epoch: [6/20], Batch Num: [62/600] Discriminator Loss: 0.3744, Generator Loss: 4.3920 D(x): 0.8825, D(G(z)): 0.0538 Epoch: [6/20], Batch Num: [63/600] Discriminator Loss: 0.1161, Generator Loss: 4.1443 D(x): 0.9665, D(G(z)): 0.0525 Epoch: [6/20], Batch Num: [64/600] Discriminator Loss: 0.1896, Generator Loss: 3.9174 D(x): 0.9396, D(G(z)): 0.0469 Epoch: [6/20], Batch Num: [65/600] Discriminator Loss: 0.2059, Generator Loss: 3.8017 D(x): 0.9456, D(G(z)): 0.0629 Epoch: [6/20], Batch Num: [66/600] Discriminator Loss: 0.2308, Generator Loss: 4.1039 D(x): 0.9507, D(G(z)): 0.0877 Epoch: [6/20], Batch Num: [67/600] Discriminator Loss: 0.1658, Generator Loss: 4.4039 D(x): 0.9588, D(G(z)): 0.0545 Epoch: [6/20], Batch Num: [68/600] Discriminator Loss: 0.1620, Generator Loss: 4.5353 D(x): 0.9442, D(G(z)): 0.0407 Epoch: [6/20], Batch Num: [69/600] Discriminator Loss: 0.1277, Generator Loss: 4.5136 D(x): 0.9610, D(G(z)): 0.0381 Epoch: [6/20], Batch Num: [70/600] Discriminator Loss: 0.2718, Generator Loss: 3.8654 D(x): 0.9210, D(G(z)): 0.0576 Epoch: [6/20], Batch Num: [71/600] Discriminator Loss: 0.2307, Generator Loss: 3.2670 D(x): 0.9095, D(G(z)): 0.0411 Epoch: [6/20], Batch Num: [72/600] Discriminator Loss: 0.2937, Generator Loss: 3.9999 D(x): 0.9703, D(G(z)): 0.1538 Epoch: [6/20], Batch Num: [73/600] Discriminator Loss: 0.3480, Generator Loss: 4.0823 D(x): 0.8839, D(G(z)): 0.0615 Epoch: [6/20], Batch Num: [74/600] Discriminator Loss: 0.2035, Generator Loss: 4.8840 D(x): 0.9697, D(G(z)): 0.0812 Epoch: [6/20], Batch Num: [75/600] Discriminator Loss: 0.1520, Generator Loss: 4.9238 D(x): 0.9373, D(G(z)): 0.0279 Epoch: [6/20], Batch Num: [76/600] Discriminator Loss: 0.3807, Generator Loss: 4.5725 D(x): 0.8912, D(G(z)): 0.0260 Epoch: [6/20], Batch Num: [77/600] Discriminator Loss: 0.2426, Generator Loss: 3.8793 D(x): 0.9424, D(G(z)): 0.0586 Epoch: [6/20], Batch Num: [78/600] Discriminator Loss: 0.2269, Generator Loss: 3.8819 D(x): 0.9723, D(G(z)): 0.1036 Epoch: [6/20], Batch Num: [79/600] Discriminator Loss: 0.1303, Generator Loss: 4.1469 D(x): 0.9739, D(G(z)): 0.0766 Epoch: [6/20], Batch Num: [80/600] Discriminator Loss: 0.2088, Generator Loss: 5.2458 D(x): 0.9487, D(G(z)): 0.0498 Epoch: [6/20], Batch Num: [81/600] Discriminator Loss: 0.1720, Generator Loss: 4.8946 D(x): 0.9421, D(G(z)): 0.0365 Epoch: [6/20], Batch Num: [82/600] Discriminator Loss: 0.3174, Generator Loss: 4.2433 D(x): 0.8829, D(G(z)): 0.0218 Epoch: [6/20], Batch Num: [83/600] Discriminator Loss: 0.2211, Generator Loss: 3.6643 D(x): 0.9441, D(G(z)): 0.0712 Epoch: [6/20], Batch Num: [84/600] Discriminator Loss: 0.1560, Generator Loss: 3.2302 D(x): 0.9666, D(G(z)): 0.0710 Epoch: [6/20], Batch Num: [85/600] Discriminator Loss: 0.2779, Generator Loss: 4.1090 D(x): 0.9730, D(G(z)): 0.1344 Epoch: [6/20], Batch Num: [86/600] Discriminator Loss: 0.1530, Generator Loss: 4.9455 D(x): 0.9625, D(G(z)): 0.0486 Epoch: [6/20], Batch Num: [87/600] Discriminator Loss: 0.2443, Generator Loss: 4.7781 D(x): 0.9031, D(G(z)): 0.0332 Epoch: [6/20], Batch Num: [88/600] Discriminator Loss: 0.2943, Generator Loss: 4.3960 D(x): 0.8906, D(G(z)): 0.0360 Epoch: [6/20], Batch Num: [89/600] Discriminator Loss: 0.1414, Generator Loss: 3.7383 D(x): 0.9458, D(G(z)): 0.0447 Epoch: [6/20], Batch Num: [90/600] Discriminator Loss: 0.2290, Generator Loss: 3.5185 D(x): 0.9603, D(G(z)): 0.1133 Epoch: [6/20], Batch Num: [91/600] Discriminator Loss: 0.1836, Generator Loss: 4.0601 D(x): 0.9724, D(G(z)): 0.1012 Epoch: [6/20], Batch Num: [92/600] Discriminator Loss: 0.1039, Generator Loss: 4.8070 D(x): 0.9668, D(G(z)): 0.0438 Epoch: [6/20], Batch Num: [93/600] Discriminator Loss: 0.2061, Generator Loss: 5.0156 D(x): 0.9396, D(G(z)): 0.0298 Epoch: [6/20], Batch Num: [94/600] Discriminator Loss: 0.1203, Generator Loss: 5.0197 D(x): 0.9533, D(G(z)): 0.0226 Epoch: [6/20], Batch Num: [95/600] Discriminator Loss: 0.1434, Generator Loss: 5.0195 D(x): 0.9501, D(G(z)): 0.0299 Epoch: [6/20], Batch Num: [96/600] Discriminator Loss: 0.1431, Generator Loss: 4.2683 D(x): 0.9507, D(G(z)): 0.0404 Epoch: [6/20], Batch Num: [97/600] Discriminator Loss: 0.1886, Generator Loss: 4.2588 D(x): 0.9615, D(G(z)): 0.0837 Epoch: [6/20], Batch Num: [98/600] Discriminator Loss: 0.1605, Generator Loss: 4.5227 D(x): 0.9748, D(G(z)): 0.0888 Epoch: [6/20], Batch Num: [99/600] Discriminator Loss: 0.1109, Generator Loss: 4.5255 D(x): 0.9671, D(G(z)): 0.0391 Epoch: 6, Batch Num: [100/600]
Epoch: [6/20], Batch Num: [100/600] Discriminator Loss: 0.2102, Generator Loss: 4.9271 D(x): 0.9808, D(G(z)): 0.0799 Epoch: [6/20], Batch Num: [101/600] Discriminator Loss: 0.0751, Generator Loss: 5.5799 D(x): 0.9589, D(G(z)): 0.0157 Epoch: [6/20], Batch Num: [102/600] Discriminator Loss: 0.1389, Generator Loss: 5.2065 D(x): 0.9367, D(G(z)): 0.0144 Epoch: [6/20], Batch Num: [103/600] Discriminator Loss: 0.3271, Generator Loss: 4.5007 D(x): 0.9189, D(G(z)): 0.0447 Epoch: [6/20], Batch Num: [104/600] Discriminator Loss: 0.2085, Generator Loss: 3.8559 D(x): 0.9430, D(G(z)): 0.0668 Epoch: [6/20], Batch Num: [105/600] Discriminator Loss: 0.2466, Generator Loss: 4.8291 D(x): 0.9750, D(G(z)): 0.1192 Epoch: [6/20], Batch Num: [106/600] Discriminator Loss: 0.1438, Generator Loss: 5.3262 D(x): 0.9677, D(G(z)): 0.0605 Epoch: [6/20], Batch Num: [107/600] Discriminator Loss: 0.2102, Generator Loss: 5.4690 D(x): 0.9394, D(G(z)): 0.0303 Epoch: [6/20], Batch Num: [108/600] Discriminator Loss: 0.3450, Generator Loss: 5.3827 D(x): 0.9219, D(G(z)): 0.0346 Epoch: [6/20], Batch Num: [109/600] Discriminator Loss: 0.3495, Generator Loss: 4.5752 D(x): 0.8892, D(G(z)): 0.0335 Epoch: [6/20], Batch Num: [110/600] Discriminator Loss: 0.3294, Generator Loss: 3.8998 D(x): 0.9676, D(G(z)): 0.0920 Epoch: [6/20], Batch Num: [111/600] Discriminator Loss: 0.1985, Generator Loss: 3.8149 D(x): 0.9621, D(G(z)): 0.0861 Epoch: [6/20], Batch Num: [112/600] Discriminator Loss: 0.1253, Generator Loss: 4.0540 D(x): 0.9913, D(G(z)): 0.0865 Epoch: [6/20], Batch Num: [113/600] Discriminator Loss: 0.3192, Generator Loss: 5.1084 D(x): 0.9700, D(G(z)): 0.1201 Epoch: [6/20], Batch Num: [114/600] Discriminator Loss: 0.1791, Generator Loss: 5.9223 D(x): 0.9319, D(G(z)): 0.0376 Epoch: [6/20], Batch Num: [115/600] Discriminator Loss: 0.3343, Generator Loss: 5.8448 D(x): 0.8603, D(G(z)): 0.0128 Epoch: [6/20], Batch Num: [116/600] Discriminator Loss: 0.3501, Generator Loss: 4.4738 D(x): 0.8781, D(G(z)): 0.0254 Epoch: [6/20], Batch Num: [117/600] Discriminator Loss: 0.1822, Generator Loss: 3.5989 D(x): 0.9667, D(G(z)): 0.0621 Epoch: [6/20], Batch Num: [118/600] Discriminator Loss: 0.1775, Generator Loss: 3.3959 D(x): 0.9922, D(G(z)): 0.0947 Epoch: [6/20], Batch Num: [119/600] Discriminator Loss: 0.3193, Generator Loss: 4.5986 D(x): 0.9873, D(G(z)): 0.1566 Epoch: [6/20], Batch Num: [120/600] Discriminator Loss: 0.1947, Generator Loss: 5.4803 D(x): 0.9484, D(G(z)): 0.0672 Epoch: [6/20], Batch Num: [121/600] Discriminator Loss: 0.4701, Generator Loss: 5.7029 D(x): 0.9170, D(G(z)): 0.0725 Epoch: [6/20], Batch Num: [122/600] Discriminator Loss: 0.2769, Generator Loss: 5.7422 D(x): 0.9212, D(G(z)): 0.0434 Epoch: [6/20], Batch Num: [123/600] Discriminator Loss: 0.2184, Generator Loss: 5.0971 D(x): 0.9210, D(G(z)): 0.0299 Epoch: [6/20], Batch Num: [124/600] Discriminator Loss: 0.2139, Generator Loss: 5.0734 D(x): 0.9468, D(G(z)): 0.0601 Epoch: [6/20], Batch Num: [125/600] Discriminator Loss: 0.3170, Generator Loss: 4.0659 D(x): 0.9521, D(G(z)): 0.0909 Epoch: [6/20], Batch Num: [126/600] Discriminator Loss: 0.2266, Generator Loss: 4.2982 D(x): 0.9570, D(G(z)): 0.0947 Epoch: [6/20], Batch Num: [127/600] Discriminator Loss: 0.3289, Generator Loss: 4.5784 D(x): 0.9264, D(G(z)): 0.1140 Epoch: [6/20], Batch Num: [128/600] Discriminator Loss: 0.2645, Generator Loss: 5.2410 D(x): 0.9599, D(G(z)): 0.1237 Epoch: [6/20], Batch Num: [129/600] Discriminator Loss: 0.2997, Generator Loss: 5.4243 D(x): 0.9006, D(G(z)): 0.0576 Epoch: [6/20], Batch Num: [130/600] Discriminator Loss: 0.2283, Generator Loss: 4.9961 D(x): 0.9096, D(G(z)): 0.0429 Epoch: [6/20], Batch Num: [131/600] Discriminator Loss: 0.1960, Generator Loss: 4.7770 D(x): 0.9316, D(G(z)): 0.0397 Epoch: [6/20], Batch Num: [132/600] Discriminator Loss: 0.2803, Generator Loss: 4.7940 D(x): 0.9210, D(G(z)): 0.0662 Epoch: [6/20], Batch Num: [133/600] Discriminator Loss: 0.2520, Generator Loss: 4.1308 D(x): 0.9245, D(G(z)): 0.0548 Epoch: [6/20], Batch Num: [134/600] Discriminator Loss: 0.1979, Generator Loss: 4.5828 D(x): 0.9824, D(G(z)): 0.1177 Epoch: [6/20], Batch Num: [135/600] Discriminator Loss: 0.2045, Generator Loss: 5.9850 D(x): 0.9719, D(G(z)): 0.1049 Epoch: [6/20], Batch Num: [136/600] Discriminator Loss: 0.2374, Generator Loss: 7.2914 D(x): 0.9456, D(G(z)): 0.0595 Epoch: [6/20], Batch Num: [137/600] Discriminator Loss: 0.1827, Generator Loss: 7.4580 D(x): 0.9160, D(G(z)): 0.0221 Epoch: [6/20], Batch Num: [138/600] Discriminator Loss: 0.3049, Generator Loss: 5.8011 D(x): 0.8812, D(G(z)): 0.0158 Epoch: [6/20], Batch Num: [139/600] Discriminator Loss: 0.4127, Generator Loss: 4.2254 D(x): 0.8836, D(G(z)): 0.0381 Epoch: [6/20], Batch Num: [140/600] Discriminator Loss: 0.2407, Generator Loss: 3.2143 D(x): 0.9694, D(G(z)): 0.1212 Epoch: [6/20], Batch Num: [141/600] Discriminator Loss: 0.5376, Generator Loss: 4.6535 D(x): 0.9599, D(G(z)): 0.2372 Epoch: [6/20], Batch Num: [142/600] Discriminator Loss: 0.2920, Generator Loss: 6.2743 D(x): 0.9443, D(G(z)): 0.1077 Epoch: [6/20], Batch Num: [143/600] Discriminator Loss: 0.2097, Generator Loss: 7.4153 D(x): 0.9388, D(G(z)): 0.0385 Epoch: [6/20], Batch Num: [144/600] Discriminator Loss: 0.4249, Generator Loss: 6.9240 D(x): 0.8472, D(G(z)): 0.0103 Epoch: [6/20], Batch Num: [145/600] Discriminator Loss: 0.2788, Generator Loss: 5.9643 D(x): 0.8896, D(G(z)): 0.0203 Epoch: [6/20], Batch Num: [146/600] Discriminator Loss: 0.1468, Generator Loss: 5.0183 D(x): 0.9405, D(G(z)): 0.0405 Epoch: [6/20], Batch Num: [147/600] Discriminator Loss: 0.3449, Generator Loss: 3.6563 D(x): 0.9166, D(G(z)): 0.0807 Epoch: [6/20], Batch Num: [148/600] Discriminator Loss: 0.3422, Generator Loss: 3.3915 D(x): 0.9774, D(G(z)): 0.1717 Epoch: [6/20], Batch Num: [149/600] Discriminator Loss: 0.3045, Generator Loss: 4.7192 D(x): 0.9600, D(G(z)): 0.1430 Epoch: [6/20], Batch Num: [150/600] Discriminator Loss: 0.2101, Generator Loss: 5.4423 D(x): 0.9400, D(G(z)): 0.0643 Epoch: [6/20], Batch Num: [151/600] Discriminator Loss: 0.1830, Generator Loss: 6.7976 D(x): 0.9459, D(G(z)): 0.0414 Epoch: [6/20], Batch Num: [152/600] Discriminator Loss: 0.2184, Generator Loss: 6.1484 D(x): 0.8981, D(G(z)): 0.0199 Epoch: [6/20], Batch Num: [153/600] Discriminator Loss: 0.2863, Generator Loss: 4.9309 D(x): 0.8808, D(G(z)): 0.0245 Epoch: [6/20], Batch Num: [154/600] Discriminator Loss: 0.2536, Generator Loss: 3.7540 D(x): 0.9276, D(G(z)): 0.0737 Epoch: [6/20], Batch Num: [155/600] Discriminator Loss: 0.2542, Generator Loss: 3.6806 D(x): 0.9495, D(G(z)): 0.1244 Epoch: [6/20], Batch Num: [156/600] Discriminator Loss: 0.3967, Generator Loss: 3.7998 D(x): 0.9353, D(G(z)): 0.1489 Epoch: [6/20], Batch Num: [157/600] Discriminator Loss: 0.3875, Generator Loss: 4.5031 D(x): 0.9127, D(G(z)): 0.1299 Epoch: [6/20], Batch Num: [158/600] Discriminator Loss: 0.4277, Generator Loss: 5.0942 D(x): 0.9273, D(G(z)): 0.0898 Epoch: [6/20], Batch Num: [159/600] Discriminator Loss: 0.3407, Generator Loss: 4.6182 D(x): 0.8775, D(G(z)): 0.0383 Epoch: [6/20], Batch Num: [160/600] Discriminator Loss: 0.3455, Generator Loss: 3.6774 D(x): 0.8644, D(G(z)): 0.0530 Epoch: [6/20], Batch Num: [161/600] Discriminator Loss: 0.3046, Generator Loss: 3.3346 D(x): 0.9512, D(G(z)): 0.1237 Epoch: [6/20], Batch Num: [162/600] Discriminator Loss: 0.3635, Generator Loss: 3.1653 D(x): 0.9197, D(G(z)): 0.1569 Epoch: [6/20], Batch Num: [163/600] Discriminator Loss: 0.3699, Generator Loss: 3.6206 D(x): 0.8967, D(G(z)): 0.1054 Epoch: [6/20], Batch Num: [164/600] Discriminator Loss: 0.2504, Generator Loss: 4.2922 D(x): 0.9523, D(G(z)): 0.1092 Epoch: [6/20], Batch Num: [165/600] Discriminator Loss: 0.2475, Generator Loss: 4.4565 D(x): 0.9129, D(G(z)): 0.0573 Epoch: [6/20], Batch Num: [166/600] Discriminator Loss: 0.2597, Generator Loss: 4.2272 D(x): 0.9059, D(G(z)): 0.0418 Epoch: [6/20], Batch Num: [167/600] Discriminator Loss: 0.3183, Generator Loss: 3.9745 D(x): 0.9100, D(G(z)): 0.0480 Epoch: [6/20], Batch Num: [168/600] Discriminator Loss: 0.2209, Generator Loss: 3.4525 D(x): 0.9328, D(G(z)): 0.0635 Epoch: [6/20], Batch Num: [169/600] Discriminator Loss: 0.1575, Generator Loss: 2.9837 D(x): 0.9670, D(G(z)): 0.0763 Epoch: [6/20], Batch Num: [170/600] Discriminator Loss: 0.2401, Generator Loss: 3.6984 D(x): 0.9735, D(G(z)): 0.1242 Epoch: [6/20], Batch Num: [171/600] Discriminator Loss: 0.1736, Generator Loss: 4.1828 D(x): 0.9439, D(G(z)): 0.0716 Epoch: [6/20], Batch Num: [172/600] Discriminator Loss: 0.1218, Generator Loss: 4.4812 D(x): 0.9511, D(G(z)): 0.0402 Epoch: [6/20], Batch Num: [173/600] Discriminator Loss: 0.1947, Generator Loss: 4.3554 D(x): 0.9358, D(G(z)): 0.0547 Epoch: [6/20], Batch Num: [174/600] Discriminator Loss: 0.1896, Generator Loss: 4.2747 D(x): 0.9354, D(G(z)): 0.0479 Epoch: [6/20], Batch Num: [175/600] Discriminator Loss: 0.1980, Generator Loss: 4.4555 D(x): 0.9496, D(G(z)): 0.0627 Epoch: [6/20], Batch Num: [176/600] Discriminator Loss: 0.2553, Generator Loss: 4.0163 D(x): 0.9224, D(G(z)): 0.0560 Epoch: [6/20], Batch Num: [177/600] Discriminator Loss: 0.1107, Generator Loss: 4.1601 D(x): 0.9729, D(G(z)): 0.0603 Epoch: [6/20], Batch Num: [178/600] Discriminator Loss: 0.1625, Generator Loss: 3.8523 D(x): 0.9645, D(G(z)): 0.0595 Epoch: [6/20], Batch Num: [179/600] Discriminator Loss: 0.1909, Generator Loss: 4.4445 D(x): 0.9774, D(G(z)): 0.1026 Epoch: [6/20], Batch Num: [180/600] Discriminator Loss: 0.2159, Generator Loss: 4.9186 D(x): 0.9458, D(G(z)): 0.0600 Epoch: [6/20], Batch Num: [181/600] Discriminator Loss: 0.1573, Generator Loss: 4.7967 D(x): 0.9549, D(G(z)): 0.0510 Epoch: [6/20], Batch Num: [182/600] Discriminator Loss: 0.1153, Generator Loss: 4.9716 D(x): 0.9448, D(G(z)): 0.0207 Epoch: [6/20], Batch Num: [183/600] Discriminator Loss: 0.2338, Generator Loss: 4.7086 D(x): 0.9307, D(G(z)): 0.0494 Epoch: [6/20], Batch Num: [184/600] Discriminator Loss: 0.2852, Generator Loss: 4.3577 D(x): 0.9276, D(G(z)): 0.0467 Epoch: [6/20], Batch Num: [185/600] Discriminator Loss: 0.1466, Generator Loss: 3.9068 D(x): 0.9664, D(G(z)): 0.0722 Epoch: [6/20], Batch Num: [186/600] Discriminator Loss: 0.1219, Generator Loss: 4.3671 D(x): 0.9791, D(G(z)): 0.0748 Epoch: [6/20], Batch Num: [187/600] Discriminator Loss: 0.2370, Generator Loss: 4.4899 D(x): 0.9429, D(G(z)): 0.0851 Epoch: [6/20], Batch Num: [188/600] Discriminator Loss: 0.1806, Generator Loss: 4.4569 D(x): 0.9327, D(G(z)): 0.0434 Epoch: [6/20], Batch Num: [189/600] Discriminator Loss: 0.1678, Generator Loss: 4.6624 D(x): 0.9630, D(G(z)): 0.0717 Epoch: [6/20], Batch Num: [190/600] Discriminator Loss: 0.0878, Generator Loss: 4.9878 D(x): 0.9673, D(G(z)): 0.0395 Epoch: [6/20], Batch Num: [191/600] Discriminator Loss: 0.2758, Generator Loss: 4.4974 D(x): 0.9105, D(G(z)): 0.0537 Epoch: [6/20], Batch Num: [192/600] Discriminator Loss: 0.2268, Generator Loss: 4.5075 D(x): 0.9397, D(G(z)): 0.0632 Epoch: [6/20], Batch Num: [193/600] Discriminator Loss: 0.2491, Generator Loss: 4.2612 D(x): 0.9460, D(G(z)): 0.0987 Epoch: [6/20], Batch Num: [194/600] Discriminator Loss: 0.1918, Generator Loss: 4.4167 D(x): 0.9410, D(G(z)): 0.0519 Epoch: [6/20], Batch Num: [195/600] Discriminator Loss: 0.1489, Generator Loss: 4.4424 D(x): 0.9486, D(G(z)): 0.0460 Epoch: [6/20], Batch Num: [196/600] Discriminator Loss: 0.2612, Generator Loss: 4.3059 D(x): 0.9472, D(G(z)): 0.1041 Epoch: [6/20], Batch Num: [197/600] Discriminator Loss: 0.2961, Generator Loss: 3.6952 D(x): 0.8976, D(G(z)): 0.0478 Epoch: [6/20], Batch Num: [198/600] Discriminator Loss: 0.3623, Generator Loss: 4.2621 D(x): 0.9216, D(G(z)): 0.1238 Epoch: [6/20], Batch Num: [199/600] Discriminator Loss: 0.3151, Generator Loss: 4.2087 D(x): 0.8988, D(G(z)): 0.0731 Epoch: 6, Batch Num: [200/600]
Epoch: [6/20], Batch Num: [200/600] Discriminator Loss: 0.2522, Generator Loss: 4.1496 D(x): 0.9226, D(G(z)): 0.0597 Epoch: [6/20], Batch Num: [201/600] Discriminator Loss: 0.2456, Generator Loss: 3.9281 D(x): 0.9289, D(G(z)): 0.0806 Epoch: [6/20], Batch Num: [202/600] Discriminator Loss: 0.3827, Generator Loss: 4.1224 D(x): 0.9006, D(G(z)): 0.0919 Epoch: [6/20], Batch Num: [203/600] Discriminator Loss: 0.2875, Generator Loss: 3.6152 D(x): 0.9212, D(G(z)): 0.0791 Epoch: [6/20], Batch Num: [204/600] Discriminator Loss: 0.3990, Generator Loss: 3.0748 D(x): 0.8931, D(G(z)): 0.0973 Epoch: [6/20], Batch Num: [205/600] Discriminator Loss: 0.4422, Generator Loss: 3.2154 D(x): 0.9057, D(G(z)): 0.1486 Epoch: [6/20], Batch Num: [206/600] Discriminator Loss: 0.2950, Generator Loss: 4.1985 D(x): 0.9465, D(G(z)): 0.1197 Epoch: [6/20], Batch Num: [207/600] Discriminator Loss: 0.3543, Generator Loss: 5.2314 D(x): 0.9143, D(G(z)): 0.1093 Epoch: [6/20], Batch Num: [208/600] Discriminator Loss: 0.3775, Generator Loss: 4.9692 D(x): 0.8714, D(G(z)): 0.0480 Epoch: [6/20], Batch Num: [209/600] Discriminator Loss: 0.3995, Generator Loss: 4.2579 D(x): 0.8796, D(G(z)): 0.0288 Epoch: [6/20], Batch Num: [210/600] Discriminator Loss: 0.3207, Generator Loss: 3.2005 D(x): 0.8805, D(G(z)): 0.0603 Epoch: [6/20], Batch Num: [211/600] Discriminator Loss: 0.4219, Generator Loss: 3.1945 D(x): 0.9337, D(G(z)): 0.1934 Epoch: [6/20], Batch Num: [212/600] Discriminator Loss: 0.3765, Generator Loss: 3.9637 D(x): 0.9491, D(G(z)): 0.1693 Epoch: [6/20], Batch Num: [213/600] Discriminator Loss: 0.3783, Generator Loss: 4.0734 D(x): 0.8630, D(G(z)): 0.0722 Epoch: [6/20], Batch Num: [214/600] Discriminator Loss: 0.4271, Generator Loss: 4.3684 D(x): 0.8404, D(G(z)): 0.0636 Epoch: [6/20], Batch Num: [215/600] Discriminator Loss: 0.4504, Generator Loss: 4.0334 D(x): 0.8759, D(G(z)): 0.1119 Epoch: [6/20], Batch Num: [216/600] Discriminator Loss: 0.3113, Generator Loss: 3.3938 D(x): 0.8898, D(G(z)): 0.0901 Epoch: [6/20], Batch Num: [217/600] Discriminator Loss: 0.3005, Generator Loss: 3.1432 D(x): 0.9340, D(G(z)): 0.1267 Epoch: [6/20], Batch Num: [218/600] Discriminator Loss: 0.4467, Generator Loss: 3.5842 D(x): 0.9019, D(G(z)): 0.1398 Epoch: [6/20], Batch Num: [219/600] Discriminator Loss: 0.3927, Generator Loss: 4.2068 D(x): 0.9162, D(G(z)): 0.1466 Epoch: [6/20], Batch Num: [220/600] Discriminator Loss: 0.2744, Generator Loss: 4.0353 D(x): 0.8876, D(G(z)): 0.0457 Epoch: [6/20], Batch Num: [221/600] Discriminator Loss: 0.3389, Generator Loss: 4.1212 D(x): 0.8947, D(G(z)): 0.0832 Epoch: [6/20], Batch Num: [222/600] Discriminator Loss: 0.4462, Generator Loss: 3.3988 D(x): 0.8408, D(G(z)): 0.0781 Epoch: [6/20], Batch Num: [223/600] Discriminator Loss: 0.4918, Generator Loss: 2.6959 D(x): 0.8655, D(G(z)): 0.1409 Epoch: [6/20], Batch Num: [224/600] Discriminator Loss: 0.5047, Generator Loss: 2.7127 D(x): 0.9055, D(G(z)): 0.1976 Epoch: [6/20], Batch Num: [225/600] Discriminator Loss: 0.3736, Generator Loss: 3.4311 D(x): 0.9242, D(G(z)): 0.1665 Epoch: [6/20], Batch Num: [226/600] Discriminator Loss: 0.3632, Generator Loss: 3.8947 D(x): 0.9039, D(G(z)): 0.0961 Epoch: [6/20], Batch Num: [227/600] Discriminator Loss: 0.3854, Generator Loss: 4.2335 D(x): 0.8650, D(G(z)): 0.0571 Epoch: [6/20], Batch Num: [228/600] Discriminator Loss: 0.3316, Generator Loss: 4.1679 D(x): 0.8720, D(G(z)): 0.0644 Epoch: [6/20], Batch Num: [229/600] Discriminator Loss: 0.2960, Generator Loss: 3.6058 D(x): 0.9009, D(G(z)): 0.0777 Epoch: [6/20], Batch Num: [230/600] Discriminator Loss: 0.3537, Generator Loss: 3.1395 D(x): 0.8788, D(G(z)): 0.0878 Epoch: [6/20], Batch Num: [231/600] Discriminator Loss: 0.4791, Generator Loss: 3.1869 D(x): 0.8993, D(G(z)): 0.1602 Epoch: [6/20], Batch Num: [232/600] Discriminator Loss: 0.5215, Generator Loss: 3.4593 D(x): 0.9252, D(G(z)): 0.2096 Epoch: [6/20], Batch Num: [233/600] Discriminator Loss: 0.3861, Generator Loss: 4.1325 D(x): 0.9030, D(G(z)): 0.1459 Epoch: [6/20], Batch Num: [234/600] Discriminator Loss: 0.3371, Generator Loss: 4.5853 D(x): 0.8785, D(G(z)): 0.0595 Epoch: [6/20], Batch Num: [235/600] Discriminator Loss: 0.3450, Generator Loss: 4.7635 D(x): 0.8721, D(G(z)): 0.0787 Epoch: [6/20], Batch Num: [236/600] Discriminator Loss: 0.3469, Generator Loss: 3.8041 D(x): 0.8556, D(G(z)): 0.0491 Epoch: [6/20], Batch Num: [237/600] Discriminator Loss: 0.3931, Generator Loss: 3.3807 D(x): 0.8742, D(G(z)): 0.0901 Epoch: [6/20], Batch Num: [238/600] Discriminator Loss: 0.6056, Generator Loss: 3.4494 D(x): 0.9625, D(G(z)): 0.2448 Epoch: [6/20], Batch Num: [239/600] Discriminator Loss: 0.4742, Generator Loss: 4.4301 D(x): 0.9311, D(G(z)): 0.1411 Epoch: [6/20], Batch Num: [240/600] Discriminator Loss: 0.3695, Generator Loss: 5.4031 D(x): 0.9102, D(G(z)): 0.1252 Epoch: [6/20], Batch Num: [241/600] Discriminator Loss: 0.5789, Generator Loss: 5.3546 D(x): 0.8082, D(G(z)): 0.1046 Epoch: [6/20], Batch Num: [242/600] Discriminator Loss: 0.6010, Generator Loss: 4.6888 D(x): 0.7429, D(G(z)): 0.0358 Epoch: [6/20], Batch Num: [243/600] Discriminator Loss: 0.3899, Generator Loss: 3.8530 D(x): 0.8378, D(G(z)): 0.0703 Epoch: [6/20], Batch Num: [244/600] Discriminator Loss: 0.4292, Generator Loss: 2.8947 D(x): 0.8939, D(G(z)): 0.1547 Epoch: [6/20], Batch Num: [245/600] Discriminator Loss: 0.5641, Generator Loss: 2.9749 D(x): 0.9192, D(G(z)): 0.2784 Epoch: [6/20], Batch Num: [246/600] Discriminator Loss: 0.4027, Generator Loss: 3.5064 D(x): 0.9058, D(G(z)): 0.1814 Epoch: [6/20], Batch Num: [247/600] Discriminator Loss: 0.3237, Generator Loss: 4.2166 D(x): 0.8703, D(G(z)): 0.0937 Epoch: [6/20], Batch Num: [248/600] Discriminator Loss: 0.4527, Generator Loss: 3.9139 D(x): 0.8010, D(G(z)): 0.0665 Epoch: [6/20], Batch Num: [249/600] Discriminator Loss: 0.3517, Generator Loss: 3.3410 D(x): 0.8455, D(G(z)): 0.0603 Epoch: [6/20], Batch Num: [250/600] Discriminator Loss: 0.3970, Generator Loss: 3.0186 D(x): 0.8280, D(G(z)): 0.1008 Epoch: [6/20], Batch Num: [251/600] Discriminator Loss: 0.4289, Generator Loss: 2.6208 D(x): 0.8842, D(G(z)): 0.1660 Epoch: [6/20], Batch Num: [252/600] Discriminator Loss: 0.4718, Generator Loss: 3.1772 D(x): 0.8993, D(G(z)): 0.2123 Epoch: [6/20], Batch Num: [253/600] Discriminator Loss: 0.5216, Generator Loss: 3.1968 D(x): 0.8398, D(G(z)): 0.1691 Epoch: [6/20], Batch Num: [254/600] Discriminator Loss: 0.3570, Generator Loss: 3.4094 D(x): 0.8877, D(G(z)): 0.1410 Epoch: [6/20], Batch Num: [255/600] Discriminator Loss: 0.3633, Generator Loss: 3.6083 D(x): 0.8574, D(G(z)): 0.0997 Epoch: [6/20], Batch Num: [256/600] Discriminator Loss: 0.4244, Generator Loss: 3.5507 D(x): 0.8282, D(G(z)): 0.1031 Epoch: [6/20], Batch Num: [257/600] Discriminator Loss: 0.3829, Generator Loss: 3.0242 D(x): 0.8713, D(G(z)): 0.0971 Epoch: [6/20], Batch Num: [258/600] Discriminator Loss: 0.3639, Generator Loss: 2.8365 D(x): 0.9234, D(G(z)): 0.1703 Epoch: [6/20], Batch Num: [259/600] Discriminator Loss: 0.3269, Generator Loss: 3.4741 D(x): 0.9027, D(G(z)): 0.1472 Epoch: [6/20], Batch Num: [260/600] Discriminator Loss: 0.3269, Generator Loss: 3.4180 D(x): 0.8847, D(G(z)): 0.0850 Epoch: [6/20], Batch Num: [261/600] Discriminator Loss: 0.2086, Generator Loss: 3.4929 D(x): 0.9394, D(G(z)): 0.0971 Epoch: [6/20], Batch Num: [262/600] Discriminator Loss: 0.3029, Generator Loss: 3.6556 D(x): 0.8990, D(G(z)): 0.0841 Epoch: [6/20], Batch Num: [263/600] Discriminator Loss: 0.3349, Generator Loss: 3.4961 D(x): 0.8756, D(G(z)): 0.0768 Epoch: [6/20], Batch Num: [264/600] Discriminator Loss: 0.2814, Generator Loss: 3.4036 D(x): 0.9187, D(G(z)): 0.0970 Epoch: [6/20], Batch Num: [265/600] Discriminator Loss: 0.3273, Generator Loss: 3.3547 D(x): 0.8987, D(G(z)): 0.1227 Epoch: [6/20], Batch Num: [266/600] Discriminator Loss: 0.3652, Generator Loss: 2.9834 D(x): 0.8799, D(G(z)): 0.0937 Epoch: [6/20], Batch Num: [267/600] Discriminator Loss: 0.2737, Generator Loss: 3.2790 D(x): 0.9338, D(G(z)): 0.1334 Epoch: [6/20], Batch Num: [268/600] Discriminator Loss: 0.4311, Generator Loss: 3.6124 D(x): 0.9043, D(G(z)): 0.1299 Epoch: [6/20], Batch Num: [269/600] Discriminator Loss: 0.3789, Generator Loss: 3.9602 D(x): 0.8722, D(G(z)): 0.0850 Epoch: [6/20], Batch Num: [270/600] Discriminator Loss: 0.5174, Generator Loss: 3.3772 D(x): 0.8327, D(G(z)): 0.0860 Epoch: [6/20], Batch Num: [271/600] Discriminator Loss: 0.2315, Generator Loss: 3.3004 D(x): 0.9416, D(G(z)): 0.0957 Epoch: [6/20], Batch Num: [272/600] Discriminator Loss: 0.2857, Generator Loss: 3.3631 D(x): 0.9594, D(G(z)): 0.1289 Epoch: [6/20], Batch Num: [273/600] Discriminator Loss: 0.2602, Generator Loss: 4.0733 D(x): 0.9366, D(G(z)): 0.0758 Epoch: [6/20], Batch Num: [274/600] Discriminator Loss: 0.1902, Generator Loss: 4.3045 D(x): 0.9322, D(G(z)): 0.0581 Epoch: [6/20], Batch Num: [275/600] Discriminator Loss: 0.2374, Generator Loss: 4.6223 D(x): 0.9251, D(G(z)): 0.0311 Epoch: [6/20], Batch Num: [276/600] Discriminator Loss: 0.2607, Generator Loss: 3.7930 D(x): 0.9065, D(G(z)): 0.0377 Epoch: [6/20], Batch Num: [277/600] Discriminator Loss: 0.2781, Generator Loss: 3.4439 D(x): 0.9156, D(G(z)): 0.0795 Epoch: [6/20], Batch Num: [278/600] Discriminator Loss: 0.1873, Generator Loss: 3.5317 D(x): 0.9524, D(G(z)): 0.0680 Epoch: [6/20], Batch Num: [279/600] Discriminator Loss: 0.2201, Generator Loss: 3.8061 D(x): 0.9595, D(G(z)): 0.1163 Epoch: [6/20], Batch Num: [280/600] Discriminator Loss: 0.2291, Generator Loss: 4.5730 D(x): 0.9420, D(G(z)): 0.0985 Epoch: [6/20], Batch Num: [281/600] Discriminator Loss: 0.1028, Generator Loss: 4.8677 D(x): 0.9510, D(G(z)): 0.0289 Epoch: [6/20], Batch Num: [282/600] Discriminator Loss: 0.1527, Generator Loss: 5.1242 D(x): 0.9344, D(G(z)): 0.0170 Epoch: [6/20], Batch Num: [283/600] Discriminator Loss: 0.2028, Generator Loss: 4.4622 D(x): 0.9225, D(G(z)): 0.0192 Epoch: [6/20], Batch Num: [284/600] Discriminator Loss: 0.1796, Generator Loss: 3.5989 D(x): 0.9339, D(G(z)): 0.0537 Epoch: [6/20], Batch Num: [285/600] Discriminator Loss: 0.1853, Generator Loss: 3.5351 D(x): 0.9625, D(G(z)): 0.0924 Epoch: [6/20], Batch Num: [286/600] Discriminator Loss: 0.2394, Generator Loss: 4.4125 D(x): 0.9719, D(G(z)): 0.0929 Epoch: [6/20], Batch Num: [287/600] Discriminator Loss: 0.1297, Generator Loss: 5.2757 D(x): 0.9563, D(G(z)): 0.0503 Epoch: [6/20], Batch Num: [288/600] Discriminator Loss: 0.3326, Generator Loss: 4.7810 D(x): 0.8914, D(G(z)): 0.0280 Epoch: [6/20], Batch Num: [289/600] Discriminator Loss: 0.1708, Generator Loss: 4.6119 D(x): 0.9560, D(G(z)): 0.0345 Epoch: [6/20], Batch Num: [290/600] Discriminator Loss: 0.2066, Generator Loss: 4.0964 D(x): 0.9414, D(G(z)): 0.0452 Epoch: [6/20], Batch Num: [291/600] Discriminator Loss: 0.1623, Generator Loss: 4.1183 D(x): 0.9608, D(G(z)): 0.0729 Epoch: [6/20], Batch Num: [292/600] Discriminator Loss: 0.1918, Generator Loss: 4.5947 D(x): 0.9543, D(G(z)): 0.0763 Epoch: [6/20], Batch Num: [293/600] Discriminator Loss: 0.1786, Generator Loss: 4.8194 D(x): 0.9599, D(G(z)): 0.0670 Epoch: [6/20], Batch Num: [294/600] Discriminator Loss: 0.0960, Generator Loss: 4.9858 D(x): 0.9665, D(G(z)): 0.0418 Epoch: [6/20], Batch Num: [295/600] Discriminator Loss: 0.2548, Generator Loss: 4.8636 D(x): 0.9166, D(G(z)): 0.0446 Epoch: [6/20], Batch Num: [296/600] Discriminator Loss: 0.1646, Generator Loss: 4.6252 D(x): 0.9595, D(G(z)): 0.0501 Epoch: [6/20], Batch Num: [297/600] Discriminator Loss: 0.1773, Generator Loss: 4.4711 D(x): 0.9448, D(G(z)): 0.0442 Epoch: [6/20], Batch Num: [298/600] Discriminator Loss: 0.1858, Generator Loss: 4.3005 D(x): 0.9456, D(G(z)): 0.0567 Epoch: [6/20], Batch Num: [299/600] Discriminator Loss: 0.2164, Generator Loss: 4.0437 D(x): 0.9461, D(G(z)): 0.0715 Epoch: 6, Batch Num: [300/600]
Epoch: [6/20], Batch Num: [300/600] Discriminator Loss: 0.2881, Generator Loss: 4.5919 D(x): 0.9248, D(G(z)): 0.0773 Epoch: [6/20], Batch Num: [301/600] Discriminator Loss: 0.4075, Generator Loss: 4.9760 D(x): 0.9016, D(G(z)): 0.0986 Epoch: [6/20], Batch Num: [302/600] Discriminator Loss: 0.3742, Generator Loss: 4.6317 D(x): 0.9168, D(G(z)): 0.0797 Epoch: [6/20], Batch Num: [303/600] Discriminator Loss: 0.1683, Generator Loss: 4.5116 D(x): 0.9557, D(G(z)): 0.0506 Epoch: [6/20], Batch Num: [304/600] Discriminator Loss: 0.1489, Generator Loss: 4.1382 D(x): 0.9508, D(G(z)): 0.0535 Epoch: [6/20], Batch Num: [305/600] Discriminator Loss: 0.4923, Generator Loss: 4.4143 D(x): 0.9337, D(G(z)): 0.1477 Epoch: [6/20], Batch Num: [306/600] Discriminator Loss: 0.4411, Generator Loss: 4.5950 D(x): 0.8955, D(G(z)): 0.0828 Epoch: [6/20], Batch Num: [307/600] Discriminator Loss: 0.3816, Generator Loss: 3.9210 D(x): 0.8797, D(G(z)): 0.0540 Epoch: [6/20], Batch Num: [308/600] Discriminator Loss: 0.3309, Generator Loss: 4.0993 D(x): 0.9478, D(G(z)): 0.1193 Epoch: [6/20], Batch Num: [309/600] Discriminator Loss: 0.2601, Generator Loss: 4.4921 D(x): 0.9562, D(G(z)): 0.1115 Epoch: [6/20], Batch Num: [310/600] Discriminator Loss: 0.2567, Generator Loss: 4.8331 D(x): 0.9198, D(G(z)): 0.0786 Epoch: [6/20], Batch Num: [311/600] Discriminator Loss: 0.2311, Generator Loss: 5.2070 D(x): 0.9294, D(G(z)): 0.0533 Epoch: [6/20], Batch Num: [312/600] Discriminator Loss: 0.4095, Generator Loss: 4.3422 D(x): 0.8422, D(G(z)): 0.0468 Epoch: [6/20], Batch Num: [313/600] Discriminator Loss: 0.3387, Generator Loss: 4.2025 D(x): 0.9265, D(G(z)): 0.1122 Epoch: [6/20], Batch Num: [314/600] Discriminator Loss: 0.2926, Generator Loss: 4.1055 D(x): 0.9556, D(G(z)): 0.1452 Epoch: [6/20], Batch Num: [315/600] Discriminator Loss: 0.3255, Generator Loss: 4.5782 D(x): 0.9145, D(G(z)): 0.1117 Epoch: [6/20], Batch Num: [316/600] Discriminator Loss: 0.4853, Generator Loss: 4.1770 D(x): 0.8895, D(G(z)): 0.1073 Epoch: [6/20], Batch Num: [317/600] Discriminator Loss: 0.3566, Generator Loss: 4.6752 D(x): 0.8873, D(G(z)): 0.0838 Epoch: [6/20], Batch Num: [318/600] Discriminator Loss: 0.2638, Generator Loss: 4.6599 D(x): 0.9255, D(G(z)): 0.0956 Epoch: [6/20], Batch Num: [319/600] Discriminator Loss: 0.3502, Generator Loss: 3.7280 D(x): 0.8915, D(G(z)): 0.0626 Epoch: [6/20], Batch Num: [320/600] Discriminator Loss: 0.3457, Generator Loss: 4.3144 D(x): 0.9055, D(G(z)): 0.1233 Epoch: [6/20], Batch Num: [321/600] Discriminator Loss: 0.4030, Generator Loss: 4.4997 D(x): 0.9186, D(G(z)): 0.1467 Epoch: [6/20], Batch Num: [322/600] Discriminator Loss: 0.2448, Generator Loss: 3.8746 D(x): 0.9317, D(G(z)): 0.1004 Epoch: [6/20], Batch Num: [323/600] Discriminator Loss: 0.3460, Generator Loss: 4.8614 D(x): 0.8912, D(G(z)): 0.1156 Epoch: [6/20], Batch Num: [324/600] Discriminator Loss: 0.2669, Generator Loss: 4.2966 D(x): 0.8849, D(G(z)): 0.0463 Epoch: [6/20], Batch Num: [325/600] Discriminator Loss: 0.2590, Generator Loss: 4.5400 D(x): 0.9042, D(G(z)): 0.0771 Epoch: [6/20], Batch Num: [326/600] Discriminator Loss: 0.2582, Generator Loss: 4.2045 D(x): 0.9334, D(G(z)): 0.1027 Epoch: [6/20], Batch Num: [327/600] Discriminator Loss: 0.3202, Generator Loss: 4.4544 D(x): 0.9536, D(G(z)): 0.1391 Epoch: [6/20], Batch Num: [328/600] Discriminator Loss: 0.2948, Generator Loss: 4.9455 D(x): 0.8954, D(G(z)): 0.0668 Epoch: [6/20], Batch Num: [329/600] Discriminator Loss: 0.2800, Generator Loss: 4.5657 D(x): 0.8905, D(G(z)): 0.0568 Epoch: [6/20], Batch Num: [330/600] Discriminator Loss: 0.3749, Generator Loss: 4.3646 D(x): 0.8882, D(G(z)): 0.0685 Epoch: [6/20], Batch Num: [331/600] Discriminator Loss: 0.3614, Generator Loss: 3.3777 D(x): 0.9082, D(G(z)): 0.1163 Epoch: [6/20], Batch Num: [332/600] Discriminator Loss: 0.3451, Generator Loss: 3.9279 D(x): 0.9245, D(G(z)): 0.1507 Epoch: [6/20], Batch Num: [333/600] Discriminator Loss: 0.2268, Generator Loss: 4.4624 D(x): 0.9323, D(G(z)): 0.0783 Epoch: [6/20], Batch Num: [334/600] Discriminator Loss: 0.2188, Generator Loss: 4.4718 D(x): 0.9114, D(G(z)): 0.0649 Epoch: [6/20], Batch Num: [335/600] Discriminator Loss: 0.2934, Generator Loss: 4.1382 D(x): 0.9005, D(G(z)): 0.0636 Epoch: [6/20], Batch Num: [336/600] Discriminator Loss: 0.2688, Generator Loss: 3.7454 D(x): 0.9032, D(G(z)): 0.0798 Epoch: [6/20], Batch Num: [337/600] Discriminator Loss: 0.2084, Generator Loss: 4.1306 D(x): 0.9574, D(G(z)): 0.1012 Epoch: [6/20], Batch Num: [338/600] Discriminator Loss: 0.1711, Generator Loss: 4.8460 D(x): 0.9576, D(G(z)): 0.0832 Epoch: [6/20], Batch Num: [339/600] Discriminator Loss: 0.3315, Generator Loss: 4.4310 D(x): 0.8842, D(G(z)): 0.0788 Epoch: [6/20], Batch Num: [340/600] Discriminator Loss: 0.3255, Generator Loss: 3.5814 D(x): 0.8827, D(G(z)): 0.0600 Epoch: [6/20], Batch Num: [341/600] Discriminator Loss: 0.2440, Generator Loss: 3.6630 D(x): 0.9434, D(G(z)): 0.1048 Epoch: [6/20], Batch Num: [342/600] Discriminator Loss: 0.1793, Generator Loss: 4.5273 D(x): 0.9701, D(G(z)): 0.1164 Epoch: [6/20], Batch Num: [343/600] Discriminator Loss: 0.1792, Generator Loss: 5.8740 D(x): 0.9459, D(G(z)): 0.0675 Epoch: [6/20], Batch Num: [344/600] Discriminator Loss: 0.3641, Generator Loss: 4.8433 D(x): 0.8766, D(G(z)): 0.0365 Epoch: [6/20], Batch Num: [345/600] Discriminator Loss: 0.3081, Generator Loss: 3.6633 D(x): 0.8958, D(G(z)): 0.0660 Epoch: [6/20], Batch Num: [346/600] Discriminator Loss: 0.3406, Generator Loss: 3.5325 D(x): 0.9371, D(G(z)): 0.1528 Epoch: [6/20], Batch Num: [347/600] Discriminator Loss: 0.3650, Generator Loss: 4.6483 D(x): 0.9368, D(G(z)): 0.1443 Epoch: [6/20], Batch Num: [348/600] Discriminator Loss: 0.2042, Generator Loss: 5.0505 D(x): 0.9238, D(G(z)): 0.0471 Epoch: [6/20], Batch Num: [349/600] Discriminator Loss: 0.2378, Generator Loss: 4.1623 D(x): 0.8981, D(G(z)): 0.0410 Epoch: [6/20], Batch Num: [350/600] Discriminator Loss: 0.2289, Generator Loss: 4.0655 D(x): 0.9409, D(G(z)): 0.0674 Epoch: [6/20], Batch Num: [351/600] Discriminator Loss: 0.2275, Generator Loss: 3.4068 D(x): 0.9272, D(G(z)): 0.0768 Epoch: [6/20], Batch Num: [352/600] Discriminator Loss: 0.4502, Generator Loss: 4.1963 D(x): 0.9338, D(G(z)): 0.1573 Epoch: [6/20], Batch Num: [353/600] Discriminator Loss: 0.3220, Generator Loss: 4.5719 D(x): 0.9169, D(G(z)): 0.0689 Epoch: [6/20], Batch Num: [354/600] Discriminator Loss: 0.2051, Generator Loss: 4.5463 D(x): 0.9203, D(G(z)): 0.0298 Epoch: [6/20], Batch Num: [355/600] Discriminator Loss: 0.2218, Generator Loss: 3.7746 D(x): 0.9088, D(G(z)): 0.0354 Epoch: [6/20], Batch Num: [356/600] Discriminator Loss: 0.2207, Generator Loss: 3.0638 D(x): 0.9297, D(G(z)): 0.0796 Epoch: [6/20], Batch Num: [357/600] Discriminator Loss: 0.2130, Generator Loss: 3.3467 D(x): 0.9691, D(G(z)): 0.1187 Epoch: [6/20], Batch Num: [358/600] Discriminator Loss: 0.2679, Generator Loss: 4.8449 D(x): 0.9602, D(G(z)): 0.1310 Epoch: [6/20], Batch Num: [359/600] Discriminator Loss: 0.2079, Generator Loss: 5.0020 D(x): 0.9183, D(G(z)): 0.0503 Epoch: [6/20], Batch Num: [360/600] Discriminator Loss: 0.2907, Generator Loss: 4.4907 D(x): 0.8842, D(G(z)): 0.0342 Epoch: [6/20], Batch Num: [361/600] Discriminator Loss: 0.1741, Generator Loss: 3.9556 D(x): 0.9412, D(G(z)): 0.0577 Epoch: [6/20], Batch Num: [362/600] Discriminator Loss: 0.3526, Generator Loss: 3.5879 D(x): 0.9169, D(G(z)): 0.0716 Epoch: [6/20], Batch Num: [363/600] Discriminator Loss: 0.2573, Generator Loss: 3.3338 D(x): 0.9317, D(G(z)): 0.0889 Epoch: [6/20], Batch Num: [364/600] Discriminator Loss: 0.3612, Generator Loss: 3.5875 D(x): 0.9229, D(G(z)): 0.1366 Epoch: [6/20], Batch Num: [365/600] Discriminator Loss: 0.2099, Generator Loss: 4.5699 D(x): 0.9660, D(G(z)): 0.0840 Epoch: [6/20], Batch Num: [366/600] Discriminator Loss: 0.1377, Generator Loss: 5.2374 D(x): 0.9482, D(G(z)): 0.0418 Epoch: [6/20], Batch Num: [367/600] Discriminator Loss: 0.2015, Generator Loss: 5.1239 D(x): 0.9155, D(G(z)): 0.0443 Epoch: [6/20], Batch Num: [368/600] Discriminator Loss: 0.3734, Generator Loss: 4.1877 D(x): 0.8901, D(G(z)): 0.0336 Epoch: [6/20], Batch Num: [369/600] Discriminator Loss: 0.2808, Generator Loss: 3.6560 D(x): 0.9167, D(G(z)): 0.0557 Epoch: [6/20], Batch Num: [370/600] Discriminator Loss: 0.3291, Generator Loss: 3.2927 D(x): 0.9426, D(G(z)): 0.1092 Epoch: [6/20], Batch Num: [371/600] Discriminator Loss: 0.2281, Generator Loss: 3.6336 D(x): 0.9569, D(G(z)): 0.1185 Epoch: [6/20], Batch Num: [372/600] Discriminator Loss: 0.3421, Generator Loss: 4.1770 D(x): 0.9214, D(G(z)): 0.0945 Epoch: [6/20], Batch Num: [373/600] Discriminator Loss: 0.2320, Generator Loss: 4.2700 D(x): 0.9299, D(G(z)): 0.0616 Epoch: [6/20], Batch Num: [374/600] Discriminator Loss: 0.2372, Generator Loss: 4.4154 D(x): 0.9148, D(G(z)): 0.0463 Epoch: [6/20], Batch Num: [375/600] Discriminator Loss: 0.3220, Generator Loss: 3.6841 D(x): 0.8717, D(G(z)): 0.0561 Epoch: [6/20], Batch Num: [376/600] Discriminator Loss: 0.2251, Generator Loss: 3.6092 D(x): 0.9498, D(G(z)): 0.0823 Epoch: [6/20], Batch Num: [377/600] Discriminator Loss: 0.2410, Generator Loss: 3.8351 D(x): 0.9537, D(G(z)): 0.1075 Epoch: [6/20], Batch Num: [378/600] Discriminator Loss: 0.2337, Generator Loss: 4.0966 D(x): 0.9411, D(G(z)): 0.0738 Epoch: [6/20], Batch Num: [379/600] Discriminator Loss: 0.2343, Generator Loss: 4.1652 D(x): 0.9027, D(G(z)): 0.0482 Epoch: [6/20], Batch Num: [380/600] Discriminator Loss: 0.3128, Generator Loss: 3.9147 D(x): 0.9158, D(G(z)): 0.0793 Epoch: [6/20], Batch Num: [381/600] Discriminator Loss: 0.2430, Generator Loss: 3.6687 D(x): 0.9373, D(G(z)): 0.0604 Epoch: [6/20], Batch Num: [382/600] Discriminator Loss: 0.2442, Generator Loss: 3.7026 D(x): 0.9275, D(G(z)): 0.0857 Epoch: [6/20], Batch Num: [383/600] Discriminator Loss: 0.1681, Generator Loss: 4.3130 D(x): 0.9513, D(G(z)): 0.0601 Epoch: [6/20], Batch Num: [384/600] Discriminator Loss: 0.1488, Generator Loss: 3.9498 D(x): 0.9644, D(G(z)): 0.0689 Epoch: [6/20], Batch Num: [385/600] Discriminator Loss: 0.1225, Generator Loss: 4.1571 D(x): 0.9538, D(G(z)): 0.0481 Epoch: [6/20], Batch Num: [386/600] Discriminator Loss: 0.1099, Generator Loss: 4.0168 D(x): 0.9580, D(G(z)): 0.0422 Epoch: [6/20], Batch Num: [387/600] Discriminator Loss: 0.1589, Generator Loss: 4.0980 D(x): 0.9653, D(G(z)): 0.0602 Epoch: [6/20], Batch Num: [388/600] Discriminator Loss: 0.2178, Generator Loss: 4.1623 D(x): 0.9313, D(G(z)): 0.0410 Epoch: [6/20], Batch Num: [389/600] Discriminator Loss: 0.2551, Generator Loss: 3.7512 D(x): 0.9255, D(G(z)): 0.0611 Epoch: [6/20], Batch Num: [390/600] Discriminator Loss: 0.1915, Generator Loss: 3.5106 D(x): 0.9433, D(G(z)): 0.0679 Epoch: [6/20], Batch Num: [391/600] Discriminator Loss: 0.1386, Generator Loss: 3.6357 D(x): 0.9784, D(G(z)): 0.0799 Epoch: [6/20], Batch Num: [392/600] Discriminator Loss: 0.1211, Generator Loss: 4.0926 D(x): 0.9802, D(G(z)): 0.0721 Epoch: [6/20], Batch Num: [393/600] Discriminator Loss: 0.2333, Generator Loss: 4.4890 D(x): 0.9400, D(G(z)): 0.0440 Epoch: [6/20], Batch Num: [394/600] Discriminator Loss: 0.2409, Generator Loss: 4.3929 D(x): 0.9183, D(G(z)): 0.0419 Epoch: [6/20], Batch Num: [395/600] Discriminator Loss: 0.1389, Generator Loss: 4.4886 D(x): 0.9746, D(G(z)): 0.0602 Epoch: [6/20], Batch Num: [396/600] Discriminator Loss: 0.2068, Generator Loss: 4.5245 D(x): 0.9413, D(G(z)): 0.0413 Epoch: [6/20], Batch Num: [397/600] Discriminator Loss: 0.2019, Generator Loss: 4.5340 D(x): 0.9318, D(G(z)): 0.0369 Epoch: [6/20], Batch Num: [398/600] Discriminator Loss: 0.1873, Generator Loss: 3.6374 D(x): 0.9625, D(G(z)): 0.0649 Epoch: [6/20], Batch Num: [399/600] Discriminator Loss: 0.1749, Generator Loss: 3.6931 D(x): 0.9473, D(G(z)): 0.0468 Epoch: 6, Batch Num: [400/600]
Epoch: [6/20], Batch Num: [400/600] Discriminator Loss: 0.2018, Generator Loss: 3.4410 D(x): 0.9543, D(G(z)): 0.0697 Epoch: [6/20], Batch Num: [401/600] Discriminator Loss: 0.2158, Generator Loss: 3.8953 D(x): 0.9536, D(G(z)): 0.0990 Epoch: [6/20], Batch Num: [402/600] Discriminator Loss: 0.1751, Generator Loss: 4.5176 D(x): 0.9570, D(G(z)): 0.0583 Epoch: [6/20], Batch Num: [403/600] Discriminator Loss: 0.3968, Generator Loss: 4.6974 D(x): 0.9199, D(G(z)): 0.0719 Epoch: [6/20], Batch Num: [404/600] Discriminator Loss: 0.2221, Generator Loss: 4.6813 D(x): 0.9209, D(G(z)): 0.0479 Epoch: [6/20], Batch Num: [405/600] Discriminator Loss: 0.1977, Generator Loss: 4.4560 D(x): 0.9379, D(G(z)): 0.0649 Epoch: [6/20], Batch Num: [406/600] Discriminator Loss: 0.1591, Generator Loss: 4.0833 D(x): 0.9537, D(G(z)): 0.0573 Epoch: [6/20], Batch Num: [407/600] Discriminator Loss: 0.3154, Generator Loss: 3.6429 D(x): 0.9180, D(G(z)): 0.0885 Epoch: [6/20], Batch Num: [408/600] Discriminator Loss: 0.4183, Generator Loss: 3.8143 D(x): 0.9474, D(G(z)): 0.1064 Epoch: [6/20], Batch Num: [409/600] Discriminator Loss: 0.1377, Generator Loss: 4.2119 D(x): 0.9644, D(G(z)): 0.0726 Epoch: [6/20], Batch Num: [410/600] Discriminator Loss: 0.3279, Generator Loss: 4.0539 D(x): 0.9088, D(G(z)): 0.0582 Epoch: [6/20], Batch Num: [411/600] Discriminator Loss: 0.2511, Generator Loss: 4.3944 D(x): 0.9267, D(G(z)): 0.0685 Epoch: [6/20], Batch Num: [412/600] Discriminator Loss: 0.1883, Generator Loss: 4.4250 D(x): 0.9560, D(G(z)): 0.0608 Epoch: [6/20], Batch Num: [413/600] Discriminator Loss: 0.2985, Generator Loss: 4.6243 D(x): 0.9142, D(G(z)): 0.0671 Epoch: [6/20], Batch Num: [414/600] Discriminator Loss: 0.2846, Generator Loss: 4.1415 D(x): 0.9184, D(G(z)): 0.0510 Epoch: [6/20], Batch Num: [415/600] Discriminator Loss: 0.2140, Generator Loss: 3.9270 D(x): 0.9450, D(G(z)): 0.0566 Epoch: [6/20], Batch Num: [416/600] Discriminator Loss: 0.3195, Generator Loss: 4.0052 D(x): 0.9277, D(G(z)): 0.0999 Epoch: [6/20], Batch Num: [417/600] Discriminator Loss: 0.3965, Generator Loss: 3.9051 D(x): 0.8986, D(G(z)): 0.0916 Epoch: [6/20], Batch Num: [418/600] Discriminator Loss: 0.2563, Generator Loss: 4.1033 D(x): 0.9649, D(G(z)): 0.1052 Epoch: [6/20], Batch Num: [419/600] Discriminator Loss: 0.2830, Generator Loss: 4.6785 D(x): 0.8941, D(G(z)): 0.0634 Epoch: [6/20], Batch Num: [420/600] Discriminator Loss: 0.1794, Generator Loss: 4.4474 D(x): 0.9477, D(G(z)): 0.0442 Epoch: [6/20], Batch Num: [421/600] Discriminator Loss: 0.1723, Generator Loss: 4.6391 D(x): 0.9474, D(G(z)): 0.0640 Epoch: [6/20], Batch Num: [422/600] Discriminator Loss: 0.2511, Generator Loss: 4.3446 D(x): 0.9230, D(G(z)): 0.0779 Epoch: [6/20], Batch Num: [423/600] Discriminator Loss: 0.2371, Generator Loss: 4.9218 D(x): 0.9260, D(G(z)): 0.0743 Epoch: [6/20], Batch Num: [424/600] Discriminator Loss: 0.2780, Generator Loss: 4.1096 D(x): 0.8835, D(G(z)): 0.0509 Epoch: [6/20], Batch Num: [425/600] Discriminator Loss: 0.1697, Generator Loss: 3.9463 D(x): 0.9642, D(G(z)): 0.0880 Epoch: [6/20], Batch Num: [426/600] Discriminator Loss: 0.2897, Generator Loss: 4.4783 D(x): 0.9302, D(G(z)): 0.1031 Epoch: [6/20], Batch Num: [427/600] Discriminator Loss: 0.3221, Generator Loss: 4.6844 D(x): 0.8917, D(G(z)): 0.0693 Epoch: [6/20], Batch Num: [428/600] Discriminator Loss: 0.2260, Generator Loss: 4.5214 D(x): 0.9306, D(G(z)): 0.0820 Epoch: [6/20], Batch Num: [429/600] Discriminator Loss: 0.2365, Generator Loss: 4.7075 D(x): 0.9373, D(G(z)): 0.0667 Epoch: [6/20], Batch Num: [430/600] Discriminator Loss: 0.3290, Generator Loss: 4.5124 D(x): 0.9170, D(G(z)): 0.0828 Epoch: [6/20], Batch Num: [431/600] Discriminator Loss: 0.2713, Generator Loss: 3.9461 D(x): 0.9086, D(G(z)): 0.0839 Epoch: [6/20], Batch Num: [432/600] Discriminator Loss: 0.4216, Generator Loss: 3.7021 D(x): 0.8961, D(G(z)): 0.1178 Epoch: [6/20], Batch Num: [433/600] Discriminator Loss: 0.4297, Generator Loss: 4.0576 D(x): 0.8985, D(G(z)): 0.1189 Epoch: [6/20], Batch Num: [434/600] Discriminator Loss: 0.3480, Generator Loss: 4.6477 D(x): 0.9232, D(G(z)): 0.1479 Epoch: [6/20], Batch Num: [435/600] Discriminator Loss: 0.4134, Generator Loss: 4.1765 D(x): 0.8391, D(G(z)): 0.0528 Epoch: [6/20], Batch Num: [436/600] Discriminator Loss: 0.3153, Generator Loss: 4.0945 D(x): 0.9104, D(G(z)): 0.0903 Epoch: [6/20], Batch Num: [437/600] Discriminator Loss: 0.4560, Generator Loss: 3.3464 D(x): 0.8627, D(G(z)): 0.1211 Epoch: [6/20], Batch Num: [438/600] Discriminator Loss: 0.3596, Generator Loss: 3.2142 D(x): 0.8940, D(G(z)): 0.1040 Epoch: [6/20], Batch Num: [439/600] Discriminator Loss: 0.3833, Generator Loss: 3.3928 D(x): 0.8958, D(G(z)): 0.1310 Epoch: [6/20], Batch Num: [440/600] Discriminator Loss: 0.3472, Generator Loss: 4.2529 D(x): 0.9356, D(G(z)): 0.1442 Epoch: [6/20], Batch Num: [441/600] Discriminator Loss: 0.2028, Generator Loss: 4.9859 D(x): 0.9123, D(G(z)): 0.0533 Epoch: [6/20], Batch Num: [442/600] Discriminator Loss: 0.1850, Generator Loss: 4.4792 D(x): 0.9317, D(G(z)): 0.0444 Epoch: [6/20], Batch Num: [443/600] Discriminator Loss: 0.2596, Generator Loss: 5.2192 D(x): 0.9017, D(G(z)): 0.0528 Epoch: [6/20], Batch Num: [444/600] Discriminator Loss: 0.2954, Generator Loss: 4.0964 D(x): 0.8998, D(G(z)): 0.0701 Epoch: [6/20], Batch Num: [445/600] Discriminator Loss: 0.2464, Generator Loss: 4.0821 D(x): 0.9344, D(G(z)): 0.0990 Epoch: [6/20], Batch Num: [446/600] Discriminator Loss: 0.3660, Generator Loss: 5.5899 D(x): 0.9432, D(G(z)): 0.1443 Epoch: [6/20], Batch Num: [447/600] Discriminator Loss: 0.2166, Generator Loss: 6.1879 D(x): 0.9158, D(G(z)): 0.0497 Epoch: [6/20], Batch Num: [448/600] Discriminator Loss: 0.1620, Generator Loss: 6.0830 D(x): 0.9219, D(G(z)): 0.0316 Epoch: [6/20], Batch Num: [449/600] Discriminator Loss: 0.2453, Generator Loss: 5.0447 D(x): 0.8900, D(G(z)): 0.0164 Epoch: [6/20], Batch Num: [450/600] Discriminator Loss: 0.2331, Generator Loss: 3.8120 D(x): 0.9079, D(G(z)): 0.0441 Epoch: [6/20], Batch Num: [451/600] Discriminator Loss: 0.3904, Generator Loss: 4.7932 D(x): 0.9751, D(G(z)): 0.1908 Epoch: [6/20], Batch Num: [452/600] Discriminator Loss: 0.2353, Generator Loss: 6.0652 D(x): 0.9491, D(G(z)): 0.0841 Epoch: [6/20], Batch Num: [453/600] Discriminator Loss: 0.1790, Generator Loss: 6.9989 D(x): 0.9310, D(G(z)): 0.0195 Epoch: [6/20], Batch Num: [454/600] Discriminator Loss: 0.3305, Generator Loss: 6.0619 D(x): 0.8508, D(G(z)): 0.0142 Epoch: [6/20], Batch Num: [455/600] Discriminator Loss: 0.3659, Generator Loss: 4.2624 D(x): 0.8713, D(G(z)): 0.0331 Epoch: [6/20], Batch Num: [456/600] Discriminator Loss: 0.3314, Generator Loss: 3.8780 D(x): 0.9677, D(G(z)): 0.1233 Epoch: [6/20], Batch Num: [457/600] Discriminator Loss: 0.2389, Generator Loss: 4.6189 D(x): 0.9654, D(G(z)): 0.1159 Epoch: [6/20], Batch Num: [458/600] Discriminator Loss: 0.3653, Generator Loss: 5.4270 D(x): 0.9349, D(G(z)): 0.0997 Epoch: [6/20], Batch Num: [459/600] Discriminator Loss: 0.1881, Generator Loss: 6.2525 D(x): 0.9226, D(G(z)): 0.0434 Epoch: [6/20], Batch Num: [460/600] Discriminator Loss: 0.3120, Generator Loss: 5.6257 D(x): 0.8756, D(G(z)): 0.0300 Epoch: [6/20], Batch Num: [461/600] Discriminator Loss: 0.2537, Generator Loss: 4.8991 D(x): 0.9135, D(G(z)): 0.0294 Epoch: [6/20], Batch Num: [462/600] Discriminator Loss: 0.2332, Generator Loss: 4.1545 D(x): 0.9470, D(G(z)): 0.1003 Epoch: [6/20], Batch Num: [463/600] Discriminator Loss: 0.3785, Generator Loss: 5.5297 D(x): 0.9827, D(G(z)): 0.1658 Epoch: [6/20], Batch Num: [464/600] Discriminator Loss: 0.3147, Generator Loss: 5.9941 D(x): 0.9134, D(G(z)): 0.0606 Epoch: [6/20], Batch Num: [465/600] Discriminator Loss: 0.4621, Generator Loss: 5.7671 D(x): 0.8404, D(G(z)): 0.0516 Epoch: [6/20], Batch Num: [466/600] Discriminator Loss: 0.3137, Generator Loss: 4.3945 D(x): 0.8718, D(G(z)): 0.0343 Epoch: [6/20], Batch Num: [467/600] Discriminator Loss: 0.5130, Generator Loss: 3.9467 D(x): 0.9299, D(G(z)): 0.1377 Epoch: [6/20], Batch Num: [468/600] Discriminator Loss: 0.4664, Generator Loss: 4.5087 D(x): 0.9442, D(G(z)): 0.1724 Epoch: [6/20], Batch Num: [469/600] Discriminator Loss: 0.3994, Generator Loss: 6.0099 D(x): 0.8808, D(G(z)): 0.0920 Epoch: [6/20], Batch Num: [470/600] Discriminator Loss: 0.5625, Generator Loss: 5.2408 D(x): 0.8194, D(G(z)): 0.0720 Epoch: [6/20], Batch Num: [471/600] Discriminator Loss: 0.4394, Generator Loss: 3.6594 D(x): 0.8308, D(G(z)): 0.0387 Epoch: [6/20], Batch Num: [472/600] Discriminator Loss: 0.3187, Generator Loss: 3.5244 D(x): 0.9421, D(G(z)): 0.1521 Epoch: [6/20], Batch Num: [473/600] Discriminator Loss: 0.3871, Generator Loss: 4.3247 D(x): 0.9158, D(G(z)): 0.1550 Epoch: [6/20], Batch Num: [474/600] Discriminator Loss: 0.3845, Generator Loss: 3.9339 D(x): 0.8617, D(G(z)): 0.0790 Epoch: [6/20], Batch Num: [475/600] Discriminator Loss: 0.3469, Generator Loss: 4.2574 D(x): 0.8917, D(G(z)): 0.0842 Epoch: [6/20], Batch Num: [476/600] Discriminator Loss: 0.3798, Generator Loss: 3.7529 D(x): 0.8736, D(G(z)): 0.1019 Epoch: [6/20], Batch Num: [477/600] Discriminator Loss: 0.3236, Generator Loss: 3.9738 D(x): 0.9140, D(G(z)): 0.1120 Epoch: [6/20], Batch Num: [478/600] Discriminator Loss: 0.5424, Generator Loss: 3.6135 D(x): 0.8789, D(G(z)): 0.1460 Epoch: [6/20], Batch Num: [479/600] Discriminator Loss: 0.3809, Generator Loss: 4.0935 D(x): 0.8935, D(G(z)): 0.1112 Epoch: [6/20], Batch Num: [480/600] Discriminator Loss: 0.4549, Generator Loss: 4.0395 D(x): 0.8454, D(G(z)): 0.0858 Epoch: [6/20], Batch Num: [481/600] Discriminator Loss: 0.3049, Generator Loss: 3.6657 D(x): 0.8915, D(G(z)): 0.0915 Epoch: [6/20], Batch Num: [482/600] Discriminator Loss: 0.3259, Generator Loss: 3.1344 D(x): 0.8947, D(G(z)): 0.0934 Epoch: [6/20], Batch Num: [483/600] Discriminator Loss: 0.4806, Generator Loss: 3.2347 D(x): 0.8934, D(G(z)): 0.1491 Epoch: [6/20], Batch Num: [484/600] Discriminator Loss: 0.2935, Generator Loss: 3.6989 D(x): 0.9447, D(G(z)): 0.1280 Epoch: [6/20], Batch Num: [485/600] Discriminator Loss: 0.4834, Generator Loss: 3.6118 D(x): 0.8194, D(G(z)): 0.0900 Epoch: [6/20], Batch Num: [486/600] Discriminator Loss: 0.3858, Generator Loss: 3.2345 D(x): 0.8560, D(G(z)): 0.1014 Epoch: [6/20], Batch Num: [487/600] Discriminator Loss: 0.4579, Generator Loss: 3.1352 D(x): 0.8579, D(G(z)): 0.1216 Epoch: [6/20], Batch Num: [488/600] Discriminator Loss: 0.3843, Generator Loss: 3.0878 D(x): 0.8823, D(G(z)): 0.1420 Epoch: [6/20], Batch Num: [489/600] Discriminator Loss: 0.4556, Generator Loss: 3.5850 D(x): 0.9058, D(G(z)): 0.1981 Epoch: [6/20], Batch Num: [490/600] Discriminator Loss: 0.3459, Generator Loss: 4.0117 D(x): 0.8916, D(G(z)): 0.1021 Epoch: [6/20], Batch Num: [491/600] Discriminator Loss: 0.3438, Generator Loss: 3.8084 D(x): 0.8655, D(G(z)): 0.0503 Epoch: [6/20], Batch Num: [492/600] Discriminator Loss: 0.1958, Generator Loss: 3.4847 D(x): 0.9109, D(G(z)): 0.0651 Epoch: [6/20], Batch Num: [493/600] Discriminator Loss: 0.3460, Generator Loss: 2.9660 D(x): 0.8771, D(G(z)): 0.0901 Epoch: [6/20], Batch Num: [494/600] Discriminator Loss: 0.2258, Generator Loss: 3.1044 D(x): 0.9473, D(G(z)): 0.1228 Epoch: [6/20], Batch Num: [495/600] Discriminator Loss: 0.2917, Generator Loss: 3.6205 D(x): 0.9214, D(G(z)): 0.1166 Epoch: [6/20], Batch Num: [496/600] Discriminator Loss: 0.3036, Generator Loss: 4.0656 D(x): 0.8987, D(G(z)): 0.0915 Epoch: [6/20], Batch Num: [497/600] Discriminator Loss: 0.3180, Generator Loss: 4.2482 D(x): 0.8968, D(G(z)): 0.0979 Epoch: [6/20], Batch Num: [498/600] Discriminator Loss: 0.1951, Generator Loss: 4.2781 D(x): 0.9208, D(G(z)): 0.0666 Epoch: [6/20], Batch Num: [499/600] Discriminator Loss: 0.1402, Generator Loss: 4.9455 D(x): 0.9752, D(G(z)): 0.0892 Epoch: 6, Batch Num: [500/600]
Epoch: [6/20], Batch Num: [500/600] Discriminator Loss: 0.2708, Generator Loss: 4.8953 D(x): 0.8758, D(G(z)): 0.0628 Epoch: [6/20], Batch Num: [501/600] Discriminator Loss: 0.2142, Generator Loss: 4.6695 D(x): 0.9147, D(G(z)): 0.0601 Epoch: [6/20], Batch Num: [502/600] Discriminator Loss: 0.2153, Generator Loss: 4.2154 D(x): 0.9305, D(G(z)): 0.0602 Epoch: [6/20], Batch Num: [503/600] Discriminator Loss: 0.2223, Generator Loss: 4.4596 D(x): 0.9448, D(G(z)): 0.1009 Epoch: [6/20], Batch Num: [504/600] Discriminator Loss: 0.1777, Generator Loss: 4.6772 D(x): 0.9586, D(G(z)): 0.0677 Epoch: [6/20], Batch Num: [505/600] Discriminator Loss: 0.2492, Generator Loss: 6.1317 D(x): 0.9506, D(G(z)): 0.0970 Epoch: [6/20], Batch Num: [506/600] Discriminator Loss: 0.4914, Generator Loss: 6.2315 D(x): 0.8895, D(G(z)): 0.0755 Epoch: [6/20], Batch Num: [507/600] Discriminator Loss: 0.2890, Generator Loss: 5.9629 D(x): 0.8731, D(G(z)): 0.0395 Epoch: [6/20], Batch Num: [508/600] Discriminator Loss: 0.2166, Generator Loss: 4.8428 D(x): 0.9266, D(G(z)): 0.0628 Epoch: [6/20], Batch Num: [509/600] Discriminator Loss: 0.4273, Generator Loss: 4.6628 D(x): 0.9284, D(G(z)): 0.1452 Epoch: [6/20], Batch Num: [510/600] Discriminator Loss: 0.3307, Generator Loss: 5.7046 D(x): 0.9687, D(G(z)): 0.1324 Epoch: [6/20], Batch Num: [511/600] Discriminator Loss: 0.5303, Generator Loss: 5.7323 D(x): 0.8844, D(G(z)): 0.0718 Epoch: [6/20], Batch Num: [512/600] Discriminator Loss: 0.5423, Generator Loss: 5.0027 D(x): 0.8561, D(G(z)): 0.0576 Epoch: [6/20], Batch Num: [513/600] Discriminator Loss: 0.3942, Generator Loss: 2.9077 D(x): 0.8713, D(G(z)): 0.0801 Epoch: [6/20], Batch Num: [514/600] Discriminator Loss: 0.5719, Generator Loss: 3.5069 D(x): 0.9427, D(G(z)): 0.2469 Epoch: [6/20], Batch Num: [515/600] Discriminator Loss: 0.7140, Generator Loss: 4.9472 D(x): 0.8609, D(G(z)): 0.2266 Epoch: [6/20], Batch Num: [516/600] Discriminator Loss: 0.3812, Generator Loss: 5.2988 D(x): 0.8383, D(G(z)): 0.0682 Epoch: [6/20], Batch Num: [517/600] Discriminator Loss: 0.4708, Generator Loss: 4.3949 D(x): 0.8116, D(G(z)): 0.0420 Epoch: [6/20], Batch Num: [518/600] Discriminator Loss: 0.4584, Generator Loss: 3.2455 D(x): 0.8521, D(G(z)): 0.1143 Epoch: [6/20], Batch Num: [519/600] Discriminator Loss: 0.5263, Generator Loss: 2.7009 D(x): 0.9035, D(G(z)): 0.2086 Epoch: [6/20], Batch Num: [520/600] Discriminator Loss: 0.6434, Generator Loss: 2.9829 D(x): 0.8416, D(G(z)): 0.1836 Epoch: [6/20], Batch Num: [521/600] Discriminator Loss: 0.5016, Generator Loss: 3.8083 D(x): 0.8922, D(G(z)): 0.1896 Epoch: [6/20], Batch Num: [522/600] Discriminator Loss: 0.4144, Generator Loss: 4.5480 D(x): 0.8747, D(G(z)): 0.1364 Epoch: [6/20], Batch Num: [523/600] Discriminator Loss: 0.4878, Generator Loss: 4.2365 D(x): 0.8220, D(G(z)): 0.0792 Epoch: [6/20], Batch Num: [524/600] Discriminator Loss: 0.3872, Generator Loss: 3.4853 D(x): 0.8529, D(G(z)): 0.0944 Epoch: [6/20], Batch Num: [525/600] Discriminator Loss: 0.4190, Generator Loss: 3.6452 D(x): 0.8967, D(G(z)): 0.1632 Epoch: [6/20], Batch Num: [526/600] Discriminator Loss: 0.3702, Generator Loss: 3.3361 D(x): 0.8871, D(G(z)): 0.1553 Epoch: [6/20], Batch Num: [527/600] Discriminator Loss: 0.4575, Generator Loss: 3.7457 D(x): 0.8537, D(G(z)): 0.1356 Epoch: [6/20], Batch Num: [528/600] Discriminator Loss: 0.4177, Generator Loss: 3.5059 D(x): 0.8671, D(G(z)): 0.1228 Epoch: [6/20], Batch Num: [529/600] Discriminator Loss: 0.2482, Generator Loss: 4.1717 D(x): 0.9191, D(G(z)): 0.1169 Epoch: [6/20], Batch Num: [530/600] Discriminator Loss: 0.3072, Generator Loss: 4.0295 D(x): 0.8985, D(G(z)): 0.0981 Epoch: [6/20], Batch Num: [531/600] Discriminator Loss: 0.3396, Generator Loss: 3.8580 D(x): 0.8755, D(G(z)): 0.0745 Epoch: [6/20], Batch Num: [532/600] Discriminator Loss: 0.3299, Generator Loss: 3.9045 D(x): 0.8939, D(G(z)): 0.0894 Epoch: [6/20], Batch Num: [533/600] Discriminator Loss: 0.2677, Generator Loss: 3.6590 D(x): 0.9252, D(G(z)): 0.1118 Epoch: [6/20], Batch Num: [534/600] Discriminator Loss: 0.3349, Generator Loss: 3.8436 D(x): 0.9159, D(G(z)): 0.1387 Epoch: [6/20], Batch Num: [535/600] Discriminator Loss: 0.2621, Generator Loss: 4.5636 D(x): 0.9161, D(G(z)): 0.0712 Epoch: [6/20], Batch Num: [536/600] Discriminator Loss: 0.2614, Generator Loss: 5.2751 D(x): 0.9359, D(G(z)): 0.1088 Epoch: [6/20], Batch Num: [537/600] Discriminator Loss: 0.3968, Generator Loss: 4.3715 D(x): 0.8492, D(G(z)): 0.0544 Epoch: [6/20], Batch Num: [538/600] Discriminator Loss: 0.3865, Generator Loss: 3.5730 D(x): 0.8781, D(G(z)): 0.0764 Epoch: [6/20], Batch Num: [539/600] Discriminator Loss: 0.3873, Generator Loss: 2.9836 D(x): 0.9046, D(G(z)): 0.1154 Epoch: [6/20], Batch Num: [540/600] Discriminator Loss: 0.3852, Generator Loss: 4.1331 D(x): 0.9562, D(G(z)): 0.1910 Epoch: [6/20], Batch Num: [541/600] Discriminator Loss: 0.2662, Generator Loss: 5.5479 D(x): 0.9097, D(G(z)): 0.0864 Epoch: [6/20], Batch Num: [542/600] Discriminator Loss: 0.4004, Generator Loss: 4.9274 D(x): 0.8408, D(G(z)): 0.0520 Epoch: [6/20], Batch Num: [543/600] Discriminator Loss: 0.4393, Generator Loss: 3.0157 D(x): 0.8206, D(G(z)): 0.0522 Epoch: [6/20], Batch Num: [544/600] Discriminator Loss: 0.3672, Generator Loss: 2.6372 D(x): 0.9503, D(G(z)): 0.1888 Epoch: [6/20], Batch Num: [545/600] Discriminator Loss: 0.4625, Generator Loss: 4.7300 D(x): 0.9393, D(G(z)): 0.2310 Epoch: [6/20], Batch Num: [546/600] Discriminator Loss: 0.3910, Generator Loss: 4.8467 D(x): 0.8506, D(G(z)): 0.0678 Epoch: [6/20], Batch Num: [547/600] Discriminator Loss: 0.5557, Generator Loss: 4.4246 D(x): 0.8173, D(G(z)): 0.0692 Epoch: [6/20], Batch Num: [548/600] Discriminator Loss: 0.4171, Generator Loss: 3.2847 D(x): 0.8760, D(G(z)): 0.0778 Epoch: [6/20], Batch Num: [549/600] Discriminator Loss: 0.4604, Generator Loss: 3.3988 D(x): 0.8981, D(G(z)): 0.1691 Epoch: [6/20], Batch Num: [550/600] Discriminator Loss: 0.5825, Generator Loss: 3.3637 D(x): 0.8728, D(G(z)): 0.1615 Epoch: [6/20], Batch Num: [551/600] Discriminator Loss: 0.6153, Generator Loss: 3.8832 D(x): 0.8574, D(G(z)): 0.1943 Epoch: [6/20], Batch Num: [552/600] Discriminator Loss: 0.5931, Generator Loss: 3.5102 D(x): 0.7949, D(G(z)): 0.0908 Epoch: [6/20], Batch Num: [553/600] Discriminator Loss: 0.4221, Generator Loss: 3.2276 D(x): 0.8768, D(G(z)): 0.1271 Epoch: [6/20], Batch Num: [554/600] Discriminator Loss: 0.5592, Generator Loss: 3.2454 D(x): 0.8580, D(G(z)): 0.1562 Epoch: [6/20], Batch Num: [555/600] Discriminator Loss: 0.4573, Generator Loss: 3.7253 D(x): 0.9074, D(G(z)): 0.1517 Epoch: [6/20], Batch Num: [556/600] Discriminator Loss: 0.2279, Generator Loss: 3.4870 D(x): 0.9060, D(G(z)): 0.0594 Epoch: [6/20], Batch Num: [557/600] Discriminator Loss: 0.4080, Generator Loss: 3.2863 D(x): 0.8566, D(G(z)): 0.0868 Epoch: [6/20], Batch Num: [558/600] Discriminator Loss: 0.2932, Generator Loss: 3.0081 D(x): 0.9070, D(G(z)): 0.0727 Epoch: [6/20], Batch Num: [559/600] Discriminator Loss: 0.4525, Generator Loss: 3.4558 D(x): 0.9109, D(G(z)): 0.1596 Epoch: [6/20], Batch Num: [560/600] Discriminator Loss: 0.3235, Generator Loss: 3.7339 D(x): 0.9179, D(G(z)): 0.1092 Epoch: [6/20], Batch Num: [561/600] Discriminator Loss: 0.3323, Generator Loss: 3.8435 D(x): 0.8662, D(G(z)): 0.0551 Epoch: [6/20], Batch Num: [562/600] Discriminator Loss: 0.2089, Generator Loss: 3.8197 D(x): 0.9396, D(G(z)): 0.0775 Epoch: [6/20], Batch Num: [563/600] Discriminator Loss: 0.1694, Generator Loss: 3.9688 D(x): 0.9413, D(G(z)): 0.0644 Epoch: [6/20], Batch Num: [564/600] Discriminator Loss: 0.2793, Generator Loss: 4.0995 D(x): 0.9201, D(G(z)): 0.0780 Epoch: [6/20], Batch Num: [565/600] Discriminator Loss: 0.2776, Generator Loss: 4.0558 D(x): 0.9093, D(G(z)): 0.0818 Epoch: [6/20], Batch Num: [566/600] Discriminator Loss: 0.1844, Generator Loss: 3.7299 D(x): 0.9279, D(G(z)): 0.0632 Epoch: [6/20], Batch Num: [567/600] Discriminator Loss: 0.1708, Generator Loss: 3.7706 D(x): 0.9430, D(G(z)): 0.0772 Epoch: [6/20], Batch Num: [568/600] Discriminator Loss: 0.2592, Generator Loss: 3.9619 D(x): 0.9416, D(G(z)): 0.0777 Epoch: [6/20], Batch Num: [569/600] Discriminator Loss: 0.1245, Generator Loss: 3.9499 D(x): 0.9720, D(G(z)): 0.0708 Epoch: [6/20], Batch Num: [570/600] Discriminator Loss: 0.1588, Generator Loss: 4.1737 D(x): 0.9381, D(G(z)): 0.0416 Epoch: [6/20], Batch Num: [571/600] Discriminator Loss: 0.1546, Generator Loss: 4.6316 D(x): 0.9615, D(G(z)): 0.0448 Epoch: [6/20], Batch Num: [572/600] Discriminator Loss: 0.1431, Generator Loss: 4.3549 D(x): 0.9504, D(G(z)): 0.0357 Epoch: [6/20], Batch Num: [573/600] Discriminator Loss: 0.1306, Generator Loss: 4.3491 D(x): 0.9308, D(G(z)): 0.0276 Epoch: [6/20], Batch Num: [574/600] Discriminator Loss: 0.2005, Generator Loss: 3.7879 D(x): 0.9280, D(G(z)): 0.0491 Epoch: [6/20], Batch Num: [575/600] Discriminator Loss: 0.1725, Generator Loss: 3.1827 D(x): 0.9572, D(G(z)): 0.0669 Epoch: [6/20], Batch Num: [576/600] Discriminator Loss: 0.2939, Generator Loss: 3.8008 D(x): 0.9680, D(G(z)): 0.1588 Epoch: [6/20], Batch Num: [577/600] Discriminator Loss: 0.3764, Generator Loss: 5.2113 D(x): 0.9482, D(G(z)): 0.1466 Epoch: [6/20], Batch Num: [578/600] Discriminator Loss: 0.2790, Generator Loss: 5.3532 D(x): 0.8761, D(G(z)): 0.0185 Epoch: [6/20], Batch Num: [579/600] Discriminator Loss: 0.2396, Generator Loss: 4.8661 D(x): 0.8924, D(G(z)): 0.0118 Epoch: [6/20], Batch Num: [580/600] Discriminator Loss: 0.2670, Generator Loss: 4.3546 D(x): 0.9063, D(G(z)): 0.0433 Epoch: [6/20], Batch Num: [581/600] Discriminator Loss: 0.2436, Generator Loss: 3.1942 D(x): 0.9037, D(G(z)): 0.0481 Epoch: [6/20], Batch Num: [582/600] Discriminator Loss: 0.2979, Generator Loss: 3.1293 D(x): 0.9537, D(G(z)): 0.1422 Epoch: [6/20], Batch Num: [583/600] Discriminator Loss: 0.3429, Generator Loss: 4.5306 D(x): 0.9627, D(G(z)): 0.1886 Epoch: [6/20], Batch Num: [584/600] Discriminator Loss: 0.3413, Generator Loss: 4.8217 D(x): 0.8752, D(G(z)): 0.0370 Epoch: [6/20], Batch Num: [585/600] Discriminator Loss: 0.2644, Generator Loss: 4.9682 D(x): 0.8911, D(G(z)): 0.0268 Epoch: [6/20], Batch Num: [586/600] Discriminator Loss: 0.2707, Generator Loss: 4.3326 D(x): 0.8936, D(G(z)): 0.0297 Epoch: [6/20], Batch Num: [587/600] Discriminator Loss: 0.2891, Generator Loss: 4.1931 D(x): 0.9168, D(G(z)): 0.0708 Epoch: [6/20], Batch Num: [588/600] Discriminator Loss: 0.2963, Generator Loss: 3.7675 D(x): 0.9507, D(G(z)): 0.1142 Epoch: [6/20], Batch Num: [589/600] Discriminator Loss: 0.3823, Generator Loss: 3.8858 D(x): 0.9274, D(G(z)): 0.1096 Epoch: [6/20], Batch Num: [590/600] Discriminator Loss: 0.3307, Generator Loss: 4.3058 D(x): 0.9277, D(G(z)): 0.1097 Epoch: [6/20], Batch Num: [591/600] Discriminator Loss: 0.3009, Generator Loss: 4.3692 D(x): 0.9135, D(G(z)): 0.0840 Epoch: [6/20], Batch Num: [592/600] Discriminator Loss: 0.3825, Generator Loss: 4.3377 D(x): 0.8966, D(G(z)): 0.0548 Epoch: [6/20], Batch Num: [593/600] Discriminator Loss: 0.3163, Generator Loss: 3.9374 D(x): 0.9077, D(G(z)): 0.0724 Epoch: [6/20], Batch Num: [594/600] Discriminator Loss: 0.2508, Generator Loss: 3.7784 D(x): 0.9186, D(G(z)): 0.0786 Epoch: [6/20], Batch Num: [595/600] Discriminator Loss: 0.2543, Generator Loss: 4.0549 D(x): 0.9551, D(G(z)): 0.1083 Epoch: [6/20], Batch Num: [596/600] Discriminator Loss: 0.1850, Generator Loss: 4.3017 D(x): 0.9324, D(G(z)): 0.0660 Epoch: [6/20], Batch Num: [597/600] Discriminator Loss: 0.3024, Generator Loss: 4.1694 D(x): 0.9062, D(G(z)): 0.0415 Epoch: [6/20], Batch Num: [598/600] Discriminator Loss: 0.3874, Generator Loss: 3.7637 D(x): 0.8838, D(G(z)): 0.0699 Epoch: [6/20], Batch Num: [599/600] Discriminator Loss: 0.3121, Generator Loss: 3.6808 D(x): 0.9086, D(G(z)): 0.0740 Epoch: 7, Batch Num: [0/600]
Epoch: [7/20], Batch Num: [0/600] Discriminator Loss: 0.2915, Generator Loss: 3.5438 D(x): 0.9127, D(G(z)): 0.1098 Epoch: [7/20], Batch Num: [1/600] Discriminator Loss: 0.3691, Generator Loss: 3.9567 D(x): 0.9206, D(G(z)): 0.1340 Epoch: [7/20], Batch Num: [2/600] Discriminator Loss: 0.2331, Generator Loss: 4.4415 D(x): 0.9125, D(G(z)): 0.0586 Epoch: [7/20], Batch Num: [3/600] Discriminator Loss: 0.2546, Generator Loss: 4.2082 D(x): 0.9252, D(G(z)): 0.0639 Epoch: [7/20], Batch Num: [4/600] Discriminator Loss: 0.3475, Generator Loss: 3.7615 D(x): 0.8948, D(G(z)): 0.0848 Epoch: [7/20], Batch Num: [5/600] Discriminator Loss: 0.3326, Generator Loss: 3.7920 D(x): 0.9182, D(G(z)): 0.1059 Epoch: [7/20], Batch Num: [6/600] Discriminator Loss: 0.3353, Generator Loss: 4.2407 D(x): 0.9336, D(G(z)): 0.1128 Epoch: [7/20], Batch Num: [7/600] Discriminator Loss: 0.2374, Generator Loss: 4.2718 D(x): 0.9291, D(G(z)): 0.0693 Epoch: [7/20], Batch Num: [8/600] Discriminator Loss: 0.2353, Generator Loss: 4.3081 D(x): 0.9445, D(G(z)): 0.0706 Epoch: [7/20], Batch Num: [9/600] Discriminator Loss: 0.4593, Generator Loss: 3.8367 D(x): 0.8485, D(G(z)): 0.0581 Epoch: [7/20], Batch Num: [10/600] Discriminator Loss: 0.4634, Generator Loss: 2.9007 D(x): 0.8694, D(G(z)): 0.0800 Epoch: [7/20], Batch Num: [11/600] Discriminator Loss: 0.3653, Generator Loss: 2.7389 D(x): 0.9127, D(G(z)): 0.1199 Epoch: [7/20], Batch Num: [12/600] Discriminator Loss: 0.4588, Generator Loss: 3.1673 D(x): 0.9129, D(G(z)): 0.1992 Epoch: [7/20], Batch Num: [13/600] Discriminator Loss: 0.3124, Generator Loss: 4.0503 D(x): 0.9351, D(G(z)): 0.1363 Epoch: [7/20], Batch Num: [14/600] Discriminator Loss: 0.2487, Generator Loss: 4.4337 D(x): 0.9154, D(G(z)): 0.0572 Epoch: [7/20], Batch Num: [15/600] Discriminator Loss: 0.4586, Generator Loss: 3.9545 D(x): 0.8330, D(G(z)): 0.0373 Epoch: [7/20], Batch Num: [16/600] Discriminator Loss: 0.4676, Generator Loss: 3.2223 D(x): 0.8076, D(G(z)): 0.0381 Epoch: [7/20], Batch Num: [17/600] Discriminator Loss: 0.3529, Generator Loss: 2.7501 D(x): 0.9210, D(G(z)): 0.1408 Epoch: [7/20], Batch Num: [18/600] Discriminator Loss: 0.4297, Generator Loss: 2.7633 D(x): 0.9376, D(G(z)): 0.2166 Epoch: [7/20], Batch Num: [19/600] Discriminator Loss: 0.4274, Generator Loss: 3.2972 D(x): 0.8956, D(G(z)): 0.1736 Epoch: [7/20], Batch Num: [20/600] Discriminator Loss: 0.1766, Generator Loss: 4.0235 D(x): 0.9635, D(G(z)): 0.1120 Epoch: [7/20], Batch Num: [21/600] Discriminator Loss: 0.2623, Generator Loss: 4.5372 D(x): 0.9073, D(G(z)): 0.0652 Epoch: [7/20], Batch Num: [22/600] Discriminator Loss: 0.3507, Generator Loss: 4.3593 D(x): 0.8393, D(G(z)): 0.0407 Epoch: [7/20], Batch Num: [23/600] Discriminator Loss: 0.3794, Generator Loss: 3.6431 D(x): 0.8788, D(G(z)): 0.0415 Epoch: [7/20], Batch Num: [24/600] Discriminator Loss: 0.3280, Generator Loss: 3.0967 D(x): 0.8895, D(G(z)): 0.0820 Epoch: [7/20], Batch Num: [25/600] Discriminator Loss: 0.2417, Generator Loss: 2.8322 D(x): 0.9483, D(G(z)): 0.1282 Epoch: [7/20], Batch Num: [26/600] Discriminator Loss: 0.2733, Generator Loss: 3.2141 D(x): 0.9645, D(G(z)): 0.1679 Epoch: [7/20], Batch Num: [27/600] Discriminator Loss: 0.1893, Generator Loss: 3.9150 D(x): 0.9519, D(G(z)): 0.1077 Epoch: [7/20], Batch Num: [28/600] Discriminator Loss: 0.2278, Generator Loss: 4.0738 D(x): 0.9225, D(G(z)): 0.0701 Epoch: [7/20], Batch Num: [29/600] Discriminator Loss: 0.3323, Generator Loss: 4.4564 D(x): 0.8702, D(G(z)): 0.0456 Epoch: [7/20], Batch Num: [30/600] Discriminator Loss: 0.3115, Generator Loss: 4.1066 D(x): 0.8825, D(G(z)): 0.0584 Epoch: [7/20], Batch Num: [31/600] Discriminator Loss: 0.1574, Generator Loss: 4.0557 D(x): 0.9514, D(G(z)): 0.0616 Epoch: [7/20], Batch Num: [32/600] Discriminator Loss: 0.2454, Generator Loss: 3.6892 D(x): 0.9188, D(G(z)): 0.0805 Epoch: [7/20], Batch Num: [33/600] Discriminator Loss: 0.2555, Generator Loss: 3.1555 D(x): 0.9355, D(G(z)): 0.0946 Epoch: [7/20], Batch Num: [34/600] Discriminator Loss: 0.1538, Generator Loss: 3.3108 D(x): 0.9829, D(G(z)): 0.1089 Epoch: [7/20], Batch Num: [35/600] Discriminator Loss: 0.2995, Generator Loss: 3.6176 D(x): 0.9391, D(G(z)): 0.1244 Epoch: [7/20], Batch Num: [36/600] Discriminator Loss: 0.2738, Generator Loss: 4.1639 D(x): 0.9328, D(G(z)): 0.1133 Epoch: [7/20], Batch Num: [37/600] Discriminator Loss: 0.2433, Generator Loss: 4.5253 D(x): 0.9288, D(G(z)): 0.0605 Epoch: [7/20], Batch Num: [38/600] Discriminator Loss: 0.2197, Generator Loss: 4.5665 D(x): 0.9085, D(G(z)): 0.0382 Epoch: [7/20], Batch Num: [39/600] Discriminator Loss: 0.2515, Generator Loss: 4.3798 D(x): 0.9125, D(G(z)): 0.0630 Epoch: [7/20], Batch Num: [40/600] Discriminator Loss: 0.2500, Generator Loss: 3.5939 D(x): 0.9167, D(G(z)): 0.0749 Epoch: [7/20], Batch Num: [41/600] Discriminator Loss: 0.2523, Generator Loss: 3.7177 D(x): 0.9336, D(G(z)): 0.1085 Epoch: [7/20], Batch Num: [42/600] Discriminator Loss: 0.2001, Generator Loss: 3.6477 D(x): 0.9444, D(G(z)): 0.0921 Epoch: [7/20], Batch Num: [43/600] Discriminator Loss: 0.1579, Generator Loss: 4.2859 D(x): 0.9906, D(G(z)): 0.1130 Epoch: [7/20], Batch Num: [44/600] Discriminator Loss: 0.1384, Generator Loss: 4.8634 D(x): 0.9561, D(G(z)): 0.0574 Epoch: [7/20], Batch Num: [45/600] Discriminator Loss: 0.2444, Generator Loss: 5.0869 D(x): 0.8923, D(G(z)): 0.0367 Epoch: [7/20], Batch Num: [46/600] Discriminator Loss: 0.3851, Generator Loss: 4.5721 D(x): 0.8702, D(G(z)): 0.0509 Epoch: [7/20], Batch Num: [47/600] Discriminator Loss: 0.1662, Generator Loss: 4.3093 D(x): 0.9358, D(G(z)): 0.0383 Epoch: [7/20], Batch Num: [48/600] Discriminator Loss: 0.2352, Generator Loss: 3.8819 D(x): 0.9531, D(G(z)): 0.1173 Epoch: [7/20], Batch Num: [49/600] Discriminator Loss: 0.3487, Generator Loss: 4.0641 D(x): 0.9523, D(G(z)): 0.1611 Epoch: [7/20], Batch Num: [50/600] Discriminator Loss: 0.2001, Generator Loss: 4.4669 D(x): 0.9473, D(G(z)): 0.0751 Epoch: [7/20], Batch Num: [51/600] Discriminator Loss: 0.1600, Generator Loss: 5.0063 D(x): 0.9499, D(G(z)): 0.0641 Epoch: [7/20], Batch Num: [52/600] Discriminator Loss: 0.2505, Generator Loss: 5.2797 D(x): 0.8996, D(G(z)): 0.0344 Epoch: [7/20], Batch Num: [53/600] Discriminator Loss: 0.2536, Generator Loss: 4.9645 D(x): 0.9085, D(G(z)): 0.0509 Epoch: [7/20], Batch Num: [54/600] Discriminator Loss: 0.2765, Generator Loss: 4.0309 D(x): 0.8982, D(G(z)): 0.0458 Epoch: [7/20], Batch Num: [55/600] Discriminator Loss: 0.3080, Generator Loss: 3.4533 D(x): 0.9190, D(G(z)): 0.0796 Epoch: [7/20], Batch Num: [56/600] Discriminator Loss: 0.3234, Generator Loss: 2.9897 D(x): 0.9414, D(G(z)): 0.1412 Epoch: [7/20], Batch Num: [57/600] Discriminator Loss: 0.5307, Generator Loss: 4.1876 D(x): 0.9726, D(G(z)): 0.2619 Epoch: [7/20], Batch Num: [58/600] Discriminator Loss: 0.2492, Generator Loss: 5.2062 D(x): 0.9018, D(G(z)): 0.0811 Epoch: [7/20], Batch Num: [59/600] Discriminator Loss: 0.2944, Generator Loss: 5.1712 D(x): 0.8892, D(G(z)): 0.0520 Epoch: [7/20], Batch Num: [60/600] Discriminator Loss: 0.3086, Generator Loss: 4.8279 D(x): 0.8711, D(G(z)): 0.0456 Epoch: [7/20], Batch Num: [61/600] Discriminator Loss: 0.4240, Generator Loss: 3.8285 D(x): 0.8514, D(G(z)): 0.0579 Epoch: [7/20], Batch Num: [62/600] Discriminator Loss: 0.2217, Generator Loss: 3.1211 D(x): 0.9305, D(G(z)): 0.0969 Epoch: [7/20], Batch Num: [63/600] Discriminator Loss: 0.5049, Generator Loss: 3.4230 D(x): 0.9384, D(G(z)): 0.2357 Epoch: [7/20], Batch Num: [64/600] Discriminator Loss: 0.4081, Generator Loss: 4.1234 D(x): 0.9013, D(G(z)): 0.1337 Epoch: [7/20], Batch Num: [65/600] Discriminator Loss: 0.3279, Generator Loss: 4.9016 D(x): 0.9038, D(G(z)): 0.0998 Epoch: [7/20], Batch Num: [66/600] Discriminator Loss: 0.2802, Generator Loss: 4.8601 D(x): 0.8922, D(G(z)): 0.0609 Epoch: [7/20], Batch Num: [67/600] Discriminator Loss: 0.3695, Generator Loss: 4.3677 D(x): 0.8645, D(G(z)): 0.0783 Epoch: [7/20], Batch Num: [68/600] Discriminator Loss: 0.2941, Generator Loss: 4.4912 D(x): 0.9235, D(G(z)): 0.1153 Epoch: [7/20], Batch Num: [69/600] Discriminator Loss: 0.3119, Generator Loss: 4.3860 D(x): 0.9371, D(G(z)): 0.1179 Epoch: [7/20], Batch Num: [70/600] Discriminator Loss: 0.2984, Generator Loss: 5.7246 D(x): 0.9215, D(G(z)): 0.1104 Epoch: [7/20], Batch Num: [71/600] Discriminator Loss: 0.2244, Generator Loss: 5.5691 D(x): 0.9294, D(G(z)): 0.0790 Epoch: [7/20], Batch Num: [72/600] Discriminator Loss: 0.2025, Generator Loss: 5.9319 D(x): 0.9396, D(G(z)): 0.0709 Epoch: [7/20], Batch Num: [73/600] Discriminator Loss: 0.3830, Generator Loss: 5.5639 D(x): 0.8612, D(G(z)): 0.0559 Epoch: [7/20], Batch Num: [74/600] Discriminator Loss: 0.2713, Generator Loss: 5.1399 D(x): 0.9225, D(G(z)): 0.0861 Epoch: [7/20], Batch Num: [75/600] Discriminator Loss: 0.2800, Generator Loss: 4.4686 D(x): 0.9045, D(G(z)): 0.0753 Epoch: [7/20], Batch Num: [76/600] Discriminator Loss: 0.5516, Generator Loss: 6.0560 D(x): 0.9280, D(G(z)): 0.2232 Epoch: [7/20], Batch Num: [77/600] Discriminator Loss: 0.4086, Generator Loss: 6.1784 D(x): 0.8384, D(G(z)): 0.0692 Epoch: [7/20], Batch Num: [78/600] Discriminator Loss: 0.5061, Generator Loss: 4.4908 D(x): 0.8115, D(G(z)): 0.0422 Epoch: [7/20], Batch Num: [79/600] Discriminator Loss: 0.3035, Generator Loss: 3.3257 D(x): 0.9092, D(G(z)): 0.1065 Epoch: [7/20], Batch Num: [80/600] Discriminator Loss: 0.5961, Generator Loss: 4.3507 D(x): 0.9355, D(G(z)): 0.2530 Epoch: [7/20], Batch Num: [81/600] Discriminator Loss: 0.3068, Generator Loss: 5.6625 D(x): 0.9243, D(G(z)): 0.1015 Epoch: [7/20], Batch Num: [82/600] Discriminator Loss: 0.5469, Generator Loss: 5.0158 D(x): 0.8009, D(G(z)): 0.0517 Epoch: [7/20], Batch Num: [83/600] Discriminator Loss: 0.7012, Generator Loss: 2.7822 D(x): 0.7731, D(G(z)): 0.0461 Epoch: [7/20], Batch Num: [84/600] Discriminator Loss: 0.4909, Generator Loss: 2.7294 D(x): 0.9274, D(G(z)): 0.2206 Epoch: [7/20], Batch Num: [85/600] Discriminator Loss: 0.4674, Generator Loss: 2.9345 D(x): 0.9304, D(G(z)): 0.1975 Epoch: [7/20], Batch Num: [86/600] Discriminator Loss: 0.5152, Generator Loss: 4.3745 D(x): 0.9276, D(G(z)): 0.1989 Epoch: [7/20], Batch Num: [87/600] Discriminator Loss: 0.3831, Generator Loss: 5.1150 D(x): 0.8597, D(G(z)): 0.0500 Epoch: [7/20], Batch Num: [88/600] Discriminator Loss: 0.4336, Generator Loss: 4.9029 D(x): 0.8332, D(G(z)): 0.0558 Epoch: [7/20], Batch Num: [89/600] Discriminator Loss: 0.5089, Generator Loss: 3.2860 D(x): 0.8026, D(G(z)): 0.0625 Epoch: [7/20], Batch Num: [90/600] Discriminator Loss: 0.4912, Generator Loss: 2.6700 D(x): 0.8988, D(G(z)): 0.1901 Epoch: [7/20], Batch Num: [91/600] Discriminator Loss: 0.4233, Generator Loss: 2.8076 D(x): 0.9123, D(G(z)): 0.1949 Epoch: [7/20], Batch Num: [92/600] Discriminator Loss: 0.3046, Generator Loss: 3.2701 D(x): 0.8992, D(G(z)): 0.1101 Epoch: [7/20], Batch Num: [93/600] Discriminator Loss: 0.3059, Generator Loss: 3.6499 D(x): 0.9083, D(G(z)): 0.1036 Epoch: [7/20], Batch Num: [94/600] Discriminator Loss: 0.3515, Generator Loss: 3.6585 D(x): 0.8908, D(G(z)): 0.0652 Epoch: [7/20], Batch Num: [95/600] Discriminator Loss: 0.3632, Generator Loss: 3.2536 D(x): 0.8730, D(G(z)): 0.0657 Epoch: [7/20], Batch Num: [96/600] Discriminator Loss: 0.3456, Generator Loss: 3.3066 D(x): 0.8730, D(G(z)): 0.0710 Epoch: [7/20], Batch Num: [97/600] Discriminator Loss: 0.2951, Generator Loss: 2.8496 D(x): 0.9068, D(G(z)): 0.0824 Epoch: [7/20], Batch Num: [98/600] Discriminator Loss: 0.2639, Generator Loss: 2.7102 D(x): 0.9372, D(G(z)): 0.1188 Epoch: [7/20], Batch Num: [99/600] Discriminator Loss: 0.3637, Generator Loss: 3.2954 D(x): 0.9195, D(G(z)): 0.1496 Epoch: 7, Batch Num: [100/600]
Epoch: [7/20], Batch Num: [100/600] Discriminator Loss: 0.1547, Generator Loss: 3.9251 D(x): 0.9680, D(G(z)): 0.0762 Epoch: [7/20], Batch Num: [101/600] Discriminator Loss: 0.1721, Generator Loss: 4.3288 D(x): 0.9386, D(G(z)): 0.0507 Epoch: [7/20], Batch Num: [102/600] Discriminator Loss: 0.2005, Generator Loss: 4.3109 D(x): 0.9122, D(G(z)): 0.0174 Epoch: [7/20], Batch Num: [103/600] Discriminator Loss: 0.2105, Generator Loss: 4.1030 D(x): 0.9170, D(G(z)): 0.0261 Epoch: [7/20], Batch Num: [104/600] Discriminator Loss: 0.2666, Generator Loss: 3.3884 D(x): 0.9030, D(G(z)): 0.0470 Epoch: [7/20], Batch Num: [105/600] Discriminator Loss: 0.2071, Generator Loss: 3.1203 D(x): 0.9542, D(G(z)): 0.0933 Epoch: [7/20], Batch Num: [106/600] Discriminator Loss: 0.1770, Generator Loss: 3.5723 D(x): 0.9737, D(G(z)): 0.0951 Epoch: [7/20], Batch Num: [107/600] Discriminator Loss: 0.0957, Generator Loss: 4.2692 D(x): 0.9853, D(G(z)): 0.0682 Epoch: [7/20], Batch Num: [108/600] Discriminator Loss: 0.2718, Generator Loss: 4.7262 D(x): 0.9343, D(G(z)): 0.0559 Epoch: [7/20], Batch Num: [109/600] Discriminator Loss: 0.2016, Generator Loss: 4.9567 D(x): 0.9352, D(G(z)): 0.0320 Epoch: [7/20], Batch Num: [110/600] Discriminator Loss: 0.2031, Generator Loss: 4.6199 D(x): 0.9132, D(G(z)): 0.0315 Epoch: [7/20], Batch Num: [111/600] Discriminator Loss: 0.2157, Generator Loss: 3.8389 D(x): 0.9064, D(G(z)): 0.0340 Epoch: [7/20], Batch Num: [112/600] Discriminator Loss: 0.0922, Generator Loss: 3.1191 D(x): 0.9849, D(G(z)): 0.0579 Epoch: [7/20], Batch Num: [113/600] Discriminator Loss: 0.1825, Generator Loss: 3.6602 D(x): 0.9697, D(G(z)): 0.1008 Epoch: [7/20], Batch Num: [114/600] Discriminator Loss: 0.2754, Generator Loss: 4.6401 D(x): 0.9442, D(G(z)): 0.0899 Epoch: [7/20], Batch Num: [115/600] Discriminator Loss: 0.1211, Generator Loss: 5.0168 D(x): 0.9652, D(G(z)): 0.0478 Epoch: [7/20], Batch Num: [116/600] Discriminator Loss: 0.1787, Generator Loss: 5.2831 D(x): 0.9295, D(G(z)): 0.0277 Epoch: [7/20], Batch Num: [117/600] Discriminator Loss: 0.2153, Generator Loss: 5.0833 D(x): 0.9215, D(G(z)): 0.0253 Epoch: [7/20], Batch Num: [118/600] Discriminator Loss: 0.2765, Generator Loss: 4.2528 D(x): 0.9067, D(G(z)): 0.0398 Epoch: [7/20], Batch Num: [119/600] Discriminator Loss: 0.2797, Generator Loss: 4.2119 D(x): 0.9219, D(G(z)): 0.0461 Epoch: [7/20], Batch Num: [120/600] Discriminator Loss: 0.2827, Generator Loss: 4.4612 D(x): 0.9626, D(G(z)): 0.1239 Epoch: [7/20], Batch Num: [121/600] Discriminator Loss: 0.3446, Generator Loss: 4.8569 D(x): 0.9195, D(G(z)): 0.0856 Epoch: [7/20], Batch Num: [122/600] Discriminator Loss: 0.2759, Generator Loss: 4.5197 D(x): 0.8966, D(G(z)): 0.0402 Epoch: [7/20], Batch Num: [123/600] Discriminator Loss: 0.2320, Generator Loss: 4.5345 D(x): 0.9182, D(G(z)): 0.0511 Epoch: [7/20], Batch Num: [124/600] Discriminator Loss: 0.1721, Generator Loss: 4.0435 D(x): 0.9544, D(G(z)): 0.0738 Epoch: [7/20], Batch Num: [125/600] Discriminator Loss: 0.2090, Generator Loss: 3.9841 D(x): 0.9439, D(G(z)): 0.0803 Epoch: [7/20], Batch Num: [126/600] Discriminator Loss: 0.2621, Generator Loss: 3.8390 D(x): 0.9198, D(G(z)): 0.0534 Epoch: [7/20], Batch Num: [127/600] Discriminator Loss: 0.2055, Generator Loss: 3.6245 D(x): 0.9415, D(G(z)): 0.0929 Epoch: [7/20], Batch Num: [128/600] Discriminator Loss: 0.2489, Generator Loss: 4.0659 D(x): 0.9467, D(G(z)): 0.0814 Epoch: [7/20], Batch Num: [129/600] Discriminator Loss: 0.2007, Generator Loss: 4.2664 D(x): 0.9384, D(G(z)): 0.0591 Epoch: [7/20], Batch Num: [130/600] Discriminator Loss: 0.1773, Generator Loss: 4.2170 D(x): 0.9366, D(G(z)): 0.0587 Epoch: [7/20], Batch Num: [131/600] Discriminator Loss: 0.3033, Generator Loss: 3.9353 D(x): 0.9232, D(G(z)): 0.0905 Epoch: [7/20], Batch Num: [132/600] Discriminator Loss: 0.2610, Generator Loss: 3.9840 D(x): 0.9223, D(G(z)): 0.0809 Epoch: [7/20], Batch Num: [133/600] Discriminator Loss: 0.1954, Generator Loss: 4.0177 D(x): 0.9534, D(G(z)): 0.0733 Epoch: [7/20], Batch Num: [134/600] Discriminator Loss: 0.2635, Generator Loss: 3.9980 D(x): 0.9266, D(G(z)): 0.0929 Epoch: [7/20], Batch Num: [135/600] Discriminator Loss: 0.2833, Generator Loss: 4.1745 D(x): 0.9075, D(G(z)): 0.0565 Epoch: [7/20], Batch Num: [136/600] Discriminator Loss: 0.2741, Generator Loss: 4.1581 D(x): 0.9126, D(G(z)): 0.0585 Epoch: [7/20], Batch Num: [137/600] Discriminator Loss: 0.2224, Generator Loss: 3.7671 D(x): 0.9379, D(G(z)): 0.0702 Epoch: [7/20], Batch Num: [138/600] Discriminator Loss: 0.1423, Generator Loss: 3.6259 D(x): 0.9540, D(G(z)): 0.0648 Epoch: [7/20], Batch Num: [139/600] Discriminator Loss: 0.1180, Generator Loss: 4.0679 D(x): 0.9621, D(G(z)): 0.0570 Epoch: [7/20], Batch Num: [140/600] Discriminator Loss: 0.2409, Generator Loss: 4.3146 D(x): 0.9384, D(G(z)): 0.0742 Epoch: [7/20], Batch Num: [141/600] Discriminator Loss: 0.2031, Generator Loss: 3.9453 D(x): 0.9048, D(G(z)): 0.0339 Epoch: [7/20], Batch Num: [142/600] Discriminator Loss: 0.3085, Generator Loss: 3.6968 D(x): 0.9254, D(G(z)): 0.0676 Epoch: [7/20], Batch Num: [143/600] Discriminator Loss: 0.2834, Generator Loss: 3.5521 D(x): 0.9187, D(G(z)): 0.0612 Epoch: [7/20], Batch Num: [144/600] Discriminator Loss: 0.3269, Generator Loss: 3.7432 D(x): 0.9344, D(G(z)): 0.1180 Epoch: [7/20], Batch Num: [145/600] Discriminator Loss: 0.3503, Generator Loss: 4.2567 D(x): 0.9154, D(G(z)): 0.0745 Epoch: [7/20], Batch Num: [146/600] Discriminator Loss: 0.1842, Generator Loss: 4.3956 D(x): 0.9356, D(G(z)): 0.0464 Epoch: [7/20], Batch Num: [147/600] Discriminator Loss: 0.2473, Generator Loss: 3.9308 D(x): 0.9097, D(G(z)): 0.0407 Epoch: [7/20], Batch Num: [148/600] Discriminator Loss: 0.1536, Generator Loss: 3.7024 D(x): 0.9557, D(G(z)): 0.0642 Epoch: [7/20], Batch Num: [149/600] Discriminator Loss: 0.2137, Generator Loss: 3.6938 D(x): 0.9485, D(G(z)): 0.0718 Epoch: [7/20], Batch Num: [150/600] Discriminator Loss: 0.2303, Generator Loss: 3.4287 D(x): 0.9354, D(G(z)): 0.0678 Epoch: [7/20], Batch Num: [151/600] Discriminator Loss: 0.2424, Generator Loss: 4.2214 D(x): 0.9506, D(G(z)): 0.0942 Epoch: [7/20], Batch Num: [152/600] Discriminator Loss: 0.3034, Generator Loss: 4.3698 D(x): 0.9233, D(G(z)): 0.0932 Epoch: [7/20], Batch Num: [153/600] Discriminator Loss: 0.3095, Generator Loss: 4.1378 D(x): 0.8893, D(G(z)): 0.0370 Epoch: [7/20], Batch Num: [154/600] Discriminator Loss: 0.4180, Generator Loss: 3.8431 D(x): 0.9377, D(G(z)): 0.0800 Epoch: [7/20], Batch Num: [155/600] Discriminator Loss: 0.2253, Generator Loss: 3.9712 D(x): 0.9524, D(G(z)): 0.0672 Epoch: [7/20], Batch Num: [156/600] Discriminator Loss: 0.1523, Generator Loss: 4.2143 D(x): 0.9555, D(G(z)): 0.0681 Epoch: [7/20], Batch Num: [157/600] Discriminator Loss: 0.3187, Generator Loss: 4.0698 D(x): 0.8831, D(G(z)): 0.0501 Epoch: [7/20], Batch Num: [158/600] Discriminator Loss: 0.2371, Generator Loss: 3.3303 D(x): 0.9286, D(G(z)): 0.0860 Epoch: [7/20], Batch Num: [159/600] Discriminator Loss: 0.2251, Generator Loss: 3.9717 D(x): 0.9758, D(G(z)): 0.1041 Epoch: [7/20], Batch Num: [160/600] Discriminator Loss: 0.2477, Generator Loss: 3.9877 D(x): 0.9190, D(G(z)): 0.0549 Epoch: [7/20], Batch Num: [161/600] Discriminator Loss: 0.1447, Generator Loss: 4.3395 D(x): 0.9744, D(G(z)): 0.0753 Epoch: [7/20], Batch Num: [162/600] Discriminator Loss: 0.2587, Generator Loss: 4.9644 D(x): 0.9525, D(G(z)): 0.0828 Epoch: [7/20], Batch Num: [163/600] Discriminator Loss: 0.2645, Generator Loss: 4.8506 D(x): 0.9081, D(G(z)): 0.0383 Epoch: [7/20], Batch Num: [164/600] Discriminator Loss: 0.2870, Generator Loss: 4.3344 D(x): 0.9029, D(G(z)): 0.0521 Epoch: [7/20], Batch Num: [165/600] Discriminator Loss: 0.2011, Generator Loss: 3.9957 D(x): 0.9511, D(G(z)): 0.0646 Epoch: [7/20], Batch Num: [166/600] Discriminator Loss: 0.2846, Generator Loss: 3.9917 D(x): 0.9435, D(G(z)): 0.0803 Epoch: [7/20], Batch Num: [167/600] Discriminator Loss: 0.2099, Generator Loss: 3.8400 D(x): 0.9327, D(G(z)): 0.0825 Epoch: [7/20], Batch Num: [168/600] Discriminator Loss: 0.1985, Generator Loss: 4.0642 D(x): 0.9753, D(G(z)): 0.1064 Epoch: [7/20], Batch Num: [169/600] Discriminator Loss: 0.2324, Generator Loss: 4.3223 D(x): 0.9124, D(G(z)): 0.0507 Epoch: [7/20], Batch Num: [170/600] Discriminator Loss: 0.2170, Generator Loss: 3.8920 D(x): 0.9182, D(G(z)): 0.0473 Epoch: [7/20], Batch Num: [171/600] Discriminator Loss: 0.2119, Generator Loss: 4.0104 D(x): 0.9492, D(G(z)): 0.0717 Epoch: [7/20], Batch Num: [172/600] Discriminator Loss: 0.2410, Generator Loss: 4.3914 D(x): 0.9373, D(G(z)): 0.0715 Epoch: [7/20], Batch Num: [173/600] Discriminator Loss: 0.1822, Generator Loss: 4.4092 D(x): 0.9410, D(G(z)): 0.0638 Epoch: [7/20], Batch Num: [174/600] Discriminator Loss: 0.1833, Generator Loss: 4.5506 D(x): 0.9377, D(G(z)): 0.0354 Epoch: [7/20], Batch Num: [175/600] Discriminator Loss: 0.2574, Generator Loss: 3.9963 D(x): 0.9149, D(G(z)): 0.0337 Epoch: [7/20], Batch Num: [176/600] Discriminator Loss: 0.1683, Generator Loss: 3.6419 D(x): 0.9483, D(G(z)): 0.0743 Epoch: [7/20], Batch Num: [177/600] Discriminator Loss: 0.2452, Generator Loss: 4.1723 D(x): 0.9590, D(G(z)): 0.1092 Epoch: [7/20], Batch Num: [178/600] Discriminator Loss: 0.2506, Generator Loss: 4.9102 D(x): 0.9586, D(G(z)): 0.1058 Epoch: [7/20], Batch Num: [179/600] Discriminator Loss: 0.1215, Generator Loss: 5.5180 D(x): 0.9726, D(G(z)): 0.0466 Epoch: [7/20], Batch Num: [180/600] Discriminator Loss: 0.2078, Generator Loss: 5.5317 D(x): 0.9415, D(G(z)): 0.0389 Epoch: [7/20], Batch Num: [181/600] Discriminator Loss: 0.1370, Generator Loss: 5.1789 D(x): 0.9532, D(G(z)): 0.0264 Epoch: [7/20], Batch Num: [182/600] Discriminator Loss: 0.4388, Generator Loss: 4.8833 D(x): 0.8799, D(G(z)): 0.0339 Epoch: [7/20], Batch Num: [183/600] Discriminator Loss: 0.2172, Generator Loss: 3.7727 D(x): 0.9393, D(G(z)): 0.0428 Epoch: [7/20], Batch Num: [184/600] Discriminator Loss: 0.2930, Generator Loss: 3.4869 D(x): 0.9504, D(G(z)): 0.1076 Epoch: [7/20], Batch Num: [185/600] Discriminator Loss: 0.2680, Generator Loss: 4.8187 D(x): 0.9902, D(G(z)): 0.1483 Epoch: [7/20], Batch Num: [186/600] Discriminator Loss: 0.1412, Generator Loss: 5.6152 D(x): 0.9515, D(G(z)): 0.0457 Epoch: [7/20], Batch Num: [187/600] Discriminator Loss: 0.3343, Generator Loss: 5.4848 D(x): 0.8731, D(G(z)): 0.0248 Epoch: [7/20], Batch Num: [188/600] Discriminator Loss: 0.1695, Generator Loss: 4.9115 D(x): 0.9389, D(G(z)): 0.0260 Epoch: [7/20], Batch Num: [189/600] Discriminator Loss: 0.2245, Generator Loss: 4.8414 D(x): 0.9466, D(G(z)): 0.0405 Epoch: [7/20], Batch Num: [190/600] Discriminator Loss: 0.1523, Generator Loss: 4.5022 D(x): 0.9474, D(G(z)): 0.0361 Epoch: [7/20], Batch Num: [191/600] Discriminator Loss: 0.2168, Generator Loss: 4.1120 D(x): 0.9488, D(G(z)): 0.0739 Epoch: [7/20], Batch Num: [192/600] Discriminator Loss: 0.2758, Generator Loss: 3.7338 D(x): 0.9444, D(G(z)): 0.0715 Epoch: [7/20], Batch Num: [193/600] Discriminator Loss: 0.3570, Generator Loss: 4.6984 D(x): 0.9428, D(G(z)): 0.1520 Epoch: [7/20], Batch Num: [194/600] Discriminator Loss: 0.2212, Generator Loss: 5.5076 D(x): 0.9393, D(G(z)): 0.0593 Epoch: [7/20], Batch Num: [195/600] Discriminator Loss: 0.2100, Generator Loss: 5.8367 D(x): 0.9344, D(G(z)): 0.0640 Epoch: [7/20], Batch Num: [196/600] Discriminator Loss: 0.2140, Generator Loss: 5.6931 D(x): 0.9171, D(G(z)): 0.0273 Epoch: [7/20], Batch Num: [197/600] Discriminator Loss: 0.4057, Generator Loss: 5.2137 D(x): 0.8621, D(G(z)): 0.0202 Epoch: [7/20], Batch Num: [198/600] Discriminator Loss: 0.3279, Generator Loss: 4.2478 D(x): 0.8892, D(G(z)): 0.0578 Epoch: [7/20], Batch Num: [199/600] Discriminator Loss: 0.4317, Generator Loss: 3.6436 D(x): 0.9031, D(G(z)): 0.1063 Epoch: 7, Batch Num: [200/600]
Epoch: [7/20], Batch Num: [200/600] Discriminator Loss: 0.3593, Generator Loss: 3.5671 D(x): 0.9195, D(G(z)): 0.0960 Epoch: [7/20], Batch Num: [201/600] Discriminator Loss: 0.4019, Generator Loss: 3.5719 D(x): 0.9259, D(G(z)): 0.1347 Epoch: [7/20], Batch Num: [202/600] Discriminator Loss: 0.2875, Generator Loss: 3.8636 D(x): 0.9105, D(G(z)): 0.0982 Epoch: [7/20], Batch Num: [203/600] Discriminator Loss: 0.2334, Generator Loss: 4.0502 D(x): 0.9076, D(G(z)): 0.0675 Epoch: [7/20], Batch Num: [204/600] Discriminator Loss: 0.3636, Generator Loss: 3.5945 D(x): 0.8709, D(G(z)): 0.0794 Epoch: [7/20], Batch Num: [205/600] Discriminator Loss: 0.2455, Generator Loss: 3.8901 D(x): 0.9256, D(G(z)): 0.0889 Epoch: [7/20], Batch Num: [206/600] Discriminator Loss: 0.3075, Generator Loss: 3.3440 D(x): 0.8984, D(G(z)): 0.0834 Epoch: [7/20], Batch Num: [207/600] Discriminator Loss: 0.3304, Generator Loss: 3.7564 D(x): 0.9078, D(G(z)): 0.1165 Epoch: [7/20], Batch Num: [208/600] Discriminator Loss: 0.3625, Generator Loss: 3.1936 D(x): 0.8904, D(G(z)): 0.1341 Epoch: [7/20], Batch Num: [209/600] Discriminator Loss: 0.3817, Generator Loss: 3.4585 D(x): 0.8952, D(G(z)): 0.1222 Epoch: [7/20], Batch Num: [210/600] Discriminator Loss: 0.2457, Generator Loss: 3.4072 D(x): 0.9304, D(G(z)): 0.0982 Epoch: [7/20], Batch Num: [211/600] Discriminator Loss: 0.2786, Generator Loss: 3.5356 D(x): 0.9096, D(G(z)): 0.0989 Epoch: [7/20], Batch Num: [212/600] Discriminator Loss: 0.3573, Generator Loss: 3.6006 D(x): 0.8942, D(G(z)): 0.1035 Epoch: [7/20], Batch Num: [213/600] Discriminator Loss: 0.3198, Generator Loss: 3.7884 D(x): 0.9034, D(G(z)): 0.0919 Epoch: [7/20], Batch Num: [214/600] Discriminator Loss: 0.2834, Generator Loss: 3.7825 D(x): 0.8839, D(G(z)): 0.0678 Epoch: [7/20], Batch Num: [215/600] Discriminator Loss: 0.2757, Generator Loss: 3.6125 D(x): 0.9100, D(G(z)): 0.0961 Epoch: [7/20], Batch Num: [216/600] Discriminator Loss: 0.4245, Generator Loss: 3.5109 D(x): 0.8639, D(G(z)): 0.1011 Epoch: [7/20], Batch Num: [217/600] Discriminator Loss: 0.2758, Generator Loss: 3.2649 D(x): 0.9142, D(G(z)): 0.0969 Epoch: [7/20], Batch Num: [218/600] Discriminator Loss: 0.2615, Generator Loss: 3.3306 D(x): 0.9180, D(G(z)): 0.0919 Epoch: [7/20], Batch Num: [219/600] Discriminator Loss: 0.2977, Generator Loss: 3.6992 D(x): 0.9329, D(G(z)): 0.1195 Epoch: [7/20], Batch Num: [220/600] Discriminator Loss: 0.2940, Generator Loss: 3.8473 D(x): 0.9036, D(G(z)): 0.0848 Epoch: [7/20], Batch Num: [221/600] Discriminator Loss: 0.2142, Generator Loss: 4.1954 D(x): 0.9280, D(G(z)): 0.0557 Epoch: [7/20], Batch Num: [222/600] Discriminator Loss: 0.3159, Generator Loss: 3.9513 D(x): 0.8906, D(G(z)): 0.0638 Epoch: [7/20], Batch Num: [223/600] Discriminator Loss: 0.2700, Generator Loss: 3.5213 D(x): 0.8926, D(G(z)): 0.0574 Epoch: [7/20], Batch Num: [224/600] Discriminator Loss: 0.2078, Generator Loss: 3.2752 D(x): 0.9556, D(G(z)): 0.0999 Epoch: [7/20], Batch Num: [225/600] Discriminator Loss: 0.2865, Generator Loss: 3.8063 D(x): 0.9538, D(G(z)): 0.1503 Epoch: [7/20], Batch Num: [226/600] Discriminator Loss: 0.1980, Generator Loss: 4.8160 D(x): 0.9519, D(G(z)): 0.1001 Epoch: [7/20], Batch Num: [227/600] Discriminator Loss: 0.4570, Generator Loss: 5.1509 D(x): 0.8261, D(G(z)): 0.0565 Epoch: [7/20], Batch Num: [228/600] Discriminator Loss: 0.4557, Generator Loss: 3.7992 D(x): 0.8414, D(G(z)): 0.0339 Epoch: [7/20], Batch Num: [229/600] Discriminator Loss: 0.1960, Generator Loss: 3.1127 D(x): 0.9379, D(G(z)): 0.0730 Epoch: [7/20], Batch Num: [230/600] Discriminator Loss: 0.2160, Generator Loss: 3.4121 D(x): 0.9690, D(G(z)): 0.1257 Epoch: [7/20], Batch Num: [231/600] Discriminator Loss: 0.2013, Generator Loss: 3.7658 D(x): 0.9408, D(G(z)): 0.0885 Epoch: [7/20], Batch Num: [232/600] Discriminator Loss: 0.1888, Generator Loss: 4.4261 D(x): 0.9483, D(G(z)): 0.0867 Epoch: [7/20], Batch Num: [233/600] Discriminator Loss: 0.2210, Generator Loss: 4.7504 D(x): 0.9155, D(G(z)): 0.0498 Epoch: [7/20], Batch Num: [234/600] Discriminator Loss: 0.1851, Generator Loss: 5.0547 D(x): 0.9341, D(G(z)): 0.0513 Epoch: [7/20], Batch Num: [235/600] Discriminator Loss: 0.1483, Generator Loss: 4.4897 D(x): 0.9414, D(G(z)): 0.0370 Epoch: [7/20], Batch Num: [236/600] Discriminator Loss: 0.3014, Generator Loss: 3.9652 D(x): 0.8804, D(G(z)): 0.0524 Epoch: [7/20], Batch Num: [237/600] Discriminator Loss: 0.2156, Generator Loss: 3.6317 D(x): 0.9378, D(G(z)): 0.0820 Epoch: [7/20], Batch Num: [238/600] Discriminator Loss: 0.3154, Generator Loss: 3.3626 D(x): 0.9212, D(G(z)): 0.1274 Epoch: [7/20], Batch Num: [239/600] Discriminator Loss: 0.4072, Generator Loss: 4.1129 D(x): 0.9549, D(G(z)): 0.1794 Epoch: [7/20], Batch Num: [240/600] Discriminator Loss: 0.2800, Generator Loss: 4.3824 D(x): 0.8920, D(G(z)): 0.0535 Epoch: [7/20], Batch Num: [241/600] Discriminator Loss: 0.2481, Generator Loss: 4.6225 D(x): 0.9069, D(G(z)): 0.0395 Epoch: [7/20], Batch Num: [242/600] Discriminator Loss: 0.2135, Generator Loss: 4.4464 D(x): 0.9183, D(G(z)): 0.0394 Epoch: [7/20], Batch Num: [243/600] Discriminator Loss: 0.2846, Generator Loss: 3.4027 D(x): 0.9038, D(G(z)): 0.0709 Epoch: [7/20], Batch Num: [244/600] Discriminator Loss: 0.3421, Generator Loss: 2.8061 D(x): 0.8994, D(G(z)): 0.0990 Epoch: [7/20], Batch Num: [245/600] Discriminator Loss: 0.3367, Generator Loss: 3.1342 D(x): 0.9516, D(G(z)): 0.1768 Epoch: [7/20], Batch Num: [246/600] Discriminator Loss: 0.3310, Generator Loss: 4.1127 D(x): 0.9326, D(G(z)): 0.1325 Epoch: [7/20], Batch Num: [247/600] Discriminator Loss: 0.3594, Generator Loss: 4.4340 D(x): 0.8863, D(G(z)): 0.0589 Epoch: [7/20], Batch Num: [248/600] Discriminator Loss: 0.1892, Generator Loss: 4.1010 D(x): 0.9194, D(G(z)): 0.0357 Epoch: [7/20], Batch Num: [249/600] Discriminator Loss: 0.3674, Generator Loss: 3.5848 D(x): 0.8839, D(G(z)): 0.0573 Epoch: [7/20], Batch Num: [250/600] Discriminator Loss: 0.2539, Generator Loss: 3.2537 D(x): 0.9071, D(G(z)): 0.0639 Epoch: [7/20], Batch Num: [251/600] Discriminator Loss: 0.1754, Generator Loss: 3.1658 D(x): 0.9730, D(G(z)): 0.1139 Epoch: [7/20], Batch Num: [252/600] Discriminator Loss: 0.3013, Generator Loss: 3.5571 D(x): 0.9215, D(G(z)): 0.1071 Epoch: [7/20], Batch Num: [253/600] Discriminator Loss: 0.3494, Generator Loss: 3.4803 D(x): 0.8979, D(G(z)): 0.0820 Epoch: [7/20], Batch Num: [254/600] Discriminator Loss: 0.1923, Generator Loss: 3.5076 D(x): 0.9543, D(G(z)): 0.0986 Epoch: [7/20], Batch Num: [255/600] Discriminator Loss: 0.2461, Generator Loss: 3.8403 D(x): 0.9221, D(G(z)): 0.0708 Epoch: [7/20], Batch Num: [256/600] Discriminator Loss: 0.1121, Generator Loss: 4.1479 D(x): 0.9601, D(G(z)): 0.0390 Epoch: [7/20], Batch Num: [257/600] Discriminator Loss: 0.2990, Generator Loss: 3.9146 D(x): 0.9311, D(G(z)): 0.0660 Epoch: [7/20], Batch Num: [258/600] Discriminator Loss: 0.1892, Generator Loss: 3.7270 D(x): 0.9365, D(G(z)): 0.0623 Epoch: [7/20], Batch Num: [259/600] Discriminator Loss: 0.1850, Generator Loss: 3.1608 D(x): 0.9481, D(G(z)): 0.0516 Epoch: [7/20], Batch Num: [260/600] Discriminator Loss: 0.1893, Generator Loss: 3.3004 D(x): 0.9678, D(G(z)): 0.0969 Epoch: [7/20], Batch Num: [261/600] Discriminator Loss: 0.2489, Generator Loss: 4.1527 D(x): 0.9485, D(G(z)): 0.0788 Epoch: [7/20], Batch Num: [262/600] Discriminator Loss: 0.2844, Generator Loss: 4.6657 D(x): 0.9371, D(G(z)): 0.0852 Epoch: [7/20], Batch Num: [263/600] Discriminator Loss: 0.2130, Generator Loss: 5.6181 D(x): 0.9470, D(G(z)): 0.0542 Epoch: [7/20], Batch Num: [264/600] Discriminator Loss: 0.2825, Generator Loss: 5.0149 D(x): 0.8790, D(G(z)): 0.0143 Epoch: [7/20], Batch Num: [265/600] Discriminator Loss: 0.2130, Generator Loss: 3.9324 D(x): 0.9085, D(G(z)): 0.0216 Epoch: [7/20], Batch Num: [266/600] Discriminator Loss: 0.1600, Generator Loss: 2.7747 D(x): 0.9499, D(G(z)): 0.0623 Epoch: [7/20], Batch Num: [267/600] Discriminator Loss: 0.2445, Generator Loss: 3.2083 D(x): 0.9736, D(G(z)): 0.1195 Epoch: [7/20], Batch Num: [268/600] Discriminator Loss: 0.3390, Generator Loss: 4.3534 D(x): 0.9594, D(G(z)): 0.1463 Epoch: [7/20], Batch Num: [269/600] Discriminator Loss: 0.2461, Generator Loss: 4.5270 D(x): 0.9061, D(G(z)): 0.0502 Epoch: [7/20], Batch Num: [270/600] Discriminator Loss: 0.3980, Generator Loss: 4.9312 D(x): 0.8995, D(G(z)): 0.0600 Epoch: [7/20], Batch Num: [271/600] Discriminator Loss: 0.2424, Generator Loss: 4.4410 D(x): 0.8948, D(G(z)): 0.0332 Epoch: [7/20], Batch Num: [272/600] Discriminator Loss: 0.2627, Generator Loss: 3.8033 D(x): 0.8976, D(G(z)): 0.0244 Epoch: [7/20], Batch Num: [273/600] Discriminator Loss: 0.2206, Generator Loss: 3.0039 D(x): 0.9332, D(G(z)): 0.0721 Epoch: [7/20], Batch Num: [274/600] Discriminator Loss: 0.3009, Generator Loss: 3.4250 D(x): 0.9580, D(G(z)): 0.1404 Epoch: [7/20], Batch Num: [275/600] Discriminator Loss: 0.2557, Generator Loss: 4.1148 D(x): 0.9452, D(G(z)): 0.1060 Epoch: [7/20], Batch Num: [276/600] Discriminator Loss: 0.1083, Generator Loss: 4.5425 D(x): 0.9694, D(G(z)): 0.0575 Epoch: [7/20], Batch Num: [277/600] Discriminator Loss: 0.2431, Generator Loss: 5.1695 D(x): 0.9192, D(G(z)): 0.0543 Epoch: [7/20], Batch Num: [278/600] Discriminator Loss: 0.1549, Generator Loss: 4.7256 D(x): 0.9348, D(G(z)): 0.0331 Epoch: [7/20], Batch Num: [279/600] Discriminator Loss: 0.0904, Generator Loss: 4.9953 D(x): 0.9698, D(G(z)): 0.0280 Epoch: [7/20], Batch Num: [280/600] Discriminator Loss: 0.1526, Generator Loss: 5.0059 D(x): 0.9816, D(G(z)): 0.0616 Epoch: [7/20], Batch Num: [281/600] Discriminator Loss: 0.1634, Generator Loss: 5.0128 D(x): 0.9464, D(G(z)): 0.0438 Epoch: [7/20], Batch Num: [282/600] Discriminator Loss: 0.2991, Generator Loss: 4.6104 D(x): 0.9384, D(G(z)): 0.0722 Epoch: [7/20], Batch Num: [283/600] Discriminator Loss: 0.1591, Generator Loss: 3.9678 D(x): 0.9311, D(G(z)): 0.0326 Epoch: [7/20], Batch Num: [284/600] Discriminator Loss: 0.1502, Generator Loss: 3.9002 D(x): 0.9715, D(G(z)): 0.0554 Epoch: [7/20], Batch Num: [285/600] Discriminator Loss: 0.2031, Generator Loss: 4.1779 D(x): 0.9575, D(G(z)): 0.1030 Epoch: [7/20], Batch Num: [286/600] Discriminator Loss: 0.2173, Generator Loss: 4.8090 D(x): 0.9662, D(G(z)): 0.0843 Epoch: [7/20], Batch Num: [287/600] Discriminator Loss: 0.3039, Generator Loss: 5.0506 D(x): 0.9347, D(G(z)): 0.0440 Epoch: [7/20], Batch Num: [288/600] Discriminator Loss: 0.2257, Generator Loss: 5.1283 D(x): 0.9152, D(G(z)): 0.0240 Epoch: [7/20], Batch Num: [289/600] Discriminator Loss: 0.2529, Generator Loss: 4.9427 D(x): 0.9430, D(G(z)): 0.0586 Epoch: [7/20], Batch Num: [290/600] Discriminator Loss: 0.1773, Generator Loss: 4.8380 D(x): 0.9118, D(G(z)): 0.0197 Epoch: [7/20], Batch Num: [291/600] Discriminator Loss: 0.1592, Generator Loss: 3.9922 D(x): 0.9594, D(G(z)): 0.0549 Epoch: [7/20], Batch Num: [292/600] Discriminator Loss: 0.1345, Generator Loss: 4.2161 D(x): 0.9579, D(G(z)): 0.0531 Epoch: [7/20], Batch Num: [293/600] Discriminator Loss: 0.1817, Generator Loss: 4.3075 D(x): 0.9500, D(G(z)): 0.0671 Epoch: [7/20], Batch Num: [294/600] Discriminator Loss: 0.1696, Generator Loss: 4.6060 D(x): 0.9632, D(G(z)): 0.0776 Epoch: [7/20], Batch Num: [295/600] Discriminator Loss: 0.2149, Generator Loss: 5.3933 D(x): 0.9371, D(G(z)): 0.0423 Epoch: [7/20], Batch Num: [296/600] Discriminator Loss: 0.1538, Generator Loss: 5.5617 D(x): 0.9483, D(G(z)): 0.0334 Epoch: [7/20], Batch Num: [297/600] Discriminator Loss: 0.2203, Generator Loss: 5.6679 D(x): 0.9440, D(G(z)): 0.0507 Epoch: [7/20], Batch Num: [298/600] Discriminator Loss: 0.2476, Generator Loss: 4.9469 D(x): 0.9132, D(G(z)): 0.0218 Epoch: [7/20], Batch Num: [299/600] Discriminator Loss: 0.1686, Generator Loss: 4.5746 D(x): 0.9464, D(G(z)): 0.0518 Epoch: 7, Batch Num: [300/600]
Epoch: [7/20], Batch Num: [300/600] Discriminator Loss: 0.2077, Generator Loss: 3.8056 D(x): 0.9289, D(G(z)): 0.0572 Epoch: [7/20], Batch Num: [301/600] Discriminator Loss: 0.3204, Generator Loss: 4.6292 D(x): 0.9606, D(G(z)): 0.1375 Epoch: [7/20], Batch Num: [302/600] Discriminator Loss: 0.3830, Generator Loss: 5.1436 D(x): 0.9267, D(G(z)): 0.1028 Epoch: [7/20], Batch Num: [303/600] Discriminator Loss: 0.1401, Generator Loss: 5.2466 D(x): 0.9490, D(G(z)): 0.0409 Epoch: [7/20], Batch Num: [304/600] Discriminator Loss: 0.1794, Generator Loss: 5.2272 D(x): 0.9222, D(G(z)): 0.0303 Epoch: [7/20], Batch Num: [305/600] Discriminator Loss: 0.1743, Generator Loss: 4.5316 D(x): 0.9164, D(G(z)): 0.0199 Epoch: [7/20], Batch Num: [306/600] Discriminator Loss: 0.1970, Generator Loss: 4.0533 D(x): 0.9392, D(G(z)): 0.0600 Epoch: [7/20], Batch Num: [307/600] Discriminator Loss: 0.1577, Generator Loss: 3.8771 D(x): 0.9831, D(G(z)): 0.0993 Epoch: [7/20], Batch Num: [308/600] Discriminator Loss: 0.1131, Generator Loss: 4.3012 D(x): 0.9631, D(G(z)): 0.0574 Epoch: [7/20], Batch Num: [309/600] Discriminator Loss: 0.1769, Generator Loss: 4.6210 D(x): 0.9310, D(G(z)): 0.0567 Epoch: [7/20], Batch Num: [310/600] Discriminator Loss: 0.2141, Generator Loss: 3.8619 D(x): 0.9139, D(G(z)): 0.0432 Epoch: [7/20], Batch Num: [311/600] Discriminator Loss: 0.2509, Generator Loss: 4.4762 D(x): 0.9292, D(G(z)): 0.0786 Epoch: [7/20], Batch Num: [312/600] Discriminator Loss: 0.1843, Generator Loss: 4.5906 D(x): 0.9699, D(G(z)): 0.0878 Epoch: [7/20], Batch Num: [313/600] Discriminator Loss: 0.2082, Generator Loss: 4.8813 D(x): 0.9492, D(G(z)): 0.0716 Epoch: [7/20], Batch Num: [314/600] Discriminator Loss: 0.3106, Generator Loss: 4.0378 D(x): 0.8916, D(G(z)): 0.0545 Epoch: [7/20], Batch Num: [315/600] Discriminator Loss: 0.1761, Generator Loss: 4.1411 D(x): 0.9559, D(G(z)): 0.0630 Epoch: [7/20], Batch Num: [316/600] Discriminator Loss: 0.1485, Generator Loss: 3.9120 D(x): 0.9502, D(G(z)): 0.0574 Epoch: [7/20], Batch Num: [317/600] Discriminator Loss: 0.3078, Generator Loss: 4.2234 D(x): 0.9306, D(G(z)): 0.1040 Epoch: [7/20], Batch Num: [318/600] Discriminator Loss: 0.2174, Generator Loss: 4.4574 D(x): 0.9498, D(G(z)): 0.0713 Epoch: [7/20], Batch Num: [319/600] Discriminator Loss: 0.1931, Generator Loss: 4.3944 D(x): 0.9321, D(G(z)): 0.0522 Epoch: [7/20], Batch Num: [320/600] Discriminator Loss: 0.2763, Generator Loss: 3.9875 D(x): 0.8986, D(G(z)): 0.0524 Epoch: [7/20], Batch Num: [321/600] Discriminator Loss: 0.1945, Generator Loss: 3.6900 D(x): 0.9465, D(G(z)): 0.0731 Epoch: [7/20], Batch Num: [322/600] Discriminator Loss: 0.2921, Generator Loss: 4.4065 D(x): 0.9537, D(G(z)): 0.1236 Epoch: [7/20], Batch Num: [323/600] Discriminator Loss: 0.2368, Generator Loss: 5.4200 D(x): 0.9447, D(G(z)): 0.0878 Epoch: [7/20], Batch Num: [324/600] Discriminator Loss: 0.3752, Generator Loss: 4.6907 D(x): 0.8487, D(G(z)): 0.0365 Epoch: [7/20], Batch Num: [325/600] Discriminator Loss: 0.1446, Generator Loss: 4.2536 D(x): 0.9518, D(G(z)): 0.0528 Epoch: [7/20], Batch Num: [326/600] Discriminator Loss: 0.2215, Generator Loss: 3.7658 D(x): 0.9238, D(G(z)): 0.0588 Epoch: [7/20], Batch Num: [327/600] Discriminator Loss: 0.2771, Generator Loss: 3.6527 D(x): 0.9397, D(G(z)): 0.1066 Epoch: [7/20], Batch Num: [328/600] Discriminator Loss: 0.2849, Generator Loss: 4.6979 D(x): 0.9717, D(G(z)): 0.1466 Epoch: [7/20], Batch Num: [329/600] Discriminator Loss: 0.2202, Generator Loss: 5.6143 D(x): 0.9163, D(G(z)): 0.0531 Epoch: [7/20], Batch Num: [330/600] Discriminator Loss: 0.2254, Generator Loss: 5.2103 D(x): 0.9281, D(G(z)): 0.0308 Epoch: [7/20], Batch Num: [331/600] Discriminator Loss: 0.2793, Generator Loss: 4.9132 D(x): 0.8948, D(G(z)): 0.0551 Epoch: [7/20], Batch Num: [332/600] Discriminator Loss: 0.2125, Generator Loss: 4.2474 D(x): 0.9298, D(G(z)): 0.0737 Epoch: [7/20], Batch Num: [333/600] Discriminator Loss: 0.1796, Generator Loss: 4.1783 D(x): 0.9591, D(G(z)): 0.0701 Epoch: [7/20], Batch Num: [334/600] Discriminator Loss: 0.1727, Generator Loss: 4.3258 D(x): 0.9507, D(G(z)): 0.0743 Epoch: [7/20], Batch Num: [335/600] Discriminator Loss: 0.2739, Generator Loss: 4.5302 D(x): 0.9082, D(G(z)): 0.0566 Epoch: [7/20], Batch Num: [336/600] Discriminator Loss: 0.2622, Generator Loss: 4.6872 D(x): 0.9568, D(G(z)): 0.0891 Epoch: [7/20], Batch Num: [337/600] Discriminator Loss: 0.2198, Generator Loss: 4.3338 D(x): 0.9131, D(G(z)): 0.0559 Epoch: [7/20], Batch Num: [338/600] Discriminator Loss: 0.2665, Generator Loss: 4.2119 D(x): 0.9187, D(G(z)): 0.0750 Epoch: [7/20], Batch Num: [339/600] Discriminator Loss: 0.2521, Generator Loss: 4.4900 D(x): 0.9455, D(G(z)): 0.0881 Epoch: [7/20], Batch Num: [340/600] Discriminator Loss: 0.3071, Generator Loss: 4.2439 D(x): 0.9068, D(G(z)): 0.0852 Epoch: [7/20], Batch Num: [341/600] Discriminator Loss: 0.2148, Generator Loss: 4.8107 D(x): 0.9637, D(G(z)): 0.0975 Epoch: [7/20], Batch Num: [342/600] Discriminator Loss: 0.3322, Generator Loss: 4.6267 D(x): 0.8988, D(G(z)): 0.0579 Epoch: [7/20], Batch Num: [343/600] Discriminator Loss: 0.2508, Generator Loss: 4.5768 D(x): 0.8957, D(G(z)): 0.0418 Epoch: [7/20], Batch Num: [344/600] Discriminator Loss: 0.1411, Generator Loss: 4.2137 D(x): 0.9521, D(G(z)): 0.0514 Epoch: [7/20], Batch Num: [345/600] Discriminator Loss: 0.2451, Generator Loss: 4.0313 D(x): 0.9400, D(G(z)): 0.0861 Epoch: [7/20], Batch Num: [346/600] Discriminator Loss: 0.2498, Generator Loss: 3.8649 D(x): 0.9418, D(G(z)): 0.0969 Epoch: [7/20], Batch Num: [347/600] Discriminator Loss: 0.4223, Generator Loss: 4.0213 D(x): 0.9189, D(G(z)): 0.1300 Epoch: [7/20], Batch Num: [348/600] Discriminator Loss: 0.2685, Generator Loss: 4.8499 D(x): 0.9269, D(G(z)): 0.0630 Epoch: [7/20], Batch Num: [349/600] Discriminator Loss: 0.2320, Generator Loss: 4.8599 D(x): 0.9229, D(G(z)): 0.0788 Epoch: [7/20], Batch Num: [350/600] Discriminator Loss: 0.2589, Generator Loss: 4.3552 D(x): 0.9090, D(G(z)): 0.0414 Epoch: [7/20], Batch Num: [351/600] Discriminator Loss: 0.2044, Generator Loss: 3.7889 D(x): 0.9338, D(G(z)): 0.0523 Epoch: [7/20], Batch Num: [352/600] Discriminator Loss: 0.2785, Generator Loss: 3.7177 D(x): 0.9072, D(G(z)): 0.0912 Epoch: [7/20], Batch Num: [353/600] Discriminator Loss: 0.2913, Generator Loss: 3.8212 D(x): 0.9431, D(G(z)): 0.1112 Epoch: [7/20], Batch Num: [354/600] Discriminator Loss: 0.2105, Generator Loss: 4.0910 D(x): 0.9523, D(G(z)): 0.0886 Epoch: [7/20], Batch Num: [355/600] Discriminator Loss: 0.3284, Generator Loss: 5.0509 D(x): 0.9301, D(G(z)): 0.0995 Epoch: [7/20], Batch Num: [356/600] Discriminator Loss: 0.3809, Generator Loss: 5.0990 D(x): 0.8722, D(G(z)): 0.0507 Epoch: [7/20], Batch Num: [357/600] Discriminator Loss: 0.2826, Generator Loss: 4.3122 D(x): 0.9015, D(G(z)): 0.0612 Epoch: [7/20], Batch Num: [358/600] Discriminator Loss: 0.2590, Generator Loss: 3.7792 D(x): 0.9217, D(G(z)): 0.0652 Epoch: [7/20], Batch Num: [359/600] Discriminator Loss: 0.2543, Generator Loss: 3.8273 D(x): 0.9386, D(G(z)): 0.0993 Epoch: [7/20], Batch Num: [360/600] Discriminator Loss: 0.2424, Generator Loss: 3.9090 D(x): 0.9410, D(G(z)): 0.0986 Epoch: [7/20], Batch Num: [361/600] Discriminator Loss: 0.2805, Generator Loss: 4.6435 D(x): 0.9383, D(G(z)): 0.0727 Epoch: [7/20], Batch Num: [362/600] Discriminator Loss: 0.2584, Generator Loss: 5.2777 D(x): 0.9362, D(G(z)): 0.0729 Epoch: [7/20], Batch Num: [363/600] Discriminator Loss: 0.3340, Generator Loss: 5.0083 D(x): 0.8914, D(G(z)): 0.0501 Epoch: [7/20], Batch Num: [364/600] Discriminator Loss: 0.4527, Generator Loss: 4.2926 D(x): 0.8852, D(G(z)): 0.0760 Epoch: [7/20], Batch Num: [365/600] Discriminator Loss: 0.3426, Generator Loss: 3.1194 D(x): 0.8905, D(G(z)): 0.0876 Epoch: [7/20], Batch Num: [366/600] Discriminator Loss: 0.3287, Generator Loss: 3.5082 D(x): 0.9623, D(G(z)): 0.1420 Epoch: [7/20], Batch Num: [367/600] Discriminator Loss: 0.3896, Generator Loss: 4.5122 D(x): 0.9232, D(G(z)): 0.1503 Epoch: [7/20], Batch Num: [368/600] Discriminator Loss: 0.3383, Generator Loss: 4.9677 D(x): 0.9324, D(G(z)): 0.0807 Epoch: [7/20], Batch Num: [369/600] Discriminator Loss: 0.4250, Generator Loss: 4.9576 D(x): 0.8511, D(G(z)): 0.0390 Epoch: [7/20], Batch Num: [370/600] Discriminator Loss: 0.4698, Generator Loss: 4.2541 D(x): 0.8381, D(G(z)): 0.0600 Epoch: [7/20], Batch Num: [371/600] Discriminator Loss: 0.1821, Generator Loss: 3.6994 D(x): 0.9480, D(G(z)): 0.0696 Epoch: [7/20], Batch Num: [372/600] Discriminator Loss: 0.2448, Generator Loss: 3.1952 D(x): 0.9437, D(G(z)): 0.1087 Epoch: [7/20], Batch Num: [373/600] Discriminator Loss: 0.3245, Generator Loss: 4.2061 D(x): 0.9820, D(G(z)): 0.1654 Epoch: [7/20], Batch Num: [374/600] Discriminator Loss: 0.2915, Generator Loss: 5.0888 D(x): 0.9122, D(G(z)): 0.0746 Epoch: [7/20], Batch Num: [375/600] Discriminator Loss: 0.2022, Generator Loss: 5.4200 D(x): 0.9121, D(G(z)): 0.0292 Epoch: [7/20], Batch Num: [376/600] Discriminator Loss: 0.2337, Generator Loss: 4.9899 D(x): 0.9121, D(G(z)): 0.0356 Epoch: [7/20], Batch Num: [377/600] Discriminator Loss: 0.2445, Generator Loss: 5.0047 D(x): 0.9290, D(G(z)): 0.0889 Epoch: [7/20], Batch Num: [378/600] Discriminator Loss: 0.2301, Generator Loss: 4.6773 D(x): 0.9266, D(G(z)): 0.0588 Epoch: [7/20], Batch Num: [379/600] Discriminator Loss: 0.1638, Generator Loss: 4.3033 D(x): 0.9558, D(G(z)): 0.0678 Epoch: [7/20], Batch Num: [380/600] Discriminator Loss: 0.2525, Generator Loss: 4.3632 D(x): 0.9082, D(G(z)): 0.0626 Epoch: [7/20], Batch Num: [381/600] Discriminator Loss: 0.2653, Generator Loss: 4.1280 D(x): 0.9216, D(G(z)): 0.0912 Epoch: [7/20], Batch Num: [382/600] Discriminator Loss: 0.1982, Generator Loss: 4.8287 D(x): 0.9430, D(G(z)): 0.0888 Epoch: [7/20], Batch Num: [383/600] Discriminator Loss: 0.3678, Generator Loss: 5.3729 D(x): 0.8986, D(G(z)): 0.0816 Epoch: [7/20], Batch Num: [384/600] Discriminator Loss: 0.2878, Generator Loss: 5.1399 D(x): 0.9073, D(G(z)): 0.0474 Epoch: [7/20], Batch Num: [385/600] Discriminator Loss: 0.2056, Generator Loss: 4.5684 D(x): 0.9141, D(G(z)): 0.0363 Epoch: [7/20], Batch Num: [386/600] Discriminator Loss: 0.2531, Generator Loss: 3.8958 D(x): 0.9089, D(G(z)): 0.0392 Epoch: [7/20], Batch Num: [387/600] Discriminator Loss: 0.2306, Generator Loss: 3.4673 D(x): 0.9554, D(G(z)): 0.1064 Epoch: [7/20], Batch Num: [388/600] Discriminator Loss: 0.3479, Generator Loss: 4.3482 D(x): 0.9406, D(G(z)): 0.1470 Epoch: [7/20], Batch Num: [389/600] Discriminator Loss: 0.2413, Generator Loss: 4.5692 D(x): 0.9449, D(G(z)): 0.0915 Epoch: [7/20], Batch Num: [390/600] Discriminator Loss: 0.2963, Generator Loss: 5.1588 D(x): 0.9204, D(G(z)): 0.0806 Epoch: [7/20], Batch Num: [391/600] Discriminator Loss: 0.1906, Generator Loss: 4.7003 D(x): 0.9121, D(G(z)): 0.0344 Epoch: [7/20], Batch Num: [392/600] Discriminator Loss: 0.0992, Generator Loss: 4.7765 D(x): 0.9594, D(G(z)): 0.0360 Epoch: [7/20], Batch Num: [393/600] Discriminator Loss: 0.1790, Generator Loss: 4.4190 D(x): 0.9360, D(G(z)): 0.0500 Epoch: [7/20], Batch Num: [394/600] Discriminator Loss: 0.2861, Generator Loss: 4.2621 D(x): 0.9453, D(G(z)): 0.1193 Epoch: [7/20], Batch Num: [395/600] Discriminator Loss: 0.3163, Generator Loss: 4.9872 D(x): 0.9411, D(G(z)): 0.1095 Epoch: [7/20], Batch Num: [396/600] Discriminator Loss: 0.2131, Generator Loss: 5.5305 D(x): 0.9227, D(G(z)): 0.0456 Epoch: [7/20], Batch Num: [397/600] Discriminator Loss: 0.2592, Generator Loss: 4.4463 D(x): 0.9060, D(G(z)): 0.0393 Epoch: [7/20], Batch Num: [398/600] Discriminator Loss: 0.3922, Generator Loss: 3.4837 D(x): 0.8533, D(G(z)): 0.0652 Epoch: [7/20], Batch Num: [399/600] Discriminator Loss: 0.3148, Generator Loss: 3.4909 D(x): 0.9523, D(G(z)): 0.1276 Epoch: 7, Batch Num: [400/600]
Epoch: [7/20], Batch Num: [400/600] Discriminator Loss: 0.3094, Generator Loss: 4.2217 D(x): 0.9245, D(G(z)): 0.1314 Epoch: [7/20], Batch Num: [401/600] Discriminator Loss: 0.3761, Generator Loss: 4.3338 D(x): 0.8574, D(G(z)): 0.0756 Epoch: [7/20], Batch Num: [402/600] Discriminator Loss: 0.5214, Generator Loss: 4.3980 D(x): 0.8864, D(G(z)): 0.1335 Epoch: [7/20], Batch Num: [403/600] Discriminator Loss: 0.3482, Generator Loss: 4.6515 D(x): 0.9190, D(G(z)): 0.1104 Epoch: [7/20], Batch Num: [404/600] Discriminator Loss: 0.4774, Generator Loss: 4.2219 D(x): 0.8561, D(G(z)): 0.0604 Epoch: [7/20], Batch Num: [405/600] Discriminator Loss: 0.2749, Generator Loss: 3.6085 D(x): 0.8979, D(G(z)): 0.0619 Epoch: [7/20], Batch Num: [406/600] Discriminator Loss: 0.3105, Generator Loss: 3.1188 D(x): 0.9045, D(G(z)): 0.0912 Epoch: [7/20], Batch Num: [407/600] Discriminator Loss: 0.3307, Generator Loss: 3.8967 D(x): 0.9725, D(G(z)): 0.1597 Epoch: [7/20], Batch Num: [408/600] Discriminator Loss: 0.2081, Generator Loss: 4.5873 D(x): 0.9446, D(G(z)): 0.0928 Epoch: [7/20], Batch Num: [409/600] Discriminator Loss: 0.2145, Generator Loss: 5.3884 D(x): 0.9181, D(G(z)): 0.0464 Epoch: [7/20], Batch Num: [410/600] Discriminator Loss: 0.4392, Generator Loss: 4.6050 D(x): 0.8603, D(G(z)): 0.0276 Epoch: [7/20], Batch Num: [411/600] Discriminator Loss: 0.2896, Generator Loss: 3.6988 D(x): 0.8690, D(G(z)): 0.0357 Epoch: [7/20], Batch Num: [412/600] Discriminator Loss: 0.3253, Generator Loss: 2.5278 D(x): 0.9447, D(G(z)): 0.1106 Epoch: [7/20], Batch Num: [413/600] Discriminator Loss: 0.5937, Generator Loss: 4.1323 D(x): 0.9638, D(G(z)): 0.2724 Epoch: [7/20], Batch Num: [414/600] Discriminator Loss: 0.2337, Generator Loss: 5.4065 D(x): 0.9198, D(G(z)): 0.0817 Epoch: [7/20], Batch Num: [415/600] Discriminator Loss: 0.4669, Generator Loss: 5.2607 D(x): 0.8413, D(G(z)): 0.0447 Epoch: [7/20], Batch Num: [416/600] Discriminator Loss: 0.4104, Generator Loss: 4.4893 D(x): 0.8222, D(G(z)): 0.0207 Epoch: [7/20], Batch Num: [417/600] Discriminator Loss: 0.2478, Generator Loss: 3.5907 D(x): 0.9223, D(G(z)): 0.0681 Epoch: [7/20], Batch Num: [418/600] Discriminator Loss: 0.2137, Generator Loss: 3.2849 D(x): 0.9629, D(G(z)): 0.1247 Epoch: [7/20], Batch Num: [419/600] Discriminator Loss: 0.3370, Generator Loss: 3.2188 D(x): 0.9548, D(G(z)): 0.1488 Epoch: [7/20], Batch Num: [420/600] Discriminator Loss: 0.3694, Generator Loss: 4.4303 D(x): 0.9410, D(G(z)): 0.1607 Epoch: [7/20], Batch Num: [421/600] Discriminator Loss: 0.2743, Generator Loss: 4.9066 D(x): 0.9145, D(G(z)): 0.0777 Epoch: [7/20], Batch Num: [422/600] Discriminator Loss: 0.4740, Generator Loss: 4.5698 D(x): 0.8147, D(G(z)): 0.0277 Epoch: [7/20], Batch Num: [423/600] Discriminator Loss: 0.3106, Generator Loss: 3.5034 D(x): 0.8875, D(G(z)): 0.0596 Epoch: [7/20], Batch Num: [424/600] Discriminator Loss: 0.2711, Generator Loss: 3.0421 D(x): 0.9185, D(G(z)): 0.0903 Epoch: [7/20], Batch Num: [425/600] Discriminator Loss: 0.3244, Generator Loss: 3.4692 D(x): 0.9783, D(G(z)): 0.1972 Epoch: [7/20], Batch Num: [426/600] Discriminator Loss: 0.2497, Generator Loss: 4.6282 D(x): 0.9567, D(G(z)): 0.1355 Epoch: [7/20], Batch Num: [427/600] Discriminator Loss: 0.5426, Generator Loss: 4.4962 D(x): 0.8119, D(G(z)): 0.0594 Epoch: [7/20], Batch Num: [428/600] Discriminator Loss: 0.3512, Generator Loss: 4.4266 D(x): 0.8760, D(G(z)): 0.0540 Epoch: [7/20], Batch Num: [429/600] Discriminator Loss: 0.4570, Generator Loss: 3.3346 D(x): 0.8295, D(G(z)): 0.0484 Epoch: [7/20], Batch Num: [430/600] Discriminator Loss: 0.2523, Generator Loss: 2.4408 D(x): 0.9235, D(G(z)): 0.0942 Epoch: [7/20], Batch Num: [431/600] Discriminator Loss: 0.4074, Generator Loss: 3.0765 D(x): 0.9594, D(G(z)): 0.2271 Epoch: [7/20], Batch Num: [432/600] Discriminator Loss: 0.4384, Generator Loss: 4.1273 D(x): 0.9161, D(G(z)): 0.1773 Epoch: [7/20], Batch Num: [433/600] Discriminator Loss: 0.2777, Generator Loss: 4.4793 D(x): 0.8841, D(G(z)): 0.0455 Epoch: [7/20], Batch Num: [434/600] Discriminator Loss: 0.2272, Generator Loss: 4.5767 D(x): 0.8911, D(G(z)): 0.0317 Epoch: [7/20], Batch Num: [435/600] Discriminator Loss: 0.2519, Generator Loss: 4.0207 D(x): 0.8926, D(G(z)): 0.0390 Epoch: [7/20], Batch Num: [436/600] Discriminator Loss: 0.2087, Generator Loss: 3.4546 D(x): 0.9206, D(G(z)): 0.0528 Epoch: [7/20], Batch Num: [437/600] Discriminator Loss: 0.3135, Generator Loss: 3.2288 D(x): 0.9289, D(G(z)): 0.1170 Epoch: [7/20], Batch Num: [438/600] Discriminator Loss: 0.3857, Generator Loss: 3.6122 D(x): 0.9204, D(G(z)): 0.1416 Epoch: [7/20], Batch Num: [439/600] Discriminator Loss: 0.3020, Generator Loss: 3.7444 D(x): 0.9147, D(G(z)): 0.1011 Epoch: [7/20], Batch Num: [440/600] Discriminator Loss: 0.3164, Generator Loss: 3.9749 D(x): 0.9068, D(G(z)): 0.0776 Epoch: [7/20], Batch Num: [441/600] Discriminator Loss: 0.3227, Generator Loss: 4.2507 D(x): 0.8858, D(G(z)): 0.0809 Epoch: [7/20], Batch Num: [442/600] Discriminator Loss: 0.2175, Generator Loss: 3.6761 D(x): 0.9185, D(G(z)): 0.0729 Epoch: [7/20], Batch Num: [443/600] Discriminator Loss: 0.3440, Generator Loss: 3.8974 D(x): 0.8965, D(G(z)): 0.0985 Epoch: [7/20], Batch Num: [444/600] Discriminator Loss: 0.2946, Generator Loss: 4.0830 D(x): 0.9138, D(G(z)): 0.1015 Epoch: [7/20], Batch Num: [445/600] Discriminator Loss: 0.3127, Generator Loss: 4.0278 D(x): 0.9182, D(G(z)): 0.1106 Epoch: [7/20], Batch Num: [446/600] Discriminator Loss: 0.3203, Generator Loss: 4.0334 D(x): 0.8820, D(G(z)): 0.0664 Epoch: [7/20], Batch Num: [447/600] Discriminator Loss: 0.2428, Generator Loss: 4.5566 D(x): 0.9268, D(G(z)): 0.0578 Epoch: [7/20], Batch Num: [448/600] Discriminator Loss: 0.2173, Generator Loss: 4.3761 D(x): 0.9332, D(G(z)): 0.0760 Epoch: [7/20], Batch Num: [449/600] Discriminator Loss: 0.2644, Generator Loss: 4.2854 D(x): 0.9007, D(G(z)): 0.0664 Epoch: [7/20], Batch Num: [450/600] Discriminator Loss: 0.3002, Generator Loss: 4.6130 D(x): 0.9221, D(G(z)): 0.1075 Epoch: [7/20], Batch Num: [451/600] Discriminator Loss: 0.2675, Generator Loss: 4.8198 D(x): 0.9608, D(G(z)): 0.1090 Epoch: [7/20], Batch Num: [452/600] Discriminator Loss: 0.3726, Generator Loss: 5.2992 D(x): 0.8721, D(G(z)): 0.0817 Epoch: [7/20], Batch Num: [453/600] Discriminator Loss: 0.2526, Generator Loss: 5.0835 D(x): 0.9102, D(G(z)): 0.0748 Epoch: [7/20], Batch Num: [454/600] Discriminator Loss: 0.3057, Generator Loss: 4.0839 D(x): 0.8841, D(G(z)): 0.0642 Epoch: [7/20], Batch Num: [455/600] Discriminator Loss: 0.4930, Generator Loss: 3.8543 D(x): 0.8958, D(G(z)): 0.1366 Epoch: [7/20], Batch Num: [456/600] Discriminator Loss: 0.2991, Generator Loss: 4.4169 D(x): 0.9498, D(G(z)): 0.1449 Epoch: [7/20], Batch Num: [457/600] Discriminator Loss: 0.3201, Generator Loss: 5.3880 D(x): 0.9243, D(G(z)): 0.1113 Epoch: [7/20], Batch Num: [458/600] Discriminator Loss: 0.3444, Generator Loss: 4.9822 D(x): 0.8926, D(G(z)): 0.0553 Epoch: [7/20], Batch Num: [459/600] Discriminator Loss: 0.3240, Generator Loss: 4.6812 D(x): 0.8906, D(G(z)): 0.0800 Epoch: [7/20], Batch Num: [460/600] Discriminator Loss: 0.4420, Generator Loss: 3.7421 D(x): 0.8743, D(G(z)): 0.0833 Epoch: [7/20], Batch Num: [461/600] Discriminator Loss: 0.4344, Generator Loss: 3.5890 D(x): 0.9111, D(G(z)): 0.1689 Epoch: [7/20], Batch Num: [462/600] Discriminator Loss: 0.4477, Generator Loss: 3.9913 D(x): 0.8966, D(G(z)): 0.1720 Epoch: [7/20], Batch Num: [463/600] Discriminator Loss: 0.3228, Generator Loss: 4.4569 D(x): 0.9045, D(G(z)): 0.1130 Epoch: [7/20], Batch Num: [464/600] Discriminator Loss: 0.3892, Generator Loss: 3.9804 D(x): 0.8533, D(G(z)): 0.0676 Epoch: [7/20], Batch Num: [465/600] Discriminator Loss: 0.5027, Generator Loss: 3.2008 D(x): 0.8521, D(G(z)): 0.0884 Epoch: [7/20], Batch Num: [466/600] Discriminator Loss: 0.3638, Generator Loss: 3.1263 D(x): 0.9441, D(G(z)): 0.1388 Epoch: [7/20], Batch Num: [467/600] Discriminator Loss: 0.3822, Generator Loss: 4.1247 D(x): 0.9140, D(G(z)): 0.1603 Epoch: [7/20], Batch Num: [468/600] Discriminator Loss: 0.4641, Generator Loss: 4.0984 D(x): 0.8632, D(G(z)): 0.1194 Epoch: [7/20], Batch Num: [469/600] Discriminator Loss: 0.3676, Generator Loss: 4.4048 D(x): 0.8740, D(G(z)): 0.1006 Epoch: [7/20], Batch Num: [470/600] Discriminator Loss: 0.3939, Generator Loss: 4.0442 D(x): 0.8580, D(G(z)): 0.0758 Epoch: [7/20], Batch Num: [471/600] Discriminator Loss: 0.3226, Generator Loss: 3.1742 D(x): 0.8954, D(G(z)): 0.0863 Epoch: [7/20], Batch Num: [472/600] Discriminator Loss: 0.4307, Generator Loss: 2.8403 D(x): 0.8732, D(G(z)): 0.1043 Epoch: [7/20], Batch Num: [473/600] Discriminator Loss: 0.4892, Generator Loss: 3.3026 D(x): 0.9182, D(G(z)): 0.2062 Epoch: [7/20], Batch Num: [474/600] Discriminator Loss: 0.4121, Generator Loss: 3.5961 D(x): 0.9072, D(G(z)): 0.1457 Epoch: [7/20], Batch Num: [475/600] Discriminator Loss: 0.2676, Generator Loss: 3.9050 D(x): 0.9061, D(G(z)): 0.0819 Epoch: [7/20], Batch Num: [476/600] Discriminator Loss: 0.3326, Generator Loss: 4.1374 D(x): 0.8831, D(G(z)): 0.0717 Epoch: [7/20], Batch Num: [477/600] Discriminator Loss: 0.3871, Generator Loss: 3.9356 D(x): 0.8622, D(G(z)): 0.0850 Epoch: [7/20], Batch Num: [478/600] Discriminator Loss: 0.4229, Generator Loss: 3.1313 D(x): 0.8256, D(G(z)): 0.0644 Epoch: [7/20], Batch Num: [479/600] Discriminator Loss: 0.3715, Generator Loss: 3.4729 D(x): 0.9263, D(G(z)): 0.1592 Epoch: [7/20], Batch Num: [480/600] Discriminator Loss: 0.3887, Generator Loss: 3.3879 D(x): 0.9102, D(G(z)): 0.1683 Epoch: [7/20], Batch Num: [481/600] Discriminator Loss: 0.3230, Generator Loss: 4.0384 D(x): 0.9316, D(G(z)): 0.1627 Epoch: [7/20], Batch Num: [482/600] Discriminator Loss: 0.3241, Generator Loss: 4.5174 D(x): 0.8856, D(G(z)): 0.0866 Epoch: [7/20], Batch Num: [483/600] Discriminator Loss: 0.2637, Generator Loss: 4.8956 D(x): 0.8844, D(G(z)): 0.0547 Epoch: [7/20], Batch Num: [484/600] Discriminator Loss: 0.3343, Generator Loss: 4.3353 D(x): 0.8689, D(G(z)): 0.0478 Epoch: [7/20], Batch Num: [485/600] Discriminator Loss: 0.3457, Generator Loss: 3.7023 D(x): 0.8737, D(G(z)): 0.0719 Epoch: [7/20], Batch Num: [486/600] Discriminator Loss: 0.2279, Generator Loss: 3.7651 D(x): 0.9549, D(G(z)): 0.1243 Epoch: [7/20], Batch Num: [487/600] Discriminator Loss: 0.3499, Generator Loss: 3.1594 D(x): 0.9049, D(G(z)): 0.1240 Epoch: [7/20], Batch Num: [488/600] Discriminator Loss: 0.2829, Generator Loss: 3.7646 D(x): 0.9667, D(G(z)): 0.1351 Epoch: [7/20], Batch Num: [489/600] Discriminator Loss: 0.3137, Generator Loss: 4.5320 D(x): 0.9204, D(G(z)): 0.1016 Epoch: [7/20], Batch Num: [490/600] Discriminator Loss: 0.3625, Generator Loss: 4.6636 D(x): 0.8660, D(G(z)): 0.0700 Epoch: [7/20], Batch Num: [491/600] Discriminator Loss: 0.2521, Generator Loss: 4.1337 D(x): 0.9120, D(G(z)): 0.0671 Epoch: [7/20], Batch Num: [492/600] Discriminator Loss: 0.3040, Generator Loss: 4.2182 D(x): 0.8687, D(G(z)): 0.0715 Epoch: [7/20], Batch Num: [493/600] Discriminator Loss: 0.2005, Generator Loss: 3.9004 D(x): 0.9459, D(G(z)): 0.0812 Epoch: [7/20], Batch Num: [494/600] Discriminator Loss: 0.3152, Generator Loss: 3.9609 D(x): 0.9027, D(G(z)): 0.0883 Epoch: [7/20], Batch Num: [495/600] Discriminator Loss: 0.3611, Generator Loss: 4.2531 D(x): 0.9310, D(G(z)): 0.1388 Epoch: [7/20], Batch Num: [496/600] Discriminator Loss: 0.1817, Generator Loss: 4.4688 D(x): 0.9478, D(G(z)): 0.0767 Epoch: [7/20], Batch Num: [497/600] Discriminator Loss: 0.2078, Generator Loss: 4.4704 D(x): 0.9040, D(G(z)): 0.0562 Epoch: [7/20], Batch Num: [498/600] Discriminator Loss: 0.2255, Generator Loss: 4.5427 D(x): 0.9093, D(G(z)): 0.0709 Epoch: [7/20], Batch Num: [499/600] Discriminator Loss: 0.3606, Generator Loss: 4.1934 D(x): 0.8680, D(G(z)): 0.0654 Epoch: 7, Batch Num: [500/600]
Epoch: [7/20], Batch Num: [500/600] Discriminator Loss: 0.1986, Generator Loss: 3.9867 D(x): 0.9503, D(G(z)): 0.0816 Epoch: [7/20], Batch Num: [501/600] Discriminator Loss: 0.2667, Generator Loss: 3.6074 D(x): 0.9416, D(G(z)): 0.1229 Epoch: [7/20], Batch Num: [502/600] Discriminator Loss: 0.2954, Generator Loss: 4.8514 D(x): 0.9342, D(G(z)): 0.1214 Epoch: [7/20], Batch Num: [503/600] Discriminator Loss: 0.2082, Generator Loss: 5.4174 D(x): 0.9251, D(G(z)): 0.0414 Epoch: [7/20], Batch Num: [504/600] Discriminator Loss: 0.2683, Generator Loss: 4.9641 D(x): 0.8921, D(G(z)): 0.0398 Epoch: [7/20], Batch Num: [505/600] Discriminator Loss: 0.1679, Generator Loss: 4.4355 D(x): 0.9324, D(G(z)): 0.0578 Epoch: [7/20], Batch Num: [506/600] Discriminator Loss: 0.3509, Generator Loss: 4.3216 D(x): 0.9045, D(G(z)): 0.0922 Epoch: [7/20], Batch Num: [507/600] Discriminator Loss: 0.3527, Generator Loss: 4.1304 D(x): 0.9181, D(G(z)): 0.0878 Epoch: [7/20], Batch Num: [508/600] Discriminator Loss: 0.2176, Generator Loss: 4.8420 D(x): 0.9520, D(G(z)): 0.0952 Epoch: [7/20], Batch Num: [509/600] Discriminator Loss: 0.2707, Generator Loss: 4.8443 D(x): 0.9037, D(G(z)): 0.0735 Epoch: [7/20], Batch Num: [510/600] Discriminator Loss: 0.3783, Generator Loss: 4.4489 D(x): 0.8962, D(G(z)): 0.0550 Epoch: [7/20], Batch Num: [511/600] Discriminator Loss: 0.3166, Generator Loss: 3.7532 D(x): 0.9112, D(G(z)): 0.0662 Epoch: [7/20], Batch Num: [512/600] Discriminator Loss: 0.2496, Generator Loss: 3.9108 D(x): 0.9442, D(G(z)): 0.1091 Epoch: [7/20], Batch Num: [513/600] Discriminator Loss: 0.3046, Generator Loss: 4.9628 D(x): 0.9475, D(G(z)): 0.1271 Epoch: [7/20], Batch Num: [514/600] Discriminator Loss: 0.2982, Generator Loss: 4.9181 D(x): 0.8996, D(G(z)): 0.0494 Epoch: [7/20], Batch Num: [515/600] Discriminator Loss: 0.2640, Generator Loss: 4.5864 D(x): 0.9128, D(G(z)): 0.0689 Epoch: [7/20], Batch Num: [516/600] Discriminator Loss: 0.3127, Generator Loss: 4.0150 D(x): 0.8918, D(G(z)): 0.0699 Epoch: [7/20], Batch Num: [517/600] Discriminator Loss: 0.2995, Generator Loss: 3.9672 D(x): 0.9460, D(G(z)): 0.1219 Epoch: [7/20], Batch Num: [518/600] Discriminator Loss: 0.3354, Generator Loss: 4.0647 D(x): 0.8948, D(G(z)): 0.0897 Epoch: [7/20], Batch Num: [519/600] Discriminator Loss: 0.3034, Generator Loss: 3.7723 D(x): 0.9057, D(G(z)): 0.0887 Epoch: [7/20], Batch Num: [520/600] Discriminator Loss: 0.3664, Generator Loss: 3.8371 D(x): 0.8899, D(G(z)): 0.0862 Epoch: [7/20], Batch Num: [521/600] Discriminator Loss: 0.3342, Generator Loss: 4.1195 D(x): 0.9454, D(G(z)): 0.1319 Epoch: [7/20], Batch Num: [522/600] Discriminator Loss: 0.2631, Generator Loss: 4.2655 D(x): 0.9147, D(G(z)): 0.0545 Epoch: [7/20], Batch Num: [523/600] Discriminator Loss: 0.2607, Generator Loss: 3.9190 D(x): 0.9121, D(G(z)): 0.0772 Epoch: [7/20], Batch Num: [524/600] Discriminator Loss: 0.3713, Generator Loss: 3.9355 D(x): 0.8826, D(G(z)): 0.0693 Epoch: [7/20], Batch Num: [525/600] Discriminator Loss: 0.3721, Generator Loss: 3.3851 D(x): 0.9007, D(G(z)): 0.1022 Epoch: [7/20], Batch Num: [526/600] Discriminator Loss: 0.4159, Generator Loss: 3.5747 D(x): 0.9223, D(G(z)): 0.1599 Epoch: [7/20], Batch Num: [527/600] Discriminator Loss: 0.2923, Generator Loss: 4.3355 D(x): 0.9394, D(G(z)): 0.1104 Epoch: [7/20], Batch Num: [528/600] Discriminator Loss: 0.3170, Generator Loss: 4.2291 D(x): 0.9099, D(G(z)): 0.0672 Epoch: [7/20], Batch Num: [529/600] Discriminator Loss: 0.3187, Generator Loss: 4.4777 D(x): 0.8945, D(G(z)): 0.0637 Epoch: [7/20], Batch Num: [530/600] Discriminator Loss: 0.2609, Generator Loss: 3.5727 D(x): 0.9112, D(G(z)): 0.0504 Epoch: [7/20], Batch Num: [531/600] Discriminator Loss: 0.3431, Generator Loss: 3.3595 D(x): 0.9274, D(G(z)): 0.1397 Epoch: [7/20], Batch Num: [532/600] Discriminator Loss: 0.3534, Generator Loss: 4.2230 D(x): 0.9335, D(G(z)): 0.1292 Epoch: [7/20], Batch Num: [533/600] Discriminator Loss: 0.2329, Generator Loss: 4.7254 D(x): 0.9213, D(G(z)): 0.0670 Epoch: [7/20], Batch Num: [534/600] Discriminator Loss: 0.1444, Generator Loss: 4.7093 D(x): 0.9394, D(G(z)): 0.0522 Epoch: [7/20], Batch Num: [535/600] Discriminator Loss: 0.4309, Generator Loss: 3.6997 D(x): 0.8400, D(G(z)): 0.0437 Epoch: [7/20], Batch Num: [536/600] Discriminator Loss: 0.1744, Generator Loss: 3.5195 D(x): 0.9425, D(G(z)): 0.0693 Epoch: [7/20], Batch Num: [537/600] Discriminator Loss: 0.1825, Generator Loss: 3.3973 D(x): 0.9561, D(G(z)): 0.1003 Epoch: [7/20], Batch Num: [538/600] Discriminator Loss: 0.4039, Generator Loss: 4.1842 D(x): 0.9484, D(G(z)): 0.1659 Epoch: [7/20], Batch Num: [539/600] Discriminator Loss: 0.3695, Generator Loss: 4.3979 D(x): 0.8814, D(G(z)): 0.0790 Epoch: [7/20], Batch Num: [540/600] Discriminator Loss: 0.1938, Generator Loss: 4.3262 D(x): 0.9171, D(G(z)): 0.0409 Epoch: [7/20], Batch Num: [541/600] Discriminator Loss: 0.3749, Generator Loss: 3.4694 D(x): 0.8470, D(G(z)): 0.0344 Epoch: [7/20], Batch Num: [542/600] Discriminator Loss: 0.2910, Generator Loss: 3.1232 D(x): 0.9320, D(G(z)): 0.1107 Epoch: [7/20], Batch Num: [543/600] Discriminator Loss: 0.2266, Generator Loss: 3.2163 D(x): 0.9710, D(G(z)): 0.1336 Epoch: [7/20], Batch Num: [544/600] Discriminator Loss: 0.3390, Generator Loss: 3.8502 D(x): 0.9202, D(G(z)): 0.1391 Epoch: [7/20], Batch Num: [545/600] Discriminator Loss: 0.3073, Generator Loss: 4.1100 D(x): 0.8797, D(G(z)): 0.0695 Epoch: [7/20], Batch Num: [546/600] Discriminator Loss: 0.3785, Generator Loss: 4.1796 D(x): 0.8643, D(G(z)): 0.0569 Epoch: [7/20], Batch Num: [547/600] Discriminator Loss: 0.2464, Generator Loss: 3.4659 D(x): 0.9113, D(G(z)): 0.0753 Epoch: [7/20], Batch Num: [548/600] Discriminator Loss: 0.2763, Generator Loss: 3.1543 D(x): 0.9285, D(G(z)): 0.0965 Epoch: [7/20], Batch Num: [549/600] Discriminator Loss: 0.4107, Generator Loss: 3.6082 D(x): 0.9281, D(G(z)): 0.1646 Epoch: [7/20], Batch Num: [550/600] Discriminator Loss: 0.2600, Generator Loss: 4.6730 D(x): 0.9496, D(G(z)): 0.1031 Epoch: [7/20], Batch Num: [551/600] Discriminator Loss: 0.2539, Generator Loss: 4.7896 D(x): 0.8957, D(G(z)): 0.0522 Epoch: [7/20], Batch Num: [552/600] Discriminator Loss: 0.2983, Generator Loss: 4.2335 D(x): 0.8868, D(G(z)): 0.0364 Epoch: [7/20], Batch Num: [553/600] Discriminator Loss: 0.3113, Generator Loss: 3.6550 D(x): 0.8764, D(G(z)): 0.0503 Epoch: [7/20], Batch Num: [554/600] Discriminator Loss: 0.2906, Generator Loss: 2.8478 D(x): 0.8969, D(G(z)): 0.0502 Epoch: [7/20], Batch Num: [555/600] Discriminator Loss: 0.2943, Generator Loss: 3.3255 D(x): 0.9924, D(G(z)): 0.1691 Epoch: [7/20], Batch Num: [556/600] Discriminator Loss: 0.3293, Generator Loss: 4.7109 D(x): 0.9592, D(G(z)): 0.1654 Epoch: [7/20], Batch Num: [557/600] Discriminator Loss: 0.2720, Generator Loss: 4.8811 D(x): 0.8914, D(G(z)): 0.0464 Epoch: [7/20], Batch Num: [558/600] Discriminator Loss: 0.5516, Generator Loss: 4.5798 D(x): 0.8285, D(G(z)): 0.0251 Epoch: [7/20], Batch Num: [559/600] Discriminator Loss: 0.2632, Generator Loss: 3.8788 D(x): 0.9093, D(G(z)): 0.0504 Epoch: [7/20], Batch Num: [560/600] Discriminator Loss: 0.3777, Generator Loss: 3.0767 D(x): 0.9116, D(G(z)): 0.0920 Epoch: [7/20], Batch Num: [561/600] Discriminator Loss: 0.2410, Generator Loss: 3.0303 D(x): 0.9709, D(G(z)): 0.1267 Epoch: [7/20], Batch Num: [562/600] Discriminator Loss: 0.3227, Generator Loss: 3.7996 D(x): 0.9554, D(G(z)): 0.1446 Epoch: [7/20], Batch Num: [563/600] Discriminator Loss: 0.1947, Generator Loss: 4.4499 D(x): 0.9433, D(G(z)): 0.0758 Epoch: [7/20], Batch Num: [564/600] Discriminator Loss: 0.3234, Generator Loss: 4.3432 D(x): 0.8791, D(G(z)): 0.0405 Epoch: [7/20], Batch Num: [565/600] Discriminator Loss: 0.2775, Generator Loss: 4.1492 D(x): 0.9104, D(G(z)): 0.0230 Epoch: [7/20], Batch Num: [566/600] Discriminator Loss: 0.2290, Generator Loss: 3.5767 D(x): 0.9203, D(G(z)): 0.0610 Epoch: [7/20], Batch Num: [567/600] Discriminator Loss: 0.2823, Generator Loss: 3.1601 D(x): 0.9341, D(G(z)): 0.0896 Epoch: [7/20], Batch Num: [568/600] Discriminator Loss: 0.2262, Generator Loss: 3.3667 D(x): 0.9493, D(G(z)): 0.1062 Epoch: [7/20], Batch Num: [569/600] Discriminator Loss: 0.3186, Generator Loss: 3.8940 D(x): 0.9095, D(G(z)): 0.1096 Epoch: [7/20], Batch Num: [570/600] Discriminator Loss: 0.3556, Generator Loss: 4.1022 D(x): 0.9191, D(G(z)): 0.0898 Epoch: [7/20], Batch Num: [571/600] Discriminator Loss: 0.3075, Generator Loss: 4.4625 D(x): 0.9111, D(G(z)): 0.0733 Epoch: [7/20], Batch Num: [572/600] Discriminator Loss: 0.3757, Generator Loss: 3.7345 D(x): 0.8631, D(G(z)): 0.0611 Epoch: [7/20], Batch Num: [573/600] Discriminator Loss: 0.2543, Generator Loss: 3.0938 D(x): 0.9102, D(G(z)): 0.0600 Epoch: [7/20], Batch Num: [574/600] Discriminator Loss: 0.2946, Generator Loss: 2.8487 D(x): 0.9323, D(G(z)): 0.1068 Epoch: [7/20], Batch Num: [575/600] Discriminator Loss: 0.2259, Generator Loss: 3.5125 D(x): 0.9747, D(G(z)): 0.1517 Epoch: [7/20], Batch Num: [576/600] Discriminator Loss: 0.2975, Generator Loss: 3.8601 D(x): 0.9211, D(G(z)): 0.0890 Epoch: [7/20], Batch Num: [577/600] Discriminator Loss: 0.4910, Generator Loss: 4.0608 D(x): 0.8708, D(G(z)): 0.0894 Epoch: [7/20], Batch Num: [578/600] Discriminator Loss: 0.2757, Generator Loss: 4.2343 D(x): 0.9131, D(G(z)): 0.0611 Epoch: [7/20], Batch Num: [579/600] Discriminator Loss: 0.1560, Generator Loss: 4.2372 D(x): 0.9439, D(G(z)): 0.0618 Epoch: [7/20], Batch Num: [580/600] Discriminator Loss: 0.3766, Generator Loss: 4.1529 D(x): 0.9010, D(G(z)): 0.0789 Epoch: [7/20], Batch Num: [581/600] Discriminator Loss: 0.2727, Generator Loss: 4.0346 D(x): 0.9222, D(G(z)): 0.0626 Epoch: [7/20], Batch Num: [582/600] Discriminator Loss: 0.1860, Generator Loss: 3.8190 D(x): 0.9411, D(G(z)): 0.0643 Epoch: [7/20], Batch Num: [583/600] Discriminator Loss: 0.1977, Generator Loss: 3.8583 D(x): 0.9366, D(G(z)): 0.0649 Epoch: [7/20], Batch Num: [584/600] Discriminator Loss: 0.2622, Generator Loss: 3.8998 D(x): 0.9312, D(G(z)): 0.0715 Epoch: [7/20], Batch Num: [585/600] Discriminator Loss: 0.1719, Generator Loss: 3.8072 D(x): 0.9686, D(G(z)): 0.0767 Epoch: [7/20], Batch Num: [586/600] Discriminator Loss: 0.3177, Generator Loss: 3.8531 D(x): 0.9021, D(G(z)): 0.0525 Epoch: [7/20], Batch Num: [587/600] Discriminator Loss: 0.3785, Generator Loss: 3.6559 D(x): 0.8793, D(G(z)): 0.0794 Epoch: [7/20], Batch Num: [588/600] Discriminator Loss: 0.3232, Generator Loss: 3.9293 D(x): 0.9374, D(G(z)): 0.1235 Epoch: [7/20], Batch Num: [589/600] Discriminator Loss: 0.2113, Generator Loss: 4.2802 D(x): 0.9687, D(G(z)): 0.0977 Epoch: [7/20], Batch Num: [590/600] Discriminator Loss: 0.3152, Generator Loss: 4.1818 D(x): 0.8928, D(G(z)): 0.0457 Epoch: [7/20], Batch Num: [591/600] Discriminator Loss: 0.2520, Generator Loss: 3.7692 D(x): 0.9028, D(G(z)): 0.0428 Epoch: [7/20], Batch Num: [592/600] Discriminator Loss: 0.2989, Generator Loss: 3.3596 D(x): 0.9142, D(G(z)): 0.0843 Epoch: [7/20], Batch Num: [593/600] Discriminator Loss: 0.1876, Generator Loss: 3.2874 D(x): 0.9649, D(G(z)): 0.0995 Epoch: [7/20], Batch Num: [594/600] Discriminator Loss: 0.2115, Generator Loss: 4.3060 D(x): 0.9644, D(G(z)): 0.0987 Epoch: [7/20], Batch Num: [595/600] Discriminator Loss: 0.2421, Generator Loss: 4.2359 D(x): 0.9235, D(G(z)): 0.0848 Epoch: [7/20], Batch Num: [596/600] Discriminator Loss: 0.4528, Generator Loss: 4.4945 D(x): 0.8740, D(G(z)): 0.0733 Epoch: [7/20], Batch Num: [597/600] Discriminator Loss: 0.2843, Generator Loss: 3.8141 D(x): 0.9006, D(G(z)): 0.0425 Epoch: [7/20], Batch Num: [598/600] Discriminator Loss: 0.4463, Generator Loss: 3.0126 D(x): 0.8733, D(G(z)): 0.0712 Epoch: [7/20], Batch Num: [599/600] Discriminator Loss: 0.2908, Generator Loss: 3.0027 D(x): 0.9660, D(G(z)): 0.1522 Epoch: 8, Batch Num: [0/600]
Epoch: [8/20], Batch Num: [0/600] Discriminator Loss: 0.2972, Generator Loss: 3.8671 D(x): 0.9553, D(G(z)): 0.1476 Epoch: [8/20], Batch Num: [1/600] Discriminator Loss: 0.3030, Generator Loss: 4.4037 D(x): 0.9168, D(G(z)): 0.0766 Epoch: [8/20], Batch Num: [2/600] Discriminator Loss: 0.3984, Generator Loss: 4.1119 D(x): 0.8328, D(G(z)): 0.0430 Epoch: [8/20], Batch Num: [3/600] Discriminator Loss: 0.2880, Generator Loss: 3.1991 D(x): 0.8838, D(G(z)): 0.0513 Epoch: [8/20], Batch Num: [4/600] Discriminator Loss: 0.2419, Generator Loss: 2.5141 D(x): 0.9297, D(G(z)): 0.0935 Epoch: [8/20], Batch Num: [5/600] Discriminator Loss: 0.4253, Generator Loss: 2.8198 D(x): 0.9446, D(G(z)): 0.2139 Epoch: [8/20], Batch Num: [6/600] Discriminator Loss: 0.3938, Generator Loss: 3.8721 D(x): 0.9237, D(G(z)): 0.1640 Epoch: [8/20], Batch Num: [7/600] Discriminator Loss: 0.3496, Generator Loss: 4.6216 D(x): 0.8870, D(G(z)): 0.0822 Epoch: [8/20], Batch Num: [8/600] Discriminator Loss: 0.4140, Generator Loss: 4.4749 D(x): 0.8482, D(G(z)): 0.0413 Epoch: [8/20], Batch Num: [9/600] Discriminator Loss: 0.4921, Generator Loss: 3.5448 D(x): 0.8316, D(G(z)): 0.0387 Epoch: [8/20], Batch Num: [10/600] Discriminator Loss: 0.2822, Generator Loss: 2.8835 D(x): 0.9232, D(G(z)): 0.0851 Epoch: [8/20], Batch Num: [11/600] Discriminator Loss: 0.3421, Generator Loss: 3.1035 D(x): 0.9601, D(G(z)): 0.1574 Epoch: [8/20], Batch Num: [12/600] Discriminator Loss: 0.2978, Generator Loss: 3.0082 D(x): 0.9242, D(G(z)): 0.1131 Epoch: [8/20], Batch Num: [13/600] Discriminator Loss: 0.2376, Generator Loss: 3.7274 D(x): 0.9629, D(G(z)): 0.1178 Epoch: [8/20], Batch Num: [14/600] Discriminator Loss: 0.2851, Generator Loss: 4.2297 D(x): 0.9329, D(G(z)): 0.0926 Epoch: [8/20], Batch Num: [15/600] Discriminator Loss: 0.4073, Generator Loss: 4.6372 D(x): 0.8623, D(G(z)): 0.0667 Epoch: [8/20], Batch Num: [16/600] Discriminator Loss: 0.3454, Generator Loss: 4.0462 D(x): 0.8774, D(G(z)): 0.0268 Epoch: [8/20], Batch Num: [17/600] Discriminator Loss: 0.3149, Generator Loss: 4.1662 D(x): 0.8794, D(G(z)): 0.0591 Epoch: [8/20], Batch Num: [18/600] Discriminator Loss: 0.2807, Generator Loss: 3.0999 D(x): 0.9635, D(G(z)): 0.1085 Epoch: [8/20], Batch Num: [19/600] Discriminator Loss: 0.2025, Generator Loss: 3.5785 D(x): 0.9593, D(G(z)): 0.1051 Epoch: [8/20], Batch Num: [20/600] Discriminator Loss: 0.3301, Generator Loss: 4.4041 D(x): 0.9117, D(G(z)): 0.1155 Epoch: [8/20], Batch Num: [21/600] Discriminator Loss: 0.3697, Generator Loss: 3.7663 D(x): 0.8787, D(G(z)): 0.0510 Epoch: [8/20], Batch Num: [22/600] Discriminator Loss: 0.3217, Generator Loss: 3.6192 D(x): 0.8820, D(G(z)): 0.0666 Epoch: [8/20], Batch Num: [23/600] Discriminator Loss: 0.2027, Generator Loss: 3.0806 D(x): 0.9237, D(G(z)): 0.0652 Epoch: [8/20], Batch Num: [24/600] Discriminator Loss: 0.4076, Generator Loss: 3.1018 D(x): 0.8987, D(G(z)): 0.1195 Epoch: [8/20], Batch Num: [25/600] Discriminator Loss: 0.3723, Generator Loss: 3.1137 D(x): 0.9158, D(G(z)): 0.1399 Epoch: [8/20], Batch Num: [26/600] Discriminator Loss: 0.3013, Generator Loss: 3.1111 D(x): 0.9179, D(G(z)): 0.1192 Epoch: [8/20], Batch Num: [27/600] Discriminator Loss: 0.4440, Generator Loss: 3.2741 D(x): 0.8798, D(G(z)): 0.1133 Epoch: [8/20], Batch Num: [28/600] Discriminator Loss: 0.3597, Generator Loss: 3.1988 D(x): 0.8885, D(G(z)): 0.0863 Epoch: [8/20], Batch Num: [29/600] Discriminator Loss: 0.2473, Generator Loss: 3.3935 D(x): 0.9386, D(G(z)): 0.1113 Epoch: [8/20], Batch Num: [30/600] Discriminator Loss: 0.2895, Generator Loss: 3.2554 D(x): 0.9014, D(G(z)): 0.0631 Epoch: [8/20], Batch Num: [31/600] Discriminator Loss: 0.3333, Generator Loss: 3.1231 D(x): 0.8771, D(G(z)): 0.0753 Epoch: [8/20], Batch Num: [32/600] Discriminator Loss: 0.4118, Generator Loss: 2.9606 D(x): 0.9084, D(G(z)): 0.1283 Epoch: [8/20], Batch Num: [33/600] Discriminator Loss: 0.3356, Generator Loss: 3.3132 D(x): 0.9164, D(G(z)): 0.1271 Epoch: [8/20], Batch Num: [34/600] Discriminator Loss: 0.2900, Generator Loss: 3.9851 D(x): 0.9229, D(G(z)): 0.1040 Epoch: [8/20], Batch Num: [35/600] Discriminator Loss: 0.3080, Generator Loss: 3.8337 D(x): 0.8748, D(G(z)): 0.0524 Epoch: [8/20], Batch Num: [36/600] Discriminator Loss: 0.2343, Generator Loss: 3.6530 D(x): 0.9286, D(G(z)): 0.0795 Epoch: [8/20], Batch Num: [37/600] Discriminator Loss: 0.2165, Generator Loss: 3.6896 D(x): 0.9326, D(G(z)): 0.0726 Epoch: [8/20], Batch Num: [38/600] Discriminator Loss: 0.3075, Generator Loss: 3.6012 D(x): 0.8945, D(G(z)): 0.0911 Epoch: [8/20], Batch Num: [39/600] Discriminator Loss: 0.3396, Generator Loss: 3.6923 D(x): 0.8940, D(G(z)): 0.1003 Epoch: [8/20], Batch Num: [40/600] Discriminator Loss: 0.4957, Generator Loss: 3.3705 D(x): 0.8545, D(G(z)): 0.0978 Epoch: [8/20], Batch Num: [41/600] Discriminator Loss: 0.2681, Generator Loss: 3.2738 D(x): 0.9364, D(G(z)): 0.1182 Epoch: [8/20], Batch Num: [42/600] Discriminator Loss: 0.2122, Generator Loss: 3.5918 D(x): 0.9347, D(G(z)): 0.0916 Epoch: [8/20], Batch Num: [43/600] Discriminator Loss: 0.3363, Generator Loss: 3.7750 D(x): 0.8906, D(G(z)): 0.0858 Epoch: [8/20], Batch Num: [44/600] Discriminator Loss: 0.2941, Generator Loss: 3.7038 D(x): 0.9062, D(G(z)): 0.0867 Epoch: [8/20], Batch Num: [45/600] Discriminator Loss: 0.4352, Generator Loss: 3.4975 D(x): 0.8846, D(G(z)): 0.1123 Epoch: [8/20], Batch Num: [46/600] Discriminator Loss: 0.3606, Generator Loss: 3.9533 D(x): 0.9077, D(G(z)): 0.1054 Epoch: [8/20], Batch Num: [47/600] Discriminator Loss: 0.3700, Generator Loss: 3.9009 D(x): 0.9048, D(G(z)): 0.1023 Epoch: [8/20], Batch Num: [48/600] Discriminator Loss: 0.2519, Generator Loss: 4.0460 D(x): 0.9253, D(G(z)): 0.0694 Epoch: [8/20], Batch Num: [49/600] Discriminator Loss: 0.3113, Generator Loss: 4.1005 D(x): 0.8959, D(G(z)): 0.0739 Epoch: [8/20], Batch Num: [50/600] Discriminator Loss: 0.3545, Generator Loss: 3.6899 D(x): 0.8856, D(G(z)): 0.0890 Epoch: [8/20], Batch Num: [51/600] Discriminator Loss: 0.3509, Generator Loss: 3.1707 D(x): 0.9144, D(G(z)): 0.0934 Epoch: [8/20], Batch Num: [52/600] Discriminator Loss: 0.3891, Generator Loss: 3.4638 D(x): 0.8834, D(G(z)): 0.1204 Epoch: [8/20], Batch Num: [53/600] Discriminator Loss: 0.3625, Generator Loss: 4.3502 D(x): 0.9447, D(G(z)): 0.1494 Epoch: [8/20], Batch Num: [54/600] Discriminator Loss: 0.2900, Generator Loss: 4.3275 D(x): 0.8725, D(G(z)): 0.0453 Epoch: [8/20], Batch Num: [55/600] Discriminator Loss: 0.3352, Generator Loss: 4.1160 D(x): 0.8897, D(G(z)): 0.0891 Epoch: [8/20], Batch Num: [56/600] Discriminator Loss: 0.3033, Generator Loss: 3.2814 D(x): 0.8999, D(G(z)): 0.0703 Epoch: [8/20], Batch Num: [57/600] Discriminator Loss: 0.3740, Generator Loss: 3.2889 D(x): 0.8905, D(G(z)): 0.1137 Epoch: [8/20], Batch Num: [58/600] Discriminator Loss: 0.2870, Generator Loss: 3.6483 D(x): 0.9402, D(G(z)): 0.1101 Epoch: [8/20], Batch Num: [59/600] Discriminator Loss: 0.4603, Generator Loss: 3.8878 D(x): 0.8673, D(G(z)): 0.1185 Epoch: [8/20], Batch Num: [60/600] Discriminator Loss: 0.5497, Generator Loss: 3.4921 D(x): 0.8425, D(G(z)): 0.0964 Epoch: [8/20], Batch Num: [61/600] Discriminator Loss: 0.3332, Generator Loss: 3.2701 D(x): 0.8770, D(G(z)): 0.0756 Epoch: [8/20], Batch Num: [62/600] Discriminator Loss: 0.2936, Generator Loss: 3.5365 D(x): 0.9101, D(G(z)): 0.1153 Epoch: [8/20], Batch Num: [63/600] Discriminator Loss: 0.3230, Generator Loss: 3.6090 D(x): 0.9407, D(G(z)): 0.1321 Epoch: [8/20], Batch Num: [64/600] Discriminator Loss: 0.2972, Generator Loss: 4.0728 D(x): 0.9294, D(G(z)): 0.1075 Epoch: [8/20], Batch Num: [65/600] Discriminator Loss: 0.3052, Generator Loss: 4.7897 D(x): 0.8897, D(G(z)): 0.0618 Epoch: [8/20], Batch Num: [66/600] Discriminator Loss: 0.3357, Generator Loss: 4.2001 D(x): 0.8698, D(G(z)): 0.0589 Epoch: [8/20], Batch Num: [67/600] Discriminator Loss: 0.3801, Generator Loss: 3.5011 D(x): 0.8568, D(G(z)): 0.0428 Epoch: [8/20], Batch Num: [68/600] Discriminator Loss: 0.4058, Generator Loss: 2.9934 D(x): 0.9405, D(G(z)): 0.1639 Epoch: [8/20], Batch Num: [69/600] Discriminator Loss: 0.3621, Generator Loss: 3.7986 D(x): 0.9362, D(G(z)): 0.1361 Epoch: [8/20], Batch Num: [70/600] Discriminator Loss: 0.4140, Generator Loss: 4.2117 D(x): 0.9219, D(G(z)): 0.1308 Epoch: [8/20], Batch Num: [71/600] Discriminator Loss: 0.4410, Generator Loss: 5.0273 D(x): 0.8669, D(G(z)): 0.0657 Epoch: [8/20], Batch Num: [72/600] Discriminator Loss: 0.3531, Generator Loss: 4.8249 D(x): 0.8671, D(G(z)): 0.0572 Epoch: [8/20], Batch Num: [73/600] Discriminator Loss: 0.3648, Generator Loss: 4.0711 D(x): 0.8547, D(G(z)): 0.0673 Epoch: [8/20], Batch Num: [74/600] Discriminator Loss: 0.2754, Generator Loss: 3.0788 D(x): 0.9172, D(G(z)): 0.0883 Epoch: [8/20], Batch Num: [75/600] Discriminator Loss: 0.2819, Generator Loss: 3.3893 D(x): 0.9482, D(G(z)): 0.1312 Epoch: [8/20], Batch Num: [76/600] Discriminator Loss: 0.3327, Generator Loss: 4.3341 D(x): 0.9492, D(G(z)): 0.1675 Epoch: [8/20], Batch Num: [77/600] Discriminator Loss: 0.3657, Generator Loss: 4.4229 D(x): 0.8511, D(G(z)): 0.0825 Epoch: [8/20], Batch Num: [78/600] Discriminator Loss: 0.2943, Generator Loss: 4.5839 D(x): 0.8854, D(G(z)): 0.0653 Epoch: [8/20], Batch Num: [79/600] Discriminator Loss: 0.4855, Generator Loss: 3.9631 D(x): 0.8508, D(G(z)): 0.0964 Epoch: [8/20], Batch Num: [80/600] Discriminator Loss: 0.4243, Generator Loss: 4.0959 D(x): 0.9065, D(G(z)): 0.1192 Epoch: [8/20], Batch Num: [81/600] Discriminator Loss: 0.5149, Generator Loss: 3.4443 D(x): 0.9033, D(G(z)): 0.1479 Epoch: [8/20], Batch Num: [82/600] Discriminator Loss: 0.2481, Generator Loss: 4.0930 D(x): 0.9211, D(G(z)): 0.0805 Epoch: [8/20], Batch Num: [83/600] Discriminator Loss: 0.2652, Generator Loss: 4.2778 D(x): 0.9205, D(G(z)): 0.0962 Epoch: [8/20], Batch Num: [84/600] Discriminator Loss: 0.3444, Generator Loss: 4.4191 D(x): 0.8651, D(G(z)): 0.0892 Epoch: [8/20], Batch Num: [85/600] Discriminator Loss: 0.3420, Generator Loss: 3.9518 D(x): 0.8746, D(G(z)): 0.0995 Epoch: [8/20], Batch Num: [86/600] Discriminator Loss: 0.2671, Generator Loss: 4.1814 D(x): 0.9212, D(G(z)): 0.1000 Epoch: [8/20], Batch Num: [87/600] Discriminator Loss: 0.3269, Generator Loss: 3.6936 D(x): 0.9218, D(G(z)): 0.1162 Epoch: [8/20], Batch Num: [88/600] Discriminator Loss: 0.4257, Generator Loss: 4.6989 D(x): 0.8779, D(G(z)): 0.0959 Epoch: [8/20], Batch Num: [89/600] Discriminator Loss: 0.3774, Generator Loss: 4.1801 D(x): 0.8598, D(G(z)): 0.0627 Epoch: [8/20], Batch Num: [90/600] Discriminator Loss: 0.3880, Generator Loss: 3.6064 D(x): 0.8676, D(G(z)): 0.1025 Epoch: [8/20], Batch Num: [91/600] Discriminator Loss: 0.2967, Generator Loss: 3.5224 D(x): 0.9238, D(G(z)): 0.1040 Epoch: [8/20], Batch Num: [92/600] Discriminator Loss: 0.2898, Generator Loss: 3.4925 D(x): 0.9428, D(G(z)): 0.1193 Epoch: [8/20], Batch Num: [93/600] Discriminator Loss: 0.3013, Generator Loss: 4.0275 D(x): 0.9134, D(G(z)): 0.1212 Epoch: [8/20], Batch Num: [94/600] Discriminator Loss: 0.2843, Generator Loss: 4.7131 D(x): 0.9238, D(G(z)): 0.0765 Epoch: [8/20], Batch Num: [95/600] Discriminator Loss: 0.2931, Generator Loss: 4.5978 D(x): 0.8919, D(G(z)): 0.0821 Epoch: [8/20], Batch Num: [96/600] Discriminator Loss: 0.3287, Generator Loss: 4.2293 D(x): 0.8738, D(G(z)): 0.0785 Epoch: [8/20], Batch Num: [97/600] Discriminator Loss: 0.3416, Generator Loss: 4.1006 D(x): 0.8804, D(G(z)): 0.1031 Epoch: [8/20], Batch Num: [98/600] Discriminator Loss: 0.3240, Generator Loss: 3.9315 D(x): 0.9130, D(G(z)): 0.0947 Epoch: [8/20], Batch Num: [99/600] Discriminator Loss: 0.3630, Generator Loss: 3.5637 D(x): 0.8837, D(G(z)): 0.1055 Epoch: 8, Batch Num: [100/600]
Epoch: [8/20], Batch Num: [100/600] Discriminator Loss: 0.2708, Generator Loss: 3.4144 D(x): 0.9240, D(G(z)): 0.0937 Epoch: [8/20], Batch Num: [101/600] Discriminator Loss: 0.3345, Generator Loss: 4.1711 D(x): 0.9113, D(G(z)): 0.1270 Epoch: [8/20], Batch Num: [102/600] Discriminator Loss: 0.2460, Generator Loss: 4.8013 D(x): 0.9272, D(G(z)): 0.0539 Epoch: [8/20], Batch Num: [103/600] Discriminator Loss: 0.3271, Generator Loss: 5.3758 D(x): 0.8922, D(G(z)): 0.0883 Epoch: [8/20], Batch Num: [104/600] Discriminator Loss: 0.3235, Generator Loss: 4.4775 D(x): 0.8769, D(G(z)): 0.0533 Epoch: [8/20], Batch Num: [105/600] Discriminator Loss: 0.3577, Generator Loss: 3.8007 D(x): 0.8997, D(G(z)): 0.0703 Epoch: [8/20], Batch Num: [106/600] Discriminator Loss: 0.4591, Generator Loss: 3.3894 D(x): 0.8949, D(G(z)): 0.1190 Epoch: [8/20], Batch Num: [107/600] Discriminator Loss: 0.5135, Generator Loss: 4.2245 D(x): 0.9029, D(G(z)): 0.1761 Epoch: [8/20], Batch Num: [108/600] Discriminator Loss: 0.3506, Generator Loss: 4.4838 D(x): 0.9054, D(G(z)): 0.1060 Epoch: [8/20], Batch Num: [109/600] Discriminator Loss: 0.3594, Generator Loss: 4.2348 D(x): 0.8858, D(G(z)): 0.0697 Epoch: [8/20], Batch Num: [110/600] Discriminator Loss: 0.2582, Generator Loss: 4.2725 D(x): 0.9032, D(G(z)): 0.0532 Epoch: [8/20], Batch Num: [111/600] Discriminator Loss: 0.2682, Generator Loss: 3.9380 D(x): 0.9205, D(G(z)): 0.0795 Epoch: [8/20], Batch Num: [112/600] Discriminator Loss: 0.3660, Generator Loss: 3.3906 D(x): 0.8803, D(G(z)): 0.0909 Epoch: [8/20], Batch Num: [113/600] Discriminator Loss: 0.3792, Generator Loss: 3.3978 D(x): 0.9372, D(G(z)): 0.1738 Epoch: [8/20], Batch Num: [114/600] Discriminator Loss: 0.4270, Generator Loss: 4.3988 D(x): 0.9151, D(G(z)): 0.1390 Epoch: [8/20], Batch Num: [115/600] Discriminator Loss: 0.3106, Generator Loss: 4.5260 D(x): 0.9060, D(G(z)): 0.0528 Epoch: [8/20], Batch Num: [116/600] Discriminator Loss: 0.5483, Generator Loss: 4.3664 D(x): 0.8420, D(G(z)): 0.0450 Epoch: [8/20], Batch Num: [117/600] Discriminator Loss: 0.5209, Generator Loss: 3.4270 D(x): 0.8545, D(G(z)): 0.1038 Epoch: [8/20], Batch Num: [118/600] Discriminator Loss: 0.3916, Generator Loss: 3.0160 D(x): 0.9232, D(G(z)): 0.1556 Epoch: [8/20], Batch Num: [119/600] Discriminator Loss: 0.4851, Generator Loss: 3.3791 D(x): 0.9036, D(G(z)): 0.1565 Epoch: [8/20], Batch Num: [120/600] Discriminator Loss: 0.2956, Generator Loss: 4.1154 D(x): 0.9312, D(G(z)): 0.1300 Epoch: [8/20], Batch Num: [121/600] Discriminator Loss: 0.3946, Generator Loss: 4.1857 D(x): 0.8777, D(G(z)): 0.0757 Epoch: [8/20], Batch Num: [122/600] Discriminator Loss: 0.5525, Generator Loss: 3.5084 D(x): 0.7799, D(G(z)): 0.0570 Epoch: [8/20], Batch Num: [123/600] Discriminator Loss: 0.4272, Generator Loss: 2.6289 D(x): 0.8337, D(G(z)): 0.0844 Epoch: [8/20], Batch Num: [124/600] Discriminator Loss: 0.3814, Generator Loss: 2.6631 D(x): 0.9452, D(G(z)): 0.1963 Epoch: [8/20], Batch Num: [125/600] Discriminator Loss: 0.4178, Generator Loss: 3.0164 D(x): 0.9144, D(G(z)): 0.1914 Epoch: [8/20], Batch Num: [126/600] Discriminator Loss: 0.4252, Generator Loss: 3.7567 D(x): 0.9103, D(G(z)): 0.1679 Epoch: [8/20], Batch Num: [127/600] Discriminator Loss: 0.3456, Generator Loss: 4.3626 D(x): 0.8734, D(G(z)): 0.0623 Epoch: [8/20], Batch Num: [128/600] Discriminator Loss: 0.4531, Generator Loss: 3.7081 D(x): 0.8190, D(G(z)): 0.0680 Epoch: [8/20], Batch Num: [129/600] Discriminator Loss: 0.5510, Generator Loss: 2.7252 D(x): 0.8004, D(G(z)): 0.0568 Epoch: [8/20], Batch Num: [130/600] Discriminator Loss: 0.4924, Generator Loss: 2.1664 D(x): 0.8853, D(G(z)): 0.1702 Epoch: [8/20], Batch Num: [131/600] Discriminator Loss: 0.5907, Generator Loss: 2.2781 D(x): 0.9275, D(G(z)): 0.2540 Epoch: [8/20], Batch Num: [132/600] Discriminator Loss: 0.3484, Generator Loss: 3.3790 D(x): 0.9594, D(G(z)): 0.2147 Epoch: [8/20], Batch Num: [133/600] Discriminator Loss: 0.3503, Generator Loss: 4.3182 D(x): 0.8836, D(G(z)): 0.0804 Epoch: [8/20], Batch Num: [134/600] Discriminator Loss: 0.4810, Generator Loss: 4.7329 D(x): 0.8219, D(G(z)): 0.0358 Epoch: [8/20], Batch Num: [135/600] Discriminator Loss: 0.5887, Generator Loss: 3.8905 D(x): 0.7770, D(G(z)): 0.0345 Epoch: [8/20], Batch Num: [136/600] Discriminator Loss: 0.3392, Generator Loss: 2.9333 D(x): 0.8646, D(G(z)): 0.0525 Epoch: [8/20], Batch Num: [137/600] Discriminator Loss: 0.4667, Generator Loss: 2.4439 D(x): 0.8638, D(G(z)): 0.1412 Epoch: [8/20], Batch Num: [138/600] Discriminator Loss: 0.4522, Generator Loss: 2.3906 D(x): 0.9330, D(G(z)): 0.2265 Epoch: [8/20], Batch Num: [139/600] Discriminator Loss: 0.4106, Generator Loss: 2.6703 D(x): 0.9394, D(G(z)): 0.2229 Epoch: [8/20], Batch Num: [140/600] Discriminator Loss: 0.3307, Generator Loss: 3.5659 D(x): 0.9283, D(G(z)): 0.1577 Epoch: [8/20], Batch Num: [141/600] Discriminator Loss: 0.3407, Generator Loss: 3.8553 D(x): 0.8644, D(G(z)): 0.0707 Epoch: [8/20], Batch Num: [142/600] Discriminator Loss: 0.3713, Generator Loss: 4.0330 D(x): 0.8357, D(G(z)): 0.0502 Epoch: [8/20], Batch Num: [143/600] Discriminator Loss: 0.3676, Generator Loss: 3.5049 D(x): 0.8443, D(G(z)): 0.0612 Epoch: [8/20], Batch Num: [144/600] Discriminator Loss: 0.2910, Generator Loss: 3.1948 D(x): 0.8658, D(G(z)): 0.0543 Epoch: [8/20], Batch Num: [145/600] Discriminator Loss: 0.3033, Generator Loss: 2.6982 D(x): 0.9084, D(G(z)): 0.1115 Epoch: [8/20], Batch Num: [146/600] Discriminator Loss: 0.3905, Generator Loss: 2.6384 D(x): 0.8976, D(G(z)): 0.1529 Epoch: [8/20], Batch Num: [147/600] Discriminator Loss: 0.2848, Generator Loss: 2.5240 D(x): 0.9552, D(G(z)): 0.1627 Epoch: [8/20], Batch Num: [148/600] Discriminator Loss: 0.2765, Generator Loss: 3.2064 D(x): 0.9523, D(G(z)): 0.1589 Epoch: [8/20], Batch Num: [149/600] Discriminator Loss: 0.3210, Generator Loss: 3.7269 D(x): 0.8801, D(G(z)): 0.0845 Epoch: [8/20], Batch Num: [150/600] Discriminator Loss: 0.4139, Generator Loss: 3.4128 D(x): 0.8399, D(G(z)): 0.0746 Epoch: [8/20], Batch Num: [151/600] Discriminator Loss: 0.3514, Generator Loss: 3.5024 D(x): 0.8568, D(G(z)): 0.0794 Epoch: [8/20], Batch Num: [152/600] Discriminator Loss: 0.3181, Generator Loss: 3.0432 D(x): 0.8909, D(G(z)): 0.0944 Epoch: [8/20], Batch Num: [153/600] Discriminator Loss: 0.3328, Generator Loss: 3.0952 D(x): 0.9079, D(G(z)): 0.1413 Epoch: [8/20], Batch Num: [154/600] Discriminator Loss: 0.3168, Generator Loss: 3.0022 D(x): 0.9226, D(G(z)): 0.1468 Epoch: [8/20], Batch Num: [155/600] Discriminator Loss: 0.3246, Generator Loss: 3.1277 D(x): 0.9389, D(G(z)): 0.1436 Epoch: [8/20], Batch Num: [156/600] Discriminator Loss: 0.2821, Generator Loss: 3.9412 D(x): 0.9376, D(G(z)): 0.0900 Epoch: [8/20], Batch Num: [157/600] Discriminator Loss: 0.3599, Generator Loss: 4.4888 D(x): 0.8847, D(G(z)): 0.0797 Epoch: [8/20], Batch Num: [158/600] Discriminator Loss: 0.2301, Generator Loss: 4.0080 D(x): 0.9011, D(G(z)): 0.0598 Epoch: [8/20], Batch Num: [159/600] Discriminator Loss: 0.3527, Generator Loss: 3.9684 D(x): 0.8887, D(G(z)): 0.0783 Epoch: [8/20], Batch Num: [160/600] Discriminator Loss: 0.4212, Generator Loss: 3.3855 D(x): 0.8661, D(G(z)): 0.1038 Epoch: [8/20], Batch Num: [161/600] Discriminator Loss: 0.3318, Generator Loss: 3.6198 D(x): 0.9357, D(G(z)): 0.1604 Epoch: [8/20], Batch Num: [162/600] Discriminator Loss: 0.3400, Generator Loss: 3.6534 D(x): 0.9187, D(G(z)): 0.1178 Epoch: [8/20], Batch Num: [163/600] Discriminator Loss: 0.3594, Generator Loss: 3.9005 D(x): 0.9072, D(G(z)): 0.1408 Epoch: [8/20], Batch Num: [164/600] Discriminator Loss: 0.4570, Generator Loss: 3.9718 D(x): 0.8659, D(G(z)): 0.1013 Epoch: [8/20], Batch Num: [165/600] Discriminator Loss: 0.3193, Generator Loss: 3.4721 D(x): 0.8934, D(G(z)): 0.0945 Epoch: [8/20], Batch Num: [166/600] Discriminator Loss: 0.4060, Generator Loss: 2.6861 D(x): 0.8546, D(G(z)): 0.0909 Epoch: [8/20], Batch Num: [167/600] Discriminator Loss: 0.3887, Generator Loss: 2.8512 D(x): 0.9027, D(G(z)): 0.1890 Epoch: [8/20], Batch Num: [168/600] Discriminator Loss: 0.3645, Generator Loss: 3.5687 D(x): 0.9367, D(G(z)): 0.1900 Epoch: [8/20], Batch Num: [169/600] Discriminator Loss: 0.4084, Generator Loss: 3.8327 D(x): 0.8643, D(G(z)): 0.1117 Epoch: [8/20], Batch Num: [170/600] Discriminator Loss: 0.4077, Generator Loss: 4.1742 D(x): 0.8394, D(G(z)): 0.1082 Epoch: [8/20], Batch Num: [171/600] Discriminator Loss: 0.3717, Generator Loss: 3.6801 D(x): 0.8643, D(G(z)): 0.0965 Epoch: [8/20], Batch Num: [172/600] Discriminator Loss: 0.3629, Generator Loss: 3.1919 D(x): 0.8819, D(G(z)): 0.1065 Epoch: [8/20], Batch Num: [173/600] Discriminator Loss: 0.4405, Generator Loss: 3.0998 D(x): 0.8851, D(G(z)): 0.1629 Epoch: [8/20], Batch Num: [174/600] Discriminator Loss: 0.3860, Generator Loss: 3.3416 D(x): 0.9170, D(G(z)): 0.1497 Epoch: [8/20], Batch Num: [175/600] Discriminator Loss: 0.2628, Generator Loss: 3.6080 D(x): 0.8846, D(G(z)): 0.0828 Epoch: [8/20], Batch Num: [176/600] Discriminator Loss: 0.2890, Generator Loss: 3.5216 D(x): 0.9078, D(G(z)): 0.0981 Epoch: [8/20], Batch Num: [177/600] Discriminator Loss: 0.3698, Generator Loss: 3.8338 D(x): 0.8866, D(G(z)): 0.1103 Epoch: [8/20], Batch Num: [178/600] Discriminator Loss: 0.3124, Generator Loss: 3.5790 D(x): 0.8916, D(G(z)): 0.0832 Epoch: [8/20], Batch Num: [179/600] Discriminator Loss: 0.2684, Generator Loss: 3.9017 D(x): 0.9110, D(G(z)): 0.0963 Epoch: [8/20], Batch Num: [180/600] Discriminator Loss: 0.3095, Generator Loss: 3.4668 D(x): 0.9043, D(G(z)): 0.0829 Epoch: [8/20], Batch Num: [181/600] Discriminator Loss: 0.2707, Generator Loss: 3.4718 D(x): 0.9235, D(G(z)): 0.0993 Epoch: [8/20], Batch Num: [182/600] Discriminator Loss: 0.3116, Generator Loss: 3.7607 D(x): 0.9132, D(G(z)): 0.1103 Epoch: [8/20], Batch Num: [183/600] Discriminator Loss: 0.2821, Generator Loss: 3.3846 D(x): 0.9279, D(G(z)): 0.0880 Epoch: [8/20], Batch Num: [184/600] Discriminator Loss: 0.4176, Generator Loss: 3.6460 D(x): 0.8951, D(G(z)): 0.1034 Epoch: [8/20], Batch Num: [185/600] Discriminator Loss: 0.3569, Generator Loss: 4.1108 D(x): 0.9055, D(G(z)): 0.1155 Epoch: [8/20], Batch Num: [186/600] Discriminator Loss: 0.2828, Generator Loss: 4.3208 D(x): 0.9092, D(G(z)): 0.0853 Epoch: [8/20], Batch Num: [187/600] Discriminator Loss: 0.3925, Generator Loss: 3.2820 D(x): 0.8591, D(G(z)): 0.0730 Epoch: [8/20], Batch Num: [188/600] Discriminator Loss: 0.3528, Generator Loss: 3.5890 D(x): 0.9087, D(G(z)): 0.1200 Epoch: [8/20], Batch Num: [189/600] Discriminator Loss: 0.3306, Generator Loss: 3.6215 D(x): 0.9113, D(G(z)): 0.1169 Epoch: [8/20], Batch Num: [190/600] Discriminator Loss: 0.2997, Generator Loss: 4.4238 D(x): 0.9335, D(G(z)): 0.1303 Epoch: [8/20], Batch Num: [191/600] Discriminator Loss: 0.3477, Generator Loss: 4.6819 D(x): 0.8739, D(G(z)): 0.0684 Epoch: [8/20], Batch Num: [192/600] Discriminator Loss: 0.3202, Generator Loss: 3.4149 D(x): 0.8346, D(G(z)): 0.0408 Epoch: [8/20], Batch Num: [193/600] Discriminator Loss: 0.3375, Generator Loss: 3.3862 D(x): 0.9219, D(G(z)): 0.1389 Epoch: [8/20], Batch Num: [194/600] Discriminator Loss: 0.4081, Generator Loss: 4.1327 D(x): 0.9332, D(G(z)): 0.1843 Epoch: [8/20], Batch Num: [195/600] Discriminator Loss: 0.4018, Generator Loss: 4.5585 D(x): 0.8836, D(G(z)): 0.1042 Epoch: [8/20], Batch Num: [196/600] Discriminator Loss: 0.2858, Generator Loss: 4.5193 D(x): 0.9124, D(G(z)): 0.0564 Epoch: [8/20], Batch Num: [197/600] Discriminator Loss: 0.3602, Generator Loss: 3.6016 D(x): 0.8515, D(G(z)): 0.0467 Epoch: [8/20], Batch Num: [198/600] Discriminator Loss: 0.3608, Generator Loss: 2.9426 D(x): 0.8775, D(G(z)): 0.0922 Epoch: [8/20], Batch Num: [199/600] Discriminator Loss: 0.4907, Generator Loss: 3.4923 D(x): 0.9405, D(G(z)): 0.1999 Epoch: 8, Batch Num: [200/600]
Epoch: [8/20], Batch Num: [200/600] Discriminator Loss: 0.3382, Generator Loss: 4.0488 D(x): 0.9202, D(G(z)): 0.1147 Epoch: [8/20], Batch Num: [201/600] Discriminator Loss: 0.2842, Generator Loss: 4.4123 D(x): 0.9108, D(G(z)): 0.0830 Epoch: [8/20], Batch Num: [202/600] Discriminator Loss: 0.3579, Generator Loss: 4.7336 D(x): 0.8653, D(G(z)): 0.0707 Epoch: [8/20], Batch Num: [203/600] Discriminator Loss: 0.3974, Generator Loss: 4.1545 D(x): 0.8599, D(G(z)): 0.0844 Epoch: [8/20], Batch Num: [204/600] Discriminator Loss: 0.5486, Generator Loss: 3.0643 D(x): 0.8460, D(G(z)): 0.1016 Epoch: [8/20], Batch Num: [205/600] Discriminator Loss: 0.3994, Generator Loss: 2.7916 D(x): 0.9007, D(G(z)): 0.1352 Epoch: [8/20], Batch Num: [206/600] Discriminator Loss: 0.3516, Generator Loss: 2.9320 D(x): 0.9245, D(G(z)): 0.1472 Epoch: [8/20], Batch Num: [207/600] Discriminator Loss: 0.4436, Generator Loss: 4.1923 D(x): 0.9051, D(G(z)): 0.1706 Epoch: [8/20], Batch Num: [208/600] Discriminator Loss: 0.4093, Generator Loss: 4.1260 D(x): 0.8545, D(G(z)): 0.0678 Epoch: [8/20], Batch Num: [209/600] Discriminator Loss: 0.5976, Generator Loss: 3.4486 D(x): 0.8020, D(G(z)): 0.0437 Epoch: [8/20], Batch Num: [210/600] Discriminator Loss: 0.3641, Generator Loss: 2.5986 D(x): 0.8680, D(G(z)): 0.0914 Epoch: [8/20], Batch Num: [211/600] Discriminator Loss: 0.3346, Generator Loss: 2.3751 D(x): 0.9228, D(G(z)): 0.1569 Epoch: [8/20], Batch Num: [212/600] Discriminator Loss: 0.4416, Generator Loss: 2.4966 D(x): 0.9264, D(G(z)): 0.2049 Epoch: [8/20], Batch Num: [213/600] Discriminator Loss: 0.3678, Generator Loss: 3.4740 D(x): 0.9209, D(G(z)): 0.1548 Epoch: [8/20], Batch Num: [214/600] Discriminator Loss: 0.3576, Generator Loss: 3.8234 D(x): 0.8763, D(G(z)): 0.0863 Epoch: [8/20], Batch Num: [215/600] Discriminator Loss: 0.4295, Generator Loss: 4.0149 D(x): 0.8613, D(G(z)): 0.0783 Epoch: [8/20], Batch Num: [216/600] Discriminator Loss: 0.5140, Generator Loss: 3.4562 D(x): 0.8361, D(G(z)): 0.0818 Epoch: [8/20], Batch Num: [217/600] Discriminator Loss: 0.4065, Generator Loss: 3.0906 D(x): 0.8606, D(G(z)): 0.0901 Epoch: [8/20], Batch Num: [218/600] Discriminator Loss: 0.2710, Generator Loss: 2.5216 D(x): 0.9287, D(G(z)): 0.1069 Epoch: [8/20], Batch Num: [219/600] Discriminator Loss: 0.4785, Generator Loss: 2.3395 D(x): 0.8609, D(G(z)): 0.1342 Epoch: [8/20], Batch Num: [220/600] Discriminator Loss: 0.4271, Generator Loss: 2.6909 D(x): 0.9276, D(G(z)): 0.2163 Epoch: [8/20], Batch Num: [221/600] Discriminator Loss: 0.3466, Generator Loss: 3.6853 D(x): 0.9507, D(G(z)): 0.1796 Epoch: [8/20], Batch Num: [222/600] Discriminator Loss: 0.3671, Generator Loss: 3.9859 D(x): 0.8750, D(G(z)): 0.0628 Epoch: [8/20], Batch Num: [223/600] Discriminator Loss: 0.3316, Generator Loss: 4.1919 D(x): 0.8543, D(G(z)): 0.0578 Epoch: [8/20], Batch Num: [224/600] Discriminator Loss: 0.3137, Generator Loss: 3.5729 D(x): 0.8743, D(G(z)): 0.0476 Epoch: [8/20], Batch Num: [225/600] Discriminator Loss: 0.2960, Generator Loss: 3.0127 D(x): 0.8658, D(G(z)): 0.0560 Epoch: [8/20], Batch Num: [226/600] Discriminator Loss: 0.2275, Generator Loss: 2.8023 D(x): 0.9384, D(G(z)): 0.0952 Epoch: [8/20], Batch Num: [227/600] Discriminator Loss: 0.3901, Generator Loss: 2.5705 D(x): 0.8900, D(G(z)): 0.1400 Epoch: [8/20], Batch Num: [228/600] Discriminator Loss: 0.3537, Generator Loss: 2.9067 D(x): 0.9148, D(G(z)): 0.1559 Epoch: [8/20], Batch Num: [229/600] Discriminator Loss: 0.3240, Generator Loss: 3.3118 D(x): 0.9434, D(G(z)): 0.1634 Epoch: [8/20], Batch Num: [230/600] Discriminator Loss: 0.3345, Generator Loss: 3.7007 D(x): 0.8887, D(G(z)): 0.1017 Epoch: [8/20], Batch Num: [231/600] Discriminator Loss: 0.2454, Generator Loss: 3.6937 D(x): 0.8986, D(G(z)): 0.0625 Epoch: [8/20], Batch Num: [232/600] Discriminator Loss: 0.2522, Generator Loss: 3.7985 D(x): 0.8855, D(G(z)): 0.0487 Epoch: [8/20], Batch Num: [233/600] Discriminator Loss: 0.2797, Generator Loss: 3.3297 D(x): 0.8958, D(G(z)): 0.0636 Epoch: [8/20], Batch Num: [234/600] Discriminator Loss: 0.1892, Generator Loss: 3.1851 D(x): 0.9304, D(G(z)): 0.0616 Epoch: [8/20], Batch Num: [235/600] Discriminator Loss: 0.3779, Generator Loss: 2.7614 D(x): 0.9031, D(G(z)): 0.1358 Epoch: [8/20], Batch Num: [236/600] Discriminator Loss: 0.3643, Generator Loss: 3.3022 D(x): 0.9420, D(G(z)): 0.1736 Epoch: [8/20], Batch Num: [237/600] Discriminator Loss: 0.2646, Generator Loss: 4.1089 D(x): 0.9627, D(G(z)): 0.1183 Epoch: [8/20], Batch Num: [238/600] Discriminator Loss: 0.2891, Generator Loss: 4.5439 D(x): 0.8953, D(G(z)): 0.0496 Epoch: [8/20], Batch Num: [239/600] Discriminator Loss: 0.3781, Generator Loss: 4.1033 D(x): 0.8403, D(G(z)): 0.0335 Epoch: [8/20], Batch Num: [240/600] Discriminator Loss: 0.2704, Generator Loss: 3.5021 D(x): 0.8811, D(G(z)): 0.0501 Epoch: [8/20], Batch Num: [241/600] Discriminator Loss: 0.1953, Generator Loss: 2.8758 D(x): 0.9339, D(G(z)): 0.0690 Epoch: [8/20], Batch Num: [242/600] Discriminator Loss: 0.2064, Generator Loss: 2.9222 D(x): 0.9816, D(G(z)): 0.1310 Epoch: [8/20], Batch Num: [243/600] Discriminator Loss: 0.1777, Generator Loss: 3.4940 D(x): 0.9712, D(G(z)): 0.1081 Epoch: [8/20], Batch Num: [244/600] Discriminator Loss: 0.2460, Generator Loss: 3.7695 D(x): 0.9294, D(G(z)): 0.1028 Epoch: [8/20], Batch Num: [245/600] Discriminator Loss: 0.1407, Generator Loss: 4.9120 D(x): 0.9553, D(G(z)): 0.0561 Epoch: [8/20], Batch Num: [246/600] Discriminator Loss: 0.2502, Generator Loss: 4.9258 D(x): 0.9042, D(G(z)): 0.0472 Epoch: [8/20], Batch Num: [247/600] Discriminator Loss: 0.2907, Generator Loss: 4.4318 D(x): 0.8739, D(G(z)): 0.0493 Epoch: [8/20], Batch Num: [248/600] Discriminator Loss: 0.1955, Generator Loss: 3.4466 D(x): 0.9275, D(G(z)): 0.0694 Epoch: [8/20], Batch Num: [249/600] Discriminator Loss: 0.4246, Generator Loss: 3.4970 D(x): 0.9099, D(G(z)): 0.1608 Epoch: [8/20], Batch Num: [250/600] Discriminator Loss: 0.3130, Generator Loss: 4.7942 D(x): 0.9426, D(G(z)): 0.1381 Epoch: [8/20], Batch Num: [251/600] Discriminator Loss: 0.2993, Generator Loss: 4.4530 D(x): 0.8701, D(G(z)): 0.0590 Epoch: [8/20], Batch Num: [252/600] Discriminator Loss: 0.2368, Generator Loss: 4.4189 D(x): 0.9067, D(G(z)): 0.0530 Epoch: [8/20], Batch Num: [253/600] Discriminator Loss: 0.2938, Generator Loss: 3.7805 D(x): 0.9172, D(G(z)): 0.0923 Epoch: [8/20], Batch Num: [254/600] Discriminator Loss: 0.3334, Generator Loss: 3.5314 D(x): 0.9172, D(G(z)): 0.1045 Epoch: [8/20], Batch Num: [255/600] Discriminator Loss: 0.3019, Generator Loss: 3.9515 D(x): 0.9406, D(G(z)): 0.1275 Epoch: [8/20], Batch Num: [256/600] Discriminator Loss: 0.2889, Generator Loss: 4.2248 D(x): 0.9222, D(G(z)): 0.0962 Epoch: [8/20], Batch Num: [257/600] Discriminator Loss: 0.1855, Generator Loss: 5.0595 D(x): 0.9405, D(G(z)): 0.0708 Epoch: [8/20], Batch Num: [258/600] Discriminator Loss: 0.4113, Generator Loss: 4.2522 D(x): 0.8440, D(G(z)): 0.0391 Epoch: [8/20], Batch Num: [259/600] Discriminator Loss: 0.2424, Generator Loss: 3.3187 D(x): 0.9166, D(G(z)): 0.0627 Epoch: [8/20], Batch Num: [260/600] Discriminator Loss: 0.3198, Generator Loss: 3.6734 D(x): 0.9321, D(G(z)): 0.1079 Epoch: [8/20], Batch Num: [261/600] Discriminator Loss: 0.3730, Generator Loss: 3.7993 D(x): 0.9310, D(G(z)): 0.1631 Epoch: [8/20], Batch Num: [262/600] Discriminator Loss: 0.4648, Generator Loss: 4.2438 D(x): 0.8980, D(G(z)): 0.1255 Epoch: [8/20], Batch Num: [263/600] Discriminator Loss: 0.3498, Generator Loss: 4.9872 D(x): 0.8878, D(G(z)): 0.0644 Epoch: [8/20], Batch Num: [264/600] Discriminator Loss: 0.3019, Generator Loss: 4.3074 D(x): 0.8747, D(G(z)): 0.0469 Epoch: [8/20], Batch Num: [265/600] Discriminator Loss: 0.4255, Generator Loss: 3.3506 D(x): 0.8502, D(G(z)): 0.0409 Epoch: [8/20], Batch Num: [266/600] Discriminator Loss: 0.2605, Generator Loss: 2.9101 D(x): 0.9434, D(G(z)): 0.1119 Epoch: [8/20], Batch Num: [267/600] Discriminator Loss: 0.4365, Generator Loss: 3.1987 D(x): 0.9233, D(G(z)): 0.1853 Epoch: [8/20], Batch Num: [268/600] Discriminator Loss: 0.3343, Generator Loss: 3.7746 D(x): 0.9225, D(G(z)): 0.1299 Epoch: [8/20], Batch Num: [269/600] Discriminator Loss: 0.2519, Generator Loss: 4.4100 D(x): 0.9103, D(G(z)): 0.0776 Epoch: [8/20], Batch Num: [270/600] Discriminator Loss: 0.4157, Generator Loss: 4.3142 D(x): 0.8530, D(G(z)): 0.0628 Epoch: [8/20], Batch Num: [271/600] Discriminator Loss: 0.4944, Generator Loss: 3.1687 D(x): 0.8157, D(G(z)): 0.0441 Epoch: [8/20], Batch Num: [272/600] Discriminator Loss: 0.2638, Generator Loss: 2.7905 D(x): 0.9391, D(G(z)): 0.1105 Epoch: [8/20], Batch Num: [273/600] Discriminator Loss: 0.3062, Generator Loss: 2.8598 D(x): 0.9627, D(G(z)): 0.1811 Epoch: [8/20], Batch Num: [274/600] Discriminator Loss: 0.4387, Generator Loss: 3.2644 D(x): 0.8901, D(G(z)): 0.1350 Epoch: [8/20], Batch Num: [275/600] Discriminator Loss: 0.3408, Generator Loss: 3.8489 D(x): 0.9160, D(G(z)): 0.1216 Epoch: [8/20], Batch Num: [276/600] Discriminator Loss: 0.3527, Generator Loss: 3.9460 D(x): 0.8748, D(G(z)): 0.0638 Epoch: [8/20], Batch Num: [277/600] Discriminator Loss: 0.3234, Generator Loss: 4.1225 D(x): 0.8895, D(G(z)): 0.0676 Epoch: [8/20], Batch Num: [278/600] Discriminator Loss: 0.4242, Generator Loss: 3.8000 D(x): 0.8252, D(G(z)): 0.0514 Epoch: [8/20], Batch Num: [279/600] Discriminator Loss: 0.4137, Generator Loss: 2.7970 D(x): 0.8792, D(G(z)): 0.0963 Epoch: [8/20], Batch Num: [280/600] Discriminator Loss: 0.3405, Generator Loss: 3.1598 D(x): 0.9626, D(G(z)): 0.1802 Epoch: [8/20], Batch Num: [281/600] Discriminator Loss: 0.3718, Generator Loss: 3.4648 D(x): 0.9181, D(G(z)): 0.1595 Epoch: [8/20], Batch Num: [282/600] Discriminator Loss: 0.2287, Generator Loss: 4.0116 D(x): 0.9271, D(G(z)): 0.0698 Epoch: [8/20], Batch Num: [283/600] Discriminator Loss: 0.3847, Generator Loss: 4.0611 D(x): 0.8662, D(G(z)): 0.0674 Epoch: [8/20], Batch Num: [284/600] Discriminator Loss: 0.4147, Generator Loss: 3.5988 D(x): 0.8539, D(G(z)): 0.0589 Epoch: [8/20], Batch Num: [285/600] Discriminator Loss: 0.2404, Generator Loss: 3.1640 D(x): 0.9073, D(G(z)): 0.0760 Epoch: [8/20], Batch Num: [286/600] Discriminator Loss: 0.3906, Generator Loss: 2.8101 D(x): 0.8959, D(G(z)): 0.1180 Epoch: [8/20], Batch Num: [287/600] Discriminator Loss: 0.3578, Generator Loss: 3.3132 D(x): 0.9483, D(G(z)): 0.1771 Epoch: [8/20], Batch Num: [288/600] Discriminator Loss: 0.2889, Generator Loss: 4.5767 D(x): 0.9182, D(G(z)): 0.0996 Epoch: [8/20], Batch Num: [289/600] Discriminator Loss: 0.2000, Generator Loss: 4.1131 D(x): 0.9251, D(G(z)): 0.0732 Epoch: [8/20], Batch Num: [290/600] Discriminator Loss: 0.3633, Generator Loss: 4.3961 D(x): 0.8650, D(G(z)): 0.0604 Epoch: [8/20], Batch Num: [291/600] Discriminator Loss: 0.3900, Generator Loss: 3.7017 D(x): 0.8454, D(G(z)): 0.0451 Epoch: [8/20], Batch Num: [292/600] Discriminator Loss: 0.2206, Generator Loss: 2.6620 D(x): 0.9286, D(G(z)): 0.0756 Epoch: [8/20], Batch Num: [293/600] Discriminator Loss: 0.4341, Generator Loss: 3.4904 D(x): 0.9361, D(G(z)): 0.1913 Epoch: [8/20], Batch Num: [294/600] Discriminator Loss: 0.3324, Generator Loss: 3.6620 D(x): 0.8988, D(G(z)): 0.1162 Epoch: [8/20], Batch Num: [295/600] Discriminator Loss: 0.2748, Generator Loss: 4.1811 D(x): 0.9225, D(G(z)): 0.0961 Epoch: [8/20], Batch Num: [296/600] Discriminator Loss: 0.2150, Generator Loss: 4.4925 D(x): 0.9077, D(G(z)): 0.0489 Epoch: [8/20], Batch Num: [297/600] Discriminator Loss: 0.2980, Generator Loss: 4.4052 D(x): 0.8872, D(G(z)): 0.0589 Epoch: [8/20], Batch Num: [298/600] Discriminator Loss: 0.3101, Generator Loss: 3.8000 D(x): 0.8677, D(G(z)): 0.0451 Epoch: [8/20], Batch Num: [299/600] Discriminator Loss: 0.2818, Generator Loss: 3.0343 D(x): 0.9065, D(G(z)): 0.0984 Epoch: 8, Batch Num: [300/600]
Epoch: [8/20], Batch Num: [300/600] Discriminator Loss: 0.3204, Generator Loss: 3.1982 D(x): 0.9298, D(G(z)): 0.1213 Epoch: [8/20], Batch Num: [301/600] Discriminator Loss: 0.3484, Generator Loss: 3.5425 D(x): 0.9305, D(G(z)): 0.1413 Epoch: [8/20], Batch Num: [302/600] Discriminator Loss: 0.2361, Generator Loss: 3.9834 D(x): 0.9327, D(G(z)): 0.0992 Epoch: [8/20], Batch Num: [303/600] Discriminator Loss: 0.2433, Generator Loss: 4.2917 D(x): 0.9144, D(G(z)): 0.0879 Epoch: [8/20], Batch Num: [304/600] Discriminator Loss: 0.1885, Generator Loss: 3.9562 D(x): 0.9155, D(G(z)): 0.0472 Epoch: [8/20], Batch Num: [305/600] Discriminator Loss: 0.2695, Generator Loss: 4.0996 D(x): 0.8929, D(G(z)): 0.0396 Epoch: [8/20], Batch Num: [306/600] Discriminator Loss: 0.2440, Generator Loss: 3.5804 D(x): 0.9207, D(G(z)): 0.0610 Epoch: [8/20], Batch Num: [307/600] Discriminator Loss: 0.2216, Generator Loss: 2.9776 D(x): 0.9318, D(G(z)): 0.0799 Epoch: [8/20], Batch Num: [308/600] Discriminator Loss: 0.2437, Generator Loss: 3.5605 D(x): 0.9564, D(G(z)): 0.1366 Epoch: [8/20], Batch Num: [309/600] Discriminator Loss: 0.3698, Generator Loss: 3.9644 D(x): 0.9437, D(G(z)): 0.1516 Epoch: [8/20], Batch Num: [310/600] Discriminator Loss: 0.2029, Generator Loss: 4.9400 D(x): 0.9679, D(G(z)): 0.0938 Epoch: [8/20], Batch Num: [311/600] Discriminator Loss: 0.4468, Generator Loss: 5.0082 D(x): 0.8204, D(G(z)): 0.0353 Epoch: [8/20], Batch Num: [312/600] Discriminator Loss: 0.3580, Generator Loss: 4.2270 D(x): 0.8390, D(G(z)): 0.0146 Epoch: [8/20], Batch Num: [313/600] Discriminator Loss: 0.2498, Generator Loss: 2.7670 D(x): 0.9093, D(G(z)): 0.0465 Epoch: [8/20], Batch Num: [314/600] Discriminator Loss: 0.3011, Generator Loss: 3.2132 D(x): 0.9798, D(G(z)): 0.1650 Epoch: [8/20], Batch Num: [315/600] Discriminator Loss: 0.2690, Generator Loss: 3.9906 D(x): 0.9767, D(G(z)): 0.1570 Epoch: [8/20], Batch Num: [316/600] Discriminator Loss: 0.2866, Generator Loss: 4.8138 D(x): 0.9307, D(G(z)): 0.0664 Epoch: [8/20], Batch Num: [317/600] Discriminator Loss: 0.1803, Generator Loss: 5.2433 D(x): 0.9247, D(G(z)): 0.0492 Epoch: [8/20], Batch Num: [318/600] Discriminator Loss: 0.4567, Generator Loss: 4.9964 D(x): 0.8640, D(G(z)): 0.0467 Epoch: [8/20], Batch Num: [319/600] Discriminator Loss: 0.2143, Generator Loss: 4.2381 D(x): 0.9031, D(G(z)): 0.0243 Epoch: [8/20], Batch Num: [320/600] Discriminator Loss: 0.3585, Generator Loss: 3.5741 D(x): 0.8899, D(G(z)): 0.0828 Epoch: [8/20], Batch Num: [321/600] Discriminator Loss: 0.3028, Generator Loss: 3.0713 D(x): 0.9344, D(G(z)): 0.1140 Epoch: [8/20], Batch Num: [322/600] Discriminator Loss: 0.3528, Generator Loss: 3.5537 D(x): 0.9459, D(G(z)): 0.1530 Epoch: [8/20], Batch Num: [323/600] Discriminator Loss: 0.2011, Generator Loss: 4.2101 D(x): 0.9590, D(G(z)): 0.0917 Epoch: [8/20], Batch Num: [324/600] Discriminator Loss: 0.3825, Generator Loss: 4.6254 D(x): 0.8783, D(G(z)): 0.0919 Epoch: [8/20], Batch Num: [325/600] Discriminator Loss: 0.4049, Generator Loss: 4.1869 D(x): 0.8450, D(G(z)): 0.0551 Epoch: [8/20], Batch Num: [326/600] Discriminator Loss: 0.2236, Generator Loss: 3.9840 D(x): 0.8835, D(G(z)): 0.0390 Epoch: [8/20], Batch Num: [327/600] Discriminator Loss: 0.2184, Generator Loss: 3.5678 D(x): 0.9284, D(G(z)): 0.0696 Epoch: [8/20], Batch Num: [328/600] Discriminator Loss: 0.2566, Generator Loss: 3.8322 D(x): 0.9484, D(G(z)): 0.1293 Epoch: [8/20], Batch Num: [329/600] Discriminator Loss: 0.2185, Generator Loss: 3.9975 D(x): 0.9545, D(G(z)): 0.1102 Epoch: [8/20], Batch Num: [330/600] Discriminator Loss: 0.2589, Generator Loss: 3.7629 D(x): 0.9324, D(G(z)): 0.0919 Epoch: [8/20], Batch Num: [331/600] Discriminator Loss: 0.2498, Generator Loss: 4.6784 D(x): 0.9254, D(G(z)): 0.0728 Epoch: [8/20], Batch Num: [332/600] Discriminator Loss: 0.2316, Generator Loss: 4.7750 D(x): 0.9302, D(G(z)): 0.0815 Epoch: [8/20], Batch Num: [333/600] Discriminator Loss: 0.4435, Generator Loss: 4.3966 D(x): 0.8412, D(G(z)): 0.0396 Epoch: [8/20], Batch Num: [334/600] Discriminator Loss: 0.1950, Generator Loss: 4.1177 D(x): 0.9174, D(G(z)): 0.0374 Epoch: [8/20], Batch Num: [335/600] Discriminator Loss: 0.2510, Generator Loss: 3.3865 D(x): 0.9216, D(G(z)): 0.0928 Epoch: [8/20], Batch Num: [336/600] Discriminator Loss: 0.4190, Generator Loss: 3.4028 D(x): 0.9373, D(G(z)): 0.1515 Epoch: [8/20], Batch Num: [337/600] Discriminator Loss: 0.4421, Generator Loss: 3.5937 D(x): 0.9193, D(G(z)): 0.1284 Epoch: [8/20], Batch Num: [338/600] Discriminator Loss: 0.3184, Generator Loss: 4.1145 D(x): 0.9277, D(G(z)): 0.0867 Epoch: [8/20], Batch Num: [339/600] Discriminator Loss: 0.3282, Generator Loss: 4.4191 D(x): 0.9046, D(G(z)): 0.0709 Epoch: [8/20], Batch Num: [340/600] Discriminator Loss: 0.3072, Generator Loss: 4.4881 D(x): 0.8787, D(G(z)): 0.0668 Epoch: [8/20], Batch Num: [341/600] Discriminator Loss: 0.3214, Generator Loss: 3.9604 D(x): 0.8946, D(G(z)): 0.0626 Epoch: [8/20], Batch Num: [342/600] Discriminator Loss: 0.3006, Generator Loss: 3.8982 D(x): 0.9245, D(G(z)): 0.1119 Epoch: [8/20], Batch Num: [343/600] Discriminator Loss: 0.3760, Generator Loss: 3.4005 D(x): 0.9112, D(G(z)): 0.1129 Epoch: [8/20], Batch Num: [344/600] Discriminator Loss: 0.3044, Generator Loss: 4.4066 D(x): 0.9463, D(G(z)): 0.1479 Epoch: [8/20], Batch Num: [345/600] Discriminator Loss: 0.3185, Generator Loss: 4.3540 D(x): 0.8929, D(G(z)): 0.0694 Epoch: [8/20], Batch Num: [346/600] Discriminator Loss: 0.3004, Generator Loss: 4.3324 D(x): 0.9115, D(G(z)): 0.0551 Epoch: [8/20], Batch Num: [347/600] Discriminator Loss: 0.1685, Generator Loss: 4.3367 D(x): 0.9311, D(G(z)): 0.0606 Epoch: [8/20], Batch Num: [348/600] Discriminator Loss: 0.3349, Generator Loss: 3.9204 D(x): 0.8832, D(G(z)): 0.0507 Epoch: [8/20], Batch Num: [349/600] Discriminator Loss: 0.2119, Generator Loss: 3.2624 D(x): 0.9161, D(G(z)): 0.0640 Epoch: [8/20], Batch Num: [350/600] Discriminator Loss: 0.3319, Generator Loss: 3.2524 D(x): 0.9409, D(G(z)): 0.1307 Epoch: [8/20], Batch Num: [351/600] Discriminator Loss: 0.2691, Generator Loss: 3.5410 D(x): 0.9417, D(G(z)): 0.1166 Epoch: [8/20], Batch Num: [352/600] Discriminator Loss: 0.2126, Generator Loss: 3.6454 D(x): 0.9103, D(G(z)): 0.0675 Epoch: [8/20], Batch Num: [353/600] Discriminator Loss: 0.2576, Generator Loss: 4.1319 D(x): 0.9252, D(G(z)): 0.0868 Epoch: [8/20], Batch Num: [354/600] Discriminator Loss: 0.3019, Generator Loss: 4.0051 D(x): 0.9239, D(G(z)): 0.1014 Epoch: [8/20], Batch Num: [355/600] Discriminator Loss: 0.2637, Generator Loss: 4.2176 D(x): 0.9152, D(G(z)): 0.0860 Epoch: [8/20], Batch Num: [356/600] Discriminator Loss: 0.4906, Generator Loss: 4.0628 D(x): 0.8516, D(G(z)): 0.0870 Epoch: [8/20], Batch Num: [357/600] Discriminator Loss: 0.3254, Generator Loss: 3.7323 D(x): 0.9026, D(G(z)): 0.0911 Epoch: [8/20], Batch Num: [358/600] Discriminator Loss: 0.3545, Generator Loss: 3.3663 D(x): 0.9017, D(G(z)): 0.0895 Epoch: [8/20], Batch Num: [359/600] Discriminator Loss: 0.4308, Generator Loss: 3.6322 D(x): 0.9217, D(G(z)): 0.1419 Epoch: [8/20], Batch Num: [360/600] Discriminator Loss: 0.2249, Generator Loss: 3.7312 D(x): 0.9435, D(G(z)): 0.0800 Epoch: [8/20], Batch Num: [361/600] Discriminator Loss: 0.3757, Generator Loss: 3.8350 D(x): 0.8660, D(G(z)): 0.0537 Epoch: [8/20], Batch Num: [362/600] Discriminator Loss: 0.3307, Generator Loss: 3.4853 D(x): 0.9379, D(G(z)): 0.1311 Epoch: [8/20], Batch Num: [363/600] Discriminator Loss: 0.3481, Generator Loss: 3.7011 D(x): 0.9014, D(G(z)): 0.1201 Epoch: [8/20], Batch Num: [364/600] Discriminator Loss: 0.2151, Generator Loss: 3.8242 D(x): 0.9259, D(G(z)): 0.0648 Epoch: [8/20], Batch Num: [365/600] Discriminator Loss: 0.2627, Generator Loss: 4.2483 D(x): 0.9184, D(G(z)): 0.0882 Epoch: [8/20], Batch Num: [366/600] Discriminator Loss: 0.4088, Generator Loss: 3.7066 D(x): 0.8629, D(G(z)): 0.0750 Epoch: [8/20], Batch Num: [367/600] Discriminator Loss: 0.3318, Generator Loss: 3.2257 D(x): 0.9078, D(G(z)): 0.0936 Epoch: [8/20], Batch Num: [368/600] Discriminator Loss: 0.2386, Generator Loss: 3.1783 D(x): 0.9158, D(G(z)): 0.0958 Epoch: [8/20], Batch Num: [369/600] Discriminator Loss: 0.3710, Generator Loss: 3.0828 D(x): 0.8978, D(G(z)): 0.1240 Epoch: [8/20], Batch Num: [370/600] Discriminator Loss: 0.3182, Generator Loss: 3.4845 D(x): 0.9147, D(G(z)): 0.1088 Epoch: [8/20], Batch Num: [371/600] Discriminator Loss: 0.3110, Generator Loss: 3.9884 D(x): 0.9378, D(G(z)): 0.1284 Epoch: [8/20], Batch Num: [372/600] Discriminator Loss: 0.2577, Generator Loss: 4.3323 D(x): 0.8975, D(G(z)): 0.0667 Epoch: [8/20], Batch Num: [373/600] Discriminator Loss: 0.2453, Generator Loss: 4.1424 D(x): 0.9073, D(G(z)): 0.0661 Epoch: [8/20], Batch Num: [374/600] Discriminator Loss: 0.3092, Generator Loss: 4.2203 D(x): 0.8747, D(G(z)): 0.0393 Epoch: [8/20], Batch Num: [375/600] Discriminator Loss: 0.2824, Generator Loss: 3.0579 D(x): 0.8980, D(G(z)): 0.0691 Epoch: [8/20], Batch Num: [376/600] Discriminator Loss: 0.2490, Generator Loss: 2.5552 D(x): 0.9299, D(G(z)): 0.0962 Epoch: [8/20], Batch Num: [377/600] Discriminator Loss: 0.4382, Generator Loss: 3.0997 D(x): 0.9166, D(G(z)): 0.1768 Epoch: [8/20], Batch Num: [378/600] Discriminator Loss: 0.3739, Generator Loss: 3.7287 D(x): 0.9313, D(G(z)): 0.1743 Epoch: [8/20], Batch Num: [379/600] Discriminator Loss: 0.3790, Generator Loss: 4.7740 D(x): 0.8807, D(G(z)): 0.0987 Epoch: [8/20], Batch Num: [380/600] Discriminator Loss: 0.4073, Generator Loss: 4.5643 D(x): 0.8658, D(G(z)): 0.0591 Epoch: [8/20], Batch Num: [381/600] Discriminator Loss: 0.2985, Generator Loss: 3.9681 D(x): 0.8969, D(G(z)): 0.0519 Epoch: [8/20], Batch Num: [382/600] Discriminator Loss: 0.3951, Generator Loss: 3.4297 D(x): 0.8683, D(G(z)): 0.0716 Epoch: [8/20], Batch Num: [383/600] Discriminator Loss: 0.4044, Generator Loss: 3.1556 D(x): 0.9191, D(G(z)): 0.1208 Epoch: [8/20], Batch Num: [384/600] Discriminator Loss: 0.3929, Generator Loss: 3.3238 D(x): 0.9188, D(G(z)): 0.1465 Epoch: [8/20], Batch Num: [385/600] Discriminator Loss: 0.3800, Generator Loss: 3.4626 D(x): 0.8871, D(G(z)): 0.1127 Epoch: [8/20], Batch Num: [386/600] Discriminator Loss: 0.3083, Generator Loss: 3.9299 D(x): 0.9041, D(G(z)): 0.0740 Epoch: [8/20], Batch Num: [387/600] Discriminator Loss: 0.2830, Generator Loss: 3.8984 D(x): 0.8977, D(G(z)): 0.0549 Epoch: [8/20], Batch Num: [388/600] Discriminator Loss: 0.2212, Generator Loss: 3.3234 D(x): 0.9248, D(G(z)): 0.0738 Epoch: [8/20], Batch Num: [389/600] Discriminator Loss: 0.4275, Generator Loss: 3.3579 D(x): 0.8844, D(G(z)): 0.1023 Epoch: [8/20], Batch Num: [390/600] Discriminator Loss: 0.3198, Generator Loss: 2.9965 D(x): 0.9039, D(G(z)): 0.1077 Epoch: [8/20], Batch Num: [391/600] Discriminator Loss: 0.2542, Generator Loss: 3.1518 D(x): 0.9332, D(G(z)): 0.1157 Epoch: [8/20], Batch Num: [392/600] Discriminator Loss: 0.2730, Generator Loss: 3.5869 D(x): 0.9447, D(G(z)): 0.1235 Epoch: [8/20], Batch Num: [393/600] Discriminator Loss: 0.5306, Generator Loss: 3.9907 D(x): 0.8476, D(G(z)): 0.0895 Epoch: [8/20], Batch Num: [394/600] Discriminator Loss: 0.2501, Generator Loss: 4.0189 D(x): 0.9177, D(G(z)): 0.0662 Epoch: [8/20], Batch Num: [395/600] Discriminator Loss: 0.2815, Generator Loss: 3.7702 D(x): 0.8998, D(G(z)): 0.0718 Epoch: [8/20], Batch Num: [396/600] Discriminator Loss: 0.3282, Generator Loss: 3.4351 D(x): 0.9008, D(G(z)): 0.0872 Epoch: [8/20], Batch Num: [397/600] Discriminator Loss: 0.3007, Generator Loss: 3.6849 D(x): 0.9211, D(G(z)): 0.1091 Epoch: [8/20], Batch Num: [398/600] Discriminator Loss: 0.2871, Generator Loss: 3.6557 D(x): 0.9129, D(G(z)): 0.1000 Epoch: [8/20], Batch Num: [399/600] Discriminator Loss: 0.2434, Generator Loss: 3.8345 D(x): 0.9278, D(G(z)): 0.0716 Epoch: 8, Batch Num: [400/600]
Epoch: [8/20], Batch Num: [400/600] Discriminator Loss: 0.3934, Generator Loss: 3.7179 D(x): 0.8659, D(G(z)): 0.0859 Epoch: [8/20], Batch Num: [401/600] Discriminator Loss: 0.2789, Generator Loss: 3.3502 D(x): 0.9152, D(G(z)): 0.0774 Epoch: [8/20], Batch Num: [402/600] Discriminator Loss: 0.3636, Generator Loss: 3.6724 D(x): 0.9094, D(G(z)): 0.1268 Epoch: [8/20], Batch Num: [403/600] Discriminator Loss: 0.3359, Generator Loss: 3.4826 D(x): 0.9011, D(G(z)): 0.1015 Epoch: [8/20], Batch Num: [404/600] Discriminator Loss: 0.2131, Generator Loss: 3.8234 D(x): 0.9494, D(G(z)): 0.0909 Epoch: [8/20], Batch Num: [405/600] Discriminator Loss: 0.2684, Generator Loss: 4.5445 D(x): 0.9174, D(G(z)): 0.0907 Epoch: [8/20], Batch Num: [406/600] Discriminator Loss: 0.1518, Generator Loss: 4.6907 D(x): 0.9487, D(G(z)): 0.0559 Epoch: [8/20], Batch Num: [407/600] Discriminator Loss: 0.1756, Generator Loss: 4.2935 D(x): 0.9252, D(G(z)): 0.0349 Epoch: [8/20], Batch Num: [408/600] Discriminator Loss: 0.1886, Generator Loss: 4.0413 D(x): 0.9307, D(G(z)): 0.0425 Epoch: [8/20], Batch Num: [409/600] Discriminator Loss: 0.2399, Generator Loss: 3.2960 D(x): 0.9165, D(G(z)): 0.0632 Epoch: [8/20], Batch Num: [410/600] Discriminator Loss: 0.4016, Generator Loss: 4.3062 D(x): 0.9546, D(G(z)): 0.1756 Epoch: [8/20], Batch Num: [411/600] Discriminator Loss: 0.4932, Generator Loss: 5.1013 D(x): 0.8895, D(G(z)): 0.1104 Epoch: [8/20], Batch Num: [412/600] Discriminator Loss: 0.3196, Generator Loss: 5.5938 D(x): 0.9145, D(G(z)): 0.0688 Epoch: [8/20], Batch Num: [413/600] Discriminator Loss: 0.3309, Generator Loss: 5.0161 D(x): 0.8996, D(G(z)): 0.0343 Epoch: [8/20], Batch Num: [414/600] Discriminator Loss: 0.2745, Generator Loss: 4.0637 D(x): 0.8928, D(G(z)): 0.0221 Epoch: [8/20], Batch Num: [415/600] Discriminator Loss: 0.4439, Generator Loss: 3.8044 D(x): 0.9468, D(G(z)): 0.1582 Epoch: [8/20], Batch Num: [416/600] Discriminator Loss: 0.3596, Generator Loss: 4.6974 D(x): 0.9232, D(G(z)): 0.1155 Epoch: [8/20], Batch Num: [417/600] Discriminator Loss: 0.3533, Generator Loss: 3.9547 D(x): 0.8758, D(G(z)): 0.0632 Epoch: [8/20], Batch Num: [418/600] Discriminator Loss: 0.2531, Generator Loss: 3.5841 D(x): 0.9249, D(G(z)): 0.0802 Epoch: [8/20], Batch Num: [419/600] Discriminator Loss: 0.2138, Generator Loss: 3.8690 D(x): 0.9452, D(G(z)): 0.1044 Epoch: [8/20], Batch Num: [420/600] Discriminator Loss: 0.1871, Generator Loss: 4.1543 D(x): 0.9362, D(G(z)): 0.0708 Epoch: [8/20], Batch Num: [421/600] Discriminator Loss: 0.3995, Generator Loss: 4.0363 D(x): 0.8768, D(G(z)): 0.0465 Epoch: [8/20], Batch Num: [422/600] Discriminator Loss: 0.2815, Generator Loss: 3.4383 D(x): 0.9056, D(G(z)): 0.0579 Epoch: [8/20], Batch Num: [423/600] Discriminator Loss: 0.2026, Generator Loss: 2.8998 D(x): 0.9484, D(G(z)): 0.0903 Epoch: [8/20], Batch Num: [424/600] Discriminator Loss: 0.3418, Generator Loss: 3.3985 D(x): 0.9368, D(G(z)): 0.1448 Epoch: [8/20], Batch Num: [425/600] Discriminator Loss: 0.3918, Generator Loss: 4.3659 D(x): 0.9159, D(G(z)): 0.1282 Epoch: [8/20], Batch Num: [426/600] Discriminator Loss: 0.2227, Generator Loss: 4.6940 D(x): 0.9081, D(G(z)): 0.0496 Epoch: [8/20], Batch Num: [427/600] Discriminator Loss: 0.4250, Generator Loss: 3.9952 D(x): 0.8594, D(G(z)): 0.0416 Epoch: [8/20], Batch Num: [428/600] Discriminator Loss: 0.2142, Generator Loss: 3.7490 D(x): 0.9110, D(G(z)): 0.0590 Epoch: [8/20], Batch Num: [429/600] Discriminator Loss: 0.2654, Generator Loss: 2.7358 D(x): 0.9256, D(G(z)): 0.0737 Epoch: [8/20], Batch Num: [430/600] Discriminator Loss: 0.4372, Generator Loss: 3.3479 D(x): 0.9396, D(G(z)): 0.1785 Epoch: [8/20], Batch Num: [431/600] Discriminator Loss: 0.4909, Generator Loss: 4.3747 D(x): 0.9157, D(G(z)): 0.1835 Epoch: [8/20], Batch Num: [432/600] Discriminator Loss: 0.3370, Generator Loss: 4.7559 D(x): 0.8740, D(G(z)): 0.0584 Epoch: [8/20], Batch Num: [433/600] Discriminator Loss: 0.2726, Generator Loss: 4.5098 D(x): 0.8952, D(G(z)): 0.0335 Epoch: [8/20], Batch Num: [434/600] Discriminator Loss: 0.1624, Generator Loss: 4.1587 D(x): 0.9253, D(G(z)): 0.0314 Epoch: [8/20], Batch Num: [435/600] Discriminator Loss: 0.3457, Generator Loss: 4.0700 D(x): 0.9135, D(G(z)): 0.1080 Epoch: [8/20], Batch Num: [436/600] Discriminator Loss: 0.2695, Generator Loss: 3.7242 D(x): 0.9171, D(G(z)): 0.0801 Epoch: [8/20], Batch Num: [437/600] Discriminator Loss: 0.3242, Generator Loss: 3.8561 D(x): 0.8854, D(G(z)): 0.0854 Epoch: [8/20], Batch Num: [438/600] Discriminator Loss: 0.2080, Generator Loss: 3.3600 D(x): 0.9310, D(G(z)): 0.0875 Epoch: [8/20], Batch Num: [439/600] Discriminator Loss: 0.3086, Generator Loss: 3.3917 D(x): 0.9031, D(G(z)): 0.1071 Epoch: [8/20], Batch Num: [440/600] Discriminator Loss: 0.3581, Generator Loss: 3.8111 D(x): 0.9178, D(G(z)): 0.1360 Epoch: [8/20], Batch Num: [441/600] Discriminator Loss: 0.3385, Generator Loss: 3.9616 D(x): 0.9164, D(G(z)): 0.1249 Epoch: [8/20], Batch Num: [442/600] Discriminator Loss: 0.2773, Generator Loss: 4.2141 D(x): 0.8825, D(G(z)): 0.0564 Epoch: [8/20], Batch Num: [443/600] Discriminator Loss: 0.4614, Generator Loss: 3.3740 D(x): 0.8283, D(G(z)): 0.0540 Epoch: [8/20], Batch Num: [444/600] Discriminator Loss: 0.3605, Generator Loss: 2.9402 D(x): 0.9032, D(G(z)): 0.1219 Epoch: [8/20], Batch Num: [445/600] Discriminator Loss: 0.5693, Generator Loss: 3.6317 D(x): 0.9079, D(G(z)): 0.2272 Epoch: [8/20], Batch Num: [446/600] Discriminator Loss: 0.3981, Generator Loss: 4.4645 D(x): 0.9066, D(G(z)): 0.1373 Epoch: [8/20], Batch Num: [447/600] Discriminator Loss: 0.4888, Generator Loss: 4.4223 D(x): 0.7973, D(G(z)): 0.0502 Epoch: [8/20], Batch Num: [448/600] Discriminator Loss: 0.3196, Generator Loss: 3.6056 D(x): 0.8673, D(G(z)): 0.0528 Epoch: [8/20], Batch Num: [449/600] Discriminator Loss: 0.3555, Generator Loss: 2.8721 D(x): 0.8791, D(G(z)): 0.0868 Epoch: [8/20], Batch Num: [450/600] Discriminator Loss: 0.4143, Generator Loss: 2.5453 D(x): 0.9220, D(G(z)): 0.1802 Epoch: [8/20], Batch Num: [451/600] Discriminator Loss: 0.4171, Generator Loss: 3.4536 D(x): 0.9506, D(G(z)): 0.2137 Epoch: [8/20], Batch Num: [452/600] Discriminator Loss: 0.4742, Generator Loss: 3.8974 D(x): 0.8279, D(G(z)): 0.1023 Epoch: [8/20], Batch Num: [453/600] Discriminator Loss: 0.2663, Generator Loss: 4.0517 D(x): 0.8984, D(G(z)): 0.0750 Epoch: [8/20], Batch Num: [454/600] Discriminator Loss: 0.4154, Generator Loss: 3.7255 D(x): 0.8367, D(G(z)): 0.0437 Epoch: [8/20], Batch Num: [455/600] Discriminator Loss: 0.3422, Generator Loss: 3.2974 D(x): 0.8966, D(G(z)): 0.0943 Epoch: [8/20], Batch Num: [456/600] Discriminator Loss: 0.4560, Generator Loss: 3.4298 D(x): 0.9073, D(G(z)): 0.1722 Epoch: [8/20], Batch Num: [457/600] Discriminator Loss: 0.2967, Generator Loss: 3.6076 D(x): 0.9378, D(G(z)): 0.1394 Epoch: [8/20], Batch Num: [458/600] Discriminator Loss: 0.3146, Generator Loss: 3.6549 D(x): 0.9048, D(G(z)): 0.0917 Epoch: [8/20], Batch Num: [459/600] Discriminator Loss: 0.3254, Generator Loss: 4.4530 D(x): 0.9050, D(G(z)): 0.0965 Epoch: [8/20], Batch Num: [460/600] Discriminator Loss: 0.3312, Generator Loss: 4.0373 D(x): 0.8647, D(G(z)): 0.0745 Epoch: [8/20], Batch Num: [461/600] Discriminator Loss: 0.3267, Generator Loss: 3.9887 D(x): 0.8611, D(G(z)): 0.0573 Epoch: [8/20], Batch Num: [462/600] Discriminator Loss: 0.2876, Generator Loss: 2.9335 D(x): 0.9082, D(G(z)): 0.1045 Epoch: [8/20], Batch Num: [463/600] Discriminator Loss: 0.2930, Generator Loss: 2.5440 D(x): 0.9297, D(G(z)): 0.1049 Epoch: [8/20], Batch Num: [464/600] Discriminator Loss: 0.3243, Generator Loss: 2.8780 D(x): 0.9185, D(G(z)): 0.1449 Epoch: [8/20], Batch Num: [465/600] Discriminator Loss: 0.3196, Generator Loss: 3.4630 D(x): 0.9040, D(G(z)): 0.1100 Epoch: [8/20], Batch Num: [466/600] Discriminator Loss: 0.3719, Generator Loss: 4.3599 D(x): 0.8734, D(G(z)): 0.0850 Epoch: [8/20], Batch Num: [467/600] Discriminator Loss: 0.4642, Generator Loss: 3.9563 D(x): 0.8494, D(G(z)): 0.1089 Epoch: [8/20], Batch Num: [468/600] Discriminator Loss: 0.4942, Generator Loss: 3.0477 D(x): 0.8433, D(G(z)): 0.1279 Epoch: [8/20], Batch Num: [469/600] Discriminator Loss: 0.3295, Generator Loss: 2.7984 D(x): 0.9287, D(G(z)): 0.1183 Epoch: [8/20], Batch Num: [470/600] Discriminator Loss: 0.3546, Generator Loss: 3.0644 D(x): 0.9246, D(G(z)): 0.1495 Epoch: [8/20], Batch Num: [471/600] Discriminator Loss: 0.3005, Generator Loss: 3.6803 D(x): 0.9213, D(G(z)): 0.1101 Epoch: [8/20], Batch Num: [472/600] Discriminator Loss: 0.3048, Generator Loss: 4.1174 D(x): 0.9048, D(G(z)): 0.0987 Epoch: [8/20], Batch Num: [473/600] Discriminator Loss: 0.2956, Generator Loss: 4.4539 D(x): 0.8883, D(G(z)): 0.0711 Epoch: [8/20], Batch Num: [474/600] Discriminator Loss: 0.4512, Generator Loss: 3.8727 D(x): 0.8176, D(G(z)): 0.0705 Epoch: [8/20], Batch Num: [475/600] Discriminator Loss: 0.2715, Generator Loss: 3.1930 D(x): 0.8884, D(G(z)): 0.0624 Epoch: [8/20], Batch Num: [476/600] Discriminator Loss: 0.3344, Generator Loss: 2.1923 D(x): 0.8895, D(G(z)): 0.0974 Epoch: [8/20], Batch Num: [477/600] Discriminator Loss: 0.3907, Generator Loss: 2.5650 D(x): 0.9554, D(G(z)): 0.2069 Epoch: [8/20], Batch Num: [478/600] Discriminator Loss: 0.5166, Generator Loss: 4.1374 D(x): 0.9294, D(G(z)): 0.2151 Epoch: [8/20], Batch Num: [479/600] Discriminator Loss: 0.2960, Generator Loss: 4.6652 D(x): 0.8778, D(G(z)): 0.0550 Epoch: [8/20], Batch Num: [480/600] Discriminator Loss: 0.5625, Generator Loss: 4.6099 D(x): 0.7775, D(G(z)): 0.0196 Epoch: [8/20], Batch Num: [481/600] Discriminator Loss: 0.4566, Generator Loss: 3.2437 D(x): 0.8220, D(G(z)): 0.0471 Epoch: [8/20], Batch Num: [482/600] Discriminator Loss: 0.2573, Generator Loss: 2.3753 D(x): 0.9446, D(G(z)): 0.1089 Epoch: [8/20], Batch Num: [483/600] Discriminator Loss: 0.4227, Generator Loss: 2.7621 D(x): 0.9686, D(G(z)): 0.2036 Epoch: [8/20], Batch Num: [484/600] Discriminator Loss: 0.3447, Generator Loss: 3.5673 D(x): 0.9392, D(G(z)): 0.1707 Epoch: [8/20], Batch Num: [485/600] Discriminator Loss: 0.3938, Generator Loss: 4.2305 D(x): 0.8621, D(G(z)): 0.0870 Epoch: [8/20], Batch Num: [486/600] Discriminator Loss: 0.3945, Generator Loss: 4.0671 D(x): 0.8558, D(G(z)): 0.0380 Epoch: [8/20], Batch Num: [487/600] Discriminator Loss: 0.3264, Generator Loss: 3.6992 D(x): 0.8853, D(G(z)): 0.0522 Epoch: [8/20], Batch Num: [488/600] Discriminator Loss: 0.2183, Generator Loss: 3.1183 D(x): 0.9251, D(G(z)): 0.0680 Epoch: [8/20], Batch Num: [489/600] Discriminator Loss: 0.2839, Generator Loss: 3.1013 D(x): 0.9291, D(G(z)): 0.1148 Epoch: [8/20], Batch Num: [490/600] Discriminator Loss: 0.3199, Generator Loss: 3.1678 D(x): 0.9353, D(G(z)): 0.1438 Epoch: [8/20], Batch Num: [491/600] Discriminator Loss: 0.3077, Generator Loss: 4.2309 D(x): 0.9343, D(G(z)): 0.1165 Epoch: [8/20], Batch Num: [492/600] Discriminator Loss: 0.2311, Generator Loss: 4.7968 D(x): 0.8875, D(G(z)): 0.0267 Epoch: [8/20], Batch Num: [493/600] Discriminator Loss: 0.3334, Generator Loss: 4.1890 D(x): 0.8724, D(G(z)): 0.0356 Epoch: [8/20], Batch Num: [494/600] Discriminator Loss: 0.3018, Generator Loss: 3.5575 D(x): 0.8760, D(G(z)): 0.0534 Epoch: [8/20], Batch Num: [495/600] Discriminator Loss: 0.1981, Generator Loss: 3.2655 D(x): 0.9630, D(G(z)): 0.0916 Epoch: [8/20], Batch Num: [496/600] Discriminator Loss: 0.3044, Generator Loss: 3.4518 D(x): 0.9491, D(G(z)): 0.1222 Epoch: [8/20], Batch Num: [497/600] Discriminator Loss: 0.1974, Generator Loss: 3.7046 D(x): 0.9455, D(G(z)): 0.0685 Epoch: [8/20], Batch Num: [498/600] Discriminator Loss: 0.2340, Generator Loss: 4.1437 D(x): 0.9432, D(G(z)): 0.0676 Epoch: [8/20], Batch Num: [499/600] Discriminator Loss: 0.1631, Generator Loss: 4.6345 D(x): 0.9421, D(G(z)): 0.0331 Epoch: 8, Batch Num: [500/600]
Epoch: [8/20], Batch Num: [500/600] Discriminator Loss: 0.1932, Generator Loss: 4.1089 D(x): 0.9166, D(G(z)): 0.0471 Epoch: [8/20], Batch Num: [501/600] Discriminator Loss: 0.1718, Generator Loss: 3.8499 D(x): 0.9310, D(G(z)): 0.0527 Epoch: [8/20], Batch Num: [502/600] Discriminator Loss: 0.2533, Generator Loss: 3.6971 D(x): 0.9248, D(G(z)): 0.0832 Epoch: [8/20], Batch Num: [503/600] Discriminator Loss: 0.2357, Generator Loss: 3.8354 D(x): 0.9435, D(G(z)): 0.0975 Epoch: [8/20], Batch Num: [504/600] Discriminator Loss: 0.3915, Generator Loss: 3.4466 D(x): 0.8909, D(G(z)): 0.1052 Epoch: [8/20], Batch Num: [505/600] Discriminator Loss: 0.2817, Generator Loss: 3.7807 D(x): 0.9080, D(G(z)): 0.0882 Epoch: [8/20], Batch Num: [506/600] Discriminator Loss: 0.3872, Generator Loss: 3.6457 D(x): 0.8989, D(G(z)): 0.1052 Epoch: [8/20], Batch Num: [507/600] Discriminator Loss: 0.3701, Generator Loss: 3.6065 D(x): 0.8859, D(G(z)): 0.0995 Epoch: [8/20], Batch Num: [508/600] Discriminator Loss: 0.3830, Generator Loss: 3.9568 D(x): 0.9011, D(G(z)): 0.1027 Epoch: [8/20], Batch Num: [509/600] Discriminator Loss: 0.1711, Generator Loss: 4.0618 D(x): 0.9549, D(G(z)): 0.0755 Epoch: [8/20], Batch Num: [510/600] Discriminator Loss: 0.1327, Generator Loss: 4.1665 D(x): 0.9635, D(G(z)): 0.0612 Epoch: [8/20], Batch Num: [511/600] Discriminator Loss: 0.1768, Generator Loss: 4.6932 D(x): 0.9589, D(G(z)): 0.0757 Epoch: [8/20], Batch Num: [512/600] Discriminator Loss: 0.4001, Generator Loss: 4.0653 D(x): 0.8665, D(G(z)): 0.0405 Epoch: [8/20], Batch Num: [513/600] Discriminator Loss: 0.4484, Generator Loss: 4.1443 D(x): 0.9047, D(G(z)): 0.0963 Epoch: [8/20], Batch Num: [514/600] Discriminator Loss: 0.2780, Generator Loss: 3.6804 D(x): 0.9123, D(G(z)): 0.0687 Epoch: [8/20], Batch Num: [515/600] Discriminator Loss: 0.4668, Generator Loss: 3.2535 D(x): 0.8590, D(G(z)): 0.1006 Epoch: [8/20], Batch Num: [516/600] Discriminator Loss: 0.2692, Generator Loss: 3.6172 D(x): 0.9596, D(G(z)): 0.1367 Epoch: [8/20], Batch Num: [517/600] Discriminator Loss: 0.2555, Generator Loss: 3.8731 D(x): 0.9264, D(G(z)): 0.0941 Epoch: [8/20], Batch Num: [518/600] Discriminator Loss: 0.3612, Generator Loss: 4.5156 D(x): 0.8837, D(G(z)): 0.0789 Epoch: [8/20], Batch Num: [519/600] Discriminator Loss: 0.2649, Generator Loss: 4.3349 D(x): 0.9073, D(G(z)): 0.0555 Epoch: [8/20], Batch Num: [520/600] Discriminator Loss: 0.1821, Generator Loss: 3.9204 D(x): 0.9295, D(G(z)): 0.0534 Epoch: [8/20], Batch Num: [521/600] Discriminator Loss: 0.2978, Generator Loss: 3.6991 D(x): 0.9059, D(G(z)): 0.0713 Epoch: [8/20], Batch Num: [522/600] Discriminator Loss: 0.2292, Generator Loss: 3.5787 D(x): 0.9306, D(G(z)): 0.0911 Epoch: [8/20], Batch Num: [523/600] Discriminator Loss: 0.3218, Generator Loss: 3.7861 D(x): 0.9245, D(G(z)): 0.1384 Epoch: [8/20], Batch Num: [524/600] Discriminator Loss: 0.3475, Generator Loss: 4.3713 D(x): 0.8971, D(G(z)): 0.1091 Epoch: [8/20], Batch Num: [525/600] Discriminator Loss: 0.2819, Generator Loss: 4.6032 D(x): 0.9150, D(G(z)): 0.0661 Epoch: [8/20], Batch Num: [526/600] Discriminator Loss: 0.4323, Generator Loss: 4.6227 D(x): 0.8689, D(G(z)): 0.0463 Epoch: [8/20], Batch Num: [527/600] Discriminator Loss: 0.2948, Generator Loss: 3.6015 D(x): 0.8686, D(G(z)): 0.0606 Epoch: [8/20], Batch Num: [528/600] Discriminator Loss: 0.2625, Generator Loss: 3.3714 D(x): 0.9266, D(G(z)): 0.1021 Epoch: [8/20], Batch Num: [529/600] Discriminator Loss: 0.2412, Generator Loss: 3.7289 D(x): 0.9540, D(G(z)): 0.1100 Epoch: [8/20], Batch Num: [530/600] Discriminator Loss: 0.3166, Generator Loss: 3.9936 D(x): 0.9266, D(G(z)): 0.1161 Epoch: [8/20], Batch Num: [531/600] Discriminator Loss: 0.2075, Generator Loss: 4.6632 D(x): 0.9214, D(G(z)): 0.0630 Epoch: [8/20], Batch Num: [532/600] Discriminator Loss: 0.3719, Generator Loss: 4.1129 D(x): 0.8563, D(G(z)): 0.0666 Epoch: [8/20], Batch Num: [533/600] Discriminator Loss: 0.3797, Generator Loss: 3.6735 D(x): 0.8790, D(G(z)): 0.0821 Epoch: [8/20], Batch Num: [534/600] Discriminator Loss: 0.3879, Generator Loss: 3.7770 D(x): 0.9530, D(G(z)): 0.1592 Epoch: [8/20], Batch Num: [535/600] Discriminator Loss: 0.4718, Generator Loss: 4.2698 D(x): 0.8895, D(G(z)): 0.1305 Epoch: [8/20], Batch Num: [536/600] Discriminator Loss: 0.3998, Generator Loss: 3.9906 D(x): 0.8885, D(G(z)): 0.0930 Epoch: [8/20], Batch Num: [537/600] Discriminator Loss: 0.3835, Generator Loss: 3.6340 D(x): 0.8618, D(G(z)): 0.0855 Epoch: [8/20], Batch Num: [538/600] Discriminator Loss: 0.2359, Generator Loss: 3.8047 D(x): 0.9199, D(G(z)): 0.0807 Epoch: [8/20], Batch Num: [539/600] Discriminator Loss: 0.2533, Generator Loss: 3.6458 D(x): 0.9327, D(G(z)): 0.1002 Epoch: [8/20], Batch Num: [540/600] Discriminator Loss: 0.2269, Generator Loss: 3.9098 D(x): 0.9382, D(G(z)): 0.0893 Epoch: [8/20], Batch Num: [541/600] Discriminator Loss: 0.2351, Generator Loss: 3.9422 D(x): 0.9052, D(G(z)): 0.0678 Epoch: [8/20], Batch Num: [542/600] Discriminator Loss: 0.3423, Generator Loss: 3.8420 D(x): 0.8841, D(G(z)): 0.0903 Epoch: [8/20], Batch Num: [543/600] Discriminator Loss: 0.2584, Generator Loss: 3.8533 D(x): 0.9156, D(G(z)): 0.0833 Epoch: [8/20], Batch Num: [544/600] Discriminator Loss: 0.3455, Generator Loss: 3.3957 D(x): 0.8781, D(G(z)): 0.0776 Epoch: [8/20], Batch Num: [545/600] Discriminator Loss: 0.2983, Generator Loss: 3.4519 D(x): 0.9132, D(G(z)): 0.1089 Epoch: [8/20], Batch Num: [546/600] Discriminator Loss: 0.3384, Generator Loss: 4.3489 D(x): 0.9550, D(G(z)): 0.1513 Epoch: [8/20], Batch Num: [547/600] Discriminator Loss: 0.3324, Generator Loss: 4.7951 D(x): 0.8997, D(G(z)): 0.0889 Epoch: [8/20], Batch Num: [548/600] Discriminator Loss: 0.3011, Generator Loss: 4.9558 D(x): 0.8791, D(G(z)): 0.0468 Epoch: [8/20], Batch Num: [549/600] Discriminator Loss: 0.3188, Generator Loss: 3.9366 D(x): 0.8875, D(G(z)): 0.0618 Epoch: [8/20], Batch Num: [550/600] Discriminator Loss: 0.2654, Generator Loss: 3.6215 D(x): 0.9087, D(G(z)): 0.0737 Epoch: [8/20], Batch Num: [551/600] Discriminator Loss: 0.3839, Generator Loss: 3.6944 D(x): 0.9406, D(G(z)): 0.1559 Epoch: [8/20], Batch Num: [552/600] Discriminator Loss: 0.4868, Generator Loss: 4.0539 D(x): 0.8845, D(G(z)): 0.1643 Epoch: [8/20], Batch Num: [553/600] Discriminator Loss: 0.4546, Generator Loss: 4.6918 D(x): 0.8663, D(G(z)): 0.1254 Epoch: [8/20], Batch Num: [554/600] Discriminator Loss: 0.3803, Generator Loss: 3.9513 D(x): 0.8437, D(G(z)): 0.0735 Epoch: [8/20], Batch Num: [555/600] Discriminator Loss: 0.5177, Generator Loss: 3.2412 D(x): 0.8238, D(G(z)): 0.0936 Epoch: [8/20], Batch Num: [556/600] Discriminator Loss: 0.3754, Generator Loss: 2.3381 D(x): 0.8871, D(G(z)): 0.1318 Epoch: [8/20], Batch Num: [557/600] Discriminator Loss: 0.3406, Generator Loss: 2.5405 D(x): 0.9472, D(G(z)): 0.1894 Epoch: [8/20], Batch Num: [558/600] Discriminator Loss: 0.4441, Generator Loss: 3.6586 D(x): 0.9194, D(G(z)): 0.2127 Epoch: [8/20], Batch Num: [559/600] Discriminator Loss: 0.3853, Generator Loss: 4.3465 D(x): 0.8870, D(G(z)): 0.1126 Epoch: [8/20], Batch Num: [560/600] Discriminator Loss: 0.4578, Generator Loss: 4.2369 D(x): 0.8422, D(G(z)): 0.0559 Epoch: [8/20], Batch Num: [561/600] Discriminator Loss: 0.4973, Generator Loss: 3.3753 D(x): 0.7863, D(G(z)): 0.0446 Epoch: [8/20], Batch Num: [562/600] Discriminator Loss: 0.4673, Generator Loss: 2.3349 D(x): 0.8353, D(G(z)): 0.0981 Epoch: [8/20], Batch Num: [563/600] Discriminator Loss: 0.4162, Generator Loss: 2.2138 D(x): 0.9436, D(G(z)): 0.2270 Epoch: [8/20], Batch Num: [564/600] Discriminator Loss: 0.4773, Generator Loss: 2.5513 D(x): 0.9139, D(G(z)): 0.2288 Epoch: [8/20], Batch Num: [565/600] Discriminator Loss: 0.3628, Generator Loss: 3.7022 D(x): 0.9267, D(G(z)): 0.1806 Epoch: [8/20], Batch Num: [566/600] Discriminator Loss: 0.4182, Generator Loss: 4.4852 D(x): 0.8577, D(G(z)): 0.0879 Epoch: [8/20], Batch Num: [567/600] Discriminator Loss: 0.5750, Generator Loss: 4.0976 D(x): 0.7703, D(G(z)): 0.0526 Epoch: [8/20], Batch Num: [568/600] Discriminator Loss: 0.5452, Generator Loss: 3.0511 D(x): 0.7856, D(G(z)): 0.0629 Epoch: [8/20], Batch Num: [569/600] Discriminator Loss: 0.3206, Generator Loss: 2.8100 D(x): 0.9129, D(G(z)): 0.1253 Epoch: [8/20], Batch Num: [570/600] Discriminator Loss: 0.3482, Generator Loss: 2.4632 D(x): 0.9299, D(G(z)): 0.1587 Epoch: [8/20], Batch Num: [571/600] Discriminator Loss: 0.3518, Generator Loss: 2.8900 D(x): 0.9075, D(G(z)): 0.1460 Epoch: [8/20], Batch Num: [572/600] Discriminator Loss: 0.4282, Generator Loss: 3.1903 D(x): 0.8632, D(G(z)): 0.1504 Epoch: [8/20], Batch Num: [573/600] Discriminator Loss: 0.4269, Generator Loss: 3.5820 D(x): 0.8622, D(G(z)): 0.1259 Epoch: [8/20], Batch Num: [574/600] Discriminator Loss: 0.2966, Generator Loss: 3.8343 D(x): 0.9119, D(G(z)): 0.1101 Epoch: [8/20], Batch Num: [575/600] Discriminator Loss: 0.3250, Generator Loss: 4.0682 D(x): 0.8840, D(G(z)): 0.0618 Epoch: [8/20], Batch Num: [576/600] Discriminator Loss: 0.4339, Generator Loss: 4.0753 D(x): 0.8395, D(G(z)): 0.0743 Epoch: [8/20], Batch Num: [577/600] Discriminator Loss: 0.4240, Generator Loss: 2.8554 D(x): 0.8416, D(G(z)): 0.0835 Epoch: [8/20], Batch Num: [578/600] Discriminator Loss: 0.3567, Generator Loss: 2.8928 D(x): 0.9163, D(G(z)): 0.1496 Epoch: [8/20], Batch Num: [579/600] Discriminator Loss: 0.3295, Generator Loss: 3.3616 D(x): 0.9546, D(G(z)): 0.1860 Epoch: [8/20], Batch Num: [580/600] Discriminator Loss: 0.3178, Generator Loss: 3.8892 D(x): 0.9082, D(G(z)): 0.1173 Epoch: [8/20], Batch Num: [581/600] Discriminator Loss: 0.2270, Generator Loss: 4.4001 D(x): 0.9302, D(G(z)): 0.0907 Epoch: [8/20], Batch Num: [582/600] Discriminator Loss: 0.5279, Generator Loss: 4.5433 D(x): 0.8288, D(G(z)): 0.0651 Epoch: [8/20], Batch Num: [583/600] Discriminator Loss: 0.3365, Generator Loss: 3.8801 D(x): 0.8548, D(G(z)): 0.0540 Epoch: [8/20], Batch Num: [584/600] Discriminator Loss: 0.4387, Generator Loss: 3.1058 D(x): 0.8609, D(G(z)): 0.0939 Epoch: [8/20], Batch Num: [585/600] Discriminator Loss: 0.3964, Generator Loss: 2.6654 D(x): 0.9200, D(G(z)): 0.1770 Epoch: [8/20], Batch Num: [586/600] Discriminator Loss: 0.3817, Generator Loss: 3.5965 D(x): 0.9227, D(G(z)): 0.1674 Epoch: [8/20], Batch Num: [587/600] Discriminator Loss: 0.3835, Generator Loss: 3.5346 D(x): 0.8943, D(G(z)): 0.1168 Epoch: [8/20], Batch Num: [588/600] Discriminator Loss: 0.3877, Generator Loss: 3.9548 D(x): 0.8617, D(G(z)): 0.0941 Epoch: [8/20], Batch Num: [589/600] Discriminator Loss: 0.3444, Generator Loss: 3.5284 D(x): 0.8963, D(G(z)): 0.0969 Epoch: [8/20], Batch Num: [590/600] Discriminator Loss: 0.4074, Generator Loss: 3.3736 D(x): 0.8614, D(G(z)): 0.0847 Epoch: [8/20], Batch Num: [591/600] Discriminator Loss: 0.3401, Generator Loss: 3.3644 D(x): 0.8946, D(G(z)): 0.1028 Epoch: [8/20], Batch Num: [592/600] Discriminator Loss: 0.4137, Generator Loss: 2.9730 D(x): 0.8853, D(G(z)): 0.1313 Epoch: [8/20], Batch Num: [593/600] Discriminator Loss: 0.2978, Generator Loss: 3.0128 D(x): 0.9516, D(G(z)): 0.1583 Epoch: [8/20], Batch Num: [594/600] Discriminator Loss: 0.4466, Generator Loss: 3.5151 D(x): 0.8502, D(G(z)): 0.1247 Epoch: [8/20], Batch Num: [595/600] Discriminator Loss: 0.3802, Generator Loss: 3.8457 D(x): 0.8903, D(G(z)): 0.1044 Epoch: [8/20], Batch Num: [596/600] Discriminator Loss: 0.3015, Generator Loss: 4.1149 D(x): 0.8867, D(G(z)): 0.0766 Epoch: [8/20], Batch Num: [597/600] Discriminator Loss: 0.4189, Generator Loss: 3.8379 D(x): 0.8889, D(G(z)): 0.0851 Epoch: [8/20], Batch Num: [598/600] Discriminator Loss: 0.3236, Generator Loss: 3.3222 D(x): 0.8992, D(G(z)): 0.0768 Epoch: [8/20], Batch Num: [599/600] Discriminator Loss: 0.3255, Generator Loss: 3.0499 D(x): 0.8976, D(G(z)): 0.1158 Epoch: 9, Batch Num: [0/600]
Epoch: [9/20], Batch Num: [0/600] Discriminator Loss: 0.3256, Generator Loss: 3.1667 D(x): 0.9334, D(G(z)): 0.1597 Epoch: [9/20], Batch Num: [1/600] Discriminator Loss: 0.4365, Generator Loss: 3.5704 D(x): 0.8622, D(G(z)): 0.1020 Epoch: [9/20], Batch Num: [2/600] Discriminator Loss: 0.3171, Generator Loss: 3.6814 D(x): 0.9262, D(G(z)): 0.1245 Epoch: [9/20], Batch Num: [3/600] Discriminator Loss: 0.2851, Generator Loss: 3.9183 D(x): 0.8994, D(G(z)): 0.0837 Epoch: [9/20], Batch Num: [4/600] Discriminator Loss: 0.3380, Generator Loss: 3.8295 D(x): 0.8576, D(G(z)): 0.0647 Epoch: [9/20], Batch Num: [5/600] Discriminator Loss: 0.4038, Generator Loss: 3.3155 D(x): 0.8958, D(G(z)): 0.1123 Epoch: [9/20], Batch Num: [6/600] Discriminator Loss: 0.3269, Generator Loss: 3.2793 D(x): 0.9131, D(G(z)): 0.1184 Epoch: [9/20], Batch Num: [7/600] Discriminator Loss: 0.3019, Generator Loss: 3.5225 D(x): 0.9347, D(G(z)): 0.1311 Epoch: [9/20], Batch Num: [8/600] Discriminator Loss: 0.3348, Generator Loss: 3.5183 D(x): 0.9091, D(G(z)): 0.1115 Epoch: [9/20], Batch Num: [9/600] Discriminator Loss: 0.3632, Generator Loss: 3.7469 D(x): 0.8802, D(G(z)): 0.0951 Epoch: [9/20], Batch Num: [10/600] Discriminator Loss: 0.3482, Generator Loss: 3.8100 D(x): 0.8638, D(G(z)): 0.0633 Epoch: [9/20], Batch Num: [11/600] Discriminator Loss: 0.3874, Generator Loss: 3.4225 D(x): 0.8704, D(G(z)): 0.1115 Epoch: [9/20], Batch Num: [12/600] Discriminator Loss: 0.2900, Generator Loss: 3.5381 D(x): 0.9389, D(G(z)): 0.1222 Epoch: [9/20], Batch Num: [13/600] Discriminator Loss: 0.3291, Generator Loss: 3.9571 D(x): 0.9513, D(G(z)): 0.1568 Epoch: [9/20], Batch Num: [14/600] Discriminator Loss: 0.2322, Generator Loss: 4.5629 D(x): 0.9123, D(G(z)): 0.0715 Epoch: [9/20], Batch Num: [15/600] Discriminator Loss: 0.2145, Generator Loss: 4.7362 D(x): 0.9150, D(G(z)): 0.0511 Epoch: [9/20], Batch Num: [16/600] Discriminator Loss: 0.1872, Generator Loss: 4.8347 D(x): 0.9092, D(G(z)): 0.0326 Epoch: [9/20], Batch Num: [17/600] Discriminator Loss: 0.3584, Generator Loss: 3.7835 D(x): 0.8627, D(G(z)): 0.0391 Epoch: [9/20], Batch Num: [18/600] Discriminator Loss: 0.2802, Generator Loss: 3.0185 D(x): 0.9174, D(G(z)): 0.0875 Epoch: [9/20], Batch Num: [19/600] Discriminator Loss: 0.2387, Generator Loss: 3.5306 D(x): 0.9813, D(G(z)): 0.1556 Epoch: [9/20], Batch Num: [20/600] Discriminator Loss: 0.3693, Generator Loss: 4.2105 D(x): 0.9133, D(G(z)): 0.1387 Epoch: [9/20], Batch Num: [21/600] Discriminator Loss: 0.4332, Generator Loss: 4.9221 D(x): 0.8854, D(G(z)): 0.0707 Epoch: [9/20], Batch Num: [22/600] Discriminator Loss: 0.2749, Generator Loss: 4.6405 D(x): 0.8956, D(G(z)): 0.0465 Epoch: [9/20], Batch Num: [23/600] Discriminator Loss: 0.3469, Generator Loss: 4.6532 D(x): 0.8941, D(G(z)): 0.0528 Epoch: [9/20], Batch Num: [24/600] Discriminator Loss: 0.3546, Generator Loss: 3.4820 D(x): 0.8698, D(G(z)): 0.0679 Epoch: [9/20], Batch Num: [25/600] Discriminator Loss: 0.3319, Generator Loss: 3.1020 D(x): 0.9307, D(G(z)): 0.1327 Epoch: [9/20], Batch Num: [26/600] Discriminator Loss: 0.3383, Generator Loss: 3.4163 D(x): 0.9212, D(G(z)): 0.1461 Epoch: [9/20], Batch Num: [27/600] Discriminator Loss: 0.2174, Generator Loss: 4.2461 D(x): 0.9650, D(G(z)): 0.1224 Epoch: [9/20], Batch Num: [28/600] Discriminator Loss: 0.2865, Generator Loss: 4.9014 D(x): 0.9073, D(G(z)): 0.0765 Epoch: [9/20], Batch Num: [29/600] Discriminator Loss: 0.2503, Generator Loss: 4.9103 D(x): 0.8844, D(G(z)): 0.0186 Epoch: [9/20], Batch Num: [30/600] Discriminator Loss: 0.2172, Generator Loss: 4.2401 D(x): 0.9067, D(G(z)): 0.0353 Epoch: [9/20], Batch Num: [31/600] Discriminator Loss: 0.3232, Generator Loss: 3.4294 D(x): 0.9180, D(G(z)): 0.1102 Epoch: [9/20], Batch Num: [32/600] Discriminator Loss: 0.3914, Generator Loss: 3.8976 D(x): 0.9381, D(G(z)): 0.1360 Epoch: [9/20], Batch Num: [33/600] Discriminator Loss: 0.4594, Generator Loss: 3.8999 D(x): 0.8842, D(G(z)): 0.1038 Epoch: [9/20], Batch Num: [34/600] Discriminator Loss: 0.2755, Generator Loss: 3.6768 D(x): 0.8929, D(G(z)): 0.0664 Epoch: [9/20], Batch Num: [35/600] Discriminator Loss: 0.3039, Generator Loss: 4.0235 D(x): 0.9313, D(G(z)): 0.1131 Epoch: [9/20], Batch Num: [36/600] Discriminator Loss: 0.5064, Generator Loss: 3.4662 D(x): 0.8478, D(G(z)): 0.0929 Epoch: [9/20], Batch Num: [37/600] Discriminator Loss: 0.3782, Generator Loss: 3.8010 D(x): 0.8832, D(G(z)): 0.1329 Epoch: [9/20], Batch Num: [38/600] Discriminator Loss: 0.3743, Generator Loss: 3.2686 D(x): 0.8970, D(G(z)): 0.1297 Epoch: [9/20], Batch Num: [39/600] Discriminator Loss: 0.4350, Generator Loss: 2.8817 D(x): 0.8651, D(G(z)): 0.0801 Epoch: [9/20], Batch Num: [40/600] Discriminator Loss: 0.3738, Generator Loss: 3.2832 D(x): 0.9270, D(G(z)): 0.1665 Epoch: [9/20], Batch Num: [41/600] Discriminator Loss: 0.3293, Generator Loss: 3.1622 D(x): 0.9014, D(G(z)): 0.1194 Epoch: [9/20], Batch Num: [42/600] Discriminator Loss: 0.3658, Generator Loss: 3.7605 D(x): 0.8827, D(G(z)): 0.0943 Epoch: 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4.2952 D(x): 0.9103, D(G(z)): 0.0459 Epoch: [9/20], Batch Num: [52/600] Discriminator Loss: 0.3154, Generator Loss: 4.1999 D(x): 0.9017, D(G(z)): 0.0766 Epoch: [9/20], Batch Num: [53/600] Discriminator Loss: 0.4965, Generator Loss: 3.0892 D(x): 0.8688, D(G(z)): 0.1107 Epoch: [9/20], Batch Num: [54/600] Discriminator Loss: 0.3237, Generator Loss: 2.9016 D(x): 0.9078, D(G(z)): 0.1016 Epoch: [9/20], Batch Num: [55/600] Discriminator Loss: 0.4160, Generator Loss: 3.4086 D(x): 0.9041, D(G(z)): 0.1868 Epoch: [9/20], Batch Num: [56/600] Discriminator Loss: 0.3241, Generator Loss: 3.7152 D(x): 0.8953, D(G(z)): 0.1027 Epoch: [9/20], Batch Num: [57/600] Discriminator Loss: 0.3473, Generator Loss: 3.9956 D(x): 0.9249, D(G(z)): 0.0940 Epoch: [9/20], Batch Num: [58/600] Discriminator Loss: 0.3900, Generator Loss: 4.4159 D(x): 0.8708, D(G(z)): 0.0715 Epoch: [9/20], Batch Num: [59/600] Discriminator Loss: 0.4474, Generator Loss: 4.1359 D(x): 0.8660, D(G(z)): 0.0657 Epoch: [9/20], Batch Num: [60/600] Discriminator Loss: 0.3710, Generator Loss: 2.9123 D(x): 0.8695, D(G(z)): 0.0693 Epoch: [9/20], Batch Num: [61/600] Discriminator Loss: 0.5135, Generator Loss: 2.5798 D(x): 0.8846, D(G(z)): 0.1575 Epoch: [9/20], Batch Num: [62/600] Discriminator Loss: 0.3726, Generator Loss: 2.7955 D(x): 0.9525, D(G(z)): 0.1822 Epoch: [9/20], Batch Num: [63/600] Discriminator Loss: 0.4369, Generator Loss: 3.3868 D(x): 0.8656, D(G(z)): 0.1023 Epoch: [9/20], Batch Num: [64/600] Discriminator Loss: 0.4043, Generator Loss: 3.5101 D(x): 0.8880, D(G(z)): 0.1021 Epoch: [9/20], Batch Num: [65/600] Discriminator Loss: 0.3485, Generator Loss: 3.9838 D(x): 0.8824, D(G(z)): 0.0936 Epoch: [9/20], Batch Num: [66/600] Discriminator Loss: 0.2480, Generator Loss: 3.9022 D(x): 0.9115, D(G(z)): 0.0787 Epoch: [9/20], Batch Num: [67/600] Discriminator Loss: 0.3644, Generator Loss: 3.2809 D(x): 0.8487, D(G(z)): 0.0807 Epoch: [9/20], Batch Num: [68/600] Discriminator Loss: 0.2865, Generator Loss: 3.0380 D(x): 0.9332, D(G(z)): 0.1051 Epoch: [9/20], Batch Num: [69/600] Discriminator Loss: 0.3114, Generator Loss: 3.4067 D(x): 0.9195, D(G(z)): 0.1348 Epoch: [9/20], Batch Num: [70/600] Discriminator Loss: 0.4691, Generator Loss: 3.7276 D(x): 0.8827, D(G(z)): 0.1406 Epoch: [9/20], Batch Num: [71/600] Discriminator Loss: 0.4792, Generator Loss: 3.4767 D(x): 0.8590, D(G(z)): 0.1061 Epoch: [9/20], Batch Num: [72/600] Discriminator Loss: 0.6685, Generator Loss: 3.5642 D(x): 0.8297, D(G(z)): 0.1355 Epoch: [9/20], Batch Num: [73/600] Discriminator Loss: 0.5008, Generator Loss: 3.0102 D(x): 0.8592, D(G(z)): 0.1143 Epoch: [9/20], Batch Num: [74/600] Discriminator Loss: 0.3512, Generator Loss: 3.3782 D(x): 0.8928, D(G(z)): 0.1130 Epoch: [9/20], Batch Num: [75/600] Discriminator Loss: 0.3273, Generator Loss: 3.2238 D(x): 0.9333, D(G(z)): 0.1326 Epoch: [9/20], Batch Num: [76/600] Discriminator Loss: 0.3893, Generator Loss: 3.8665 D(x): 0.9015, D(G(z)): 0.1289 Epoch: [9/20], Batch Num: [77/600] Discriminator Loss: 0.3313, Generator Loss: 3.7927 D(x): 0.8907, D(G(z)): 0.0893 Epoch: [9/20], Batch Num: [78/600] Discriminator Loss: 0.4509, Generator Loss: 4.0036 D(x): 0.8703, D(G(z)): 0.0754 Epoch: [9/20], Batch Num: [79/600] Discriminator Loss: 0.5299, Generator Loss: 2.8537 D(x): 0.8152, D(G(z)): 0.0714 Epoch: [9/20], Batch Num: [80/600] Discriminator Loss: 0.5835, Generator Loss: 2.6972 D(x): 0.8800, D(G(z)): 0.1809 Epoch: [9/20], Batch Num: [81/600] Discriminator Loss: 0.2508, Generator Loss: 2.8699 D(x): 0.9606, D(G(z)): 0.1543 Epoch: [9/20], Batch Num: [82/600] Discriminator Loss: 0.3221, Generator Loss: 3.7870 D(x): 0.9305, D(G(z)): 0.1281 Epoch: [9/20], Batch Num: [83/600] Discriminator Loss: 0.3985, Generator Loss: 3.9510 D(x): 0.8428, D(G(z)): 0.0658 Epoch: [9/20], Batch Num: [84/600] Discriminator Loss: 0.4871, Generator Loss: 3.8230 D(x): 0.8524, D(G(z)): 0.0702 Epoch: [9/20], Batch Num: [85/600] Discriminator Loss: 0.2440, Generator Loss: 3.6487 D(x): 0.9009, D(G(z)): 0.0572 Epoch: [9/20], Batch Num: [86/600] Discriminator Loss: 0.3371, Generator Loss: 2.8898 D(x): 0.8963, D(G(z)): 0.0865 Epoch: [9/20], Batch Num: [87/600] Discriminator Loss: 0.3533, Generator Loss: 2.7513 D(x): 0.9150, D(G(z)): 0.1232 Epoch: [9/20], Batch Num: [88/600] Discriminator Loss: 0.3995, Generator Loss: 2.8381 D(x): 0.9215, D(G(z)): 0.1705 Epoch: [9/20], Batch Num: [89/600] Discriminator Loss: 0.3270, Generator Loss: 3.7674 D(x): 0.9285, D(G(z)): 0.1496 Epoch: [9/20], Batch Num: [90/600] Discriminator Loss: 0.3159, Generator Loss: 4.0650 D(x): 0.8981, D(G(z)): 0.0741 Epoch: [9/20], Batch Num: [91/600] Discriminator Loss: 0.3212, Generator Loss: 3.9232 D(x): 0.8814, D(G(z)): 0.0573 Epoch: [9/20], Batch Num: [92/600] Discriminator Loss: 0.3263, Generator Loss: 3.7013 D(x): 0.8675, D(G(z)): 0.0475 Epoch: [9/20], Batch Num: [93/600] Discriminator Loss: 0.2432, Generator Loss: 3.3172 D(x): 0.9143, D(G(z)): 0.0633 Epoch: [9/20], Batch Num: [94/600] Discriminator Loss: 0.3692, Generator Loss: 3.2219 D(x): 0.9241, D(G(z)): 0.1380 Epoch: [9/20], Batch Num: [95/600] Discriminator Loss: 0.3430, Generator Loss: 3.4042 D(x): 0.9371, D(G(z)): 0.1316 Epoch: [9/20], Batch Num: [96/600] Discriminator Loss: 0.2524, Generator Loss: 3.5920 D(x): 0.9478, D(G(z)): 0.1144 Epoch: [9/20], Batch Num: [97/600] Discriminator Loss: 0.3744, Generator Loss: 3.7539 D(x): 0.8741, D(G(z)): 0.0888 Epoch: [9/20], Batch Num: [98/600] Discriminator Loss: 0.5510, Generator Loss: 3.5174 D(x): 0.8409, D(G(z)): 0.1001 Epoch: [9/20], Batch Num: [99/600] Discriminator Loss: 0.7405, Generator Loss: 2.7531 D(x): 0.7959, D(G(z)): 0.1026 Epoch: 9, Batch Num: [100/600]
Epoch: [9/20], Batch Num: [100/600] Discriminator Loss: 0.3922, Generator Loss: 2.2030 D(x): 0.9008, D(G(z)): 0.1655 Epoch: [9/20], Batch Num: [101/600] Discriminator Loss: 0.4467, Generator Loss: 2.7157 D(x): 0.9211, D(G(z)): 0.2170 Epoch: [9/20], Batch Num: [102/600] Discriminator Loss: 0.6164, Generator Loss: 3.5962 D(x): 0.8913, D(G(z)): 0.2295 Epoch: [9/20], Batch Num: [103/600] Discriminator Loss: 0.3939, Generator Loss: 4.0356 D(x): 0.8497, D(G(z)): 0.0863 Epoch: [9/20], Batch Num: [104/600] Discriminator Loss: 0.4318, Generator Loss: 3.8985 D(x): 0.8260, D(G(z)): 0.0575 Epoch: [9/20], Batch Num: [105/600] Discriminator Loss: 0.6140, Generator Loss: 2.9188 D(x): 0.7959, D(G(z)): 0.0889 Epoch: [9/20], Batch Num: [106/600] Discriminator Loss: 0.4443, Generator Loss: 2.2304 D(x): 0.8516, D(G(z)): 0.1013 Epoch: [9/20], Batch Num: [107/600] Discriminator Loss: 0.3519, Generator Loss: 1.9808 D(x): 0.9181, D(G(z)): 0.1612 Epoch: [9/20], Batch Num: [108/600] Discriminator Loss: 0.3951, Generator Loss: 2.4288 D(x): 0.9407, D(G(z)): 0.2160 Epoch: [9/20], Batch Num: [109/600] Discriminator Loss: 0.3471, Generator Loss: 3.3394 D(x): 0.9242, D(G(z)): 0.1812 Epoch: [9/20], Batch Num: [110/600] Discriminator Loss: 0.2580, Generator Loss: 3.9781 D(x): 0.9241, D(G(z)): 0.0867 Epoch: [9/20], Batch Num: [111/600] Discriminator Loss: 0.3743, Generator Loss: 4.4859 D(x): 0.8518, D(G(z)): 0.0551 Epoch: [9/20], Batch Num: [112/600] Discriminator Loss: 0.4193, Generator Loss: 3.8134 D(x): 0.8127, D(G(z)): 0.0398 Epoch: [9/20], Batch Num: [113/600] Discriminator Loss: 0.3461, Generator Loss: 3.2486 D(x): 0.8791, D(G(z)): 0.0709 Epoch: [9/20], Batch Num: [114/600] Discriminator Loss: 0.4028, Generator Loss: 2.5628 D(x): 0.8849, D(G(z)): 0.1415 Epoch: [9/20], Batch Num: [115/600] Discriminator Loss: 0.3954, Generator Loss: 2.6932 D(x): 0.9483, D(G(z)): 0.1830 Epoch: [9/20], Batch Num: [116/600] Discriminator Loss: 0.4500, Generator Loss: 3.2289 D(x): 0.9067, D(G(z)): 0.1717 Epoch: [9/20], Batch Num: [117/600] Discriminator Loss: 0.3632, Generator Loss: 3.3402 D(x): 0.8896, D(G(z)): 0.0993 Epoch: [9/20], Batch Num: [118/600] Discriminator Loss: 0.2636, Generator Loss: 4.1743 D(x): 0.9360, D(G(z)): 0.1187 Epoch: [9/20], Batch Num: [119/600] Discriminator Loss: 0.4770, Generator Loss: 3.7700 D(x): 0.8294, D(G(z)): 0.0645 Epoch: [9/20], Batch Num: [120/600] Discriminator Loss: 0.4556, Generator Loss: 3.4093 D(x): 0.8179, D(G(z)): 0.0630 Epoch: [9/20], Batch Num: [121/600] Discriminator Loss: 0.3746, Generator Loss: 2.7203 D(x): 0.8545, D(G(z)): 0.0976 Epoch: [9/20], Batch Num: [122/600] Discriminator Loss: 0.6755, Generator Loss: 2.5213 D(x): 0.8690, D(G(z)): 0.2413 Epoch: [9/20], Batch Num: [123/600] Discriminator Loss: 0.3424, Generator Loss: 2.9391 D(x): 0.9405, D(G(z)): 0.1777 Epoch: [9/20], Batch Num: [124/600] Discriminator Loss: 0.3859, Generator Loss: 3.9299 D(x): 0.9340, D(G(z)): 0.1832 Epoch: [9/20], Batch Num: [125/600] Discriminator Loss: 0.3583, Generator Loss: 4.1676 D(x): 0.8377, D(G(z)): 0.0602 Epoch: [9/20], Batch Num: [126/600] Discriminator Loss: 0.4101, Generator Loss: 3.5511 D(x): 0.8162, D(G(z)): 0.0495 Epoch: [9/20], Batch Num: [127/600] Discriminator Loss: 0.4297, Generator Loss: 2.9757 D(x): 0.8082, D(G(z)): 0.0464 Epoch: [9/20], Batch Num: [128/600] Discriminator Loss: 0.3394, Generator Loss: 2.2732 D(x): 0.9090, D(G(z)): 0.1437 Epoch: [9/20], Batch Num: [129/600] Discriminator Loss: 0.5130, Generator Loss: 2.4531 D(x): 0.9594, D(G(z)): 0.2715 Epoch: [9/20], Batch Num: [130/600] Discriminator Loss: 0.4240, Generator Loss: 3.3887 D(x): 0.9038, D(G(z)): 0.1838 Epoch: [9/20], Batch Num: [131/600] Discriminator Loss: 0.3945, Generator Loss: 3.9119 D(x): 0.8720, D(G(z)): 0.1193 Epoch: [9/20], Batch Num: [132/600] Discriminator Loss: 0.5176, Generator Loss: 4.1526 D(x): 0.8253, D(G(z)): 0.0913 Epoch: [9/20], Batch Num: [133/600] Discriminator Loss: 0.3479, Generator Loss: 4.0320 D(x): 0.8801, D(G(z)): 0.0894 Epoch: [9/20], Batch Num: [134/600] Discriminator Loss: 0.5833, Generator Loss: 3.4534 D(x): 0.7632, D(G(z)): 0.0488 Epoch: [9/20], Batch Num: [135/600] Discriminator Loss: 0.3615, Generator Loss: 2.5546 D(x): 0.8796, D(G(z)): 0.1108 Epoch: [9/20], Batch Num: [136/600] Discriminator Loss: 0.5078, Generator Loss: 2.5487 D(x): 0.8783, D(G(z)): 0.1874 Epoch: [9/20], Batch Num: [137/600] Discriminator Loss: 0.4071, Generator Loss: 2.6286 D(x): 0.9431, D(G(z)): 0.2242 Epoch: [9/20], Batch Num: [138/600] Discriminator Loss: 0.4494, Generator Loss: 3.3483 D(x): 0.8625, D(G(z)): 0.1334 Epoch: [9/20], Batch Num: [139/600] Discriminator Loss: 0.4244, Generator Loss: 3.1112 D(x): 0.8521, D(G(z)): 0.1163 Epoch: [9/20], Batch Num: [140/600] Discriminator Loss: 0.4725, Generator Loss: 2.7823 D(x): 0.8389, D(G(z)): 0.0943 Epoch: [9/20], Batch Num: [141/600] Discriminator Loss: 0.3684, Generator Loss: 2.8455 D(x): 0.8616, D(G(z)): 0.1114 Epoch: [9/20], Batch Num: [142/600] Discriminator Loss: 0.4489, Generator Loss: 2.9583 D(x): 0.8749, D(G(z)): 0.1325 Epoch: [9/20], Batch Num: [143/600] Discriminator Loss: 0.3489, Generator Loss: 2.9281 D(x): 0.8676, D(G(z)): 0.1206 Epoch: [9/20], Batch Num: [144/600] Discriminator Loss: 0.3746, Generator Loss: 3.0051 D(x): 0.8910, D(G(z)): 0.1278 Epoch: [9/20], Batch Num: [145/600] Discriminator Loss: 0.3081, Generator Loss: 3.0536 D(x): 0.8956, D(G(z)): 0.0971 Epoch: [9/20], Batch Num: [146/600] Discriminator Loss: 0.4393, Generator Loss: 3.3567 D(x): 0.8611, D(G(z)): 0.1127 Epoch: [9/20], Batch Num: [147/600] Discriminator Loss: 0.4132, Generator Loss: 2.9954 D(x): 0.8519, D(G(z)): 0.1212 Epoch: [9/20], Batch Num: [148/600] Discriminator Loss: 0.3709, Generator Loss: 2.7798 D(x): 0.8753, D(G(z)): 0.1048 Epoch: [9/20], Batch Num: [149/600] Discriminator Loss: 0.3367, Generator Loss: 3.2957 D(x): 0.9413, D(G(z)): 0.1873 Epoch: [9/20], Batch Num: [150/600] Discriminator Loss: 0.3773, Generator Loss: 3.9880 D(x): 0.8807, D(G(z)): 0.1166 Epoch: [9/20], Batch Num: [151/600] Discriminator Loss: 0.3814, Generator Loss: 3.9571 D(x): 0.8444, D(G(z)): 0.0610 Epoch: [9/20], Batch Num: [152/600] Discriminator Loss: 0.4136, Generator Loss: 3.3254 D(x): 0.8341, D(G(z)): 0.0598 Epoch: [9/20], Batch Num: [153/600] Discriminator Loss: 0.2419, Generator Loss: 3.1060 D(x): 0.9361, D(G(z)): 0.0950 Epoch: [9/20], Batch Num: [154/600] Discriminator Loss: 0.3321, Generator Loss: 3.2586 D(x): 0.9368, D(G(z)): 0.1644 Epoch: [9/20], Batch Num: [155/600] Discriminator Loss: 0.4151, Generator Loss: 3.6307 D(x): 0.8843, D(G(z)): 0.1680 Epoch: [9/20], Batch Num: [156/600] Discriminator Loss: 0.2244, Generator Loss: 4.2260 D(x): 0.9310, D(G(z)): 0.0901 Epoch: [9/20], Batch Num: [157/600] Discriminator Loss: 0.3810, Generator Loss: 4.2596 D(x): 0.8478, D(G(z)): 0.0298 Epoch: [9/20], Batch Num: [158/600] Discriminator Loss: 0.3359, Generator Loss: 3.9147 D(x): 0.8845, D(G(z)): 0.0824 Epoch: [9/20], Batch Num: [159/600] Discriminator Loss: 0.2934, Generator Loss: 3.7946 D(x): 0.9054, D(G(z)): 0.1009 Epoch: [9/20], Batch Num: [160/600] Discriminator Loss: 0.2957, Generator Loss: 3.8362 D(x): 0.9168, D(G(z)): 0.0913 Epoch: [9/20], Batch Num: [161/600] Discriminator Loss: 0.2888, Generator Loss: 3.5515 D(x): 0.9275, D(G(z)): 0.0948 Epoch: [9/20], Batch Num: [162/600] Discriminator Loss: 0.2917, Generator Loss: 3.9498 D(x): 0.9405, D(G(z)): 0.1147 Epoch: [9/20], Batch Num: [163/600] Discriminator Loss: 0.2165, Generator Loss: 4.3360 D(x): 0.9470, D(G(z)): 0.0819 Epoch: [9/20], Batch Num: [164/600] Discriminator Loss: 0.2730, Generator Loss: 4.4611 D(x): 0.9033, D(G(z)): 0.0496 Epoch: [9/20], Batch Num: [165/600] Discriminator Loss: 0.2817, Generator Loss: 3.9577 D(x): 0.8718, D(G(z)): 0.0336 Epoch: [9/20], Batch Num: [166/600] Discriminator Loss: 0.2815, Generator Loss: 3.5549 D(x): 0.9056, D(G(z)): 0.0734 Epoch: [9/20], Batch Num: [167/600] Discriminator Loss: 0.3295, Generator Loss: 3.3679 D(x): 0.9306, D(G(z)): 0.1211 Epoch: [9/20], Batch Num: [168/600] Discriminator Loss: 0.2506, Generator Loss: 3.4669 D(x): 0.9422, D(G(z)): 0.1143 Epoch: [9/20], Batch Num: [169/600] Discriminator Loss: 0.1924, Generator Loss: 4.2085 D(x): 0.9500, D(G(z)): 0.0874 Epoch: [9/20], Batch Num: [170/600] Discriminator Loss: 0.3110, Generator Loss: 4.1633 D(x): 0.9017, D(G(z)): 0.0533 Epoch: [9/20], Batch Num: [171/600] Discriminator Loss: 0.2292, Generator Loss: 4.1720 D(x): 0.9337, D(G(z)): 0.0691 Epoch: [9/20], Batch Num: [172/600] Discriminator Loss: 0.2789, Generator Loss: 4.4384 D(x): 0.9181, D(G(z)): 0.0755 Epoch: [9/20], Batch Num: [173/600] Discriminator Loss: 0.3051, Generator Loss: 3.9856 D(x): 0.9050, D(G(z)): 0.0838 Epoch: [9/20], Batch Num: [174/600] Discriminator Loss: 0.3224, Generator Loss: 3.9275 D(x): 0.9157, D(G(z)): 0.1016 Epoch: [9/20], Batch Num: [175/600] Discriminator Loss: 0.3888, Generator Loss: 3.9394 D(x): 0.8837, D(G(z)): 0.1141 Epoch: [9/20], Batch Num: [176/600] Discriminator Loss: 0.2821, Generator Loss: 4.0498 D(x): 0.9203, D(G(z)): 0.0802 Epoch: [9/20], Batch Num: [177/600] Discriminator Loss: 0.2870, Generator Loss: 3.9688 D(x): 0.8994, D(G(z)): 0.0883 Epoch: [9/20], Batch Num: [178/600] Discriminator Loss: 0.2160, Generator Loss: 4.3948 D(x): 0.9541, D(G(z)): 0.1044 Epoch: [9/20], Batch Num: [179/600] Discriminator Loss: 0.2693, Generator Loss: 3.9082 D(x): 0.8893, D(G(z)): 0.0639 Epoch: [9/20], Batch Num: [180/600] Discriminator Loss: 0.2731, Generator Loss: 3.5378 D(x): 0.9317, D(G(z)): 0.0972 Epoch: [9/20], Batch Num: [181/600] Discriminator Loss: 0.2643, Generator Loss: 4.0293 D(x): 0.9541, D(G(z)): 0.1044 Epoch: [9/20], Batch Num: [182/600] Discriminator Loss: 0.3522, Generator Loss: 4.4055 D(x): 0.9099, D(G(z)): 0.1142 Epoch: [9/20], Batch Num: [183/600] Discriminator Loss: 0.2439, Generator Loss: 5.7970 D(x): 0.9438, D(G(z)): 0.0916 Epoch: [9/20], Batch Num: [184/600] Discriminator Loss: 0.3635, Generator Loss: 5.3108 D(x): 0.8701, D(G(z)): 0.0654 Epoch: [9/20], Batch Num: [185/600] Discriminator Loss: 0.4320, Generator Loss: 3.8794 D(x): 0.8470, D(G(z)): 0.0619 Epoch: [9/20], Batch Num: [186/600] Discriminator Loss: 0.3514, Generator Loss: 3.8271 D(x): 0.9075, D(G(z)): 0.1041 Epoch: [9/20], Batch Num: [187/600] Discriminator Loss: 0.5123, Generator Loss: 3.6901 D(x): 0.9189, D(G(z)): 0.1595 Epoch: [9/20], Batch Num: [188/600] Discriminator Loss: 0.4235, Generator Loss: 3.7378 D(x): 0.9000, D(G(z)): 0.1292 Epoch: [9/20], Batch Num: [189/600] Discriminator Loss: 0.5133, Generator Loss: 4.1119 D(x): 0.8693, D(G(z)): 0.1108 Epoch: [9/20], Batch Num: [190/600] Discriminator Loss: 0.3576, Generator Loss: 4.0139 D(x): 0.8942, D(G(z)): 0.0941 Epoch: [9/20], Batch Num: [191/600] Discriminator Loss: 0.4840, Generator Loss: 3.7037 D(x): 0.8447, D(G(z)): 0.0821 Epoch: [9/20], Batch Num: [192/600] Discriminator Loss: 0.3183, Generator Loss: 3.1978 D(x): 0.9208, D(G(z)): 0.1138 Epoch: [9/20], Batch Num: [193/600] Discriminator Loss: 0.4319, Generator Loss: 3.0272 D(x): 0.8778, D(G(z)): 0.1301 Epoch: [9/20], Batch Num: [194/600] Discriminator Loss: 0.5029, Generator Loss: 4.0122 D(x): 0.9006, D(G(z)): 0.1873 Epoch: [9/20], Batch Num: [195/600] Discriminator Loss: 0.3782, Generator Loss: 4.2169 D(x): 0.9043, D(G(z)): 0.1040 Epoch: [9/20], Batch Num: [196/600] Discriminator Loss: 0.3964, Generator Loss: 3.8744 D(x): 0.8518, D(G(z)): 0.0664 Epoch: [9/20], Batch Num: [197/600] Discriminator Loss: 0.3491, Generator Loss: 3.6362 D(x): 0.8834, D(G(z)): 0.0920 Epoch: [9/20], Batch Num: [198/600] Discriminator Loss: 0.3916, Generator Loss: 3.2871 D(x): 0.8893, D(G(z)): 0.1172 Epoch: [9/20], Batch Num: [199/600] Discriminator Loss: 0.5556, Generator Loss: 2.8628 D(x): 0.8569, D(G(z)): 0.1666 Epoch: 9, Batch Num: [200/600]
Epoch: [9/20], Batch Num: [200/600] Discriminator Loss: 0.5310, Generator Loss: 3.5367 D(x): 0.9092, D(G(z)): 0.2217 Epoch: [9/20], Batch Num: [201/600] Discriminator Loss: 0.3794, Generator Loss: 3.2373 D(x): 0.8508, D(G(z)): 0.1011 Epoch: [9/20], Batch Num: [202/600] Discriminator Loss: 0.4562, Generator Loss: 3.4699 D(x): 0.8236, D(G(z)): 0.0876 Epoch: [9/20], Batch Num: [203/600] Discriminator Loss: 0.4829, Generator Loss: 2.7094 D(x): 0.8477, D(G(z)): 0.1111 Epoch: [9/20], Batch Num: [204/600] Discriminator Loss: 0.5008, Generator Loss: 2.6291 D(x): 0.8394, D(G(z)): 0.1711 Epoch: [9/20], Batch Num: [205/600] Discriminator Loss: 0.4705, Generator Loss: 2.4735 D(x): 0.8718, D(G(z)): 0.1795 Epoch: [9/20], Batch Num: [206/600] Discriminator Loss: 0.5881, Generator Loss: 3.0682 D(x): 0.8771, D(G(z)): 0.2368 Epoch: [9/20], Batch Num: [207/600] Discriminator Loss: 0.4302, Generator Loss: 3.7418 D(x): 0.8831, D(G(z)): 0.1423 Epoch: [9/20], Batch Num: [208/600] Discriminator Loss: 0.5393, Generator Loss: 3.5569 D(x): 0.7978, D(G(z)): 0.0874 Epoch: [9/20], Batch Num: [209/600] Discriminator Loss: 0.5626, Generator Loss: 2.8727 D(x): 0.7876, D(G(z)): 0.1001 Epoch: [9/20], Batch Num: [210/600] Discriminator Loss: 0.4362, Generator Loss: 2.6110 D(x): 0.8584, D(G(z)): 0.1364 Epoch: [9/20], Batch Num: [211/600] Discriminator Loss: 0.3415, Generator Loss: 2.7262 D(x): 0.9050, D(G(z)): 0.1669 Epoch: [9/20], Batch Num: [212/600] Discriminator Loss: 0.4953, Generator Loss: 2.7789 D(x): 0.8967, D(G(z)): 0.2095 Epoch: [9/20], Batch Num: [213/600] Discriminator Loss: 0.2793, Generator Loss: 3.3787 D(x): 0.9231, D(G(z)): 0.1289 Epoch: [9/20], Batch Num: [214/600] Discriminator Loss: 0.3492, Generator Loss: 3.7912 D(x): 0.8695, D(G(z)): 0.0872 Epoch: [9/20], Batch Num: [215/600] Discriminator Loss: 0.3375, Generator Loss: 4.3490 D(x): 0.8809, D(G(z)): 0.0870 Epoch: [9/20], Batch Num: [216/600] Discriminator Loss: 0.2772, Generator Loss: 4.0804 D(x): 0.8737, D(G(z)): 0.0511 Epoch: [9/20], Batch Num: [217/600] Discriminator Loss: 0.2279, Generator Loss: 3.6589 D(x): 0.9045, D(G(z)): 0.0699 Epoch: [9/20], Batch Num: [218/600] Discriminator Loss: 0.2937, Generator Loss: 3.2570 D(x): 0.8784, D(G(z)): 0.0805 Epoch: [9/20], Batch Num: [219/600] Discriminator Loss: 0.3923, Generator Loss: 3.2752 D(x): 0.9141, D(G(z)): 0.1512 Epoch: [9/20], Batch Num: [220/600] Discriminator Loss: 0.2724, Generator Loss: 3.2006 D(x): 0.9252, D(G(z)): 0.1122 Epoch: [9/20], Batch Num: [221/600] Discriminator Loss: 0.2473, Generator Loss: 3.8759 D(x): 0.9393, D(G(z)): 0.1232 Epoch: [9/20], Batch Num: [222/600] Discriminator Loss: 0.2439, Generator Loss: 4.3889 D(x): 0.9207, D(G(z)): 0.0921 Epoch: [9/20], Batch Num: [223/600] Discriminator Loss: 0.3274, Generator Loss: 4.2000 D(x): 0.8748, D(G(z)): 0.0784 Epoch: [9/20], Batch Num: [224/600] Discriminator Loss: 0.2777, Generator Loss: 3.8280 D(x): 0.8769, D(G(z)): 0.0608 Epoch: [9/20], Batch Num: [225/600] Discriminator Loss: 0.3235, Generator Loss: 3.5058 D(x): 0.9084, D(G(z)): 0.1138 Epoch: [9/20], Batch Num: [226/600] Discriminator Loss: 0.3735, Generator Loss: 3.2777 D(x): 0.9120, D(G(z)): 0.1419 Epoch: [9/20], Batch Num: [227/600] Discriminator Loss: 0.3898, Generator Loss: 4.0296 D(x): 0.9202, D(G(z)): 0.1444 Epoch: [9/20], Batch Num: [228/600] Discriminator Loss: 0.3126, Generator Loss: 4.7868 D(x): 0.9297, D(G(z)): 0.1153 Epoch: [9/20], Batch Num: [229/600] Discriminator Loss: 0.2309, Generator Loss: 5.2423 D(x): 0.9067, D(G(z)): 0.0422 Epoch: [9/20], Batch Num: [230/600] Discriminator Loss: 0.4585, Generator Loss: 4.1948 D(x): 0.8051, D(G(z)): 0.0582 Epoch: [9/20], Batch Num: [231/600] Discriminator Loss: 0.2498, Generator Loss: 3.3190 D(x): 0.9341, D(G(z)): 0.1145 Epoch: [9/20], Batch Num: [232/600] Discriminator Loss: 0.3324, Generator Loss: 2.8692 D(x): 0.9012, D(G(z)): 0.1120 Epoch: [9/20], Batch Num: [233/600] Discriminator Loss: 0.4334, Generator Loss: 3.4823 D(x): 0.9279, D(G(z)): 0.1913 Epoch: [9/20], Batch Num: [234/600] Discriminator Loss: 0.3679, Generator Loss: 3.9451 D(x): 0.8944, D(G(z)): 0.1442 Epoch: [9/20], Batch Num: [235/600] Discriminator Loss: 0.5706, Generator Loss: 4.8734 D(x): 0.8643, D(G(z)): 0.1560 Epoch: [9/20], Batch Num: [236/600] Discriminator Loss: 0.4950, Generator Loss: 3.9699 D(x): 0.8103, D(G(z)): 0.0479 Epoch: [9/20], Batch Num: [237/600] Discriminator Loss: 0.4007, Generator Loss: 3.2788 D(x): 0.8364, D(G(z)): 0.0568 Epoch: [9/20], Batch Num: [238/600] Discriminator Loss: 0.4041, Generator Loss: 2.7569 D(x): 0.8938, D(G(z)): 0.1406 Epoch: [9/20], Batch Num: [239/600] Discriminator Loss: 0.5532, Generator Loss: 2.8718 D(x): 0.9001, D(G(z)): 0.2031 Epoch: [9/20], Batch Num: [240/600] Discriminator Loss: 0.4317, Generator Loss: 3.5358 D(x): 0.8967, D(G(z)): 0.1493 Epoch: [9/20], Batch Num: [241/600] Discriminator Loss: 0.3376, Generator Loss: 3.6767 D(x): 0.9078, D(G(z)): 0.1200 Epoch: [9/20], Batch Num: [242/600] Discriminator Loss: 0.4064, Generator Loss: 3.6220 D(x): 0.8608, D(G(z)): 0.1069 Epoch: [9/20], Batch Num: [243/600] Discriminator Loss: 0.3471, Generator Loss: 3.7534 D(x): 0.8764, D(G(z)): 0.0854 Epoch: [9/20], Batch Num: [244/600] Discriminator Loss: 0.3613, Generator Loss: 3.3420 D(x): 0.8652, D(G(z)): 0.0830 Epoch: [9/20], Batch Num: [245/600] Discriminator Loss: 0.4491, Generator Loss: 3.2593 D(x): 0.8555, D(G(z)): 0.1045 Epoch: [9/20], Batch Num: [246/600] Discriminator Loss: 0.4582, Generator Loss: 2.8293 D(x): 0.8594, D(G(z)): 0.1519 Epoch: [9/20], Batch Num: [247/600] Discriminator Loss: 0.3748, Generator Loss: 2.8413 D(x): 0.9245, D(G(z)): 0.1764 Epoch: [9/20], Batch Num: [248/600] Discriminator Loss: 0.4162, Generator Loss: 3.2412 D(x): 0.9163, D(G(z)): 0.1945 Epoch: [9/20], Batch Num: [249/600] Discriminator Loss: 0.3977, Generator Loss: 4.0782 D(x): 0.8716, D(G(z)): 0.1233 Epoch: [9/20], Batch Num: [250/600] Discriminator Loss: 0.2911, Generator Loss: 4.6551 D(x): 0.8833, D(G(z)): 0.0903 Epoch: [9/20], Batch Num: [251/600] Discriminator Loss: 0.3879, Generator Loss: 3.9615 D(x): 0.8097, D(G(z)): 0.0388 Epoch: [9/20], Batch Num: [252/600] Discriminator Loss: 0.3506, Generator Loss: 3.4907 D(x): 0.8783, D(G(z)): 0.0846 Epoch: [9/20], Batch Num: [253/600] Discriminator Loss: 0.3118, Generator Loss: 2.9197 D(x): 0.9105, D(G(z)): 0.1026 Epoch: [9/20], Batch Num: [254/600] Discriminator Loss: 0.4301, Generator Loss: 3.0323 D(x): 0.8896, D(G(z)): 0.1639 Epoch: [9/20], Batch Num: [255/600] Discriminator Loss: 0.4047, Generator Loss: 3.2710 D(x): 0.9091, D(G(z)): 0.1766 Epoch: [9/20], Batch Num: [256/600] Discriminator Loss: 0.3580, Generator Loss: 3.7418 D(x): 0.8920, D(G(z)): 0.1332 Epoch: [9/20], Batch Num: [257/600] Discriminator Loss: 0.3005, Generator Loss: 3.8976 D(x): 0.8917, D(G(z)): 0.0691 Epoch: [9/20], Batch Num: [258/600] Discriminator Loss: 0.3192, Generator Loss: 4.2418 D(x): 0.9006, D(G(z)): 0.0772 Epoch: [9/20], Batch Num: [259/600] Discriminator Loss: 0.4424, Generator Loss: 3.8618 D(x): 0.8695, D(G(z)): 0.0884 Epoch: [9/20], Batch Num: [260/600] Discriminator Loss: 0.2673, Generator Loss: 3.9080 D(x): 0.9478, D(G(z)): 0.1164 Epoch: [9/20], Batch Num: [261/600] Discriminator Loss: 0.2983, Generator Loss: 4.1425 D(x): 0.9043, D(G(z)): 0.0857 Epoch: [9/20], Batch Num: [262/600] Discriminator Loss: 0.3616, Generator Loss: 4.0380 D(x): 0.8783, D(G(z)): 0.0905 Epoch: [9/20], Batch Num: [263/600] Discriminator Loss: 0.4276, Generator Loss: 3.6107 D(x): 0.8395, D(G(z)): 0.0749 Epoch: [9/20], Batch Num: [264/600] Discriminator Loss: 0.3160, Generator Loss: 2.9027 D(x): 0.9261, D(G(z)): 0.1237 Epoch: [9/20], Batch Num: [265/600] Discriminator Loss: 0.4086, Generator Loss: 3.2825 D(x): 0.9078, D(G(z)): 0.1538 Epoch: [9/20], Batch Num: [266/600] Discriminator Loss: 0.3995, Generator Loss: 4.3840 D(x): 0.9373, D(G(z)): 0.1817 Epoch: [9/20], Batch Num: [267/600] Discriminator Loss: 0.4086, Generator Loss: 4.7297 D(x): 0.8322, D(G(z)): 0.0611 Epoch: [9/20], Batch Num: [268/600] Discriminator Loss: 0.4950, Generator Loss: 4.1450 D(x): 0.8391, D(G(z)): 0.0839 Epoch: [9/20], Batch Num: [269/600] Discriminator Loss: 0.4512, Generator Loss: 2.5072 D(x): 0.8319, D(G(z)): 0.0756 Epoch: [9/20], Batch Num: [270/600] Discriminator Loss: 0.4905, Generator Loss: 2.4351 D(x): 0.9252, D(G(z)): 0.2133 Epoch: [9/20], Batch Num: [271/600] Discriminator Loss: 0.6704, Generator Loss: 3.6996 D(x): 0.9268, D(G(z)): 0.2815 Epoch: [9/20], Batch Num: [272/600] Discriminator Loss: 0.3134, Generator Loss: 4.5786 D(x): 0.9089, D(G(z)): 0.1110 Epoch: [9/20], Batch Num: [273/600] Discriminator Loss: 0.8305, Generator Loss: 4.2707 D(x): 0.7125, D(G(z)): 0.0456 Epoch: [9/20], Batch Num: [274/600] Discriminator Loss: 0.5196, Generator Loss: 2.8882 D(x): 0.8146, D(G(z)): 0.0464 Epoch: [9/20], Batch Num: [275/600] Discriminator Loss: 0.4032, Generator Loss: 2.1805 D(x): 0.8852, D(G(z)): 0.1268 Epoch: [9/20], Batch Num: [276/600] Discriminator Loss: 0.3903, Generator Loss: 2.2335 D(x): 0.9535, D(G(z)): 0.2311 Epoch: [9/20], Batch Num: [277/600] Discriminator Loss: 0.4377, Generator Loss: 2.8914 D(x): 0.9294, D(G(z)): 0.2326 Epoch: [9/20], Batch Num: [278/600] Discriminator Loss: 0.4798, Generator Loss: 3.7551 D(x): 0.8324, D(G(z)): 0.1147 Epoch: [9/20], Batch Num: [279/600] Discriminator Loss: 0.4976, Generator Loss: 3.5531 D(x): 0.8207, D(G(z)): 0.0795 Epoch: [9/20], Batch Num: [280/600] Discriminator Loss: 0.3927, Generator Loss: 3.1770 D(x): 0.8441, D(G(z)): 0.0817 Epoch: [9/20], Batch Num: [281/600] Discriminator Loss: 0.4016, Generator Loss: 2.5587 D(x): 0.8375, D(G(z)): 0.0759 Epoch: [9/20], Batch Num: [282/600] Discriminator Loss: 0.4383, Generator Loss: 2.4461 D(x): 0.8829, D(G(z)): 0.1670 Epoch: [9/20], Batch Num: [283/600] Discriminator Loss: 0.4349, Generator Loss: 2.4892 D(x): 0.8951, D(G(z)): 0.1760 Epoch: [9/20], Batch Num: [284/600] Discriminator Loss: 0.4310, Generator Loss: 2.9526 D(x): 0.8948, D(G(z)): 0.1813 Epoch: [9/20], Batch Num: [285/600] Discriminator Loss: 0.3466, Generator Loss: 3.2879 D(x): 0.8902, D(G(z)): 0.1307 Epoch: [9/20], Batch Num: [286/600] Discriminator Loss: 0.5472, Generator Loss: 3.3911 D(x): 0.8221, D(G(z)): 0.1001 Epoch: [9/20], Batch Num: [287/600] Discriminator Loss: 0.4509, Generator Loss: 3.0256 D(x): 0.8354, D(G(z)): 0.0802 Epoch: [9/20], Batch Num: [288/600] Discriminator Loss: 0.4200, Generator Loss: 2.6538 D(x): 0.8566, D(G(z)): 0.1315 Epoch: [9/20], Batch Num: [289/600] Discriminator Loss: 0.3395, Generator Loss: 2.7283 D(x): 0.9205, D(G(z)): 0.1442 Epoch: [9/20], Batch Num: [290/600] Discriminator Loss: 0.4839, Generator Loss: 2.7340 D(x): 0.8725, D(G(z)): 0.1728 Epoch: [9/20], Batch Num: [291/600] Discriminator Loss: 0.3132, Generator Loss: 2.9634 D(x): 0.9038, D(G(z)): 0.1429 Epoch: [9/20], Batch Num: [292/600] Discriminator Loss: 0.2993, Generator Loss: 3.0958 D(x): 0.8942, D(G(z)): 0.1029 Epoch: [9/20], Batch Num: [293/600] Discriminator Loss: 0.3210, Generator Loss: 3.6999 D(x): 0.8924, D(G(z)): 0.1184 Epoch: [9/20], Batch Num: [294/600] Discriminator Loss: 0.4909, Generator Loss: 3.1671 D(x): 0.8195, D(G(z)): 0.0862 Epoch: [9/20], Batch Num: [295/600] Discriminator Loss: 0.4579, Generator Loss: 2.7299 D(x): 0.8402, D(G(z)): 0.0978 Epoch: [9/20], Batch Num: [296/600] Discriminator Loss: 0.3822, Generator Loss: 2.8295 D(x): 0.9362, D(G(z)): 0.1624 Epoch: [9/20], Batch Num: [297/600] Discriminator Loss: 0.3258, Generator Loss: 3.2637 D(x): 0.9053, D(G(z)): 0.1417 Epoch: [9/20], Batch Num: [298/600] Discriminator Loss: 0.3533, Generator Loss: 3.4891 D(x): 0.9315, D(G(z)): 0.1439 Epoch: [9/20], Batch Num: [299/600] Discriminator Loss: 0.4552, Generator Loss: 3.9649 D(x): 0.8509, D(G(z)): 0.1021 Epoch: 9, Batch Num: [300/600]
Epoch: [9/20], Batch Num: [300/600] Discriminator Loss: 0.3940, Generator Loss: 4.2236 D(x): 0.8761, D(G(z)): 0.0762 Epoch: [9/20], Batch Num: [301/600] Discriminator Loss: 0.3604, Generator Loss: 3.9288 D(x): 0.8839, D(G(z)): 0.0767 Epoch: [9/20], Batch Num: [302/600] Discriminator Loss: 0.4135, Generator Loss: 2.8205 D(x): 0.8390, D(G(z)): 0.0857 Epoch: [9/20], Batch Num: [303/600] Discriminator Loss: 0.3193, Generator Loss: 2.7999 D(x): 0.9344, D(G(z)): 0.1566 Epoch: [9/20], Batch Num: [304/600] Discriminator Loss: 0.3513, Generator Loss: 3.4015 D(x): 0.9371, D(G(z)): 0.1703 Epoch: [9/20], Batch Num: [305/600] Discriminator Loss: 0.3914, Generator Loss: 3.4090 D(x): 0.8613, D(G(z)): 0.0805 Epoch: [9/20], Batch Num: [306/600] Discriminator Loss: 0.3141, Generator Loss: 3.4729 D(x): 0.9102, D(G(z)): 0.0999 Epoch: [9/20], Batch Num: [307/600] Discriminator Loss: 0.3414, Generator Loss: 3.3746 D(x): 0.8789, D(G(z)): 0.0792 Epoch: [9/20], Batch Num: [308/600] Discriminator Loss: 0.2451, Generator Loss: 3.5599 D(x): 0.9110, D(G(z)): 0.0845 Epoch: [9/20], Batch Num: [309/600] Discriminator Loss: 0.6341, Generator Loss: 3.2061 D(x): 0.8323, D(G(z)): 0.1127 Epoch: [9/20], Batch Num: [310/600] Discriminator Loss: 0.3747, Generator Loss: 3.0902 D(x): 0.9058, D(G(z)): 0.1448 Epoch: [9/20], Batch Num: [311/600] Discriminator Loss: 0.2998, Generator Loss: 2.9569 D(x): 0.9005, D(G(z)): 0.0833 Epoch: [9/20], Batch Num: [312/600] Discriminator Loss: 0.3796, Generator Loss: 3.3445 D(x): 0.9061, D(G(z)): 0.1250 Epoch: [9/20], Batch Num: [313/600] Discriminator Loss: 0.4504, Generator Loss: 3.1998 D(x): 0.8633, D(G(z)): 0.1257 Epoch: [9/20], Batch Num: [314/600] Discriminator Loss: 0.3209, Generator Loss: 3.0867 D(x): 0.8860, D(G(z)): 0.0983 Epoch: [9/20], Batch Num: [315/600] Discriminator Loss: 0.3664, Generator Loss: 2.8921 D(x): 0.8905, D(G(z)): 0.1179 Epoch: [9/20], Batch Num: [316/600] Discriminator Loss: 0.3689, Generator Loss: 3.3628 D(x): 0.9079, D(G(z)): 0.1377 Epoch: [9/20], Batch Num: [317/600] Discriminator Loss: 0.3229, Generator Loss: 3.6226 D(x): 0.9058, D(G(z)): 0.0903 Epoch: [9/20], Batch Num: [318/600] Discriminator Loss: 0.5354, Generator Loss: 3.5207 D(x): 0.8184, D(G(z)): 0.0769 Epoch: [9/20], Batch Num: [319/600] Discriminator Loss: 0.3028, Generator Loss: 3.1040 D(x): 0.9057, D(G(z)): 0.0782 Epoch: [9/20], Batch Num: [320/600] Discriminator Loss: 0.3062, Generator Loss: 2.9200 D(x): 0.9134, D(G(z)): 0.1268 Epoch: [9/20], Batch Num: [321/600] Discriminator Loss: 0.4567, Generator Loss: 3.2950 D(x): 0.9084, D(G(z)): 0.1704 Epoch: [9/20], Batch Num: [322/600] Discriminator Loss: 0.3821, Generator Loss: 4.0077 D(x): 0.9034, D(G(z)): 0.1168 Epoch: [9/20], Batch Num: [323/600] Discriminator Loss: 0.2437, Generator Loss: 4.3267 D(x): 0.9031, D(G(z)): 0.0516 Epoch: [9/20], Batch Num: [324/600] Discriminator Loss: 0.2845, Generator Loss: 4.1574 D(x): 0.8890, D(G(z)): 0.0614 Epoch: [9/20], Batch Num: [325/600] Discriminator Loss: 0.4140, Generator Loss: 3.4510 D(x): 0.8523, D(G(z)): 0.0656 Epoch: [9/20], Batch Num: [326/600] Discriminator Loss: 0.3004, Generator Loss: 2.9256 D(x): 0.9235, D(G(z)): 0.0915 Epoch: [9/20], Batch Num: [327/600] Discriminator Loss: 0.4806, Generator Loss: 2.6905 D(x): 0.9140, D(G(z)): 0.1777 Epoch: [9/20], Batch Num: [328/600] Discriminator Loss: 0.3444, Generator Loss: 3.8350 D(x): 0.9263, D(G(z)): 0.1425 Epoch: [9/20], Batch Num: [329/600] Discriminator Loss: 0.3668, Generator Loss: 4.4601 D(x): 0.8836, D(G(z)): 0.1045 Epoch: [9/20], Batch Num: [330/600] Discriminator Loss: 0.4307, Generator Loss: 4.4475 D(x): 0.8434, D(G(z)): 0.0538 Epoch: [9/20], Batch Num: [331/600] Discriminator Loss: 0.2564, Generator Loss: 3.9240 D(x): 0.8824, D(G(z)): 0.0471 Epoch: [9/20], Batch Num: [332/600] Discriminator Loss: 0.2273, Generator Loss: 3.0396 D(x): 0.9162, D(G(z)): 0.0814 Epoch: [9/20], Batch Num: [333/600] Discriminator Loss: 0.2798, Generator Loss: 2.8025 D(x): 0.9480, D(G(z)): 0.1367 Epoch: [9/20], Batch Num: [334/600] Discriminator Loss: 0.3593, Generator Loss: 3.2328 D(x): 0.9260, D(G(z)): 0.1490 Epoch: [9/20], Batch Num: [335/600] Discriminator Loss: 0.2959, Generator Loss: 4.1394 D(x): 0.9048, D(G(z)): 0.0922 Epoch: [9/20], Batch Num: [336/600] Discriminator Loss: 0.2720, Generator Loss: 4.0725 D(x): 0.8879, D(G(z)): 0.0474 Epoch: [9/20], Batch Num: [337/600] Discriminator Loss: 0.2812, Generator Loss: 4.0756 D(x): 0.9252, D(G(z)): 0.0781 Epoch: [9/20], Batch Num: [338/600] Discriminator Loss: 0.3151, Generator Loss: 3.8728 D(x): 0.8891, D(G(z)): 0.0717 Epoch: [9/20], Batch Num: [339/600] Discriminator Loss: 0.2349, Generator Loss: 3.4670 D(x): 0.9294, D(G(z)): 0.0830 Epoch: [9/20], Batch Num: [340/600] Discriminator Loss: 0.2991, Generator Loss: 3.1832 D(x): 0.9136, D(G(z)): 0.0998 Epoch: [9/20], Batch Num: [341/600] Discriminator Loss: 0.2807, Generator Loss: 3.6289 D(x): 0.9440, D(G(z)): 0.1152 Epoch: [9/20], Batch Num: [342/600] Discriminator Loss: 0.2374, Generator Loss: 4.1029 D(x): 0.9596, D(G(z)): 0.1222 Epoch: [9/20], Batch Num: [343/600] Discriminator Loss: 0.3532, Generator Loss: 4.2537 D(x): 0.8520, D(G(z)): 0.0539 Epoch: [9/20], Batch Num: [344/600] Discriminator Loss: 0.4009, Generator Loss: 3.5912 D(x): 0.8685, D(G(z)): 0.0734 Epoch: [9/20], Batch Num: [345/600] Discriminator Loss: 0.2852, Generator Loss: 3.3926 D(x): 0.9211, D(G(z)): 0.1088 Epoch: [9/20], Batch Num: [346/600] Discriminator Loss: 0.3826, Generator Loss: 3.2305 D(x): 0.9108, D(G(z)): 0.1390 Epoch: [9/20], Batch Num: [347/600] Discriminator Loss: 0.2424, Generator Loss: 3.5894 D(x): 0.9214, D(G(z)): 0.1010 Epoch: [9/20], Batch Num: [348/600] Discriminator Loss: 0.3725, Generator Loss: 4.3892 D(x): 0.8942, D(G(z)): 0.1172 Epoch: [9/20], Batch Num: [349/600] Discriminator Loss: 0.4120, Generator Loss: 4.2837 D(x): 0.8500, D(G(z)): 0.0661 Epoch: [9/20], Batch Num: [350/600] Discriminator Loss: 0.2807, Generator Loss: 3.6445 D(x): 0.8972, D(G(z)): 0.0703 Epoch: [9/20], Batch Num: [351/600] Discriminator Loss: 0.4526, Generator Loss: 2.8013 D(x): 0.8593, D(G(z)): 0.1007 Epoch: [9/20], Batch Num: [352/600] Discriminator Loss: 0.4954, Generator Loss: 3.1117 D(x): 0.9149, D(G(z)): 0.2091 Epoch: [9/20], Batch Num: [353/600] Discriminator Loss: 0.3856, Generator Loss: 4.1029 D(x): 0.9208, D(G(z)): 0.1429 Epoch: [9/20], Batch Num: [354/600] Discriminator Loss: 0.5517, Generator Loss: 4.0907 D(x): 0.8474, D(G(z)): 0.0805 Epoch: [9/20], Batch Num: [355/600] Discriminator Loss: 0.5379, Generator Loss: 3.2600 D(x): 0.8123, D(G(z)): 0.0840 Epoch: [9/20], Batch Num: [356/600] Discriminator Loss: 0.3724, Generator Loss: 2.3194 D(x): 0.8603, D(G(z)): 0.0844 Epoch: [9/20], Batch Num: [357/600] Discriminator Loss: 0.5923, Generator Loss: 2.8660 D(x): 0.9337, D(G(z)): 0.2283 Epoch: [9/20], Batch Num: [358/600] Discriminator Loss: 0.5098, Generator Loss: 3.5081 D(x): 0.8973, D(G(z)): 0.1863 Epoch: [9/20], Batch Num: [359/600] Discriminator Loss: 0.3666, Generator Loss: 4.4417 D(x): 0.8730, D(G(z)): 0.0979 Epoch: [9/20], Batch Num: [360/600] Discriminator Loss: 0.6540, Generator Loss: 3.7660 D(x): 0.7876, D(G(z)): 0.0899 Epoch: [9/20], Batch Num: [361/600] Discriminator Loss: 0.4306, Generator Loss: 3.0351 D(x): 0.8616, D(G(z)): 0.1047 Epoch: [9/20], Batch Num: [362/600] Discriminator Loss: 0.4405, Generator Loss: 2.8150 D(x): 0.8659, D(G(z)): 0.1133 Epoch: [9/20], Batch Num: [363/600] Discriminator Loss: 0.3604, Generator Loss: 2.4183 D(x): 0.8945, D(G(z)): 0.1433 Epoch: [9/20], Batch Num: [364/600] Discriminator Loss: 0.3809, Generator Loss: 2.9028 D(x): 0.9316, D(G(z)): 0.1775 Epoch: [9/20], Batch Num: [365/600] Discriminator Loss: 0.3295, Generator Loss: 3.1562 D(x): 0.9161, D(G(z)): 0.1311 Epoch: [9/20], Batch Num: [366/600] Discriminator Loss: 0.5111, Generator Loss: 3.5836 D(x): 0.8556, D(G(z)): 0.1166 Epoch: [9/20], Batch Num: [367/600] Discriminator Loss: 0.5622, Generator Loss: 3.3172 D(x): 0.8105, D(G(z)): 0.0557 Epoch: [9/20], Batch Num: [368/600] Discriminator Loss: 0.3644, Generator Loss: 3.0951 D(x): 0.8900, D(G(z)): 0.0940 Epoch: [9/20], Batch Num: [369/600] Discriminator Loss: 0.4768, Generator Loss: 2.8859 D(x): 0.8885, D(G(z)): 0.1523 Epoch: [9/20], Batch Num: [370/600] Discriminator Loss: 0.4357, Generator Loss: 2.5296 D(x): 0.8700, D(G(z)): 0.1185 Epoch: [9/20], Batch Num: [371/600] Discriminator Loss: 0.4178, Generator Loss: 2.7144 D(x): 0.8974, D(G(z)): 0.1620 Epoch: [9/20], Batch Num: [372/600] Discriminator Loss: 0.4323, Generator Loss: 3.1514 D(x): 0.8830, D(G(z)): 0.1464 Epoch: [9/20], Batch Num: [373/600] Discriminator Loss: 0.3578, Generator Loss: 3.5458 D(x): 0.8930, D(G(z)): 0.0958 Epoch: [9/20], Batch Num: [374/600] Discriminator Loss: 0.5628, Generator Loss: 3.1047 D(x): 0.7978, D(G(z)): 0.1074 Epoch: [9/20], Batch Num: [375/600] Discriminator Loss: 0.5734, Generator Loss: 2.8812 D(x): 0.8093, D(G(z)): 0.1295 Epoch: [9/20], Batch Num: [376/600] Discriminator Loss: 0.3801, Generator Loss: 2.2483 D(x): 0.8697, D(G(z)): 0.1275 Epoch: [9/20], Batch Num: [377/600] Discriminator Loss: 0.5393, Generator Loss: 2.1116 D(x): 0.9105, D(G(z)): 0.2220 Epoch: [9/20], Batch Num: [378/600] Discriminator Loss: 0.3341, Generator Loss: 3.1205 D(x): 0.9165, D(G(z)): 0.1470 Epoch: [9/20], Batch Num: [379/600] Discriminator Loss: 0.4679, Generator Loss: 3.2771 D(x): 0.8345, D(G(z)): 0.1280 Epoch: [9/20], Batch Num: [380/600] Discriminator Loss: 0.4078, Generator Loss: 3.3425 D(x): 0.8567, D(G(z)): 0.1117 Epoch: [9/20], Batch Num: [381/600] Discriminator Loss: 0.3797, Generator Loss: 3.0387 D(x): 0.8764, D(G(z)): 0.1149 Epoch: [9/20], Batch Num: [382/600] Discriminator Loss: 0.4590, Generator Loss: 2.6623 D(x): 0.8187, D(G(z)): 0.1026 Epoch: [9/20], Batch Num: [383/600] Discriminator Loss: 0.5074, Generator Loss: 2.4495 D(x): 0.8645, D(G(z)): 0.1737 Epoch: [9/20], Batch Num: [384/600] Discriminator Loss: 0.5114, Generator Loss: 2.4646 D(x): 0.8607, D(G(z)): 0.1875 Epoch: [9/20], Batch Num: [385/600] Discriminator Loss: 0.4104, Generator Loss: 2.8380 D(x): 0.8965, D(G(z)): 0.1751 Epoch: [9/20], Batch Num: [386/600] Discriminator Loss: 0.4240, Generator Loss: 2.6846 D(x): 0.8481, D(G(z)): 0.1281 Epoch: [9/20], Batch Num: [387/600] Discriminator Loss: 0.4439, Generator Loss: 2.9160 D(x): 0.8740, D(G(z)): 0.1291 Epoch: [9/20], Batch Num: [388/600] Discriminator Loss: 0.4867, Generator Loss: 2.7797 D(x): 0.8344, D(G(z)): 0.1269 Epoch: [9/20], Batch Num: [389/600] Discriminator Loss: 0.3474, Generator Loss: 2.5952 D(x): 0.8665, D(G(z)): 0.0942 Epoch: [9/20], Batch Num: [390/600] Discriminator Loss: 0.5040, Generator Loss: 3.0012 D(x): 0.8702, D(G(z)): 0.1724 Epoch: [9/20], Batch Num: [391/600] Discriminator Loss: 0.4465, Generator Loss: 2.7617 D(x): 0.8668, D(G(z)): 0.1611 Epoch: [9/20], Batch Num: [392/600] Discriminator Loss: 0.3930, Generator Loss: 2.6423 D(x): 0.8606, D(G(z)): 0.1206 Epoch: [9/20], Batch Num: [393/600] Discriminator Loss: 0.5158, Generator Loss: 2.9115 D(x): 0.8650, D(G(z)): 0.1633 Epoch: [9/20], Batch Num: [394/600] Discriminator Loss: 0.4464, Generator Loss: 3.3192 D(x): 0.8774, D(G(z)): 0.1561 Epoch: [9/20], Batch Num: [395/600] Discriminator Loss: 0.4983, Generator Loss: 3.4180 D(x): 0.8379, D(G(z)): 0.1176 Epoch: [9/20], Batch Num: [396/600] Discriminator Loss: 0.3722, Generator Loss: 3.6555 D(x): 0.8877, D(G(z)): 0.1044 Epoch: [9/20], Batch Num: [397/600] Discriminator Loss: 0.3974, Generator Loss: 3.5213 D(x): 0.9006, D(G(z)): 0.1068 Epoch: [9/20], Batch Num: [398/600] Discriminator Loss: 0.3944, Generator Loss: 3.3712 D(x): 0.8553, D(G(z)): 0.0988 Epoch: [9/20], Batch Num: [399/600] Discriminator Loss: 0.4171, Generator Loss: 3.0218 D(x): 0.8657, D(G(z)): 0.1002 Epoch: 9, Batch Num: [400/600]
Epoch: [9/20], Batch Num: [400/600] Discriminator Loss: 0.3512, Generator Loss: 3.0446 D(x): 0.9121, D(G(z)): 0.1480 Epoch: [9/20], Batch Num: [401/600] Discriminator Loss: 0.3768, Generator Loss: 3.0711 D(x): 0.8966, D(G(z)): 0.1479 Epoch: [9/20], Batch Num: [402/600] Discriminator Loss: 0.3988, Generator Loss: 2.9503 D(x): 0.8516, D(G(z)): 0.0889 Epoch: [9/20], Batch Num: [403/600] Discriminator Loss: 0.3831, Generator Loss: 2.9522 D(x): 0.8789, D(G(z)): 0.1188 Epoch: [9/20], Batch Num: [404/600] Discriminator Loss: 0.4854, Generator Loss: 3.3627 D(x): 0.9357, D(G(z)): 0.2336 Epoch: [9/20], Batch Num: [405/600] Discriminator Loss: 0.3403, Generator Loss: 4.4308 D(x): 0.8992, D(G(z)): 0.0970 Epoch: [9/20], Batch Num: [406/600] Discriminator Loss: 0.4626, Generator Loss: 3.9880 D(x): 0.8117, D(G(z)): 0.0735 Epoch: [9/20], Batch Num: [407/600] Discriminator Loss: 0.5454, Generator Loss: 3.1016 D(x): 0.8238, D(G(z)): 0.0777 Epoch: [9/20], Batch Num: [408/600] Discriminator Loss: 0.3913, Generator Loss: 2.5139 D(x): 0.8559, D(G(z)): 0.1037 Epoch: [9/20], Batch Num: [409/600] Discriminator Loss: 0.5707, Generator Loss: 2.2503 D(x): 0.8674, D(G(z)): 0.1882 Epoch: [9/20], Batch Num: [410/600] Discriminator Loss: 0.6224, Generator Loss: 2.7191 D(x): 0.9045, D(G(z)): 0.2754 Epoch: [9/20], Batch Num: [411/600] Discriminator Loss: 0.4595, Generator Loss: 3.5632 D(x): 0.8731, D(G(z)): 0.1616 Epoch: [9/20], Batch Num: [412/600] Discriminator Loss: 0.6204, Generator Loss: 3.9286 D(x): 0.7819, D(G(z)): 0.1036 Epoch: [9/20], Batch Num: [413/600] Discriminator Loss: 0.5707, Generator Loss: 3.3223 D(x): 0.7847, D(G(z)): 0.0886 Epoch: [9/20], Batch Num: [414/600] Discriminator Loss: 0.3704, Generator Loss: 2.9035 D(x): 0.8554, D(G(z)): 0.0942 Epoch: [9/20], Batch Num: [415/600] Discriminator Loss: 0.4054, Generator Loss: 2.2404 D(x): 0.8568, D(G(z)): 0.1426 Epoch: [9/20], Batch Num: [416/600] Discriminator Loss: 0.5144, Generator Loss: 2.5979 D(x): 0.9131, D(G(z)): 0.2206 Epoch: [9/20], Batch Num: [417/600] Discriminator Loss: 0.5231, Generator Loss: 3.1133 D(x): 0.8646, D(G(z)): 0.1611 Epoch: [9/20], Batch Num: [418/600] Discriminator Loss: 0.3747, Generator Loss: 3.5316 D(x): 0.8796, D(G(z)): 0.1445 Epoch: [9/20], Batch Num: [419/600] Discriminator Loss: 0.3767, Generator Loss: 3.3722 D(x): 0.8407, D(G(z)): 0.0726 Epoch: [9/20], Batch Num: [420/600] Discriminator Loss: 0.4166, Generator Loss: 3.5080 D(x): 0.8566, D(G(z)): 0.0727 Epoch: [9/20], Batch Num: [421/600] Discriminator Loss: 0.4332, Generator Loss: 2.5393 D(x): 0.8381, D(G(z)): 0.0978 Epoch: [9/20], Batch Num: [422/600] Discriminator Loss: 0.4577, Generator Loss: 2.3406 D(x): 0.8491, D(G(z)): 0.1519 Epoch: [9/20], Batch Num: [423/600] Discriminator Loss: 0.4189, Generator Loss: 2.4570 D(x): 0.9509, D(G(z)): 0.2312 Epoch: [9/20], Batch Num: [424/600] Discriminator Loss: 0.3922, Generator Loss: 3.1061 D(x): 0.9047, D(G(z)): 0.1784 Epoch: [9/20], Batch Num: [425/600] Discriminator Loss: 0.4333, Generator Loss: 3.6558 D(x): 0.8480, D(G(z)): 0.1231 Epoch: [9/20], Batch Num: [426/600] Discriminator Loss: 0.3679, Generator Loss: 3.8960 D(x): 0.8561, D(G(z)): 0.0738 Epoch: [9/20], Batch Num: [427/600] Discriminator Loss: 0.3756, Generator Loss: 3.2699 D(x): 0.8646, D(G(z)): 0.0805 Epoch: [9/20], Batch Num: [428/600] Discriminator Loss: 0.3909, Generator Loss: 2.9065 D(x): 0.8605, D(G(z)): 0.0954 Epoch: [9/20], Batch Num: [429/600] Discriminator Loss: 0.3302, Generator Loss: 2.4671 D(x): 0.9064, D(G(z)): 0.1058 Epoch: [9/20], Batch Num: [430/600] Discriminator Loss: 0.4725, Generator Loss: 2.8321 D(x): 0.9265, D(G(z)): 0.2115 Epoch: [9/20], Batch Num: [431/600] Discriminator Loss: 0.2419, Generator Loss: 3.3646 D(x): 0.9394, D(G(z)): 0.1121 Epoch: [9/20], Batch Num: [432/600] Discriminator Loss: 0.2653, Generator Loss: 4.0459 D(x): 0.9307, D(G(z)): 0.1094 Epoch: [9/20], Batch Num: [433/600] Discriminator Loss: 0.4885, Generator Loss: 3.7731 D(x): 0.8153, D(G(z)): 0.0519 Epoch: [9/20], Batch Num: [434/600] Discriminator Loss: 0.4256, Generator Loss: 3.4922 D(x): 0.8343, D(G(z)): 0.0658 Epoch: [9/20], Batch Num: [435/600] Discriminator Loss: 0.2476, Generator Loss: 2.5211 D(x): 0.9050, D(G(z)): 0.0893 Epoch: [9/20], Batch Num: [436/600] Discriminator Loss: 0.3648, Generator Loss: 2.3974 D(x): 0.9142, D(G(z)): 0.1713 Epoch: [9/20], Batch Num: [437/600] Discriminator Loss: 0.3630, Generator Loss: 2.6266 D(x): 0.9108, D(G(z)): 0.1693 Epoch: [9/20], Batch Num: [438/600] Discriminator Loss: 0.3937, Generator Loss: 3.2093 D(x): 0.9162, D(G(z)): 0.1697 Epoch: [9/20], Batch Num: [439/600] Discriminator Loss: 0.4202, Generator Loss: 3.8996 D(x): 0.8488, D(G(z)): 0.0992 Epoch: [9/20], Batch Num: [440/600] Discriminator Loss: 0.2890, Generator Loss: 3.8559 D(x): 0.8853, D(G(z)): 0.0723 Epoch: [9/20], Batch Num: [441/600] Discriminator Loss: 0.3291, Generator Loss: 4.0245 D(x): 0.8679, D(G(z)): 0.0702 Epoch: [9/20], Batch Num: [442/600] Discriminator Loss: 0.4425, Generator Loss: 3.2999 D(x): 0.8527, D(G(z)): 0.1032 Epoch: [9/20], Batch Num: [443/600] Discriminator Loss: 0.4487, Generator Loss: 2.9163 D(x): 0.8545, D(G(z)): 0.1154 Epoch: [9/20], Batch Num: [444/600] Discriminator Loss: 0.3600, Generator Loss: 2.7695 D(x): 0.9473, D(G(z)): 0.1632 Epoch: [9/20], Batch Num: [445/600] Discriminator Loss: 0.4721, Generator Loss: 2.8108 D(x): 0.8708, D(G(z)): 0.1460 Epoch: [9/20], Batch Num: [446/600] Discriminator Loss: 0.3668, Generator Loss: 3.6687 D(x): 0.9111, D(G(z)): 0.1341 Epoch: [9/20], Batch Num: [447/600] Discriminator Loss: 0.4070, Generator Loss: 3.7128 D(x): 0.8748, D(G(z)): 0.1160 Epoch: [9/20], Batch Num: [448/600] Discriminator Loss: 0.6196, Generator Loss: 3.6680 D(x): 0.8088, D(G(z)): 0.1100 Epoch: [9/20], Batch Num: [449/600] Discriminator Loss: 0.3870, Generator Loss: 3.1416 D(x): 0.8696, D(G(z)): 0.1001 Epoch: [9/20], Batch Num: [450/600] Discriminator Loss: 0.3186, Generator Loss: 2.7937 D(x): 0.9110, D(G(z)): 0.1259 Epoch: [9/20], Batch Num: [451/600] Discriminator Loss: 0.4837, Generator Loss: 2.5884 D(x): 0.8769, D(G(z)): 0.1668 Epoch: [9/20], Batch Num: [452/600] Discriminator Loss: 0.3527, Generator Loss: 3.3642 D(x): 0.9041, D(G(z)): 0.1398 Epoch: [9/20], Batch Num: [453/600] Discriminator Loss: 0.2894, Generator Loss: 3.7353 D(x): 0.9207, D(G(z)): 0.1172 Epoch: [9/20], Batch Num: [454/600] Discriminator Loss: 0.4172, Generator Loss: 3.9723 D(x): 0.8476, D(G(z)): 0.0972 Epoch: [9/20], Batch Num: [455/600] Discriminator Loss: 0.5847, Generator Loss: 3.0342 D(x): 0.8114, D(G(z)): 0.1243 Epoch: [9/20], Batch Num: [456/600] Discriminator Loss: 0.6246, Generator Loss: 2.6500 D(x): 0.8347, D(G(z)): 0.1528 Epoch: [9/20], Batch Num: [457/600] Discriminator Loss: 0.4032, Generator Loss: 2.4542 D(x): 0.8806, D(G(z)): 0.1623 Epoch: [9/20], Batch Num: [458/600] Discriminator Loss: 0.5115, Generator Loss: 2.9223 D(x): 0.8851, D(G(z)): 0.1756 Epoch: [9/20], Batch Num: [459/600] Discriminator Loss: 0.4171, Generator Loss: 3.2867 D(x): 0.8825, D(G(z)): 0.1451 Epoch: [9/20], Batch Num: [460/600] Discriminator Loss: 0.3849, Generator Loss: 3.4031 D(x): 0.8882, D(G(z)): 0.0990 Epoch: [9/20], Batch Num: [461/600] Discriminator Loss: 0.4084, Generator Loss: 3.1536 D(x): 0.8764, D(G(z)): 0.1001 Epoch: [9/20], Batch Num: [462/600] Discriminator Loss: 0.3409, Generator Loss: 2.9161 D(x): 0.8727, D(G(z)): 0.0869 Epoch: [9/20], Batch Num: [463/600] Discriminator Loss: 0.4784, Generator Loss: 2.8049 D(x): 0.8782, D(G(z)): 0.1249 Epoch: [9/20], Batch Num: [464/600] Discriminator Loss: 0.4524, Generator Loss: 2.5045 D(x): 0.8948, D(G(z)): 0.1339 Epoch: [9/20], Batch Num: [465/600] Discriminator Loss: 0.3758, Generator Loss: 2.7239 D(x): 0.8958, D(G(z)): 0.1400 Epoch: [9/20], Batch Num: [466/600] Discriminator Loss: 0.4796, Generator Loss: 3.0505 D(x): 0.8846, D(G(z)): 0.1750 Epoch: [9/20], Batch Num: [467/600] Discriminator Loss: 0.3932, Generator Loss: 2.9004 D(x): 0.8935, D(G(z)): 0.1308 Epoch: [9/20], Batch Num: [468/600] Discriminator Loss: 0.3973, Generator Loss: 3.0754 D(x): 0.8662, D(G(z)): 0.0908 Epoch: [9/20], Batch Num: [469/600] Discriminator Loss: 0.3628, Generator Loss: 3.3175 D(x): 0.8877, D(G(z)): 0.1046 Epoch: [9/20], Batch Num: [470/600] Discriminator Loss: 0.3055, Generator Loss: 3.4986 D(x): 0.9051, D(G(z)): 0.1045 Epoch: [9/20], Batch Num: [471/600] Discriminator Loss: 0.4038, Generator Loss: 3.1067 D(x): 0.8632, D(G(z)): 0.1009 Epoch: [9/20], Batch Num: [472/600] Discriminator Loss: 0.4735, Generator Loss: 2.9977 D(x): 0.8345, D(G(z)): 0.1076 Epoch: [9/20], Batch Num: [473/600] Discriminator Loss: 0.3751, Generator Loss: 2.6044 D(x): 0.9022, D(G(z)): 0.1487 Epoch: [9/20], Batch Num: [474/600] Discriminator Loss: 0.3940, Generator Loss: 2.3020 D(x): 0.8905, D(G(z)): 0.1408 Epoch: [9/20], Batch Num: [475/600] Discriminator Loss: 0.4478, Generator Loss: 3.4196 D(x): 0.9394, D(G(z)): 0.1947 Epoch: [9/20], Batch Num: [476/600] Discriminator Loss: 0.4270, Generator Loss: 4.0033 D(x): 0.8683, D(G(z)): 0.0911 Epoch: [9/20], Batch Num: [477/600] Discriminator Loss: 0.3302, Generator Loss: 3.9053 D(x): 0.8578, D(G(z)): 0.0564 Epoch: [9/20], Batch Num: [478/600] Discriminator Loss: 0.4585, Generator Loss: 2.8636 D(x): 0.8525, D(G(z)): 0.0697 Epoch: [9/20], Batch Num: [479/600] Discriminator Loss: 0.3283, Generator Loss: 2.8630 D(x): 0.8929, D(G(z)): 0.1064 Epoch: [9/20], Batch Num: [480/600] Discriminator Loss: 0.3658, Generator Loss: 2.2749 D(x): 0.9123, D(G(z)): 0.1432 Epoch: [9/20], Batch Num: [481/600] Discriminator Loss: 0.4208, Generator Loss: 2.4804 D(x): 0.8958, D(G(z)): 0.1659 Epoch: [9/20], Batch Num: [482/600] Discriminator Loss: 0.3836, Generator Loss: 3.1155 D(x): 0.9243, D(G(z)): 0.1790 Epoch: [9/20], Batch Num: [483/600] Discriminator Loss: 0.3692, Generator Loss: 3.5584 D(x): 0.8851, D(G(z)): 0.0849 Epoch: [9/20], Batch Num: [484/600] Discriminator Loss: 0.3148, Generator Loss: 3.3026 D(x): 0.8790, D(G(z)): 0.0623 Epoch: [9/20], Batch Num: [485/600] Discriminator Loss: 0.3986, Generator Loss: 2.8186 D(x): 0.8317, D(G(z)): 0.0779 Epoch: [9/20], Batch Num: [486/600] Discriminator Loss: 0.3901, Generator Loss: 2.3892 D(x): 0.8925, D(G(z)): 0.1216 Epoch: [9/20], Batch Num: [487/600] Discriminator Loss: 0.4309, Generator Loss: 2.7970 D(x): 0.9158, D(G(z)): 0.1592 Epoch: [9/20], Batch Num: [488/600] Discriminator Loss: 0.3832, Generator Loss: 2.9367 D(x): 0.9208, D(G(z)): 0.1443 Epoch: [9/20], Batch Num: [489/600] Discriminator Loss: 0.6131, Generator Loss: 3.1980 D(x): 0.8107, D(G(z)): 0.1360 Epoch: [9/20], Batch Num: [490/600] Discriminator Loss: 0.4112, Generator Loss: 3.2142 D(x): 0.8865, D(G(z)): 0.1118 Epoch: [9/20], Batch Num: [491/600] Discriminator Loss: 0.3533, Generator Loss: 3.4251 D(x): 0.8689, D(G(z)): 0.0756 Epoch: [9/20], Batch Num: [492/600] Discriminator Loss: 0.4985, Generator Loss: 2.7882 D(x): 0.8488, D(G(z)): 0.1016 Epoch: [9/20], Batch Num: [493/600] Discriminator Loss: 0.4044, Generator Loss: 2.4504 D(x): 0.9042, D(G(z)): 0.1575 Epoch: [9/20], Batch Num: [494/600] Discriminator Loss: 0.4810, Generator Loss: 2.4644 D(x): 0.8882, D(G(z)): 0.1867 Epoch: [9/20], Batch Num: [495/600] Discriminator Loss: 0.4571, Generator Loss: 2.9435 D(x): 0.9154, D(G(z)): 0.2016 Epoch: [9/20], Batch Num: [496/600] Discriminator Loss: 0.4717, Generator Loss: 3.5284 D(x): 0.8309, D(G(z)): 0.1103 Epoch: [9/20], Batch Num: [497/600] Discriminator Loss: 0.3586, Generator Loss: 3.3775 D(x): 0.8537, D(G(z)): 0.0825 Epoch: [9/20], Batch Num: [498/600] Discriminator Loss: 0.4369, Generator Loss: 2.9083 D(x): 0.8384, D(G(z)): 0.0975 Epoch: [9/20], Batch Num: [499/600] Discriminator Loss: 0.3753, Generator Loss: 2.4723 D(x): 0.8587, D(G(z)): 0.1152 Epoch: 9, Batch Num: [500/600]
Epoch: [9/20], Batch Num: [500/600] Discriminator Loss: 0.4349, Generator Loss: 2.4174 D(x): 0.9023, D(G(z)): 0.2105 Epoch: [9/20], Batch Num: [501/600] Discriminator Loss: 0.4463, Generator Loss: 2.8266 D(x): 0.9184, D(G(z)): 0.1783 Epoch: [9/20], Batch Num: [502/600] Discriminator Loss: 0.4452, Generator Loss: 3.1550 D(x): 0.8614, D(G(z)): 0.1360 Epoch: [9/20], Batch Num: [503/600] Discriminator Loss: 0.3105, Generator Loss: 3.4217 D(x): 0.8803, D(G(z)): 0.0763 Epoch: [9/20], Batch Num: [504/600] Discriminator Loss: 0.4907, Generator Loss: 2.9629 D(x): 0.8243, D(G(z)): 0.0873 Epoch: [9/20], Batch Num: [505/600] Discriminator Loss: 0.2993, Generator Loss: 2.6686 D(x): 0.8846, D(G(z)): 0.0929 Epoch: [9/20], Batch Num: [506/600] Discriminator Loss: 0.3809, Generator Loss: 2.6846 D(x): 0.9134, D(G(z)): 0.1484 Epoch: [9/20], Batch Num: [507/600] Discriminator Loss: 0.4916, Generator Loss: 2.7414 D(x): 0.8613, D(G(z)): 0.1570 Epoch: [9/20], Batch Num: [508/600] Discriminator Loss: 0.4126, Generator Loss: 2.9100 D(x): 0.8923, D(G(z)): 0.1587 Epoch: [9/20], Batch Num: [509/600] Discriminator Loss: 0.3696, Generator Loss: 3.4323 D(x): 0.9137, D(G(z)): 0.1309 Epoch: [9/20], Batch Num: [510/600] Discriminator Loss: 0.4234, Generator Loss: 3.4060 D(x): 0.8361, D(G(z)): 0.0714 Epoch: [9/20], Batch Num: [511/600] Discriminator Loss: 0.2862, Generator Loss: 3.5169 D(x): 0.8842, D(G(z)): 0.0892 Epoch: [9/20], Batch Num: [512/600] Discriminator Loss: 0.4559, Generator Loss: 3.1473 D(x): 0.8369, D(G(z)): 0.1143 Epoch: [9/20], Batch Num: [513/600] Discriminator Loss: 0.4283, Generator Loss: 2.6777 D(x): 0.8681, D(G(z)): 0.1180 Epoch: [9/20], Batch Num: [514/600] Discriminator Loss: 0.4652, Generator Loss: 2.7006 D(x): 0.8864, D(G(z)): 0.1669 Epoch: [9/20], Batch Num: [515/600] Discriminator Loss: 0.3462, Generator Loss: 3.4279 D(x): 0.9332, D(G(z)): 0.1763 Epoch: [9/20], Batch Num: [516/600] Discriminator Loss: 0.4337, Generator Loss: 3.6441 D(x): 0.8537, D(G(z)): 0.0995 Epoch: [9/20], Batch Num: [517/600] Discriminator Loss: 0.5208, Generator Loss: 3.0943 D(x): 0.7985, D(G(z)): 0.0747 Epoch: [9/20], Batch Num: [518/600] Discriminator Loss: 0.4316, Generator Loss: 2.6329 D(x): 0.8694, D(G(z)): 0.1137 Epoch: [9/20], Batch Num: [519/600] Discriminator Loss: 0.3247, Generator Loss: 2.5324 D(x): 0.9215, D(G(z)): 0.1588 Epoch: [9/20], Batch Num: [520/600] Discriminator Loss: 0.5902, Generator Loss: 3.0367 D(x): 0.8819, D(G(z)): 0.2180 Epoch: [9/20], Batch Num: [521/600] Discriminator Loss: 0.3932, Generator Loss: 3.2166 D(x): 0.8799, D(G(z)): 0.1289 Epoch: [9/20], Batch Num: [522/600] Discriminator Loss: 0.3577, Generator Loss: 3.4319 D(x): 0.8549, D(G(z)): 0.0957 Epoch: [9/20], Batch Num: [523/600] Discriminator Loss: 0.3657, Generator Loss: 2.8343 D(x): 0.8680, D(G(z)): 0.0933 Epoch: [9/20], Batch Num: [524/600] Discriminator Loss: 0.5898, Generator Loss: 2.4852 D(x): 0.7987, D(G(z)): 0.1090 Epoch: [9/20], Batch Num: [525/600] Discriminator Loss: 0.4409, Generator Loss: 2.6005 D(x): 0.9081, D(G(z)): 0.1967 Epoch: [9/20], Batch Num: [526/600] Discriminator Loss: 0.4366, Generator Loss: 2.7511 D(x): 0.8701, D(G(z)): 0.1593 Epoch: [9/20], Batch Num: [527/600] Discriminator Loss: 0.4378, Generator Loss: 2.9531 D(x): 0.8887, D(G(z)): 0.1439 Epoch: [9/20], Batch Num: [528/600] Discriminator Loss: 0.3679, Generator Loss: 3.6633 D(x): 0.8864, D(G(z)): 0.1201 Epoch: [9/20], Batch Num: [529/600] Discriminator Loss: 0.6193, Generator Loss: 3.6057 D(x): 0.7755, D(G(z)): 0.1005 Epoch: [9/20], Batch Num: [530/600] Discriminator Loss: 0.6003, Generator Loss: 2.4451 D(x): 0.7911, D(G(z)): 0.1125 Epoch: [9/20], Batch Num: [531/600] Discriminator Loss: 0.4451, Generator Loss: 2.2147 D(x): 0.9158, D(G(z)): 0.2041 Epoch: [9/20], Batch Num: [532/600] Discriminator Loss: 0.5048, Generator Loss: 2.6770 D(x): 0.8899, D(G(z)): 0.2133 Epoch: [9/20], Batch Num: [533/600] Discriminator Loss: 0.3598, Generator Loss: 3.2253 D(x): 0.9080, D(G(z)): 0.1500 Epoch: [9/20], Batch Num: [534/600] Discriminator Loss: 0.5008, Generator Loss: 3.0099 D(x): 0.7970, D(G(z)): 0.0617 Epoch: [9/20], Batch Num: [535/600] Discriminator Loss: 0.3751, Generator Loss: 3.0907 D(x): 0.8779, D(G(z)): 0.1064 Epoch: [9/20], Batch Num: [536/600] Discriminator Loss: 0.3647, Generator Loss: 2.4383 D(x): 0.9005, D(G(z)): 0.1499 Epoch: [9/20], Batch Num: [537/600] Discriminator Loss: 0.4066, Generator Loss: 2.5584 D(x): 0.8625, D(G(z)): 0.1245 Epoch: [9/20], Batch Num: [538/600] Discriminator Loss: 0.3534, Generator Loss: 2.6253 D(x): 0.9071, D(G(z)): 0.1476 Epoch: [9/20], Batch Num: [539/600] Discriminator Loss: 0.3409, Generator Loss: 2.8714 D(x): 0.8832, D(G(z)): 0.1303 Epoch: [9/20], Batch Num: [540/600] Discriminator Loss: 0.3562, Generator Loss: 2.9903 D(x): 0.8793, D(G(z)): 0.1067 Epoch: [9/20], Batch Num: [541/600] Discriminator Loss: 0.3927, Generator Loss: 3.6745 D(x): 0.9127, D(G(z)): 0.1576 Epoch: [9/20], Batch Num: [542/600] Discriminator Loss: 0.3412, Generator Loss: 3.4820 D(x): 0.8840, D(G(z)): 0.1013 Epoch: [9/20], Batch Num: [543/600] Discriminator Loss: 0.3166, Generator Loss: 3.7129 D(x): 0.8920, D(G(z)): 0.0772 Epoch: [9/20], Batch Num: [544/600] Discriminator Loss: 0.4865, Generator Loss: 2.8730 D(x): 0.8310, D(G(z)): 0.0731 Epoch: [9/20], Batch Num: [545/600] Discriminator Loss: 0.4668, Generator Loss: 2.2512 D(x): 0.8497, D(G(z)): 0.1370 Epoch: [9/20], Batch Num: [546/600] Discriminator Loss: 0.5578, Generator Loss: 2.7939 D(x): 0.9113, D(G(z)): 0.2606 Epoch: [9/20], Batch Num: [547/600] Discriminator Loss: 0.4208, Generator Loss: 3.2189 D(x): 0.8854, D(G(z)): 0.1781 Epoch: [9/20], Batch Num: [548/600] Discriminator Loss: 0.5042, Generator Loss: 3.5910 D(x): 0.8580, D(G(z)): 0.1414 Epoch: [9/20], Batch Num: [549/600] Discriminator Loss: 0.4514, Generator Loss: 3.7081 D(x): 0.8408, D(G(z)): 0.0667 Epoch: [9/20], Batch Num: [550/600] Discriminator Loss: 0.3919, Generator Loss: 3.0591 D(x): 0.8397, D(G(z)): 0.0771 Epoch: [9/20], Batch Num: [551/600] Discriminator Loss: 0.2554, Generator Loss: 2.7997 D(x): 0.9364, D(G(z)): 0.1278 Epoch: [9/20], Batch Num: [552/600] Discriminator Loss: 0.3975, Generator Loss: 2.6916 D(x): 0.8664, D(G(z)): 0.1142 Epoch: [9/20], Batch Num: [553/600] Discriminator Loss: 0.5710, Generator Loss: 2.4958 D(x): 0.8789, D(G(z)): 0.1901 Epoch: [9/20], Batch Num: [554/600] Discriminator Loss: 0.4536, Generator Loss: 2.9694 D(x): 0.8834, D(G(z)): 0.1791 Epoch: [9/20], Batch Num: [555/600] Discriminator Loss: 0.4483, Generator Loss: 3.4784 D(x): 0.8593, D(G(z)): 0.1283 Epoch: [9/20], Batch Num: [556/600] Discriminator Loss: 0.4174, Generator Loss: 3.4878 D(x): 0.8431, D(G(z)): 0.0906 Epoch: [9/20], Batch Num: [557/600] Discriminator Loss: 0.4518, Generator Loss: 3.2345 D(x): 0.8651, D(G(z)): 0.1062 Epoch: [9/20], Batch Num: [558/600] Discriminator Loss: 0.4306, Generator Loss: 3.0656 D(x): 0.8881, D(G(z)): 0.1306 Epoch: [9/20], Batch Num: [559/600] Discriminator Loss: 0.3857, Generator Loss: 3.0047 D(x): 0.9025, D(G(z)): 0.1519 Epoch: [9/20], Batch Num: [560/600] Discriminator Loss: 0.4631, Generator Loss: 2.8272 D(x): 0.8446, D(G(z)): 0.1033 Epoch: [9/20], Batch Num: [561/600] Discriminator Loss: 0.5690, Generator Loss: 3.1467 D(x): 0.8410, D(G(z)): 0.1801 Epoch: [9/20], Batch Num: [562/600] Discriminator Loss: 0.3491, Generator Loss: 3.2540 D(x): 0.8865, D(G(z)): 0.1147 Epoch: [9/20], Batch Num: [563/600] Discriminator Loss: 0.4531, Generator Loss: 3.0388 D(x): 0.8528, D(G(z)): 0.1169 Epoch: [9/20], Batch Num: [564/600] Discriminator Loss: 0.5116, Generator Loss: 2.8713 D(x): 0.8462, D(G(z)): 0.1304 Epoch: [9/20], Batch Num: [565/600] Discriminator Loss: 0.3754, Generator Loss: 2.4554 D(x): 0.8966, D(G(z)): 0.1494 Epoch: [9/20], Batch Num: [566/600] Discriminator Loss: 0.3919, Generator Loss: 3.1565 D(x): 0.9174, D(G(z)): 0.1817 Epoch: [9/20], Batch Num: [567/600] Discriminator Loss: 0.4734, Generator Loss: 3.5038 D(x): 0.8527, D(G(z)): 0.1511 Epoch: [9/20], Batch Num: [568/600] Discriminator Loss: 0.5483, Generator Loss: 3.6767 D(x): 0.8174, D(G(z)): 0.1032 Epoch: [9/20], Batch Num: [569/600] Discriminator Loss: 0.4656, Generator Loss: 3.0996 D(x): 0.8251, D(G(z)): 0.0997 Epoch: [9/20], Batch Num: [570/600] Discriminator Loss: 0.4696, Generator Loss: 2.6739 D(x): 0.8344, D(G(z)): 0.0984 Epoch: [9/20], Batch Num: [571/600] Discriminator Loss: 0.5156, Generator Loss: 2.4012 D(x): 0.9124, D(G(z)): 0.2214 Epoch: [9/20], Batch Num: [572/600] Discriminator Loss: 0.5537, Generator Loss: 2.7577 D(x): 0.8378, D(G(z)): 0.1827 Epoch: [9/20], Batch Num: [573/600] Discriminator Loss: 0.4169, Generator Loss: 2.8909 D(x): 0.8823, D(G(z)): 0.1489 Epoch: [9/20], Batch Num: [574/600] Discriminator Loss: 0.4328, Generator Loss: 2.9590 D(x): 0.8468, D(G(z)): 0.1009 Epoch: [9/20], Batch Num: [575/600] Discriminator Loss: 0.3631, Generator Loss: 2.7395 D(x): 0.8808, D(G(z)): 0.1159 Epoch: [9/20], Batch Num: [576/600] Discriminator Loss: 0.4065, Generator Loss: 2.7050 D(x): 0.8616, D(G(z)): 0.1172 Epoch: [9/20], Batch Num: [577/600] Discriminator Loss: 0.3724, Generator Loss: 2.7490 D(x): 0.8995, D(G(z)): 0.1425 Epoch: [9/20], Batch Num: [578/600] Discriminator Loss: 0.4042, Generator Loss: 2.8155 D(x): 0.9242, D(G(z)): 0.1827 Epoch: [9/20], Batch Num: [579/600] Discriminator Loss: 0.2709, Generator Loss: 3.7018 D(x): 0.9185, D(G(z)): 0.0913 Epoch: [9/20], Batch Num: [580/600] Discriminator Loss: 0.5223, Generator Loss: 4.1290 D(x): 0.7986, D(G(z)): 0.0934 Epoch: [9/20], Batch Num: [581/600] Discriminator Loss: 0.4127, Generator Loss: 3.2232 D(x): 0.8477, D(G(z)): 0.0734 Epoch: [9/20], Batch Num: [582/600] Discriminator Loss: 0.6226, Generator Loss: 2.8544 D(x): 0.8428, D(G(z)): 0.1617 Epoch: [9/20], Batch Num: [583/600] Discriminator Loss: 0.3960, Generator Loss: 2.8501 D(x): 0.9215, D(G(z)): 0.1605 Epoch: [9/20], Batch Num: [584/600] Discriminator Loss: 0.4621, Generator Loss: 3.2177 D(x): 0.9073, D(G(z)): 0.1834 Epoch: [9/20], Batch Num: [585/600] Discriminator Loss: 0.3759, Generator Loss: 3.5957 D(x): 0.9035, D(G(z)): 0.1308 Epoch: [9/20], Batch Num: [586/600] Discriminator Loss: 0.5196, Generator Loss: 3.4915 D(x): 0.8248, D(G(z)): 0.0883 Epoch: [9/20], Batch Num: [587/600] Discriminator Loss: 0.3691, Generator Loss: 3.2539 D(x): 0.8559, D(G(z)): 0.0692 Epoch: [9/20], Batch Num: [588/600] Discriminator Loss: 0.4435, Generator Loss: 2.9900 D(x): 0.8552, D(G(z)): 0.1121 Epoch: [9/20], Batch Num: [589/600] Discriminator Loss: 0.4041, Generator Loss: 2.6549 D(x): 0.9019, D(G(z)): 0.1606 Epoch: [9/20], Batch Num: [590/600] Discriminator Loss: 0.4486, Generator Loss: 2.6686 D(x): 0.8685, D(G(z)): 0.1716 Epoch: [9/20], Batch Num: [591/600] Discriminator Loss: 0.4793, Generator Loss: 3.0201 D(x): 0.9113, D(G(z)): 0.2099 Epoch: [9/20], Batch Num: [592/600] Discriminator Loss: 0.3888, Generator Loss: 3.1348 D(x): 0.8814, D(G(z)): 0.1097 Epoch: [9/20], Batch Num: [593/600] Discriminator Loss: 0.3471, Generator Loss: 3.5281 D(x): 0.8746, D(G(z)): 0.0893 Epoch: [9/20], Batch Num: [594/600] Discriminator Loss: 0.4341, Generator Loss: 3.4325 D(x): 0.8360, D(G(z)): 0.0880 Epoch: [9/20], Batch Num: [595/600] Discriminator Loss: 0.5408, Generator Loss: 3.1213 D(x): 0.8219, D(G(z)): 0.1018 Epoch: [9/20], Batch Num: [596/600] Discriminator Loss: 0.4401, Generator Loss: 2.2883 D(x): 0.8538, D(G(z)): 0.1014 Epoch: [9/20], Batch Num: [597/600] Discriminator Loss: 0.3491, Generator Loss: 2.1124 D(x): 0.9263, D(G(z)): 0.1655 Epoch: [9/20], Batch Num: [598/600] Discriminator Loss: 0.4868, Generator Loss: 2.6661 D(x): 0.9043, D(G(z)): 0.2012 Epoch: [9/20], Batch Num: [599/600] Discriminator Loss: 0.4465, Generator Loss: 3.3973 D(x): 0.8924, D(G(z)): 0.1674 Epoch: 10, Batch Num: [0/600]
Epoch: [10/20], Batch Num: [0/600] Discriminator Loss: 0.3456, Generator Loss: 4.0177 D(x): 0.8807, D(G(z)): 0.0801 Epoch: [10/20], Batch Num: [1/600] Discriminator Loss: 0.4137, Generator Loss: 3.9956 D(x): 0.8621, D(G(z)): 0.0619 Epoch: [10/20], Batch Num: [2/600] Discriminator Loss: 0.4791, Generator Loss: 3.4735 D(x): 0.8281, D(G(z)): 0.0690 Epoch: [10/20], Batch Num: [3/600] Discriminator Loss: 0.4044, Generator Loss: 2.4612 D(x): 0.8297, D(G(z)): 0.0716 Epoch: [10/20], Batch Num: [4/600] Discriminator Loss: 0.4209, Generator Loss: 1.9787 D(x): 0.9106, D(G(z)): 0.1881 Epoch: [10/20], Batch Num: [5/600] Discriminator Loss: 0.5817, Generator Loss: 2.5971 D(x): 0.9049, D(G(z)): 0.2651 Epoch: [10/20], Batch Num: [6/600] Discriminator Loss: 0.5024, Generator Loss: 2.8562 D(x): 0.8464, D(G(z)): 0.1390 Epoch: [10/20], Batch Num: [7/600] Discriminator Loss: 0.3403, Generator Loss: 3.5267 D(x): 0.9038, D(G(z)): 0.1174 Epoch: [10/20], Batch Num: [8/600] Discriminator Loss: 0.3471, Generator Loss: 3.3139 D(x): 0.8543, D(G(z)): 0.0730 Epoch: [10/20], Batch Num: [9/600] Discriminator Loss: 0.3375, Generator Loss: 3.1673 D(x): 0.8634, D(G(z)): 0.0764 Epoch: [10/20], Batch Num: [10/600] Discriminator Loss: 0.4080, Generator Loss: 3.1668 D(x): 0.8739, D(G(z)): 0.1124 Epoch: [10/20], Batch Num: [11/600] Discriminator Loss: 0.4912, Generator Loss: 2.6075 D(x): 0.8585, D(G(z)): 0.1519 Epoch: [10/20], Batch Num: [12/600] Discriminator Loss: 0.5226, Generator Loss: 2.7056 D(x): 0.8545, D(G(z)): 0.1644 Epoch: [10/20], Batch Num: [13/600] Discriminator Loss: 0.3394, Generator Loss: 2.9652 D(x): 0.9266, D(G(z)): 0.1409 Epoch: [10/20], Batch Num: [14/600] Discriminator Loss: 0.3476, Generator Loss: 3.2123 D(x): 0.8927, D(G(z)): 0.1309 Epoch: [10/20], Batch Num: [15/600] Discriminator Loss: 0.4090, Generator Loss: 3.4524 D(x): 0.8725, D(G(z)): 0.0984 Epoch: [10/20], Batch Num: [16/600] Discriminator Loss: 0.3809, Generator Loss: 3.6667 D(x): 0.8752, D(G(z)): 0.0727 Epoch: [10/20], Batch Num: [17/600] Discriminator Loss: 0.3151, Generator Loss: 3.2165 D(x): 0.8847, D(G(z)): 0.0925 Epoch: [10/20], Batch Num: [18/600] Discriminator Loss: 0.5131, Generator Loss: 2.5505 D(x): 0.8137, D(G(z)): 0.1026 Epoch: [10/20], Batch Num: [19/600] Discriminator Loss: 0.4683, Generator Loss: 2.5786 D(x): 0.8930, D(G(z)): 0.1781 Epoch: [10/20], Batch Num: [20/600] Discriminator Loss: 0.5982, Generator Loss: 3.0559 D(x): 0.9097, D(G(z)): 0.2348 Epoch: [10/20], Batch Num: [21/600] Discriminator Loss: 0.3545, Generator Loss: 3.6778 D(x): 0.8811, D(G(z)): 0.1108 Epoch: [10/20], Batch Num: [22/600] Discriminator Loss: 0.4676, Generator Loss: 3.4096 D(x): 0.8242, D(G(z)): 0.0926 Epoch: [10/20], Batch Num: [23/600] Discriminator Loss: 0.3846, Generator Loss: 2.7501 D(x): 0.8275, D(G(z)): 0.0654 Epoch: [10/20], Batch Num: [24/600] Discriminator Loss: 0.4632, Generator Loss: 2.1726 D(x): 0.8290, D(G(z)): 0.0986 Epoch: [10/20], Batch Num: [25/600] Discriminator Loss: 0.4511, Generator Loss: 2.0811 D(x): 0.9164, D(G(z)): 0.2126 Epoch: [10/20], Batch Num: [26/600] Discriminator Loss: 0.4924, Generator Loss: 3.0073 D(x): 0.9456, D(G(z)): 0.2549 Epoch: [10/20], Batch Num: [27/600] Discriminator Loss: 0.2867, Generator Loss: 3.7208 D(x): 0.9038, D(G(z)): 0.1095 Epoch: [10/20], Batch Num: [28/600] Discriminator Loss: 0.3752, Generator Loss: 4.2000 D(x): 0.8511, D(G(z)): 0.0769 Epoch: [10/20], Batch Num: [29/600] Discriminator Loss: 0.4915, Generator Loss: 3.9318 D(x): 0.8300, D(G(z)): 0.0614 Epoch: [10/20], Batch Num: [30/600] Discriminator Loss: 0.3216, Generator Loss: 3.8251 D(x): 0.8921, D(G(z)): 0.0546 Epoch: [10/20], Batch Num: [31/600] Discriminator Loss: 0.2587, Generator Loss: 3.5324 D(x): 0.9112, D(G(z)): 0.0505 Epoch: [10/20], Batch Num: [32/600] Discriminator Loss: 0.4520, Generator Loss: 2.5145 D(x): 0.8718, D(G(z)): 0.1054 Epoch: [10/20], Batch Num: [33/600] Discriminator Loss: 0.4085, Generator Loss: 2.6325 D(x): 0.9365, D(G(z)): 0.1792 Epoch: [10/20], Batch Num: [34/600] Discriminator Loss: 0.3464, Generator Loss: 2.8397 D(x): 0.9182, D(G(z)): 0.1505 Epoch: [10/20], Batch Num: [35/600] Discriminator Loss: 0.3853, Generator Loss: 2.7406 D(x): 0.8692, D(G(z)): 0.1100 Epoch: [10/20], Batch Num: [36/600] Discriminator Loss: 0.3566, Generator Loss: 2.8493 D(x): 0.8740, D(G(z)): 0.1097 Epoch: [10/20], Batch Num: [37/600] Discriminator Loss: 0.2566, Generator Loss: 3.1529 D(x): 0.9303, D(G(z)): 0.1099 Epoch: [10/20], Batch Num: [38/600] Discriminator Loss: 0.4071, Generator Loss: 3.1898 D(x): 0.8606, D(G(z)): 0.1108 Epoch: [10/20], Batch Num: [39/600] Discriminator Loss: 0.2873, Generator Loss: 3.1850 D(x): 0.8966, D(G(z)): 0.0869 Epoch: [10/20], Batch Num: [40/600] Discriminator Loss: 0.3437, Generator Loss: 3.1330 D(x): 0.8915, D(G(z)): 0.1039 Epoch: [10/20], Batch Num: [41/600] Discriminator Loss: 0.4036, Generator Loss: 3.4664 D(x): 0.9099, D(G(z)): 0.1289 Epoch: [10/20], Batch Num: [42/600] Discriminator Loss: 0.2836, Generator Loss: 3.3866 D(x): 0.8977, D(G(z)): 0.0694 Epoch: [10/20], Batch Num: [43/600] Discriminator Loss: 0.2987, Generator Loss: 3.3140 D(x): 0.8857, D(G(z)): 0.0834 Epoch: [10/20], Batch Num: [44/600] Discriminator Loss: 0.2018, Generator Loss: 3.1707 D(x): 0.9453, D(G(z)): 0.0836 Epoch: [10/20], Batch Num: [45/600] Discriminator Loss: 0.2803, Generator Loss: 3.2399 D(x): 0.8864, D(G(z)): 0.0780 Epoch: [10/20], Batch Num: [46/600] Discriminator Loss: 0.3993, Generator Loss: 2.9101 D(x): 0.8802, D(G(z)): 0.1047 Epoch: [10/20], Batch Num: [47/600] Discriminator Loss: 0.4463, Generator Loss: 2.7619 D(x): 0.8840, D(G(z)): 0.1449 Epoch: [10/20], Batch Num: [48/600] Discriminator Loss: 0.2914, Generator Loss: 2.8784 D(x): 0.9121, D(G(z)): 0.1203 Epoch: [10/20], Batch Num: [49/600] Discriminator Loss: 0.2617, Generator Loss: 3.4105 D(x): 0.9247, D(G(z)): 0.1049 Epoch: [10/20], Batch Num: [50/600] Discriminator Loss: 0.2485, Generator Loss: 3.6668 D(x): 0.9303, D(G(z)): 0.0872 Epoch: [10/20], Batch Num: [51/600] Discriminator Loss: 0.3892, Generator Loss: 3.8682 D(x): 0.8816, D(G(z)): 0.1106 Epoch: [10/20], Batch Num: [52/600] Discriminator Loss: 0.2789, Generator Loss: 3.9183 D(x): 0.8974, D(G(z)): 0.0512 Epoch: [10/20], Batch Num: [53/600] Discriminator Loss: 0.1734, Generator Loss: 3.5936 D(x): 0.9226, D(G(z)): 0.0356 Epoch: [10/20], Batch Num: [54/600] Discriminator Loss: 0.2301, Generator Loss: 3.2485 D(x): 0.9194, D(G(z)): 0.0662 Epoch: [10/20], Batch Num: [55/600] Discriminator Loss: 0.2889, Generator Loss: 3.1042 D(x): 0.8948, D(G(z)): 0.0878 Epoch: [10/20], Batch Num: [56/600] Discriminator Loss: 0.3041, Generator Loss: 2.7081 D(x): 0.9131, D(G(z)): 0.1299 Epoch: [10/20], Batch Num: [57/600] Discriminator Loss: 0.3689, Generator Loss: 3.3307 D(x): 0.9387, D(G(z)): 0.1690 Epoch: [10/20], Batch Num: [58/600] Discriminator Loss: 0.3029, Generator Loss: 4.0800 D(x): 0.9478, D(G(z)): 0.1552 Epoch: [10/20], Batch Num: [59/600] Discriminator Loss: 0.4230, Generator Loss: 4.5371 D(x): 0.8508, D(G(z)): 0.0486 Epoch: [10/20], Batch Num: [60/600] Discriminator Loss: 0.3590, Generator Loss: 4.2077 D(x): 0.8838, D(G(z)): 0.0715 Epoch: [10/20], Batch Num: [61/600] Discriminator Loss: 0.4061, Generator Loss: 4.1780 D(x): 0.8744, D(G(z)): 0.0604 Epoch: [10/20], Batch Num: [62/600] Discriminator Loss: 0.2184, Generator Loss: 3.9300 D(x): 0.9422, D(G(z)): 0.0834 Epoch: [10/20], Batch Num: [63/600] Discriminator Loss: 0.2164, Generator Loss: 3.2535 D(x): 0.9506, D(G(z)): 0.0838 Epoch: [10/20], Batch Num: [64/600] Discriminator Loss: 0.4643, Generator Loss: 3.2108 D(x): 0.8914, D(G(z)): 0.1177 Epoch: [10/20], Batch Num: [65/600] Discriminator Loss: 0.4238, Generator Loss: 3.3706 D(x): 0.9167, D(G(z)): 0.1108 Epoch: [10/20], Batch Num: [66/600] Discriminator Loss: 0.2282, Generator Loss: 3.6120 D(x): 0.9514, D(G(z)): 0.0966 Epoch: [10/20], Batch Num: [67/600] Discriminator Loss: 0.2158, Generator Loss: 4.1038 D(x): 0.9453, D(G(z)): 0.0908 Epoch: [10/20], Batch Num: [68/600] Discriminator Loss: 0.4010, Generator Loss: 4.2656 D(x): 0.8553, D(G(z)): 0.0541 Epoch: [10/20], Batch Num: [69/600] Discriminator Loss: 0.4525, Generator Loss: 3.4704 D(x): 0.8477, D(G(z)): 0.0590 Epoch: [10/20], Batch Num: [70/600] Discriminator Loss: 0.3847, Generator Loss: 3.2527 D(x): 0.9116, D(G(z)): 0.1299 Epoch: [10/20], Batch Num: [71/600] Discriminator Loss: 0.4554, Generator Loss: 2.8731 D(x): 0.8642, D(G(z)): 0.1423 Epoch: [10/20], Batch Num: [72/600] Discriminator Loss: 0.3474, Generator Loss: 3.1145 D(x): 0.9416, D(G(z)): 0.1570 Epoch: [10/20], Batch Num: [73/600] Discriminator Loss: 0.4783, Generator Loss: 3.2346 D(x): 0.8507, D(G(z)): 0.1102 Epoch: [10/20], Batch Num: [74/600] Discriminator Loss: 0.3969, Generator Loss: 3.2665 D(x): 0.8875, D(G(z)): 0.1093 Epoch: [10/20], Batch Num: [75/600] Discriminator Loss: 0.3497, Generator Loss: 3.2378 D(x): 0.9109, D(G(z)): 0.0998 Epoch: [10/20], Batch Num: [76/600] Discriminator Loss: 0.4281, Generator Loss: 3.0817 D(x): 0.8758, D(G(z)): 0.1247 Epoch: [10/20], Batch Num: [77/600] Discriminator Loss: 0.3132, Generator Loss: 3.1236 D(x): 0.8851, D(G(z)): 0.0904 Epoch: [10/20], Batch Num: [78/600] Discriminator Loss: 0.4766, Generator Loss: 2.7518 D(x): 0.8684, D(G(z)): 0.1313 Epoch: [10/20], Batch Num: [79/600] Discriminator Loss: 0.3669, Generator Loss: 3.0239 D(x): 0.9278, D(G(z)): 0.1471 Epoch: [10/20], Batch Num: [80/600] Discriminator Loss: 0.4182, Generator Loss: 3.5725 D(x): 0.9145, D(G(z)): 0.1482 Epoch: [10/20], Batch Num: [81/600] Discriminator Loss: 0.3157, Generator Loss: 3.4730 D(x): 0.9148, D(G(z)): 0.1050 Epoch: [10/20], Batch Num: [82/600] Discriminator Loss: 0.3158, Generator Loss: 3.7440 D(x): 0.8713, D(G(z)): 0.0648 Epoch: [10/20], Batch Num: [83/600] Discriminator Loss: 0.3558, Generator Loss: 3.3196 D(x): 0.8532, D(G(z)): 0.0580 Epoch: [10/20], Batch Num: [84/600] Discriminator Loss: 0.3813, Generator Loss: 2.8235 D(x): 0.8665, D(G(z)): 0.0881 Epoch: [10/20], Batch Num: [85/600] Discriminator Loss: 0.3256, Generator Loss: 2.3521 D(x): 0.9066, D(G(z)): 0.1316 Epoch: [10/20], Batch Num: [86/600] Discriminator Loss: 0.3828, Generator Loss: 2.8607 D(x): 0.9429, D(G(z)): 0.1873 Epoch: [10/20], Batch Num: [87/600] Discriminator Loss: 0.3026, Generator Loss: 3.4304 D(x): 0.9145, D(G(z)): 0.1250 Epoch: [10/20], Batch Num: [88/600] Discriminator Loss: 0.3953, Generator Loss: 4.0178 D(x): 0.8866, D(G(z)): 0.1140 Epoch: [10/20], Batch Num: [89/600] Discriminator Loss: 0.3837, Generator Loss: 3.9770 D(x): 0.8339, D(G(z)): 0.0469 Epoch: [10/20], Batch Num: [90/600] Discriminator Loss: 0.5797, Generator Loss: 3.5010 D(x): 0.8582, D(G(z)): 0.1128 Epoch: [10/20], Batch Num: [91/600] Discriminator Loss: 0.4454, Generator Loss: 2.7053 D(x): 0.8665, D(G(z)): 0.0887 Epoch: [10/20], Batch Num: [92/600] Discriminator Loss: 0.2538, Generator Loss: 2.5654 D(x): 0.9394, D(G(z)): 0.1213 Epoch: [10/20], Batch Num: [93/600] Discriminator Loss: 0.6640, Generator Loss: 3.3036 D(x): 0.8994, D(G(z)): 0.2190 Epoch: [10/20], Batch Num: [94/600] Discriminator Loss: 0.4458, Generator Loss: 3.7199 D(x): 0.9087, D(G(z)): 0.1381 Epoch: [10/20], Batch Num: [95/600] Discriminator Loss: 0.3623, Generator Loss: 3.9326 D(x): 0.8894, D(G(z)): 0.1124 Epoch: [10/20], Batch Num: [96/600] Discriminator Loss: 0.3376, Generator Loss: 4.0826 D(x): 0.8659, D(G(z)): 0.0619 Epoch: [10/20], Batch Num: [97/600] Discriminator Loss: 0.4860, Generator Loss: 3.2731 D(x): 0.8167, D(G(z)): 0.0535 Epoch: [10/20], Batch Num: [98/600] Discriminator Loss: 0.4236, Generator Loss: 2.8010 D(x): 0.8461, D(G(z)): 0.1106 Epoch: [10/20], Batch Num: [99/600] Discriminator Loss: 0.4961, Generator Loss: 2.3600 D(x): 0.8926, D(G(z)): 0.1632 Epoch: 10, Batch Num: [100/600]
Epoch: [10/20], Batch Num: [100/600] Discriminator Loss: 0.4745, Generator Loss: 2.1539 D(x): 0.8831, D(G(z)): 0.1694 Epoch: [10/20], Batch Num: [101/600] Discriminator Loss: 0.4436, Generator Loss: 2.6617 D(x): 0.9198, D(G(z)): 0.1919 Epoch: [10/20], Batch Num: [102/600] Discriminator Loss: 0.4305, Generator Loss: 3.3416 D(x): 0.9404, D(G(z)): 0.1984 Epoch: [10/20], Batch Num: [103/600] Discriminator Loss: 0.4111, Generator Loss: 4.0255 D(x): 0.8419, D(G(z)): 0.0741 Epoch: [10/20], Batch Num: [104/600] Discriminator Loss: 0.4534, Generator Loss: 3.6290 D(x): 0.8513, D(G(z)): 0.0764 Epoch: [10/20], Batch Num: [105/600] Discriminator Loss: 0.6216, Generator Loss: 3.1157 D(x): 0.7651, D(G(z)): 0.0767 Epoch: [10/20], Batch Num: [106/600] Discriminator Loss: 0.3625, Generator Loss: 2.5731 D(x): 0.8793, D(G(z)): 0.1130 Epoch: [10/20], Batch Num: [107/600] Discriminator Loss: 0.4512, Generator Loss: 2.3683 D(x): 0.9070, D(G(z)): 0.1802 Epoch: [10/20], Batch Num: [108/600] Discriminator Loss: 0.5720, Generator Loss: 2.6108 D(x): 0.9048, D(G(z)): 0.2209 Epoch: [10/20], Batch Num: [109/600] Discriminator Loss: 0.3856, Generator Loss: 2.9994 D(x): 0.9454, D(G(z)): 0.2038 Epoch: [10/20], Batch Num: [110/600] Discriminator Loss: 0.4455, Generator Loss: 3.4168 D(x): 0.8846, D(G(z)): 0.1668 Epoch: [10/20], Batch Num: [111/600] Discriminator Loss: 0.3127, Generator Loss: 3.8265 D(x): 0.8835, D(G(z)): 0.0797 Epoch: [10/20], Batch Num: [112/600] Discriminator Loss: 0.4597, Generator Loss: 3.6902 D(x): 0.8057, D(G(z)): 0.0551 Epoch: [10/20], Batch Num: [113/600] Discriminator Loss: 0.2840, Generator Loss: 3.1975 D(x): 0.8498, D(G(z)): 0.0452 Epoch: [10/20], Batch Num: [114/600] Discriminator Loss: 0.4433, Generator Loss: 2.6881 D(x): 0.8363, D(G(z)): 0.0915 Epoch: [10/20], Batch Num: [115/600] Discriminator Loss: 0.5031, Generator Loss: 1.8821 D(x): 0.8644, D(G(z)): 0.1816 Epoch: [10/20], Batch Num: [116/600] Discriminator Loss: 0.4083, Generator Loss: 2.3523 D(x): 0.9525, D(G(z)): 0.2369 Epoch: [10/20], Batch Num: [117/600] Discriminator Loss: 0.3971, Generator Loss: 2.7798 D(x): 0.9199, D(G(z)): 0.1723 Epoch: [10/20], Batch Num: [118/600] Discriminator Loss: 0.3218, Generator Loss: 3.7752 D(x): 0.9187, D(G(z)): 0.1400 Epoch: [10/20], Batch Num: [119/600] Discriminator Loss: 0.2861, Generator Loss: 4.2801 D(x): 0.8690, D(G(z)): 0.0491 Epoch: [10/20], Batch Num: [120/600] Discriminator Loss: 0.3210, Generator Loss: 4.0056 D(x): 0.8497, D(G(z)): 0.0426 Epoch: [10/20], Batch Num: [121/600] Discriminator Loss: 0.4781, Generator Loss: 3.4447 D(x): 0.8117, D(G(z)): 0.0590 Epoch: [10/20], Batch Num: [122/600] Discriminator Loss: 0.4583, Generator Loss: 2.7374 D(x): 0.8219, D(G(z)): 0.0747 Epoch: [10/20], Batch Num: [123/600] Discriminator Loss: 0.4262, Generator Loss: 2.2939 D(x): 0.8908, D(G(z)): 0.1688 Epoch: [10/20], Batch Num: [124/600] Discriminator Loss: 0.5243, Generator Loss: 2.8364 D(x): 0.9733, D(G(z)): 0.2572 Epoch: [10/20], Batch Num: [125/600] Discriminator Loss: 0.3220, Generator Loss: 3.3415 D(x): 0.9391, D(G(z)): 0.1593 Epoch: [10/20], Batch Num: [126/600] Discriminator Loss: 0.2826, Generator Loss: 3.6439 D(x): 0.8912, D(G(z)): 0.0845 Epoch: [10/20], Batch Num: [127/600] Discriminator Loss: 0.3449, Generator Loss: 3.9670 D(x): 0.8566, D(G(z)): 0.0594 Epoch: [10/20], Batch Num: [128/600] Discriminator Loss: 0.4484, Generator Loss: 3.7669 D(x): 0.8380, D(G(z)): 0.0516 Epoch: [10/20], Batch Num: [129/600] Discriminator Loss: 0.4842, Generator Loss: 2.9875 D(x): 0.8045, D(G(z)): 0.0684 Epoch: [10/20], Batch Num: [130/600] Discriminator Loss: 0.3250, Generator Loss: 2.4993 D(x): 0.8761, D(G(z)): 0.0981 Epoch: [10/20], Batch Num: [131/600] Discriminator Loss: 0.4181, Generator Loss: 2.1038 D(x): 0.9146, D(G(z)): 0.1802 Epoch: [10/20], Batch Num: [132/600] Discriminator Loss: 0.3910, Generator Loss: 2.4363 D(x): 0.9478, D(G(z)): 0.2082 Epoch: [10/20], Batch Num: [133/600] Discriminator Loss: 0.3423, Generator Loss: 3.2904 D(x): 0.9223, D(G(z)): 0.1616 Epoch: [10/20], Batch Num: [134/600] Discriminator Loss: 0.2981, Generator Loss: 3.7555 D(x): 0.9194, D(G(z)): 0.1142 Epoch: [10/20], Batch Num: [135/600] Discriminator Loss: 0.4404, Generator Loss: 4.2074 D(x): 0.8131, D(G(z)): 0.0570 Epoch: [10/20], Batch Num: [136/600] Discriminator Loss: 0.3765, Generator Loss: 3.3573 D(x): 0.8495, D(G(z)): 0.0524 Epoch: [10/20], Batch Num: [137/600] Discriminator Loss: 0.3305, Generator Loss: 3.2027 D(x): 0.8778, D(G(z)): 0.0684 Epoch: [10/20], Batch Num: [138/600] Discriminator Loss: 0.2941, Generator Loss: 3.0160 D(x): 0.9041, D(G(z)): 0.0975 Epoch: [10/20], Batch Num: [139/600] Discriminator Loss: 0.4795, Generator Loss: 2.7551 D(x): 0.8892, D(G(z)): 0.1503 Epoch: [10/20], Batch Num: [140/600] Discriminator Loss: 0.2470, Generator Loss: 2.8526 D(x): 0.9459, D(G(z)): 0.1243 Epoch: [10/20], Batch Num: [141/600] Discriminator Loss: 0.3959, Generator Loss: 3.0591 D(x): 0.9438, D(G(z)): 0.1769 Epoch: [10/20], Batch Num: [142/600] Discriminator Loss: 0.3293, Generator Loss: 3.3187 D(x): 0.8760, D(G(z)): 0.0832 Epoch: [10/20], Batch Num: [143/600] Discriminator Loss: 0.2918, Generator Loss: 3.5853 D(x): 0.8911, D(G(z)): 0.0794 Epoch: [10/20], Batch Num: [144/600] Discriminator Loss: 0.3132, Generator Loss: 3.1916 D(x): 0.8806, D(G(z)): 0.0810 Epoch: [10/20], Batch Num: [145/600] Discriminator Loss: 0.3633, Generator Loss: 3.1416 D(x): 0.8647, D(G(z)): 0.1000 Epoch: [10/20], Batch Num: [146/600] Discriminator Loss: 0.2505, Generator Loss: 2.9207 D(x): 0.9158, D(G(z)): 0.1009 Epoch: [10/20], Batch Num: [147/600] Discriminator Loss: 0.3147, Generator Loss: 2.5450 D(x): 0.9443, D(G(z)): 0.1476 Epoch: [10/20], Batch Num: [148/600] Discriminator Loss: 0.4431, Generator Loss: 3.3077 D(x): 0.8973, D(G(z)): 0.1738 Epoch: [10/20], Batch Num: [149/600] Discriminator Loss: 0.2983, Generator Loss: 3.5206 D(x): 0.9231, D(G(z)): 0.1332 Epoch: [10/20], Batch Num: [150/600] Discriminator Loss: 0.2174, Generator Loss: 3.9170 D(x): 0.9173, D(G(z)): 0.0595 Epoch: [10/20], Batch Num: [151/600] Discriminator Loss: 0.7173, Generator Loss: 3.6316 D(x): 0.7466, D(G(z)): 0.0439 Epoch: [10/20], Batch Num: [152/600] Discriminator Loss: 0.3135, Generator Loss: 2.6612 D(x): 0.8588, D(G(z)): 0.0646 Epoch: [10/20], Batch Num: [153/600] Discriminator Loss: 0.3484, Generator Loss: 2.6538 D(x): 0.9435, D(G(z)): 0.1665 Epoch: [10/20], Batch Num: [154/600] Discriminator Loss: 0.4185, Generator Loss: 3.2766 D(x): 0.9460, D(G(z)): 0.2090 Epoch: [10/20], Batch Num: [155/600] Discriminator Loss: 0.2049, Generator Loss: 3.7030 D(x): 0.9321, D(G(z)): 0.0826 Epoch: [10/20], Batch Num: [156/600] Discriminator Loss: 0.3718, Generator Loss: 4.0539 D(x): 0.8568, D(G(z)): 0.0961 Epoch: [10/20], Batch Num: [157/600] Discriminator Loss: 0.2955, Generator Loss: 3.9248 D(x): 0.8887, D(G(z)): 0.0544 Epoch: [10/20], Batch Num: [158/600] Discriminator Loss: 0.3706, Generator Loss: 3.5399 D(x): 0.8580, D(G(z)): 0.0641 Epoch: [10/20], Batch Num: [159/600] Discriminator Loss: 0.2629, Generator Loss: 3.5099 D(x): 0.9290, D(G(z)): 0.0913 Epoch: [10/20], Batch Num: [160/600] Discriminator Loss: 0.4417, Generator Loss: 3.2588 D(x): 0.8487, D(G(z)): 0.0884 Epoch: [10/20], Batch Num: [161/600] Discriminator Loss: 0.2635, Generator Loss: 2.9632 D(x): 0.9195, D(G(z)): 0.1119 Epoch: [10/20], Batch Num: [162/600] Discriminator Loss: 0.3779, Generator Loss: 2.8066 D(x): 0.9287, D(G(z)): 0.1530 Epoch: [10/20], Batch Num: [163/600] Discriminator Loss: 0.3129, Generator Loss: 3.2930 D(x): 0.9292, D(G(z)): 0.1336 Epoch: [10/20], Batch Num: [164/600] Discriminator Loss: 0.3912, Generator Loss: 3.5327 D(x): 0.8924, D(G(z)): 0.1277 Epoch: [10/20], Batch Num: [165/600] Discriminator Loss: 0.4011, Generator Loss: 3.8762 D(x): 0.8719, D(G(z)): 0.0684 Epoch: [10/20], Batch Num: [166/600] Discriminator Loss: 0.3006, Generator Loss: 3.9766 D(x): 0.8822, D(G(z)): 0.0704 Epoch: [10/20], Batch Num: [167/600] Discriminator Loss: 0.3077, Generator Loss: 3.2354 D(x): 0.8809, D(G(z)): 0.0593 Epoch: [10/20], Batch Num: [168/600] Discriminator Loss: 0.2469, Generator Loss: 3.0441 D(x): 0.9321, D(G(z)): 0.0982 Epoch: [10/20], Batch Num: [169/600] Discriminator Loss: 0.3803, Generator Loss: 2.8539 D(x): 0.8842, D(G(z)): 0.1256 Epoch: [10/20], Batch Num: [170/600] Discriminator Loss: 0.2811, Generator Loss: 2.6652 D(x): 0.9332, D(G(z)): 0.1425 Epoch: [10/20], Batch Num: [171/600] Discriminator Loss: 0.3736, Generator Loss: 3.8111 D(x): 0.9370, D(G(z)): 0.1755 Epoch: [10/20], Batch Num: [172/600] Discriminator Loss: 0.3634, Generator Loss: 4.1687 D(x): 0.8601, D(G(z)): 0.0712 Epoch: [10/20], Batch Num: [173/600] Discriminator Loss: 0.3155, Generator Loss: 4.1185 D(x): 0.8802, D(G(z)): 0.0506 Epoch: [10/20], Batch Num: [174/600] Discriminator Loss: 0.4110, Generator Loss: 3.7737 D(x): 0.8839, D(G(z)): 0.0878 Epoch: [10/20], Batch Num: [175/600] Discriminator Loss: 0.3321, Generator Loss: 3.3417 D(x): 0.8972, D(G(z)): 0.0975 Epoch: [10/20], Batch Num: [176/600] Discriminator Loss: 0.3330, Generator Loss: 3.0259 D(x): 0.8938, D(G(z)): 0.1086 Epoch: [10/20], Batch Num: [177/600] Discriminator Loss: 0.3433, Generator Loss: 2.9892 D(x): 0.9052, D(G(z)): 0.1306 Epoch: [10/20], Batch Num: [178/600] Discriminator Loss: 0.2891, Generator Loss: 3.0991 D(x): 0.9032, D(G(z)): 0.1059 Epoch: [10/20], Batch Num: [179/600] Discriminator Loss: 0.4475, Generator Loss: 3.2675 D(x): 0.8714, D(G(z)): 0.1421 Epoch: [10/20], Batch Num: [180/600] Discriminator Loss: 0.4133, Generator Loss: 3.1542 D(x): 0.8965, D(G(z)): 0.1382 Epoch: [10/20], Batch Num: [181/600] Discriminator Loss: 0.2699, Generator Loss: 3.5470 D(x): 0.9062, D(G(z)): 0.0987 Epoch: [10/20], Batch Num: [182/600] Discriminator Loss: 0.3816, Generator Loss: 3.7392 D(x): 0.9009, D(G(z)): 0.1237 Epoch: [10/20], Batch Num: [183/600] Discriminator Loss: 0.4086, Generator Loss: 3.8177 D(x): 0.8559, D(G(z)): 0.0864 Epoch: [10/20], Batch Num: [184/600] Discriminator Loss: 0.3209, Generator Loss: 3.5542 D(x): 0.9029, D(G(z)): 0.0694 Epoch: [10/20], Batch Num: [185/600] Discriminator Loss: 0.4792, Generator Loss: 3.2342 D(x): 0.8694, D(G(z)): 0.1224 Epoch: [10/20], Batch Num: [186/600] Discriminator Loss: 0.3648, Generator Loss: 3.5847 D(x): 0.9020, D(G(z)): 0.1280 Epoch: [10/20], Batch Num: [187/600] Discriminator Loss: 0.3741, Generator Loss: 3.3560 D(x): 0.8915, D(G(z)): 0.1010 Epoch: [10/20], Batch Num: [188/600] Discriminator Loss: 0.3696, Generator Loss: 3.6342 D(x): 0.8856, D(G(z)): 0.1092 Epoch: [10/20], Batch Num: [189/600] Discriminator Loss: 0.4165, Generator Loss: 3.1428 D(x): 0.8577, D(G(z)): 0.0771 Epoch: [10/20], Batch Num: [190/600] Discriminator Loss: 0.3336, Generator Loss: 2.5678 D(x): 0.8710, D(G(z)): 0.0744 Epoch: [10/20], Batch Num: [191/600] Discriminator Loss: 0.4879, Generator Loss: 2.6566 D(x): 0.8958, D(G(z)): 0.2000 Epoch: [10/20], Batch Num: [192/600] Discriminator Loss: 0.3443, Generator Loss: 3.2128 D(x): 0.9133, D(G(z)): 0.1352 Epoch: [10/20], Batch Num: [193/600] Discriminator Loss: 0.3285, Generator Loss: 3.7609 D(x): 0.9047, D(G(z)): 0.1160 Epoch: [10/20], Batch Num: [194/600] Discriminator Loss: 0.4035, Generator Loss: 3.9926 D(x): 0.8668, D(G(z)): 0.0779 Epoch: [10/20], Batch Num: [195/600] Discriminator Loss: 0.3298, Generator Loss: 3.3834 D(x): 0.8977, D(G(z)): 0.0826 Epoch: [10/20], Batch Num: [196/600] Discriminator Loss: 0.4149, Generator Loss: 3.4930 D(x): 0.8601, D(G(z)): 0.0661 Epoch: [10/20], Batch Num: [197/600] Discriminator Loss: 0.5944, Generator Loss: 2.6324 D(x): 0.8068, D(G(z)): 0.1233 Epoch: [10/20], Batch Num: [198/600] Discriminator Loss: 0.5057, Generator Loss: 2.6083 D(x): 0.9301, D(G(z)): 0.2441 Epoch: [10/20], Batch Num: [199/600] Discriminator Loss: 0.4581, Generator Loss: 3.5832 D(x): 0.9318, D(G(z)): 0.2093 Epoch: 10, Batch Num: [200/600]
Epoch: [10/20], Batch Num: [200/600] Discriminator Loss: 0.4303, Generator Loss: 4.0928 D(x): 0.8454, D(G(z)): 0.0830 Epoch: [10/20], Batch Num: [201/600] Discriminator Loss: 0.3642, Generator Loss: 4.4875 D(x): 0.8764, D(G(z)): 0.0536 Epoch: [10/20], Batch Num: [202/600] Discriminator Loss: 0.6185, Generator Loss: 3.7153 D(x): 0.7904, D(G(z)): 0.0624 Epoch: [10/20], Batch Num: [203/600] Discriminator Loss: 0.4037, Generator Loss: 2.2937 D(x): 0.8375, D(G(z)): 0.0725 Epoch: [10/20], Batch Num: [204/600] Discriminator Loss: 0.4648, Generator Loss: 2.1868 D(x): 0.9460, D(G(z)): 0.2374 Epoch: [10/20], Batch Num: [205/600] Discriminator Loss: 0.4555, Generator Loss: 2.7937 D(x): 0.9246, D(G(z)): 0.1997 Epoch: [10/20], Batch Num: [206/600] Discriminator Loss: 0.3809, Generator Loss: 3.5656 D(x): 0.8765, D(G(z)): 0.1342 Epoch: [10/20], Batch Num: [207/600] Discriminator Loss: 0.5331, Generator Loss: 3.6617 D(x): 0.8466, D(G(z)): 0.1022 Epoch: [10/20], Batch Num: [208/600] Discriminator Loss: 0.3863, Generator Loss: 3.6891 D(x): 0.8753, D(G(z)): 0.0791 Epoch: [10/20], Batch Num: [209/600] Discriminator Loss: 0.3701, Generator Loss: 2.8577 D(x): 0.8511, D(G(z)): 0.0516 Epoch: [10/20], Batch Num: [210/600] Discriminator Loss: 0.5199, Generator Loss: 2.4227 D(x): 0.8541, D(G(z)): 0.1645 Epoch: [10/20], Batch Num: [211/600] Discriminator Loss: 0.3705, Generator Loss: 2.1142 D(x): 0.9029, D(G(z)): 0.1520 Epoch: [10/20], Batch Num: [212/600] Discriminator Loss: 0.4430, Generator Loss: 2.4577 D(x): 0.8897, D(G(z)): 0.1826 Epoch: [10/20], Batch Num: [213/600] Discriminator Loss: 0.4170, Generator Loss: 2.6723 D(x): 0.8993, D(G(z)): 0.1661 Epoch: [10/20], Batch Num: [214/600] Discriminator Loss: 0.2487, Generator Loss: 3.3690 D(x): 0.9387, D(G(z)): 0.1200 Epoch: [10/20], Batch Num: [215/600] Discriminator Loss: 0.5157, Generator Loss: 3.5915 D(x): 0.8402, D(G(z)): 0.1173 Epoch: [10/20], Batch Num: [216/600] Discriminator Loss: 0.5370, Generator Loss: 3.2358 D(x): 0.7978, D(G(z)): 0.0542 Epoch: [10/20], Batch Num: [217/600] Discriminator Loss: 0.3632, Generator Loss: 2.7674 D(x): 0.8488, D(G(z)): 0.0698 Epoch: [10/20], Batch Num: [218/600] Discriminator Loss: 0.4349, Generator Loss: 2.4259 D(x): 0.8682, D(G(z)): 0.1636 Epoch: [10/20], Batch Num: [219/600] Discriminator Loss: 0.3430, Generator Loss: 2.3903 D(x): 0.9369, D(G(z)): 0.1701 Epoch: [10/20], Batch Num: [220/600] Discriminator Loss: 0.5505, Generator Loss: 2.7303 D(x): 0.8817, D(G(z)): 0.2168 Epoch: [10/20], Batch Num: [221/600] Discriminator Loss: 0.3707, Generator Loss: 2.9590 D(x): 0.8646, D(G(z)): 0.1124 Epoch: [10/20], Batch Num: [222/600] Discriminator Loss: 0.5022, Generator Loss: 3.1930 D(x): 0.8258, D(G(z)): 0.1096 Epoch: [10/20], Batch Num: [223/600] Discriminator Loss: 0.4109, Generator Loss: 2.9838 D(x): 0.8501, D(G(z)): 0.0950 Epoch: [10/20], Batch Num: [224/600] Discriminator Loss: 0.3325, Generator Loss: 2.6734 D(x): 0.8945, D(G(z)): 0.1198 Epoch: [10/20], Batch Num: [225/600] Discriminator Loss: 0.3546, Generator Loss: 2.8751 D(x): 0.8915, D(G(z)): 0.1258 Epoch: [10/20], Batch Num: [226/600] Discriminator Loss: 0.3829, Generator Loss: 2.6510 D(x): 0.8864, D(G(z)): 0.1264 Epoch: [10/20], Batch Num: [227/600] Discriminator Loss: 0.3691, Generator Loss: 3.0950 D(x): 0.9149, D(G(z)): 0.1514 Epoch: [10/20], Batch Num: [228/600] Discriminator Loss: 0.3313, Generator Loss: 3.1802 D(x): 0.9402, D(G(z)): 0.1493 Epoch: [10/20], Batch Num: [229/600] Discriminator Loss: 0.3629, Generator Loss: 3.5440 D(x): 0.8844, D(G(z)): 0.0983 Epoch: [10/20], Batch Num: [230/600] Discriminator Loss: 0.5030, Generator Loss: 3.5853 D(x): 0.8026, D(G(z)): 0.0538 Epoch: [10/20], Batch Num: [231/600] Discriminator Loss: 0.4397, Generator Loss: 3.1336 D(x): 0.8451, D(G(z)): 0.0911 Epoch: [10/20], Batch Num: [232/600] Discriminator Loss: 0.4172, Generator Loss: 2.7078 D(x): 0.8836, D(G(z)): 0.1271 Epoch: [10/20], Batch Num: [233/600] Discriminator Loss: 0.4101, Generator Loss: 2.5962 D(x): 0.9085, D(G(z)): 0.1724 Epoch: [10/20], Batch Num: [234/600] Discriminator Loss: 0.3722, Generator Loss: 2.7423 D(x): 0.9151, D(G(z)): 0.1800 Epoch: [10/20], Batch Num: [235/600] Discriminator Loss: 0.3615, Generator Loss: 3.4657 D(x): 0.8790, D(G(z)): 0.1158 Epoch: [10/20], Batch Num: [236/600] Discriminator Loss: 0.5654, Generator Loss: 3.1865 D(x): 0.7937, D(G(z)): 0.0991 Epoch: [10/20], Batch Num: [237/600] Discriminator Loss: 0.3929, Generator Loss: 2.8792 D(x): 0.8802, D(G(z)): 0.1263 Epoch: [10/20], Batch Num: [238/600] Discriminator Loss: 0.4026, Generator Loss: 2.8817 D(x): 0.9108, D(G(z)): 0.1316 Epoch: [10/20], Batch Num: [239/600] Discriminator Loss: 0.5571, Generator Loss: 2.6682 D(x): 0.8594, D(G(z)): 0.1629 Epoch: [10/20], Batch Num: [240/600] Discriminator Loss: 0.4008, Generator Loss: 2.9807 D(x): 0.9027, D(G(z)): 0.1295 Epoch: [10/20], Batch Num: [241/600] Discriminator Loss: 0.4819, Generator Loss: 2.9991 D(x): 0.8419, D(G(z)): 0.1322 Epoch: [10/20], Batch Num: 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2.8780 D(x): 0.8412, D(G(z)): 0.1482 Epoch: [10/20], Batch Num: [251/600] Discriminator Loss: 0.4231, Generator Loss: 2.8143 D(x): 0.8753, D(G(z)): 0.1253 Epoch: [10/20], Batch Num: [252/600] Discriminator Loss: 0.4590, Generator Loss: 2.7923 D(x): 0.8565, D(G(z)): 0.1331 Epoch: [10/20], Batch Num: [253/600] Discriminator Loss: 0.5377, Generator Loss: 2.7542 D(x): 0.8297, D(G(z)): 0.1247 Epoch: [10/20], Batch Num: [254/600] Discriminator Loss: 0.4627, Generator Loss: 2.6405 D(x): 0.8659, D(G(z)): 0.1565 Epoch: [10/20], Batch Num: [255/600] Discriminator Loss: 0.5375, Generator Loss: 2.6600 D(x): 0.8498, D(G(z)): 0.1446 Epoch: [10/20], Batch Num: [256/600] Discriminator Loss: 0.3372, Generator Loss: 2.9632 D(x): 0.9065, D(G(z)): 0.1323 Epoch: [10/20], Batch Num: [257/600] Discriminator Loss: 0.3354, Generator Loss: 2.7525 D(x): 0.8857, D(G(z)): 0.1099 Epoch: [10/20], Batch Num: [258/600] Discriminator Loss: 0.4461, Generator Loss: 2.8898 D(x): 0.8755, D(G(z)): 0.1254 Epoch: [10/20], Batch Num: [259/600] Discriminator Loss: 0.4244, Generator Loss: 3.1881 D(x): 0.8580, D(G(z)): 0.1127 Epoch: [10/20], Batch Num: [260/600] Discriminator Loss: 0.3985, Generator Loss: 2.8943 D(x): 0.8934, D(G(z)): 0.1480 Epoch: [10/20], Batch Num: [261/600] Discriminator Loss: 0.4654, Generator Loss: 3.0671 D(x): 0.8601, D(G(z)): 0.1275 Epoch: [10/20], Batch Num: [262/600] Discriminator Loss: 0.4828, Generator Loss: 3.0362 D(x): 0.8499, D(G(z)): 0.1322 Epoch: [10/20], Batch Num: [263/600] Discriminator Loss: 0.5109, Generator Loss: 2.7565 D(x): 0.8283, D(G(z)): 0.1511 Epoch: [10/20], Batch Num: [264/600] Discriminator Loss: 0.4755, Generator Loss: 2.9255 D(x): 0.8795, D(G(z)): 0.1458 Epoch: [10/20], Batch Num: [265/600] Discriminator Loss: 0.4795, Generator Loss: 2.8036 D(x): 0.8550, D(G(z)): 0.1541 Epoch: [10/20], Batch Num: [266/600] Discriminator Loss: 0.3558, Generator Loss: 3.0684 D(x): 0.8781, D(G(z)): 0.1032 Epoch: [10/20], Batch Num: [267/600] Discriminator Loss: 0.4757, Generator Loss: 2.7221 D(x): 0.8446, D(G(z)): 0.1245 Epoch: [10/20], Batch Num: [268/600] Discriminator Loss: 0.5017, Generator Loss: 2.5172 D(x): 0.8363, D(G(z)): 0.1472 Epoch: [10/20], Batch Num: [269/600] Discriminator Loss: 0.4731, Generator Loss: 2.5803 D(x): 0.8508, D(G(z)): 0.1552 Epoch: [10/20], Batch Num: [270/600] Discriminator Loss: 0.3951, Generator Loss: 2.5606 D(x): 0.9036, D(G(z)): 0.1663 Epoch: [10/20], Batch Num: [271/600] Discriminator Loss: 0.3195, Generator Loss: 2.7581 D(x): 0.8779, D(G(z)): 0.1047 Epoch: [10/20], Batch Num: [272/600] Discriminator Loss: 0.4078, Generator Loss: 3.1166 D(x): 0.9157, D(G(z)): 0.1645 Epoch: [10/20], Batch Num: [273/600] Discriminator Loss: 0.3227, Generator Loss: 3.2745 D(x): 0.8797, D(G(z)): 0.0840 Epoch: [10/20], Batch Num: [274/600] Discriminator Loss: 0.3153, Generator Loss: 3.5871 D(x): 0.8925, D(G(z)): 0.0932 Epoch: [10/20], Batch Num: [275/600] Discriminator Loss: 0.5250, Generator Loss: 3.0418 D(x): 0.8272, D(G(z)): 0.0883 Epoch: [10/20], Batch Num: [276/600] Discriminator Loss: 0.4021, Generator Loss: 2.7603 D(x): 0.8743, D(G(z)): 0.1146 Epoch: [10/20], Batch Num: [277/600] Discriminator Loss: 0.4429, Generator Loss: 2.4862 D(x): 0.9141, D(G(z)): 0.1784 Epoch: [10/20], Batch Num: [278/600] Discriminator Loss: 0.3351, Generator Loss: 2.9813 D(x): 0.9166, D(G(z)): 0.1462 Epoch: [10/20], Batch Num: [279/600] Discriminator Loss: 0.4226, Generator Loss: 3.4019 D(x): 0.8930, D(G(z)): 0.1360 Epoch: [10/20], Batch Num: [280/600] Discriminator Loss: 0.4180, Generator Loss: 3.9597 D(x): 0.8748, D(G(z)): 0.1214 Epoch: [10/20], Batch Num: [281/600] Discriminator Loss: 0.5283, Generator Loss: 3.4264 D(x): 0.8156, D(G(z)): 0.0823 Epoch: [10/20], Batch Num: [282/600] Discriminator Loss: 0.4835, Generator Loss: 2.9084 D(x): 0.8329, D(G(z)): 0.0900 Epoch: [10/20], Batch Num: [283/600] Discriminator Loss: 0.4646, Generator Loss: 2.5079 D(x): 0.8582, D(G(z)): 0.1463 Epoch: [10/20], Batch Num: [284/600] Discriminator Loss: 0.5489, Generator Loss: 2.1129 D(x): 0.8826, D(G(z)): 0.1950 Epoch: [10/20], Batch Num: [285/600] Discriminator Loss: 0.4180, Generator Loss: 2.7254 D(x): 0.9158, D(G(z)): 0.1953 Epoch: [10/20], Batch Num: [286/600] Discriminator Loss: 0.4552, Generator Loss: 3.1721 D(x): 0.8593, D(G(z)): 0.1525 Epoch: [10/20], Batch Num: [287/600] Discriminator Loss: 0.4847, Generator Loss: 3.3167 D(x): 0.8389, D(G(z)): 0.1128 Epoch: [10/20], Batch Num: [288/600] Discriminator Loss: 0.6427, Generator Loss: 3.3758 D(x): 0.8240, D(G(z)): 0.1222 Epoch: [10/20], Batch Num: [289/600] Discriminator Loss: 0.4773, Generator Loss: 3.1341 D(x): 0.8201, D(G(z)): 0.0935 Epoch: [10/20], Batch Num: [290/600] Discriminator Loss: 0.6514, Generator Loss: 2.5069 D(x): 0.7817, D(G(z)): 0.1305 Epoch: [10/20], Batch Num: [291/600] Discriminator Loss: 0.4875, Generator Loss: 2.4867 D(x): 0.9058, D(G(z)): 0.1821 Epoch: [10/20], Batch Num: [292/600] Discriminator Loss: 0.4490, Generator Loss: 2.5819 D(x): 0.9066, D(G(z)): 0.1928 Epoch: [10/20], Batch Num: [293/600] Discriminator Loss: 0.3874, Generator Loss: 2.7303 D(x): 0.9192, D(G(z)): 0.1726 Epoch: [10/20], Batch Num: [294/600] Discriminator Loss: 0.4581, Generator Loss: 3.0940 D(x): 0.8307, D(G(z)): 0.0970 Epoch: [10/20], Batch Num: [295/600] Discriminator Loss: 0.6975, Generator Loss: 2.7945 D(x): 0.7783, D(G(z)): 0.1296 Epoch: [10/20], Batch Num: [296/600] Discriminator Loss: 0.4942, Generator Loss: 2.4156 D(x): 0.8182, D(G(z)): 0.1075 Epoch: [10/20], Batch Num: [297/600] Discriminator Loss: 0.4518, Generator Loss: 2.0990 D(x): 0.9035, D(G(z)): 0.1710 Epoch: [10/20], Batch Num: [298/600] Discriminator Loss: 0.4381, Generator Loss: 2.2768 D(x): 0.8791, D(G(z)): 0.1742 Epoch: [10/20], Batch Num: [299/600] Discriminator Loss: 0.3980, Generator Loss: 2.5321 D(x): 0.8767, D(G(z)): 0.1406 Epoch: 10, Batch Num: [300/600]
Epoch: [10/20], Batch Num: [300/600] Discriminator Loss: 0.5488, Generator Loss: 2.6681 D(x): 0.8475, D(G(z)): 0.1815 Epoch: [10/20], Batch Num: [301/600] Discriminator Loss: 0.6204, Generator Loss: 2.8882 D(x): 0.8009, D(G(z)): 0.1370 Epoch: [10/20], Batch Num: [302/600] Discriminator Loss: 0.4070, Generator Loss: 2.8068 D(x): 0.8429, D(G(z)): 0.0912 Epoch: [10/20], Batch Num: [303/600] Discriminator Loss: 0.5599, Generator Loss: 2.4524 D(x): 0.8074, D(G(z)): 0.1423 Epoch: [10/20], Batch Num: [304/600] Discriminator Loss: 0.3935, Generator Loss: 2.3582 D(x): 0.8585, D(G(z)): 0.1119 Epoch: [10/20], Batch Num: [305/600] Discriminator Loss: 0.4326, Generator Loss: 1.9233 D(x): 0.8832, D(G(z)): 0.1846 Epoch: [10/20], Batch Num: [306/600] Discriminator Loss: 0.3877, Generator Loss: 1.9766 D(x): 0.8957, D(G(z)): 0.1824 Epoch: [10/20], Batch Num: [307/600] Discriminator Loss: 0.5829, Generator Loss: 2.7961 D(x): 0.8896, D(G(z)): 0.2533 Epoch: [10/20], Batch Num: [308/600] Discriminator Loss: 0.4086, Generator Loss: 3.2434 D(x): 0.8476, D(G(z)): 0.1293 Epoch: [10/20], Batch Num: [309/600] Discriminator Loss: 0.4642, Generator Loss: 3.1093 D(x): 0.8256, D(G(z)): 0.0918 Epoch: [10/20], Batch Num: [310/600] Discriminator Loss: 0.4535, Generator Loss: 3.1925 D(x): 0.8181, D(G(z)): 0.0934 Epoch: [10/20], Batch Num: [311/600] Discriminator Loss: 0.3774, Generator Loss: 2.9025 D(x): 0.8806, D(G(z)): 0.1181 Epoch: [10/20], Batch Num: [312/600] Discriminator Loss: 0.4212, Generator Loss: 2.7679 D(x): 0.9029, D(G(z)): 0.1512 Epoch: [10/20], Batch Num: [313/600] Discriminator Loss: 0.3345, Generator Loss: 2.8384 D(x): 0.9014, D(G(z)): 0.1268 Epoch: [10/20], Batch Num: [314/600] Discriminator Loss: 0.4529, Generator Loss: 2.8133 D(x): 0.8930, D(G(z)): 0.1466 Epoch: [10/20], Batch Num: [315/600] Discriminator Loss: 0.3731, Generator Loss: 2.6501 D(x): 0.8412, D(G(z)): 0.0828 Epoch: [10/20], Batch Num: [316/600] Discriminator Loss: 0.4453, Generator Loss: 2.7783 D(x): 0.8688, D(G(z)): 0.1162 Epoch: [10/20], Batch Num: [317/600] Discriminator Loss: 0.3714, Generator Loss: 2.8502 D(x): 0.9086, D(G(z)): 0.1202 Epoch: [10/20], Batch Num: [318/600] Discriminator Loss: 0.3842, Generator Loss: 2.6848 D(x): 0.8965, D(G(z)): 0.1598 Epoch: [10/20], Batch Num: [319/600] Discriminator Loss: 0.4171, Generator Loss: 3.0655 D(x): 0.8870, D(G(z)): 0.1582 Epoch: [10/20], Batch Num: [320/600] Discriminator Loss: 0.3914, Generator Loss: 3.0835 D(x): 0.8695, D(G(z)): 0.1115 Epoch: [10/20], Batch Num: [321/600] Discriminator Loss: 0.4435, Generator Loss: 2.8740 D(x): 0.8405, D(G(z)): 0.0884 Epoch: [10/20], Batch Num: [322/600] Discriminator Loss: 0.3696, Generator Loss: 2.5402 D(x): 0.8744, D(G(z)): 0.1295 Epoch: [10/20], Batch Num: [323/600] Discriminator Loss: 0.4042, Generator Loss: 2.6703 D(x): 0.8877, D(G(z)): 0.1518 Epoch: [10/20], Batch Num: [324/600] Discriminator Loss: 0.4798, Generator Loss: 2.4817 D(x): 0.8661, D(G(z)): 0.1626 Epoch: [10/20], Batch Num: [325/600] Discriminator Loss: 0.4032, Generator Loss: 2.7490 D(x): 0.9027, D(G(z)): 0.1704 Epoch: [10/20], Batch Num: [326/600] Discriminator Loss: 0.2800, Generator Loss: 3.3691 D(x): 0.9271, D(G(z)): 0.1332 Epoch: [10/20], Batch Num: [327/600] Discriminator Loss: 0.3867, Generator Loss: 3.3612 D(x): 0.8554, D(G(z)): 0.0939 Epoch: [10/20], Batch Num: [328/600] Discriminator Loss: 0.4202, Generator Loss: 2.7957 D(x): 0.8126, D(G(z)): 0.0681 Epoch: [10/20], Batch Num: [329/600] Discriminator Loss: 0.3567, Generator Loss: 2.7178 D(x): 0.8865, D(G(z)): 0.1182 Epoch: [10/20], Batch Num: [330/600] Discriminator Loss: 0.4038, Generator Loss: 2.3930 D(x): 0.8917, D(G(z)): 0.1715 Epoch: [10/20], Batch Num: [331/600] Discriminator Loss: 0.5936, Generator Loss: 2.5276 D(x): 0.8792, D(G(z)): 0.2051 Epoch: [10/20], Batch Num: [332/600] Discriminator Loss: 0.4943, Generator Loss: 3.2562 D(x): 0.8805, D(G(z)): 0.1898 Epoch: [10/20], Batch Num: [333/600] Discriminator Loss: 0.4584, Generator Loss: 3.5178 D(x): 0.8442, D(G(z)): 0.0898 Epoch: [10/20], Batch Num: [334/600] Discriminator Loss: 0.3342, Generator Loss: 3.4804 D(x): 0.8901, D(G(z)): 0.0744 Epoch: [10/20], Batch Num: [335/600] Discriminator Loss: 0.3967, Generator Loss: 3.1855 D(x): 0.8486, D(G(z)): 0.0895 Epoch: [10/20], Batch Num: [336/600] Discriminator Loss: 0.2648, Generator Loss: 2.6863 D(x): 0.9162, D(G(z)): 0.0980 Epoch: [10/20], Batch Num: [337/600] Discriminator Loss: 0.4714, Generator Loss: 2.3565 D(x): 0.8920, D(G(z)): 0.1447 Epoch: [10/20], Batch Num: [338/600] Discriminator Loss: 0.3332, Generator Loss: 2.3357 D(x): 0.9219, D(G(z)): 0.1603 Epoch: [10/20], Batch Num: [339/600] Discriminator Loss: 0.5864, Generator Loss: 2.9254 D(x): 0.8683, D(G(z)): 0.2111 Epoch: [10/20], Batch Num: [340/600] Discriminator Loss: 0.4570, Generator Loss: 3.3139 D(x): 0.8702, D(G(z)): 0.1310 Epoch: [10/20], Batch Num: [341/600] Discriminator Loss: 0.4301, Generator Loss: 2.9520 D(x): 0.8284, D(G(z)): 0.0755 Epoch: [10/20], Batch Num: [342/600] Discriminator Loss: 0.6149, Generator Loss: 2.7376 D(x): 0.8058, D(G(z)): 0.1413 Epoch: [10/20], Batch Num: [343/600] Discriminator Loss: 0.4632, Generator Loss: 2.6417 D(x): 0.9004, D(G(z)): 0.1972 Epoch: [10/20], Batch Num: [344/600] Discriminator Loss: 0.3529, Generator Loss: 3.1912 D(x): 0.8984, D(G(z)): 0.1375 Epoch: [10/20], Batch Num: [345/600] Discriminator Loss: 0.4484, Generator Loss: 2.9107 D(x): 0.8314, D(G(z)): 0.1116 Epoch: [10/20], Batch Num: [346/600] Discriminator Loss: 0.3840, Generator Loss: 3.0277 D(x): 0.8814, D(G(z)): 0.1250 Epoch: [10/20], Batch Num: [347/600] Discriminator Loss: 0.3784, Generator Loss: 3.0123 D(x): 0.8860, D(G(z)): 0.1251 Epoch: [10/20], Batch Num: [348/600] Discriminator Loss: 0.4981, Generator Loss: 2.8419 D(x): 0.8520, D(G(z)): 0.1287 Epoch: [10/20], Batch Num: [349/600] Discriminator Loss: 0.5377, Generator Loss: 2.4210 D(x): 0.8324, D(G(z)): 0.1390 Epoch: [10/20], Batch Num: [350/600] Discriminator Loss: 0.6035, Generator Loss: 2.4653 D(x): 0.8430, D(G(z)): 0.1821 Epoch: [10/20], Batch Num: [351/600] Discriminator Loss: 0.6356, Generator Loss: 2.5186 D(x): 0.8330, D(G(z)): 0.2088 Epoch: [10/20], Batch Num: [352/600] Discriminator Loss: 0.5020, Generator Loss: 2.8710 D(x): 0.8861, D(G(z)): 0.1752 Epoch: [10/20], Batch Num: [353/600] Discriminator Loss: 0.4685, Generator Loss: 2.9734 D(x): 0.8254, D(G(z)): 0.1164 Epoch: [10/20], Batch Num: [354/600] Discriminator Loss: 0.4516, Generator Loss: 3.0649 D(x): 0.8617, D(G(z)): 0.1299 Epoch: [10/20], Batch Num: [355/600] Discriminator Loss: 0.4332, Generator Loss: 2.9719 D(x): 0.8448, D(G(z)): 0.1166 Epoch: [10/20], Batch Num: [356/600] Discriminator Loss: 0.6063, Generator Loss: 2.4294 D(x): 0.8176, D(G(z)): 0.1751 Epoch: [10/20], Batch Num: [357/600] Discriminator Loss: 0.5354, Generator Loss: 2.6228 D(x): 0.8544, D(G(z)): 0.1952 Epoch: [10/20], Batch Num: [358/600] Discriminator Loss: 0.5053, Generator Loss: 2.7673 D(x): 0.8804, D(G(z)): 0.1963 Epoch: [10/20], Batch Num: [359/600] Discriminator Loss: 0.5321, Generator Loss: 2.9019 D(x): 0.8048, D(G(z)): 0.1168 Epoch: [10/20], Batch Num: [360/600] Discriminator Loss: 0.5261, Generator Loss: 2.7818 D(x): 0.8615, D(G(z)): 0.1566 Epoch: [10/20], Batch Num: [361/600] Discriminator Loss: 0.6214, Generator Loss: 2.8591 D(x): 0.8250, D(G(z)): 0.1399 Epoch: [10/20], Batch Num: [362/600] Discriminator Loss: 0.4992, Generator Loss: 2.8253 D(x): 0.8568, D(G(z)): 0.1618 Epoch: [10/20], Batch Num: [363/600] Discriminator Loss: 0.3547, Generator Loss: 2.9063 D(x): 0.8748, D(G(z)): 0.1124 Epoch: [10/20], Batch Num: [364/600] Discriminator Loss: 0.4268, Generator Loss: 2.6988 D(x): 0.8802, D(G(z)): 0.1374 Epoch: [10/20], Batch Num: [365/600] Discriminator Loss: 0.4852, Generator Loss: 2.7788 D(x): 0.8320, D(G(z)): 0.1269 Epoch: [10/20], Batch Num: [366/600] Discriminator Loss: 0.4943, Generator Loss: 2.8965 D(x): 0.8519, D(G(z)): 0.1615 Epoch: [10/20], Batch Num: [367/600] Discriminator Loss: 0.4498, Generator Loss: 2.7446 D(x): 0.8759, D(G(z)): 0.1429 Epoch: [10/20], Batch Num: [368/600] Discriminator Loss: 0.5440, Generator Loss: 2.7343 D(x): 0.8203, D(G(z)): 0.1516 Epoch: [10/20], Batch Num: [369/600] Discriminator Loss: 0.4432, Generator Loss: 3.1645 D(x): 0.8715, D(G(z)): 0.1556 Epoch: [10/20], Batch Num: [370/600] Discriminator Loss: 0.5157, Generator Loss: 2.8242 D(x): 0.7984, D(G(z)): 0.1143 Epoch: [10/20], Batch Num: [371/600] Discriminator Loss: 0.4022, Generator Loss: 2.7873 D(x): 0.8866, D(G(z)): 0.1558 Epoch: [10/20], Batch Num: [372/600] Discriminator Loss: 0.5417, Generator Loss: 2.8283 D(x): 0.8575, D(G(z)): 0.1924 Epoch: [10/20], Batch Num: [373/600] Discriminator Loss: 0.4804, Generator Loss: 3.3553 D(x): 0.8787, D(G(z)): 0.1651 Epoch: [10/20], Batch Num: [374/600] Discriminator Loss: 0.4828, Generator Loss: 3.1064 D(x): 0.8224, D(G(z)): 0.0962 Epoch: [10/20], Batch Num: [375/600] Discriminator Loss: 0.4742, Generator Loss: 2.8471 D(x): 0.8325, D(G(z)): 0.1137 Epoch: [10/20], Batch Num: [376/600] Discriminator Loss: 0.3541, Generator Loss: 2.5068 D(x): 0.8826, D(G(z)): 0.0972 Epoch: [10/20], Batch Num: [377/600] Discriminator Loss: 0.4465, Generator Loss: 2.4788 D(x): 0.8764, D(G(z)): 0.1797 Epoch: [10/20], Batch Num: [378/600] Discriminator Loss: 0.5416, Generator Loss: 2.8263 D(x): 0.8747, D(G(z)): 0.2092 Epoch: [10/20], Batch Num: [379/600] Discriminator Loss: 0.3763, Generator Loss: 3.2925 D(x): 0.9073, D(G(z)): 0.1618 Epoch: [10/20], Batch Num: [380/600] Discriminator Loss: 0.3389, Generator Loss: 3.8317 D(x): 0.8605, D(G(z)): 0.0844 Epoch: [10/20], Batch Num: [381/600] Discriminator Loss: 0.4344, Generator Loss: 3.4853 D(x): 0.8573, D(G(z)): 0.0915 Epoch: [10/20], Batch Num: [382/600] Discriminator Loss: 0.4191, Generator Loss: 3.1274 D(x): 0.8482, D(G(z)): 0.0870 Epoch: [10/20], Batch Num: [383/600] Discriminator Loss: 0.4655, Generator Loss: 2.9089 D(x): 0.8590, D(G(z)): 0.1350 Epoch: [10/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [10/20], Batch Num: [400/600] Discriminator Loss: 0.6273, Generator Loss: 3.3396 D(x): 0.8991, D(G(z)): 0.2415 Epoch: [10/20], Batch Num: [401/600] Discriminator Loss: 0.3870, Generator Loss: 3.8688 D(x): 0.9005, D(G(z)): 0.1410 Epoch: [10/20], Batch Num: [402/600] Discriminator Loss: 0.3022, Generator Loss: 3.8936 D(x): 0.8913, D(G(z)): 0.0633 Epoch: [10/20], Batch Num: [403/600] Discriminator Loss: 0.4720, Generator Loss: 3.8352 D(x): 0.8188, D(G(z)): 0.0692 Epoch: [10/20], Batch Num: [404/600] Discriminator Loss: 0.4633, Generator Loss: 2.7841 D(x): 0.8577, D(G(z)): 0.1060 Epoch: [10/20], Batch Num: [405/600] Discriminator Loss: 0.4259, Generator Loss: 2.5922 D(x): 0.9162, D(G(z)): 0.1521 Epoch: [10/20], Batch Num: [406/600] Discriminator Loss: 0.4761, Generator Loss: 3.3296 D(x): 0.9076, D(G(z)): 0.1979 Epoch: [10/20], Batch Num: [407/600] Discriminator Loss: 0.3427, Generator Loss: 3.9179 D(x): 0.8891, D(G(z)): 0.1175 Epoch: [10/20], Batch Num: [408/600] Discriminator Loss: 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0.0521 Epoch: [10/20], Batch Num: [417/600] Discriminator Loss: 0.3251, Generator Loss: 3.4337 D(x): 0.8781, D(G(z)): 0.0626 Epoch: [10/20], Batch Num: [418/600] Discriminator Loss: 0.4618, Generator Loss: 2.8182 D(x): 0.8700, D(G(z)): 0.1244 Epoch: [10/20], Batch Num: [419/600] Discriminator Loss: 0.5098, Generator Loss: 2.2173 D(x): 0.8726, D(G(z)): 0.1843 Epoch: [10/20], Batch Num: [420/600] Discriminator Loss: 0.3726, Generator Loss: 2.5866 D(x): 0.9252, D(G(z)): 0.1883 Epoch: [10/20], Batch Num: [421/600] Discriminator Loss: 0.5282, Generator Loss: 2.9198 D(x): 0.8622, D(G(z)): 0.1709 Epoch: [10/20], Batch Num: [422/600] Discriminator Loss: 0.3058, Generator Loss: 3.3081 D(x): 0.9078, D(G(z)): 0.1348 Epoch: [10/20], Batch Num: [423/600] Discriminator Loss: 0.3241, Generator Loss: 3.3787 D(x): 0.8740, D(G(z)): 0.0787 Epoch: [10/20], Batch Num: [424/600] Discriminator Loss: 0.5567, Generator Loss: 2.9086 D(x): 0.7915, D(G(z)): 0.0913 Epoch: [10/20], Batch Num: [425/600] Discriminator Loss: 0.4596, Generator Loss: 2.9729 D(x): 0.8822, D(G(z)): 0.1529 Epoch: [10/20], Batch Num: [426/600] Discriminator Loss: 0.4178, Generator Loss: 3.0715 D(x): 0.9051, D(G(z)): 0.1799 Epoch: [10/20], Batch Num: [427/600] Discriminator Loss: 0.4363, Generator Loss: 3.1882 D(x): 0.8583, D(G(z)): 0.1181 Epoch: [10/20], Batch Num: [428/600] Discriminator Loss: 0.3753, Generator Loss: 3.3597 D(x): 0.8967, D(G(z)): 0.1175 Epoch: [10/20], Batch Num: [429/600] Discriminator Loss: 0.3782, Generator Loss: 3.2014 D(x): 0.8549, D(G(z)): 0.0836 Epoch: [10/20], Batch Num: [430/600] Discriminator Loss: 0.5838, Generator Loss: 2.4601 D(x): 0.8375, D(G(z)): 0.1421 Epoch: [10/20], Batch Num: [431/600] Discriminator Loss: 0.4984, Generator Loss: 2.7000 D(x): 0.8755, D(G(z)): 0.1916 Epoch: [10/20], Batch Num: [432/600] Discriminator Loss: 0.5479, Generator Loss: 2.9564 D(x): 0.8716, D(G(z)): 0.2200 Epoch: [10/20], Batch Num: [433/600] Discriminator Loss: 0.5215, Generator Loss: 2.8331 D(x): 0.8285, D(G(z)): 0.1619 Epoch: [10/20], Batch Num: [434/600] Discriminator Loss: 0.5698, Generator Loss: 2.6763 D(x): 0.8035, D(G(z)): 0.1200 Epoch: [10/20], Batch Num: [435/600] Discriminator Loss: 0.4464, Generator Loss: 2.5043 D(x): 0.8715, D(G(z)): 0.1478 Epoch: [10/20], Batch Num: [436/600] Discriminator Loss: 0.3623, Generator Loss: 2.8889 D(x): 0.8988, D(G(z)): 0.1715 Epoch: [10/20], Batch Num: [437/600] Discriminator Loss: 0.3517, Generator Loss: 3.2384 D(x): 0.8924, D(G(z)): 0.1479 Epoch: [10/20], Batch Num: [438/600] Discriminator Loss: 0.4409, Generator Loss: 2.8969 D(x): 0.8276, D(G(z)): 0.1075 Epoch: [10/20], Batch Num: [439/600] Discriminator Loss: 0.5168, Generator Loss: 2.7048 D(x): 0.8191, D(G(z)): 0.1480 Epoch: [10/20], Batch Num: [440/600] Discriminator Loss: 0.5318, Generator Loss: 2.4245 D(x): 0.8188, D(G(z)): 0.1596 Epoch: [10/20], Batch Num: [441/600] Discriminator Loss: 0.4755, Generator Loss: 2.4742 D(x): 0.8839, D(G(z)): 0.1962 Epoch: [10/20], Batch Num: [442/600] Discriminator Loss: 0.4280, Generator Loss: 2.9683 D(x): 0.8953, D(G(z)): 0.1909 Epoch: [10/20], Batch Num: [443/600] Discriminator Loss: 0.3904, Generator Loss: 3.4802 D(x): 0.8752, D(G(z)): 0.1448 Epoch: [10/20], Batch Num: [444/600] Discriminator Loss: 0.6371, Generator Loss: 3.0722 D(x): 0.7991, D(G(z)): 0.1294 Epoch: [10/20], Batch Num: [445/600] Discriminator Loss: 0.5438, Generator Loss: 2.8852 D(x): 0.8189, D(G(z)): 0.1289 Epoch: [10/20], Batch Num: [446/600] Discriminator Loss: 0.4686, Generator Loss: 2.7632 D(x): 0.8634, D(G(z)): 0.1465 Epoch: [10/20], Batch Num: [447/600] Discriminator Loss: 0.5067, Generator Loss: 2.2435 D(x): 0.8419, D(G(z)): 0.1325 Epoch: [10/20], Batch Num: [448/600] Discriminator Loss: 0.4290, Generator Loss: 2.5981 D(x): 0.9127, D(G(z)): 0.1887 Epoch: [10/20], Batch Num: [449/600] Discriminator Loss: 0.4022, Generator Loss: 3.3957 D(x): 0.9074, D(G(z)): 0.1783 Epoch: [10/20], Batch Num: [450/600] Discriminator Loss: 0.5473, Generator Loss: 3.3772 D(x): 0.8322, D(G(z)): 0.1101 Epoch: [10/20], Batch Num: [451/600] Discriminator Loss: 0.7047, Generator Loss: 2.9260 D(x): 0.7612, D(G(z)): 0.1087 Epoch: [10/20], Batch Num: [452/600] Discriminator Loss: 0.4274, Generator Loss: 2.4812 D(x): 0.8524, D(G(z)): 0.0952 Epoch: [10/20], Batch Num: [453/600] Discriminator Loss: 0.5235, Generator Loss: 2.2516 D(x): 0.9091, D(G(z)): 0.2128 Epoch: [10/20], Batch Num: [454/600] Discriminator Loss: 0.5234, Generator Loss: 2.6152 D(x): 0.8821, D(G(z)): 0.2080 Epoch: [10/20], Batch Num: [455/600] Discriminator Loss: 0.4286, Generator Loss: 3.2297 D(x): 0.8679, D(G(z)): 0.1323 Epoch: [10/20], Batch Num: [456/600] Discriminator Loss: 0.4875, Generator Loss: 3.3338 D(x): 0.8159, D(G(z)): 0.0948 Epoch: [10/20], Batch Num: [457/600] Discriminator Loss: 0.4002, Generator Loss: 3.1396 D(x): 0.8621, D(G(z)): 0.0836 Epoch: [10/20], Batch Num: [458/600] Discriminator Loss: 0.3621, Generator Loss: 2.5289 D(x): 0.8632, D(G(z)): 0.0894 Epoch: [10/20], Batch Num: [459/600] Discriminator Loss: 0.3317, Generator Loss: 2.1222 D(x): 0.8870, D(G(z)): 0.1317 Epoch: [10/20], Batch Num: [460/600] Discriminator Loss: 0.4563, Generator Loss: 2.4681 D(x): 0.9039, D(G(z)): 0.2045 Epoch: [10/20], Batch Num: [461/600] Discriminator Loss: 0.5143, Generator Loss: 2.8681 D(x): 0.8564, D(G(z)): 0.1901 Epoch: [10/20], Batch Num: [462/600] Discriminator Loss: 0.4182, Generator Loss: 3.0490 D(x): 0.8579, D(G(z)): 0.1344 Epoch: [10/20], Batch Num: [463/600] Discriminator Loss: 0.4270, Generator Loss: 3.6424 D(x): 0.8584, D(G(z)): 0.0917 Epoch: [10/20], Batch Num: [464/600] Discriminator Loss: 0.3630, Generator Loss: 3.3017 D(x): 0.8502, D(G(z)): 0.0725 Epoch: [10/20], Batch Num: [465/600] Discriminator Loss: 0.3298, Generator Loss: 3.0457 D(x): 0.8831, D(G(z)): 0.0971 Epoch: [10/20], Batch Num: [466/600] Discriminator Loss: 0.3527, Generator Loss: 2.7479 D(x): 0.8666, D(G(z)): 0.0917 Epoch: [10/20], Batch Num: [467/600] Discriminator Loss: 0.3458, Generator Loss: 2.8101 D(x): 0.9378, D(G(z)): 0.1652 Epoch: [10/20], Batch Num: [468/600] Discriminator Loss: 0.3865, Generator Loss: 2.9627 D(x): 0.9047, D(G(z)): 0.1467 Epoch: [10/20], Batch Num: [469/600] Discriminator Loss: 0.2927, Generator Loss: 3.3644 D(x): 0.9214, D(G(z)): 0.1253 Epoch: [10/20], Batch Num: [470/600] Discriminator Loss: 0.3088, Generator Loss: 3.9814 D(x): 0.9000, D(G(z)): 0.0683 Epoch: [10/20], Batch Num: [471/600] Discriminator Loss: 0.4988, Generator Loss: 3.2573 D(x): 0.8237, D(G(z)): 0.0568 Epoch: [10/20], Batch Num: [472/600] Discriminator Loss: 0.3095, Generator Loss: 2.9249 D(x): 0.9040, D(G(z)): 0.1106 Epoch: [10/20], Batch Num: [473/600] Discriminator Loss: 0.3764, Generator Loss: 3.1511 D(x): 0.9004, D(G(z)): 0.1234 Epoch: [10/20], Batch Num: [474/600] Discriminator Loss: 0.3771, Generator Loss: 3.1958 D(x): 0.8997, D(G(z)): 0.1242 Epoch: [10/20], Batch Num: [475/600] Discriminator Loss: 0.3323, Generator Loss: 3.4922 D(x): 0.9052, D(G(z)): 0.1150 Epoch: [10/20], Batch Num: [476/600] Discriminator Loss: 0.2767, Generator Loss: 3.6087 D(x): 0.9062, D(G(z)): 0.0862 Epoch: [10/20], Batch Num: [477/600] Discriminator Loss: 0.3462, Generator Loss: 3.6164 D(x): 0.8604, D(G(z)): 0.0691 Epoch: [10/20], Batch Num: [478/600] Discriminator Loss: 0.4657, Generator Loss: 2.7024 D(x): 0.8458, D(G(z)): 0.0848 Epoch: [10/20], Batch Num: [479/600] Discriminator Loss: 0.4163, Generator Loss: 2.8619 D(x): 0.9203, D(G(z)): 0.1446 Epoch: [10/20], Batch Num: [480/600] Discriminator Loss: 0.3869, Generator Loss: 2.9619 D(x): 0.9330, D(G(z)): 0.1715 Epoch: [10/20], Batch Num: [481/600] Discriminator Loss: 0.5558, Generator Loss: 3.7713 D(x): 0.8933, D(G(z)): 0.1605 Epoch: [10/20], Batch Num: [482/600] Discriminator Loss: 0.5138, Generator Loss: 4.1842 D(x): 0.8201, D(G(z)): 0.0537 Epoch: [10/20], Batch Num: [483/600] Discriminator Loss: 0.6488, Generator Loss: 3.2338 D(x): 0.8120, D(G(z)): 0.0929 Epoch: [10/20], Batch Num: [484/600] Discriminator Loss: 0.4060, Generator Loss: 2.1814 D(x): 0.8731, D(G(z)): 0.1179 Epoch: [10/20], Batch Num: [485/600] Discriminator Loss: 0.4235, Generator Loss: 2.6618 D(x): 0.9312, D(G(z)): 0.2104 Epoch: [10/20], Batch Num: [486/600] Discriminator Loss: 0.5407, Generator Loss: 3.2453 D(x): 0.8827, D(G(z)): 0.2049 Epoch: [10/20], Batch Num: [487/600] Discriminator Loss: 0.3713, Generator Loss: 3.5899 D(x): 0.8625, D(G(z)): 0.0818 Epoch: [10/20], Batch Num: [488/600] Discriminator Loss: 0.4512, Generator Loss: 2.8993 D(x): 0.8316, D(G(z)): 0.0842 Epoch: [10/20], Batch Num: [489/600] Discriminator Loss: 0.4296, Generator Loss: 2.9825 D(x): 0.8525, D(G(z)): 0.1025 Epoch: [10/20], Batch Num: [490/600] Discriminator Loss: 0.4046, Generator Loss: 2.6368 D(x): 0.8729, D(G(z)): 0.1259 Epoch: [10/20], Batch Num: [491/600] Discriminator Loss: 0.6222, Generator Loss: 2.5567 D(x): 0.8455, D(G(z)): 0.1746 Epoch: [10/20], Batch Num: [492/600] Discriminator Loss: 0.5962, Generator Loss: 2.3642 D(x): 0.8256, D(G(z)): 0.1694 Epoch: [10/20], Batch Num: [493/600] Discriminator Loss: 0.5674, Generator Loss: 2.3846 D(x): 0.8360, D(G(z)): 0.1693 Epoch: [10/20], Batch Num: [494/600] Discriminator Loss: 0.5333, Generator Loss: 2.6911 D(x): 0.8918, D(G(z)): 0.2071 Epoch: [10/20], Batch Num: [495/600] Discriminator Loss: 0.4911, Generator Loss: 3.2285 D(x): 0.8561, D(G(z)): 0.1608 Epoch: [10/20], Batch Num: [496/600] Discriminator Loss: 0.4272, Generator Loss: 3.1249 D(x): 0.8412, D(G(z)): 0.0919 Epoch: [10/20], Batch Num: [497/600] Discriminator Loss: 0.4840, Generator Loss: 2.5778 D(x): 0.7869, D(G(z)): 0.0838 Epoch: [10/20], Batch Num: [498/600] Discriminator Loss: 0.4551, Generator Loss: 2.3962 D(x): 0.8520, D(G(z)): 0.1233 Epoch: [10/20], Batch Num: [499/600] Discriminator Loss: 0.4383, Generator Loss: 2.1737 D(x): 0.8776, D(G(z)): 0.1626 Epoch: 10, Batch Num: [500/600]
Epoch: [10/20], Batch Num: [500/600] Discriminator Loss: 0.6153, Generator Loss: 2.0950 D(x): 0.8448, D(G(z)): 0.2131 Epoch: [10/20], Batch Num: [501/600] Discriminator Loss: 0.4250, Generator Loss: 2.2784 D(x): 0.8932, D(G(z)): 0.1892 Epoch: [10/20], Batch Num: [502/600] Discriminator Loss: 0.5050, Generator Loss: 2.6096 D(x): 0.8633, D(G(z)): 0.1946 Epoch: [10/20], Batch Num: [503/600] Discriminator Loss: 0.3811, Generator Loss: 2.7976 D(x): 0.8607, D(G(z)): 0.1143 Epoch: [10/20], Batch Num: [504/600] Discriminator Loss: 0.4021, Generator Loss: 2.6738 D(x): 0.8426, D(G(z)): 0.1043 Epoch: [10/20], Batch Num: [505/600] Discriminator Loss: 0.5294, Generator Loss: 2.7526 D(x): 0.8040, D(G(z)): 0.1348 Epoch: [10/20], Batch Num: [506/600] Discriminator Loss: 0.4828, Generator Loss: 2.5366 D(x): 0.8277, D(G(z)): 0.1383 Epoch: [10/20], Batch Num: [507/600] Discriminator Loss: 0.4655, Generator Loss: 2.2651 D(x): 0.8649, D(G(z)): 0.1734 Epoch: [10/20], Batch Num: [508/600] Discriminator Loss: 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0.1428 Epoch: [10/20], Batch Num: [517/600] Discriminator Loss: 0.4284, Generator Loss: 2.3959 D(x): 0.9174, D(G(z)): 0.1990 Epoch: [10/20], Batch Num: [518/600] Discriminator Loss: 0.4061, Generator Loss: 2.9648 D(x): 0.9118, D(G(z)): 0.1783 Epoch: [10/20], Batch Num: [519/600] Discriminator Loss: 0.4276, Generator Loss: 2.8834 D(x): 0.8578, D(G(z)): 0.1113 Epoch: [10/20], Batch Num: [520/600] Discriminator Loss: 0.3891, Generator Loss: 2.9709 D(x): 0.8543, D(G(z)): 0.0888 Epoch: [10/20], Batch Num: [521/600] Discriminator Loss: 0.4899, Generator Loss: 3.0844 D(x): 0.8730, D(G(z)): 0.1615 Epoch: [10/20], Batch Num: [522/600] Discriminator Loss: 0.4214, Generator Loss: 2.9130 D(x): 0.8436, D(G(z)): 0.0985 Epoch: [10/20], Batch Num: [523/600] Discriminator Loss: 0.5530, Generator Loss: 2.5170 D(x): 0.8364, D(G(z)): 0.1373 Epoch: [10/20], Batch Num: [524/600] Discriminator Loss: 0.4270, Generator Loss: 2.4156 D(x): 0.8693, D(G(z)): 0.1302 Epoch: [10/20], Batch Num: [525/600] Discriminator Loss: 0.3627, Generator Loss: 2.7434 D(x): 0.9327, D(G(z)): 0.1849 Epoch: [10/20], Batch Num: [526/600] Discriminator Loss: 0.3585, Generator Loss: 3.3382 D(x): 0.9268, D(G(z)): 0.1515 Epoch: [10/20], Batch Num: [527/600] Discriminator Loss: 0.4835, Generator Loss: 3.6982 D(x): 0.8444, D(G(z)): 0.0937 Epoch: [10/20], Batch Num: [528/600] Discriminator Loss: 0.3200, Generator Loss: 3.4691 D(x): 0.8969, D(G(z)): 0.1077 Epoch: [10/20], Batch Num: [529/600] Discriminator Loss: 0.5285, Generator Loss: 3.1502 D(x): 0.7873, D(G(z)): 0.0700 Epoch: [10/20], Batch Num: [530/600] Discriminator Loss: 0.5252, Generator Loss: 2.3589 D(x): 0.8367, D(G(z)): 0.1238 Epoch: [10/20], Batch Num: [531/600] Discriminator Loss: 0.4372, Generator Loss: 2.1543 D(x): 0.8802, D(G(z)): 0.1548 Epoch: [10/20], Batch Num: [532/600] Discriminator Loss: 0.5101, Generator Loss: 2.5801 D(x): 0.9292, D(G(z)): 0.2514 Epoch: [10/20], Batch Num: [533/600] Discriminator Loss: 0.4564, Generator Loss: 2.7646 D(x): 0.8535, D(G(z)): 0.1460 Epoch: [10/20], Batch Num: [534/600] Discriminator Loss: 0.4225, Generator Loss: 3.0637 D(x): 0.8549, D(G(z)): 0.1123 Epoch: [10/20], Batch Num: [535/600] Discriminator Loss: 0.4881, Generator Loss: 3.1423 D(x): 0.8202, D(G(z)): 0.1273 Epoch: [10/20], Batch Num: [536/600] Discriminator Loss: 0.3653, Generator Loss: 2.8640 D(x): 0.8641, D(G(z)): 0.1062 Epoch: [10/20], Batch Num: [537/600] Discriminator Loss: 0.4648, Generator Loss: 2.5912 D(x): 0.8604, D(G(z)): 0.1311 Epoch: [10/20], Batch Num: [538/600] Discriminator Loss: 0.4637, Generator Loss: 2.9119 D(x): 0.9168, D(G(z)): 0.1746 Epoch: [10/20], Batch Num: [539/600] Discriminator Loss: 0.4316, Generator Loss: 3.5152 D(x): 0.8980, D(G(z)): 0.1471 Epoch: [10/20], Batch Num: [540/600] Discriminator Loss: 0.5486, Generator Loss: 3.4432 D(x): 0.8198, D(G(z)): 0.0957 Epoch: [10/20], Batch Num: [541/600] Discriminator Loss: 0.4242, Generator Loss: 3.3132 D(x): 0.8618, D(G(z)): 0.1177 Epoch: [10/20], Batch Num: 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2.4227 D(x): 0.8328, D(G(z)): 0.1428 Epoch: [10/20], Batch Num: [551/600] Discriminator Loss: 0.5671, Generator Loss: 2.3166 D(x): 0.8581, D(G(z)): 0.1982 Epoch: [10/20], Batch Num: [552/600] Discriminator Loss: 0.4300, Generator Loss: 2.0293 D(x): 0.8989, D(G(z)): 0.1971 Epoch: [10/20], Batch Num: [553/600] Discriminator Loss: 0.4117, Generator Loss: 2.4256 D(x): 0.9096, D(G(z)): 0.1915 Epoch: [10/20], Batch Num: [554/600] Discriminator Loss: 0.4614, Generator Loss: 2.7346 D(x): 0.8778, D(G(z)): 0.1953 Epoch: [10/20], Batch Num: [555/600] Discriminator Loss: 0.3779, Generator Loss: 3.2588 D(x): 0.9073, D(G(z)): 0.1467 Epoch: [10/20], Batch Num: [556/600] Discriminator Loss: 0.3902, Generator Loss: 3.3653 D(x): 0.8440, D(G(z)): 0.0686 Epoch: [10/20], Batch Num: [557/600] Discriminator Loss: 0.4560, Generator Loss: 3.1233 D(x): 0.8293, D(G(z)): 0.0585 Epoch: [10/20], Batch Num: [558/600] Discriminator Loss: 0.4762, Generator Loss: 3.0143 D(x): 0.8438, D(G(z)): 0.1073 Epoch: [10/20], Batch Num: [559/600] Discriminator Loss: 0.5134, Generator Loss: 2.6077 D(x): 0.8571, D(G(z)): 0.1716 Epoch: [10/20], Batch Num: [560/600] Discriminator Loss: 0.4156, Generator Loss: 2.6925 D(x): 0.9015, D(G(z)): 0.1760 Epoch: [10/20], Batch Num: [561/600] Discriminator Loss: 0.5003, Generator Loss: 3.1334 D(x): 0.8801, D(G(z)): 0.1877 Epoch: [10/20], Batch Num: [562/600] Discriminator Loss: 0.4877, Generator Loss: 2.7888 D(x): 0.8057, D(G(z)): 0.1017 Epoch: [10/20], Batch Num: [563/600] Discriminator Loss: 0.3068, Generator Loss: 2.6433 D(x): 0.8969, D(G(z)): 0.1222 Epoch: [10/20], Batch Num: [564/600] Discriminator Loss: 0.4569, Generator Loss: 2.9410 D(x): 0.8813, D(G(z)): 0.1656 Epoch: [10/20], Batch Num: [565/600] Discriminator Loss: 0.4175, Generator Loss: 2.6495 D(x): 0.8502, D(G(z)): 0.1259 Epoch: [10/20], Batch Num: [566/600] Discriminator Loss: 0.4008, Generator Loss: 2.9919 D(x): 0.8793, D(G(z)): 0.1232 Epoch: [10/20], Batch Num: [567/600] Discriminator Loss: 0.3474, Generator Loss: 3.0554 D(x): 0.8955, D(G(z)): 0.1229 Epoch: [10/20], Batch Num: [568/600] Discriminator Loss: 0.2934, Generator Loss: 3.2511 D(x): 0.9149, D(G(z)): 0.1134 Epoch: [10/20], Batch Num: [569/600] Discriminator Loss: 0.4873, Generator Loss: 3.1022 D(x): 0.8362, D(G(z)): 0.1071 Epoch: [10/20], Batch Num: [570/600] Discriminator Loss: 0.3420, Generator Loss: 2.8288 D(x): 0.8735, D(G(z)): 0.1053 Epoch: [10/20], Batch Num: [571/600] Discriminator Loss: 0.3709, Generator Loss: 2.5217 D(x): 0.9062, D(G(z)): 0.1454 Epoch: [10/20], Batch Num: [572/600] Discriminator Loss: 0.4116, Generator Loss: 3.1907 D(x): 0.9092, D(G(z)): 0.1530 Epoch: [10/20], Batch Num: [573/600] Discriminator Loss: 0.4019, Generator Loss: 3.2203 D(x): 0.8950, D(G(z)): 0.1135 Epoch: [10/20], Batch Num: [574/600] Discriminator Loss: 0.4667, Generator Loss: 3.5899 D(x): 0.9107, D(G(z)): 0.1400 Epoch: [10/20], Batch Num: [575/600] Discriminator Loss: 0.4222, Generator Loss: 3.6144 D(x): 0.8453, D(G(z)): 0.0823 Epoch: [10/20], Batch Num: [576/600] Discriminator Loss: 0.3774, Generator Loss: 3.5247 D(x): 0.8817, D(G(z)): 0.0826 Epoch: [10/20], Batch Num: [577/600] Discriminator Loss: 0.6125, Generator Loss: 2.9803 D(x): 0.8072, D(G(z)): 0.1238 Epoch: [10/20], Batch Num: [578/600] Discriminator Loss: 0.3380, Generator Loss: 2.6894 D(x): 0.9269, D(G(z)): 0.1320 Epoch: [10/20], Batch Num: [579/600] Discriminator Loss: 0.3498, Generator Loss: 2.7774 D(x): 0.9232, D(G(z)): 0.1528 Epoch: [10/20], Batch Num: [580/600] Discriminator Loss: 0.2903, Generator Loss: 3.0878 D(x): 0.9364, D(G(z)): 0.1417 Epoch: [10/20], Batch Num: [581/600] Discriminator Loss: 0.3624, Generator Loss: 3.3900 D(x): 0.8779, D(G(z)): 0.1027 Epoch: [10/20], Batch Num: [582/600] Discriminator Loss: 0.4121, Generator Loss: 3.3243 D(x): 0.8599, D(G(z)): 0.0909 Epoch: [10/20], Batch Num: [583/600] Discriminator Loss: 0.5905, Generator Loss: 2.9457 D(x): 0.8270, D(G(z)): 0.0888 Epoch: [10/20], Batch Num: [584/600] Discriminator Loss: 0.3864, Generator Loss: 2.5843 D(x): 0.8744, D(G(z)): 0.1210 Epoch: [10/20], Batch Num: [585/600] Discriminator Loss: 0.5431, Generator Loss: 2.3393 D(x): 0.8574, D(G(z)): 0.1729 Epoch: [10/20], Batch Num: [586/600] Discriminator Loss: 0.6237, Generator Loss: 2.7136 D(x): 0.8670, D(G(z)): 0.2300 Epoch: [10/20], Batch Num: [587/600] Discriminator Loss: 0.3772, Generator Loss: 3.1479 D(x): 0.9050, D(G(z)): 0.1633 Epoch: [10/20], Batch Num: [588/600] Discriminator Loss: 0.4484, Generator Loss: 3.1278 D(x): 0.8403, D(G(z)): 0.0854 Epoch: [10/20], Batch Num: [589/600] Discriminator Loss: 0.4114, Generator Loss: 2.8639 D(x): 0.8488, D(G(z)): 0.0877 Epoch: [10/20], Batch Num: [590/600] Discriminator Loss: 0.4989, Generator Loss: 2.6529 D(x): 0.8310, D(G(z)): 0.1069 Epoch: [10/20], Batch Num: [591/600] Discriminator Loss: 0.4862, Generator Loss: 2.4244 D(x): 0.8467, D(G(z)): 0.1413 Epoch: [10/20], Batch Num: [592/600] Discriminator Loss: 0.5207, Generator Loss: 2.4539 D(x): 0.8864, D(G(z)): 0.2131 Epoch: [10/20], Batch Num: [593/600] Discriminator Loss: 0.4509, Generator Loss: 3.0649 D(x): 0.9261, D(G(z)): 0.2064 Epoch: [10/20], Batch Num: [594/600] Discriminator Loss: 0.5122, Generator Loss: 3.4127 D(x): 0.8611, D(G(z)): 0.1622 Epoch: [10/20], Batch Num: [595/600] Discriminator Loss: 0.5255, Generator Loss: 3.0715 D(x): 0.7929, D(G(z)): 0.0754 Epoch: [10/20], Batch Num: [596/600] Discriminator Loss: 0.2401, Generator Loss: 3.2859 D(x): 0.9227, D(G(z)): 0.0831 Epoch: [10/20], Batch Num: [597/600] Discriminator Loss: 0.4282, Generator Loss: 3.4073 D(x): 0.9008, D(G(z)): 0.1370 Epoch: [10/20], Batch Num: [598/600] Discriminator Loss: 0.5373, Generator Loss: 3.0487 D(x): 0.8504, D(G(z)): 0.1159 Epoch: [10/20], Batch Num: [599/600] Discriminator Loss: 0.5597, Generator Loss: 2.7493 D(x): 0.8373, D(G(z)): 0.1360 Epoch: 11, Batch Num: [0/600]
Epoch: [11/20], Batch Num: [0/600] Discriminator Loss: 0.5101, Generator Loss: 2.4329 D(x): 0.8479, D(G(z)): 0.1392 Epoch: [11/20], Batch Num: [1/600] Discriminator Loss: 0.4859, Generator Loss: 2.4931 D(x): 0.9022, D(G(z)): 0.1576 Epoch: [11/20], Batch Num: [2/600] Discriminator Loss: 0.4880, Generator Loss: 2.9613 D(x): 0.9011, D(G(z)): 0.1675 Epoch: [11/20], Batch Num: [3/600] Discriminator Loss: 0.5126, Generator Loss: 3.1807 D(x): 0.8374, D(G(z)): 0.1248 Epoch: [11/20], Batch Num: [4/600] Discriminator Loss: 0.4746, Generator Loss: 3.0375 D(x): 0.8632, D(G(z)): 0.1263 Epoch: [11/20], Batch Num: [5/600] Discriminator Loss: 0.2841, Generator Loss: 3.0748 D(x): 0.8822, D(G(z)): 0.0894 Epoch: [11/20], Batch Num: [6/600] Discriminator Loss: 0.4070, Generator Loss: 2.9893 D(x): 0.8676, D(G(z)): 0.1181 Epoch: [11/20], Batch Num: [7/600] Discriminator Loss: 0.5682, Generator Loss: 2.8170 D(x): 0.8123, D(G(z)): 0.1138 Epoch: [11/20], Batch Num: [8/600] Discriminator Loss: 0.4678, Generator Loss: 2.3399 D(x): 0.8485, D(G(z)): 0.1394 Epoch: [11/20], Batch Num: [9/600] Discriminator Loss: 0.4876, Generator Loss: 2.3997 D(x): 0.8748, D(G(z)): 0.1746 Epoch: [11/20], Batch Num: [10/600] Discriminator Loss: 0.4104, Generator Loss: 2.4747 D(x): 0.9000, D(G(z)): 0.1865 Epoch: [11/20], Batch Num: [11/600] Discriminator Loss: 0.5617, Generator Loss: 2.9884 D(x): 0.8246, D(G(z)): 0.1771 Epoch: [11/20], Batch Num: [12/600] Discriminator Loss: 0.5009, Generator Loss: 3.5519 D(x): 0.8384, D(G(z)): 0.1476 Epoch: [11/20], Batch Num: [13/600] Discriminator Loss: 0.4266, Generator Loss: 2.9398 D(x): 0.8645, D(G(z)): 0.1123 Epoch: [11/20], Batch Num: [14/600] Discriminator Loss: 0.3960, Generator Loss: 3.0444 D(x): 0.8456, D(G(z)): 0.1067 Epoch: [11/20], Batch Num: [15/600] Discriminator Loss: 0.4352, Generator Loss: 2.9405 D(x): 0.8638, D(G(z)): 0.1076 Epoch: [11/20], Batch Num: [16/600] Discriminator Loss: 0.4716, Generator Loss: 2.4451 D(x): 0.8492, D(G(z)): 0.1179 Epoch: [11/20], Batch Num: [17/600] Discriminator Loss: 0.3261, Generator Loss: 2.9231 D(x): 0.9201, D(G(z)): 0.1540 Epoch: [11/20], Batch Num: [18/600] Discriminator Loss: 0.4826, Generator Loss: 2.7666 D(x): 0.8727, D(G(z)): 0.1643 Epoch: [11/20], Batch Num: [19/600] Discriminator Loss: 0.4514, Generator Loss: 2.8822 D(x): 0.8661, D(G(z)): 0.1242 Epoch: [11/20], Batch Num: [20/600] Discriminator Loss: 0.3659, Generator Loss: 2.8564 D(x): 0.8988, D(G(z)): 0.1312 Epoch: [11/20], Batch Num: [21/600] Discriminator Loss: 0.4127, Generator Loss: 3.1468 D(x): 0.8796, D(G(z)): 0.1264 Epoch: [11/20], Batch Num: [22/600] Discriminator Loss: 0.4271, Generator Loss: 2.9231 D(x): 0.8682, D(G(z)): 0.1361 Epoch: [11/20], Batch Num: [23/600] Discriminator Loss: 0.3475, Generator Loss: 3.0096 D(x): 0.8879, D(G(z)): 0.1064 Epoch: [11/20], Batch Num: [24/600] Discriminator Loss: 0.4710, Generator Loss: 2.5563 D(x): 0.8305, D(G(z)): 0.1024 Epoch: [11/20], Batch Num: [25/600] Discriminator Loss: 0.5559, Generator Loss: 2.1897 D(x): 0.8610, D(G(z)): 0.1810 Epoch: [11/20], Batch Num: [26/600] Discriminator Loss: 0.4374, Generator Loss: 2.2798 D(x): 0.8865, D(G(z)): 0.1634 Epoch: [11/20], Batch Num: [27/600] Discriminator Loss: 0.4172, Generator Loss: 2.3733 D(x): 0.8755, D(G(z)): 0.1609 Epoch: [11/20], Batch Num: [28/600] Discriminator Loss: 0.4103, Generator Loss: 2.7891 D(x): 0.9205, D(G(z)): 0.1670 Epoch: [11/20], Batch Num: [29/600] Discriminator Loss: 0.4968, Generator Loss: 2.9391 D(x): 0.8102, D(G(z)): 0.1099 Epoch: [11/20], Batch Num: [30/600] Discriminator Loss: 0.4282, Generator Loss: 3.1126 D(x): 0.8694, D(G(z)): 0.1345 Epoch: [11/20], Batch Num: [31/600] Discriminator Loss: 0.4588, Generator Loss: 3.1498 D(x): 0.8077, D(G(z)): 0.0902 Epoch: [11/20], Batch Num: [32/600] Discriminator Loss: 0.4887, Generator Loss: 2.4864 D(x): 0.8397, D(G(z)): 0.1123 Epoch: [11/20], Batch Num: [33/600] Discriminator Loss: 0.4513, Generator Loss: 2.3927 D(x): 0.9003, D(G(z)): 0.2036 Epoch: [11/20], Batch Num: [34/600] Discriminator Loss: 0.4648, Generator Loss: 2.9973 D(x): 0.8893, D(G(z)): 0.2019 Epoch: [11/20], Batch Num: [35/600] Discriminator Loss: 0.5035, Generator Loss: 3.2661 D(x): 0.8307, D(G(z)): 0.1432 Epoch: [11/20], Batch Num: [36/600] Discriminator Loss: 0.5981, Generator Loss: 2.7702 D(x): 0.7840, D(G(z)): 0.1225 Epoch: [11/20], Batch Num: [37/600] Discriminator Loss: 0.3849, Generator Loss: 2.7551 D(x): 0.8777, D(G(z)): 0.1052 Epoch: [11/20], Batch Num: [38/600] Discriminator Loss: 0.3202, Generator Loss: 2.8775 D(x): 0.9056, D(G(z)): 0.1254 Epoch: [11/20], Batch Num: [39/600] Discriminator Loss: 0.4221, Generator Loss: 2.7584 D(x): 0.8812, D(G(z)): 0.1578 Epoch: [11/20], Batch Num: [40/600] Discriminator Loss: 0.4609, Generator Loss: 3.2745 D(x): 0.8927, D(G(z)): 0.1669 Epoch: [11/20], Batch Num: [41/600] Discriminator Loss: 0.5289, Generator Loss: 3.3829 D(x): 0.8331, D(G(z)): 0.1503 Epoch: [11/20], Batch Num: [42/600] Discriminator Loss: 0.5785, Generator Loss: 3.0321 D(x): 0.8013, D(G(z)): 0.0809 Epoch: [11/20], Batch Num: [43/600] Discriminator Loss: 0.4668, Generator Loss: 2.5108 D(x): 0.8715, D(G(z)): 0.1322 Epoch: [11/20], Batch Num: [44/600] Discriminator Loss: 0.5360, Generator Loss: 2.7057 D(x): 0.8588, D(G(z)): 0.1667 Epoch: [11/20], Batch Num: [45/600] Discriminator Loss: 0.4043, Generator Loss: 2.8068 D(x): 0.9248, D(G(z)): 0.1916 Epoch: [11/20], Batch Num: [46/600] Discriminator Loss: 0.4785, Generator Loss: 3.2238 D(x): 0.8689, D(G(z)): 0.1669 Epoch: [11/20], Batch Num: [47/600] Discriminator Loss: 0.5240, Generator Loss: 3.3217 D(x): 0.8377, D(G(z)): 0.1137 Epoch: [11/20], Batch Num: [48/600] Discriminator Loss: 0.5526, Generator Loss: 3.1481 D(x): 0.7878, D(G(z)): 0.0875 Epoch: [11/20], Batch Num: [49/600] Discriminator Loss: 0.3789, Generator Loss: 2.5091 D(x): 0.8644, D(G(z)): 0.1102 Epoch: [11/20], Batch Num: [50/600] Discriminator Loss: 0.4881, Generator Loss: 2.4425 D(x): 0.8807, D(G(z)): 0.1791 Epoch: [11/20], Batch Num: [51/600] Discriminator Loss: 0.6029, Generator Loss: 2.3943 D(x): 0.8417, D(G(z)): 0.1744 Epoch: [11/20], Batch Num: [52/600] Discriminator Loss: 0.5105, Generator Loss: 2.7248 D(x): 0.8776, D(G(z)): 0.1830 Epoch: [11/20], Batch Num: [53/600] Discriminator Loss: 0.3973, Generator Loss: 2.7474 D(x): 0.8804, D(G(z)): 0.1269 Epoch: [11/20], Batch Num: [54/600] Discriminator Loss: 0.4103, Generator Loss: 2.9776 D(x): 0.8917, D(G(z)): 0.1351 Epoch: [11/20], Batch Num: [55/600] Discriminator Loss: 0.4603, Generator Loss: 3.0522 D(x): 0.8572, D(G(z)): 0.1404 Epoch: [11/20], Batch Num: [56/600] Discriminator Loss: 0.3901, Generator Loss: 2.9254 D(x): 0.8614, D(G(z)): 0.0915 Epoch: [11/20], Batch Num: [57/600] Discriminator Loss: 0.3835, Generator Loss: 2.6454 D(x): 0.8681, D(G(z)): 0.0967 Epoch: [11/20], Batch Num: [58/600] Discriminator Loss: 0.3554, Generator Loss: 2.5110 D(x): 0.8765, D(G(z)): 0.1172 Epoch: [11/20], Batch Num: [59/600] Discriminator Loss: 0.4256, Generator Loss: 2.7293 D(x): 0.9101, D(G(z)): 0.1794 Epoch: [11/20], Batch Num: [60/600] Discriminator Loss: 0.4673, Generator Loss: 3.3200 D(x): 0.8868, D(G(z)): 0.1773 Epoch: [11/20], Batch Num: [61/600] Discriminator Loss: 0.3605, Generator Loss: 3.4256 D(x): 0.8690, D(G(z)): 0.0820 Epoch: [11/20], Batch Num: [62/600] Discriminator Loss: 0.4721, Generator Loss: 3.0477 D(x): 0.8199, D(G(z)): 0.0842 Epoch: [11/20], Batch Num: [63/600] Discriminator Loss: 0.3042, Generator Loss: 2.7992 D(x): 0.8882, D(G(z)): 0.0877 Epoch: [11/20], Batch Num: [64/600] Discriminator Loss: 0.4272, Generator Loss: 2.2770 D(x): 0.8568, D(G(z)): 0.1432 Epoch: [11/20], Batch Num: [65/600] Discriminator Loss: 0.4861, Generator Loss: 2.6141 D(x): 0.9011, D(G(z)): 0.2075 Epoch: [11/20], Batch Num: [66/600] Discriminator Loss: 0.4316, Generator Loss: 3.3139 D(x): 0.9187, D(G(z)): 0.2062 Epoch: [11/20], Batch Num: [67/600] Discriminator Loss: 0.5661, Generator Loss: 3.3500 D(x): 0.8292, D(G(z)): 0.1286 Epoch: [11/20], Batch Num: [68/600] Discriminator Loss: 0.5408, Generator Loss: 3.4583 D(x): 0.8231, D(G(z)): 0.1317 Epoch: [11/20], Batch Num: [69/600] Discriminator Loss: 0.4944, Generator Loss: 3.1072 D(x): 0.8071, D(G(z)): 0.1027 Epoch: [11/20], Batch Num: [70/600] Discriminator Loss: 0.4085, Generator Loss: 2.3807 D(x): 0.8364, D(G(z)): 0.1029 Epoch: [11/20], Batch Num: [71/600] Discriminator Loss: 0.3909, Generator Loss: 2.2267 D(x): 0.8955, D(G(z)): 0.1624 Epoch: [11/20], Batch Num: [72/600] Discriminator Loss: 0.5890, Generator Loss: 2.6159 D(x): 0.8802, D(G(z)): 0.2312 Epoch: [11/20], Batch Num: [73/600] Discriminator Loss: 0.4318, Generator Loss: 2.8595 D(x): 0.8505, D(G(z)): 0.1426 Epoch: [11/20], Batch Num: [74/600] Discriminator Loss: 0.4452, Generator Loss: 3.0807 D(x): 0.8693, D(G(z)): 0.1453 Epoch: [11/20], Batch Num: [75/600] Discriminator Loss: 0.4439, Generator Loss: 3.1375 D(x): 0.8539, D(G(z)): 0.1286 Epoch: [11/20], Batch Num: [76/600] Discriminator Loss: 0.7085, Generator Loss: 2.8351 D(x): 0.7551, D(G(z)): 0.1289 Epoch: [11/20], Batch Num: [77/600] Discriminator Loss: 0.4594, Generator Loss: 2.6440 D(x): 0.8285, D(G(z)): 0.1179 Epoch: [11/20], Batch Num: [78/600] Discriminator Loss: 0.4211, Generator Loss: 2.2418 D(x): 0.9319, D(G(z)): 0.2156 Epoch: [11/20], Batch Num: [79/600] Discriminator Loss: 0.4907, Generator Loss: 2.6290 D(x): 0.8704, D(G(z)): 0.1856 Epoch: [11/20], Batch Num: [80/600] Discriminator Loss: 0.4672, Generator Loss: 3.1509 D(x): 0.8735, D(G(z)): 0.1613 Epoch: [11/20], Batch Num: [81/600] Discriminator Loss: 0.5072, Generator Loss: 3.1711 D(x): 0.8122, D(G(z)): 0.1030 Epoch: [11/20], Batch Num: [82/600] Discriminator Loss: 0.5307, Generator Loss: 2.8176 D(x): 0.8085, D(G(z)): 0.1014 Epoch: [11/20], Batch Num: [83/600] Discriminator Loss: 0.6329, Generator Loss: 2.6048 D(x): 0.8268, D(G(z)): 0.1696 Epoch: [11/20], Batch Num: [84/600] Discriminator Loss: 0.5555, Generator Loss: 2.5291 D(x): 0.8465, D(G(z)): 0.1768 Epoch: [11/20], Batch Num: [85/600] Discriminator Loss: 0.5613, Generator Loss: 2.5089 D(x): 0.8374, D(G(z)): 0.1988 Epoch: [11/20], Batch Num: [86/600] Discriminator Loss: 0.5683, Generator Loss: 2.9362 D(x): 0.8352, D(G(z)): 0.1642 Epoch: [11/20], Batch Num: [87/600] Discriminator Loss: 0.5061, Generator Loss: 2.9200 D(x): 0.8432, D(G(z)): 0.1622 Epoch: [11/20], Batch Num: [88/600] Discriminator Loss: 0.5770, Generator Loss: 2.6323 D(x): 0.7722, D(G(z)): 0.1120 Epoch: [11/20], Batch Num: [89/600] Discriminator Loss: 0.4076, Generator Loss: 2.1407 D(x): 0.8594, D(G(z)): 0.1275 Epoch: [11/20], Batch Num: [90/600] Discriminator Loss: 0.5579, Generator Loss: 2.6047 D(x): 0.8681, D(G(z)): 0.2041 Epoch: [11/20], Batch Num: [91/600] Discriminator Loss: 0.4934, Generator Loss: 2.6016 D(x): 0.8516, D(G(z)): 0.1702 Epoch: [11/20], Batch Num: [92/600] Discriminator Loss: 0.4280, Generator Loss: 3.1314 D(x): 0.8995, D(G(z)): 0.1598 Epoch: [11/20], Batch Num: [93/600] Discriminator Loss: 0.4819, Generator Loss: 3.6564 D(x): 0.8392, D(G(z)): 0.1077 Epoch: [11/20], Batch Num: [94/600] Discriminator Loss: 0.4494, Generator Loss: 3.4355 D(x): 0.8447, D(G(z)): 0.0987 Epoch: [11/20], Batch Num: [95/600] Discriminator Loss: 0.5507, Generator Loss: 2.6407 D(x): 0.7938, D(G(z)): 0.0825 Epoch: [11/20], Batch Num: [96/600] Discriminator Loss: 0.4751, Generator Loss: 2.1593 D(x): 0.8556, D(G(z)): 0.1351 Epoch: [11/20], Batch Num: [97/600] Discriminator Loss: 0.4452, Generator Loss: 2.1261 D(x): 0.9080, D(G(z)): 0.1924 Epoch: [11/20], Batch Num: [98/600] Discriminator Loss: 0.4284, Generator Loss: 2.5426 D(x): 0.9068, D(G(z)): 0.1800 Epoch: [11/20], Batch Num: [99/600] Discriminator Loss: 0.4986, Generator Loss: 2.9618 D(x): 0.9152, D(G(z)): 0.2184 Epoch: 11, Batch Num: [100/600]
Epoch: [11/20], Batch Num: [100/600] Discriminator Loss: 0.5956, Generator Loss: 3.5031 D(x): 0.8189, D(G(z)): 0.1317 Epoch: [11/20], Batch Num: [101/600] Discriminator Loss: 0.6186, Generator Loss: 3.3343 D(x): 0.7489, D(G(z)): 0.0613 Epoch: [11/20], Batch Num: [102/600] Discriminator Loss: 0.7832, Generator Loss: 2.2201 D(x): 0.7690, D(G(z)): 0.1855 Epoch: [11/20], Batch Num: [103/600] Discriminator Loss: 0.4288, Generator Loss: 2.1741 D(x): 0.9298, D(G(z)): 0.2087 Epoch: [11/20], Batch Num: [104/600] Discriminator Loss: 0.5239, Generator Loss: 2.5725 D(x): 0.8909, D(G(z)): 0.2268 Epoch: [11/20], Batch Num: [105/600] Discriminator Loss: 0.4993, Generator Loss: 2.9009 D(x): 0.8052, D(G(z)): 0.1485 Epoch: [11/20], Batch Num: [106/600] Discriminator Loss: 0.6142, Generator Loss: 2.8894 D(x): 0.7760, D(G(z)): 0.1563 Epoch: [11/20], Batch Num: [107/600] Discriminator Loss: 0.4353, Generator Loss: 2.4053 D(x): 0.8581, D(G(z)): 0.1510 Epoch: [11/20], Batch Num: [108/600] Discriminator Loss: 0.4669, Generator Loss: 2.4919 D(x): 0.8294, D(G(z)): 0.1351 Epoch: [11/20], Batch Num: [109/600] Discriminator Loss: 0.4824, Generator Loss: 2.2372 D(x): 0.8332, D(G(z)): 0.1553 Epoch: [11/20], Batch Num: [110/600] Discriminator Loss: 0.6392, Generator Loss: 2.6649 D(x): 0.8479, D(G(z)): 0.2257 Epoch: [11/20], Batch Num: [111/600] Discriminator Loss: 0.5554, Generator Loss: 2.5442 D(x): 0.8270, D(G(z)): 0.1622 Epoch: [11/20], Batch Num: [112/600] Discriminator Loss: 0.4305, Generator Loss: 2.4962 D(x): 0.8443, D(G(z)): 0.1171 Epoch: [11/20], Batch Num: [113/600] Discriminator Loss: 0.4979, Generator Loss: 2.5888 D(x): 0.8351, D(G(z)): 0.1353 Epoch: [11/20], Batch Num: [114/600] Discriminator Loss: 0.5013, Generator Loss: 2.8236 D(x): 0.8964, D(G(z)): 0.1848 Epoch: [11/20], Batch Num: [115/600] Discriminator Loss: 0.4169, Generator Loss: 2.9487 D(x): 0.8648, D(G(z)): 0.1183 Epoch: [11/20], Batch Num: [116/600] Discriminator Loss: 0.5083, Generator Loss: 2.7218 D(x): 0.8418, D(G(z)): 0.1164 Epoch: [11/20], Batch Num: [117/600] Discriminator Loss: 0.3519, Generator Loss: 2.9875 D(x): 0.9036, D(G(z)): 0.1482 Epoch: [11/20], Batch Num: [118/600] Discriminator Loss: 0.6197, Generator Loss: 2.5141 D(x): 0.7970, D(G(z)): 0.1216 Epoch: [11/20], Batch Num: [119/600] Discriminator Loss: 0.4112, Generator Loss: 2.8246 D(x): 0.8911, D(G(z)): 0.1490 Epoch: [11/20], Batch Num: [120/600] Discriminator Loss: 0.4329, Generator Loss: 2.8681 D(x): 0.8652, D(G(z)): 0.1533 Epoch: [11/20], Batch Num: [121/600] Discriminator Loss: 0.3833, Generator Loss: 2.9661 D(x): 0.8873, D(G(z)): 0.1491 Epoch: [11/20], Batch Num: [122/600] Discriminator Loss: 0.4034, Generator Loss: 2.9965 D(x): 0.8743, D(G(z)): 0.1379 Epoch: [11/20], Batch Num: [123/600] Discriminator Loss: 0.4286, Generator Loss: 3.3148 D(x): 0.8674, D(G(z)): 0.1397 Epoch: [11/20], Batch Num: [124/600] Discriminator Loss: 0.6301, Generator Loss: 2.4701 D(x): 0.7608, D(G(z)): 0.0966 Epoch: [11/20], Batch Num: [125/600] Discriminator Loss: 0.5341, Generator Loss: 2.1881 D(x): 0.8454, D(G(z)): 0.1701 Epoch: [11/20], Batch Num: [126/600] Discriminator Loss: 0.5502, Generator Loss: 2.3253 D(x): 0.9379, D(G(z)): 0.2882 Epoch: [11/20], Batch Num: [127/600] Discriminator Loss: 0.4306, Generator Loss: 2.9763 D(x): 0.8901, D(G(z)): 0.1876 Epoch: [11/20], Batch Num: [128/600] Discriminator Loss: 0.5100, Generator Loss: 3.2889 D(x): 0.8181, D(G(z)): 0.1063 Epoch: [11/20], Batch Num: [129/600] Discriminator Loss: 0.5248, Generator Loss: 3.0575 D(x): 0.7873, D(G(z)): 0.0983 Epoch: [11/20], Batch Num: [130/600] Discriminator Loss: 0.4037, Generator Loss: 2.7677 D(x): 0.8568, D(G(z)): 0.1216 Epoch: [11/20], Batch Num: [131/600] Discriminator Loss: 0.4878, Generator Loss: 2.3358 D(x): 0.8386, D(G(z)): 0.1436 Epoch: [11/20], Batch Num: [132/600] Discriminator Loss: 0.5428, Generator Loss: 2.4817 D(x): 0.8650, D(G(z)): 0.1977 Epoch: [11/20], Batch Num: [133/600] Discriminator Loss: 0.4577, Generator Loss: 2.3391 D(x): 0.8872, D(G(z)): 0.1771 Epoch: [11/20], Batch Num: [134/600] Discriminator Loss: 0.4911, Generator Loss: 2.7738 D(x): 0.8765, D(G(z)): 0.1881 Epoch: [11/20], Batch Num: [135/600] Discriminator Loss: 0.5040, Generator Loss: 2.8694 D(x): 0.8569, D(G(z)): 0.1589 Epoch: [11/20], Batch Num: [136/600] Discriminator Loss: 0.4340, Generator Loss: 3.3025 D(x): 0.8621, D(G(z)): 0.1276 Epoch: [11/20], Batch Num: [137/600] Discriminator Loss: 0.7390, Generator Loss: 2.9220 D(x): 0.7635, D(G(z)): 0.1294 Epoch: [11/20], Batch Num: [138/600] Discriminator Loss: 0.4688, Generator Loss: 2.6834 D(x): 0.8518, D(G(z)): 0.1273 Epoch: [11/20], Batch Num: [139/600] Discriminator Loss: 0.3750, Generator Loss: 1.8570 D(x): 0.8745, D(G(z)): 0.1286 Epoch: [11/20], Batch Num: [140/600] Discriminator Loss: 0.6317, Generator Loss: 2.4286 D(x): 0.9070, D(G(z)): 0.2550 Epoch: [11/20], Batch Num: [141/600] Discriminator Loss: 0.6465, Generator Loss: 3.2564 D(x): 0.8615, D(G(z)): 0.2225 Epoch: [11/20], Batch Num: [142/600] Discriminator Loss: 0.4271, Generator Loss: 3.4254 D(x): 0.8318, D(G(z)): 0.0766 Epoch: [11/20], Batch Num: [143/600] Discriminator Loss: 0.5289, Generator Loss: 2.7650 D(x): 0.7668, D(G(z)): 0.0609 Epoch: [11/20], Batch Num: [144/600] Discriminator Loss: 0.5457, Generator Loss: 2.2233 D(x): 0.7998, D(G(z)): 0.1183 Epoch: [11/20], Batch Num: [145/600] Discriminator Loss: 0.5388, Generator Loss: 2.0547 D(x): 0.8722, D(G(z)): 0.2249 Epoch: [11/20], Batch Num: [146/600] Discriminator Loss: 0.5551, Generator Loss: 2.2498 D(x): 0.9063, D(G(z)): 0.2714 Epoch: [11/20], Batch Num: [147/600] Discriminator Loss: 0.4864, Generator Loss: 2.3761 D(x): 0.8256, D(G(z)): 0.1556 Epoch: [11/20], Batch Num: [148/600] Discriminator Loss: 0.5133, Generator Loss: 2.7015 D(x): 0.8709, D(G(z)): 0.1931 Epoch: [11/20], Batch Num: [149/600] Discriminator Loss: 0.4622, Generator Loss: 2.8590 D(x): 0.8283, D(G(z)): 0.1222 Epoch: [11/20], Batch Num: [150/600] Discriminator Loss: 0.5711, Generator Loss: 2.8426 D(x): 0.8011, D(G(z)): 0.1142 Epoch: [11/20], Batch Num: [151/600] Discriminator Loss: 0.5575, Generator Loss: 2.6424 D(x): 0.8243, D(G(z)): 0.1487 Epoch: [11/20], Batch Num: [152/600] Discriminator Loss: 0.4528, Generator Loss: 2.2835 D(x): 0.8611, D(G(z)): 0.1465 Epoch: [11/20], Batch Num: [153/600] Discriminator Loss: 0.4180, Generator Loss: 2.2979 D(x): 0.8481, D(G(z)): 0.1426 Epoch: [11/20], Batch Num: [154/600] Discriminator Loss: 0.4745, Generator Loss: 2.2802 D(x): 0.8948, D(G(z)): 0.2167 Epoch: [11/20], Batch Num: [155/600] Discriminator Loss: 0.5258, Generator Loss: 2.5851 D(x): 0.8746, D(G(z)): 0.1943 Epoch: [11/20], Batch Num: [156/600] Discriminator Loss: 0.4292, Generator Loss: 3.1593 D(x): 0.8720, D(G(z)): 0.1621 Epoch: [11/20], Batch Num: [157/600] Discriminator Loss: 0.4676, Generator Loss: 2.9126 D(x): 0.8047, D(G(z)): 0.1032 Epoch: [11/20], Batch Num: [158/600] Discriminator Loss: 0.4142, Generator Loss: 3.0628 D(x): 0.8698, D(G(z)): 0.1182 Epoch: [11/20], Batch Num: [159/600] Discriminator Loss: 0.2848, Generator Loss: 2.8796 D(x): 0.9017, D(G(z)): 0.1096 Epoch: [11/20], Batch Num: [160/600] Discriminator Loss: 0.4293, Generator Loss: 2.4993 D(x): 0.8437, D(G(z)): 0.0961 Epoch: [11/20], Batch Num: [161/600] Discriminator Loss: 0.3826, Generator Loss: 2.7290 D(x): 0.8942, D(G(z)): 0.1402 Epoch: [11/20], Batch Num: [162/600] Discriminator Loss: 0.4006, Generator Loss: 2.6521 D(x): 0.8752, D(G(z)): 0.1197 Epoch: [11/20], Batch Num: [163/600] Discriminator Loss: 0.3301, Generator Loss: 2.9541 D(x): 0.8955, D(G(z)): 0.1182 Epoch: [11/20], Batch Num: [164/600] Discriminator Loss: 0.5251, Generator Loss: 2.7050 D(x): 0.8633, D(G(z)): 0.1623 Epoch: [11/20], Batch Num: [165/600] Discriminator Loss: 0.3627, Generator Loss: 3.2397 D(x): 0.9123, D(G(z)): 0.1361 Epoch: [11/20], Batch Num: [166/600] Discriminator Loss: 0.4834, Generator Loss: 3.2202 D(x): 0.8384, D(G(z)): 0.0841 Epoch: [11/20], Batch Num: [167/600] Discriminator Loss: 0.3702, Generator Loss: 2.8499 D(x): 0.8618, D(G(z)): 0.0887 Epoch: [11/20], Batch Num: [168/600] Discriminator Loss: 0.3246, Generator Loss: 2.5079 D(x): 0.9134, D(G(z)): 0.1228 Epoch: [11/20], Batch Num: [169/600] Discriminator Loss: 0.6291, Generator Loss: 2.6306 D(x): 0.8270, D(G(z)): 0.1802 Epoch: [11/20], Batch Num: [170/600] Discriminator Loss: 0.4885, Generator Loss: 2.8860 D(x): 0.8897, D(G(z)): 0.2166 Epoch: [11/20], Batch Num: [171/600] Discriminator Loss: 0.4636, Generator Loss: 3.2744 D(x): 0.8439, D(G(z)): 0.1231 Epoch: [11/20], Batch Num: [172/600] Discriminator Loss: 0.4149, Generator Loss: 3.2275 D(x): 0.8203, D(G(z)): 0.0723 Epoch: [11/20], Batch Num: [173/600] Discriminator Loss: 0.3398, Generator Loss: 2.7340 D(x): 0.8825, D(G(z)): 0.1048 Epoch: [11/20], Batch Num: [174/600] Discriminator Loss: 0.3267, Generator Loss: 2.6578 D(x): 0.9042, D(G(z)): 0.1228 Epoch: [11/20], Batch Num: [175/600] Discriminator Loss: 0.3377, Generator Loss: 2.7841 D(x): 0.9051, D(G(z)): 0.1515 Epoch: [11/20], Batch Num: [176/600] Discriminator Loss: 0.4757, Generator Loss: 2.7650 D(x): 0.8309, D(G(z)): 0.1394 Epoch: [11/20], Batch Num: [177/600] Discriminator Loss: 0.4874, Generator Loss: 2.9828 D(x): 0.8548, D(G(z)): 0.1464 Epoch: [11/20], Batch Num: [178/600] Discriminator Loss: 0.5345, Generator Loss: 3.2504 D(x): 0.8631, D(G(z)): 0.1720 Epoch: [11/20], Batch Num: [179/600] Discriminator Loss: 0.4683, Generator Loss: 3.4434 D(x): 0.8788, D(G(z)): 0.1242 Epoch: [11/20], Batch Num: [180/600] Discriminator Loss: 0.4740, Generator Loss: 3.3142 D(x): 0.8567, D(G(z)): 0.1210 Epoch: [11/20], Batch Num: [181/600] Discriminator Loss: 0.4261, Generator Loss: 3.0659 D(x): 0.8478, D(G(z)): 0.0891 Epoch: [11/20], Batch Num: [182/600] Discriminator Loss: 0.5825, Generator Loss: 2.8102 D(x): 0.8191, D(G(z)): 0.1247 Epoch: [11/20], Batch Num: [183/600] Discriminator Loss: 0.4850, Generator Loss: 2.2147 D(x): 0.8501, D(G(z)): 0.1483 Epoch: [11/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [11/20], Batch Num: [200/600] Discriminator Loss: 0.3939, Generator Loss: 2.8265 D(x): 0.8497, D(G(z)): 0.1009 Epoch: [11/20], Batch Num: [201/600] Discriminator Loss: 0.5465, Generator Loss: 2.3468 D(x): 0.7818, D(G(z)): 0.1020 Epoch: [11/20], Batch Num: [202/600] Discriminator Loss: 0.3578, Generator Loss: 2.4662 D(x): 0.9252, D(G(z)): 0.1769 Epoch: [11/20], Batch Num: [203/600] Discriminator Loss: 0.5194, Generator Loss: 2.6544 D(x): 0.8893, D(G(z)): 0.2019 Epoch: [11/20], Batch Num: [204/600] Discriminator Loss: 0.4271, Generator Loss: 2.8919 D(x): 0.8695, D(G(z)): 0.1314 Epoch: [11/20], Batch Num: [205/600] Discriminator Loss: 0.5411, Generator Loss: 2.6674 D(x): 0.8065, D(G(z)): 0.1528 Epoch: [11/20], Batch Num: [206/600] Discriminator Loss: 0.4339, Generator Loss: 2.5747 D(x): 0.8230, D(G(z)): 0.1047 Epoch: [11/20], Batch Num: [207/600] Discriminator Loss: 0.4550, Generator Loss: 2.2800 D(x): 0.8436, D(G(z)): 0.1437 Epoch: [11/20], Batch Num: [208/600] Discriminator Loss: 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0.2255 Epoch: [11/20], Batch Num: [217/600] Discriminator Loss: 0.4410, Generator Loss: 3.1388 D(x): 0.9004, D(G(z)): 0.1581 Epoch: [11/20], Batch Num: [218/600] Discriminator Loss: 0.4066, Generator Loss: 3.8103 D(x): 0.8301, D(G(z)): 0.0904 Epoch: [11/20], Batch Num: [219/600] Discriminator Loss: 0.6770, Generator Loss: 2.9675 D(x): 0.7496, D(G(z)): 0.0834 Epoch: [11/20], Batch Num: [220/600] Discriminator Loss: 0.5232, Generator Loss: 2.4954 D(x): 0.8131, D(G(z)): 0.0913 Epoch: [11/20], Batch Num: [221/600] Discriminator Loss: 0.5493, Generator Loss: 1.9668 D(x): 0.8410, D(G(z)): 0.1739 Epoch: [11/20], Batch Num: [222/600] Discriminator Loss: 0.7572, Generator Loss: 1.9794 D(x): 0.8911, D(G(z)): 0.3154 Epoch: [11/20], Batch Num: [223/600] Discriminator Loss: 0.7358, Generator Loss: 2.5990 D(x): 0.9176, D(G(z)): 0.3090 Epoch: [11/20], Batch Num: [224/600] Discriminator Loss: 0.6493, Generator Loss: 3.3132 D(x): 0.8258, D(G(z)): 0.1860 Epoch: [11/20], Batch Num: [225/600] Discriminator Loss: 0.6229, Generator Loss: 3.3548 D(x): 0.7832, D(G(z)): 0.1081 Epoch: [11/20], Batch Num: [226/600] Discriminator Loss: 0.5726, Generator Loss: 3.3691 D(x): 0.7584, D(G(z)): 0.0569 Epoch: [11/20], Batch Num: [227/600] Discriminator Loss: 0.6643, Generator Loss: 2.4924 D(x): 0.7467, D(G(z)): 0.0892 Epoch: [11/20], Batch Num: [228/600] Discriminator Loss: 0.4753, Generator Loss: 2.0391 D(x): 0.8583, D(G(z)): 0.1617 Epoch: [11/20], Batch Num: [229/600] Discriminator Loss: 0.5372, Generator Loss: 1.5694 D(x): 0.8566, D(G(z)): 0.1971 Epoch: [11/20], Batch Num: [230/600] Discriminator Loss: 0.6192, Generator Loss: 1.8057 D(x): 0.9304, D(G(z)): 0.2964 Epoch: [11/20], Batch Num: [231/600] Discriminator Loss: 0.5205, Generator Loss: 2.5202 D(x): 0.8940, D(G(z)): 0.2401 Epoch: [11/20], Batch Num: [232/600] Discriminator Loss: 0.5785, Generator Loss: 2.9393 D(x): 0.8077, D(G(z)): 0.1565 Epoch: [11/20], Batch Num: [233/600] Discriminator Loss: 0.5182, Generator Loss: 3.0178 D(x): 0.7805, D(G(z)): 0.1233 Epoch: [11/20], Batch Num: [234/600] Discriminator Loss: 0.6167, Generator Loss: 2.8055 D(x): 0.7390, D(G(z)): 0.0884 Epoch: [11/20], Batch Num: [235/600] Discriminator Loss: 0.5836, Generator Loss: 2.5833 D(x): 0.7770, D(G(z)): 0.1321 Epoch: [11/20], Batch Num: [236/600] Discriminator Loss: 0.4598, Generator Loss: 2.1873 D(x): 0.8432, D(G(z)): 0.1416 Epoch: [11/20], Batch Num: [237/600] Discriminator Loss: 0.5890, Generator Loss: 2.0028 D(x): 0.8323, D(G(z)): 0.2205 Epoch: [11/20], Batch Num: [238/600] Discriminator Loss: 0.4788, Generator Loss: 1.9814 D(x): 0.8836, D(G(z)): 0.2374 Epoch: [11/20], Batch Num: [239/600] Discriminator Loss: 0.4744, Generator Loss: 2.0308 D(x): 0.8723, D(G(z)): 0.2249 Epoch: [11/20], Batch Num: [240/600] Discriminator Loss: 0.5370, Generator Loss: 2.4857 D(x): 0.8435, D(G(z)): 0.2103 Epoch: [11/20], Batch Num: [241/600] Discriminator Loss: 0.4977, Generator Loss: 2.6365 D(x): 0.8456, D(G(z)): 0.1633 Epoch: [11/20], Batch Num: [242/600] Discriminator Loss: 0.4659, Generator Loss: 2.6930 D(x): 0.7964, D(G(z)): 0.1078 Epoch: [11/20], Batch Num: [243/600] Discriminator Loss: 0.6388, Generator Loss: 2.4287 D(x): 0.7348, D(G(z)): 0.1264 Epoch: [11/20], Batch Num: [244/600] Discriminator Loss: 0.6095, Generator Loss: 2.1950 D(x): 0.7946, D(G(z)): 0.1791 Epoch: [11/20], Batch Num: [245/600] Discriminator Loss: 0.4806, Generator Loss: 1.9620 D(x): 0.8638, D(G(z)): 0.1954 Epoch: [11/20], Batch Num: [246/600] Discriminator Loss: 0.4394, Generator Loss: 2.1800 D(x): 0.9037, D(G(z)): 0.2161 Epoch: [11/20], Batch Num: [247/600] Discriminator Loss: 0.5538, Generator Loss: 2.4349 D(x): 0.8391, D(G(z)): 0.1857 Epoch: [11/20], Batch Num: [248/600] Discriminator Loss: 0.4421, Generator Loss: 2.9122 D(x): 0.8714, D(G(z)): 0.1711 Epoch: [11/20], Batch Num: [249/600] Discriminator Loss: 0.4032, Generator Loss: 2.8600 D(x): 0.8512, D(G(z)): 0.1158 Epoch: [11/20], Batch Num: [250/600] Discriminator Loss: 0.5154, Generator Loss: 3.0374 D(x): 0.7764, D(G(z)): 0.1067 Epoch: [11/20], Batch Num: [251/600] Discriminator Loss: 0.4662, Generator Loss: 2.1124 D(x): 0.8174, D(G(z)): 0.1170 Epoch: [11/20], Batch Num: [252/600] Discriminator Loss: 0.3893, Generator Loss: 2.1008 D(x): 0.9213, D(G(z)): 0.1902 Epoch: [11/20], Batch Num: [253/600] Discriminator Loss: 0.5134, Generator Loss: 2.5052 D(x): 0.8767, D(G(z)): 0.2089 Epoch: [11/20], Batch Num: [254/600] Discriminator Loss: 0.4190, Generator Loss: 2.5967 D(x): 0.8876, D(G(z)): 0.1818 Epoch: [11/20], Batch Num: [255/600] Discriminator Loss: 0.5597, Generator Loss: 2.7558 D(x): 0.7764, D(G(z)): 0.1193 Epoch: [11/20], Batch Num: [256/600] Discriminator Loss: 0.3981, Generator Loss: 2.5481 D(x): 0.8736, D(G(z)): 0.1451 Epoch: [11/20], Batch Num: [257/600] Discriminator Loss: 0.3490, Generator Loss: 2.8511 D(x): 0.9224, D(G(z)): 0.1493 Epoch: [11/20], Batch Num: [258/600] Discriminator Loss: 0.4284, Generator Loss: 3.0183 D(x): 0.8858, D(G(z)): 0.1453 Epoch: [11/20], 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Generator Loss: 3.1582 D(x): 0.8006, D(G(z)): 0.0968 Epoch: [11/20], Batch Num: [268/600] Discriminator Loss: 0.4086, Generator Loss: 3.2643 D(x): 0.8406, D(G(z)): 0.0738 Epoch: [11/20], Batch Num: [269/600] Discriminator Loss: 0.3844, Generator Loss: 2.7293 D(x): 0.8658, D(G(z)): 0.0862 Epoch: [11/20], Batch Num: [270/600] Discriminator Loss: 0.5953, Generator Loss: 2.5664 D(x): 0.8801, D(G(z)): 0.1540 Epoch: [11/20], Batch Num: [271/600] Discriminator Loss: 0.4893, Generator Loss: 2.7312 D(x): 0.8877, D(G(z)): 0.1912 Epoch: [11/20], Batch Num: [272/600] Discriminator Loss: 0.4516, Generator Loss: 2.9464 D(x): 0.9205, D(G(z)): 0.1840 Epoch: [11/20], Batch Num: [273/600] Discriminator Loss: 0.5245, Generator Loss: 3.3386 D(x): 0.8304, D(G(z)): 0.1320 Epoch: [11/20], Batch Num: [274/600] Discriminator Loss: 0.3588, Generator Loss: 3.2692 D(x): 0.8910, D(G(z)): 0.0975 Epoch: [11/20], Batch Num: [275/600] Discriminator Loss: 0.4068, Generator Loss: 3.4841 D(x): 0.8651, D(G(z)): 0.1010 Epoch: [11/20], Batch Num: [276/600] Discriminator Loss: 0.6495, Generator Loss: 2.8940 D(x): 0.7671, D(G(z)): 0.0890 Epoch: [11/20], Batch Num: [277/600] Discriminator Loss: 0.4965, Generator Loss: 2.2889 D(x): 0.8377, D(G(z)): 0.1337 Epoch: [11/20], Batch Num: [278/600] Discriminator Loss: 0.4830, Generator Loss: 2.5273 D(x): 0.8979, D(G(z)): 0.2320 Epoch: [11/20], Batch Num: [279/600] Discriminator Loss: 0.4847, Generator Loss: 2.7743 D(x): 0.8899, D(G(z)): 0.1873 Epoch: [11/20], Batch Num: [280/600] Discriminator Loss: 0.5964, Generator Loss: 2.9751 D(x): 0.8207, D(G(z)): 0.1486 Epoch: [11/20], Batch Num: [281/600] Discriminator Loss: 0.5439, Generator Loss: 2.7222 D(x): 0.8068, D(G(z)): 0.1100 Epoch: [11/20], Batch Num: [282/600] Discriminator Loss: 0.4660, Generator Loss: 2.7908 D(x): 0.8564, D(G(z)): 0.1511 Epoch: [11/20], Batch Num: [283/600] Discriminator Loss: 0.4640, Generator Loss: 2.9379 D(x): 0.8702, D(G(z)): 0.1525 Epoch: [11/20], Batch Num: [284/600] Discriminator Loss: 0.4092, Generator Loss: 2.7043 D(x): 0.8608, D(G(z)): 0.1339 Epoch: [11/20], Batch Num: [285/600] Discriminator Loss: 0.5373, Generator Loss: 2.4557 D(x): 0.8294, D(G(z)): 0.1536 Epoch: [11/20], Batch Num: [286/600] Discriminator Loss: 0.4042, Generator Loss: 2.3609 D(x): 0.8752, D(G(z)): 0.1595 Epoch: [11/20], Batch Num: [287/600] Discriminator Loss: 0.5872, Generator Loss: 2.3562 D(x): 0.8723, D(G(z)): 0.2256 Epoch: [11/20], Batch Num: [288/600] Discriminator Loss: 0.5004, Generator Loss: 3.1599 D(x): 0.8737, D(G(z)): 0.1850 Epoch: [11/20], Batch Num: [289/600] Discriminator Loss: 0.3536, Generator Loss: 3.1874 D(x): 0.8795, D(G(z)): 0.1075 Epoch: [11/20], Batch Num: [290/600] Discriminator Loss: 0.3895, Generator Loss: 3.5000 D(x): 0.8422, D(G(z)): 0.0956 Epoch: [11/20], Batch Num: [291/600] Discriminator Loss: 0.6879, Generator Loss: 3.0900 D(x): 0.7655, D(G(z)): 0.1053 Epoch: [11/20], Batch Num: [292/600] Discriminator Loss: 0.4704, Generator Loss: 2.4099 D(x): 0.8583, D(G(z)): 0.1212 Epoch: [11/20], Batch Num: [293/600] Discriminator Loss: 0.4535, Generator Loss: 2.0677 D(x): 0.8735, D(G(z)): 0.1844 Epoch: [11/20], Batch Num: [294/600] Discriminator Loss: 0.6004, Generator Loss: 2.3249 D(x): 0.8844, D(G(z)): 0.2430 Epoch: [11/20], Batch Num: [295/600] Discriminator Loss: 0.4784, Generator Loss: 2.7360 D(x): 0.8915, D(G(z)): 0.2015 Epoch: [11/20], Batch Num: [296/600] Discriminator Loss: 0.5539, Generator Loss: 3.7221 D(x): 0.8107, D(G(z)): 0.1444 Epoch: [11/20], Batch Num: [297/600] Discriminator Loss: 0.4069, Generator Loss: 3.2957 D(x): 0.8511, D(G(z)): 0.1021 Epoch: [11/20], Batch Num: [298/600] Discriminator Loss: 0.4342, Generator Loss: 3.1397 D(x): 0.8195, D(G(z)): 0.0913 Epoch: [11/20], Batch Num: [299/600] Discriminator Loss: 0.5016, Generator Loss: 2.5357 D(x): 0.8446, D(G(z)): 0.1232 Epoch: 11, Batch Num: [300/600]
Epoch: [11/20], Batch Num: [300/600] Discriminator Loss: 0.3995, Generator Loss: 2.4323 D(x): 0.8927, D(G(z)): 0.1737 Epoch: [11/20], Batch Num: [301/600] Discriminator Loss: 0.6121, Generator Loss: 2.8335 D(x): 0.8397, D(G(z)): 0.2296 Epoch: [11/20], Batch Num: [302/600] Discriminator Loss: 0.5118, Generator Loss: 3.3118 D(x): 0.8341, D(G(z)): 0.1742 Epoch: [11/20], Batch Num: [303/600] Discriminator Loss: 0.5437, Generator Loss: 2.6116 D(x): 0.7931, D(G(z)): 0.1224 Epoch: [11/20], Batch Num: [304/600] Discriminator Loss: 0.3773, Generator Loss: 2.9898 D(x): 0.8719, D(G(z)): 0.1300 Epoch: [11/20], Batch Num: [305/600] Discriminator Loss: 0.4152, Generator Loss: 2.8062 D(x): 0.9045, D(G(z)): 0.1786 Epoch: [11/20], Batch Num: [306/600] Discriminator Loss: 0.3943, Generator Loss: 3.0035 D(x): 0.9057, D(G(z)): 0.1557 Epoch: [11/20], Batch Num: [307/600] Discriminator Loss: 0.6730, Generator Loss: 3.3034 D(x): 0.7666, D(G(z)): 0.1383 Epoch: [11/20], Batch Num: [308/600] Discriminator Loss: 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0.1776 Epoch: [11/20], Batch Num: [317/600] Discriminator Loss: 0.4720, Generator Loss: 2.5670 D(x): 0.8952, D(G(z)): 0.2088 Epoch: [11/20], Batch Num: [318/600] Discriminator Loss: 0.6429, Generator Loss: 3.1040 D(x): 0.8578, D(G(z)): 0.2077 Epoch: [11/20], Batch Num: [319/600] Discriminator Loss: 0.5506, Generator Loss: 2.8905 D(x): 0.8118, D(G(z)): 0.0856 Epoch: [11/20], Batch Num: [320/600] Discriminator Loss: 0.6225, Generator Loss: 2.9240 D(x): 0.7835, D(G(z)): 0.1142 Epoch: [11/20], Batch Num: [321/600] Discriminator Loss: 0.5039, Generator Loss: 2.8350 D(x): 0.8389, D(G(z)): 0.1537 Epoch: [11/20], Batch Num: [322/600] Discriminator Loss: 0.5054, Generator Loss: 1.9255 D(x): 0.8022, D(G(z)): 0.1055 Epoch: [11/20], Batch Num: [323/600] Discriminator Loss: 0.7840, Generator Loss: 2.0987 D(x): 0.8496, D(G(z)): 0.2963 Epoch: [11/20], Batch Num: [324/600] Discriminator Loss: 0.5570, Generator Loss: 2.9125 D(x): 0.9069, D(G(z)): 0.2417 Epoch: [11/20], Batch Num: [325/600] Discriminator Loss: 0.5040, Generator Loss: 3.3294 D(x): 0.8022, D(G(z)): 0.1226 Epoch: [11/20], Batch Num: [326/600] Discriminator Loss: 0.5892, Generator Loss: 2.7724 D(x): 0.7624, D(G(z)): 0.0832 Epoch: [11/20], Batch Num: [327/600] Discriminator Loss: 0.5745, Generator Loss: 2.5752 D(x): 0.8223, D(G(z)): 0.1319 Epoch: [11/20], Batch Num: [328/600] Discriminator Loss: 0.6191, Generator Loss: 2.4368 D(x): 0.8464, D(G(z)): 0.1963 Epoch: [11/20], Batch Num: [329/600] Discriminator Loss: 0.4827, Generator Loss: 2.5785 D(x): 0.8825, D(G(z)): 0.2047 Epoch: [11/20], Batch Num: [330/600] Discriminator Loss: 0.5474, Generator Loss: 2.3811 D(x): 0.8234, D(G(z)): 0.1833 Epoch: [11/20], Batch Num: [331/600] Discriminator Loss: 0.5076, Generator Loss: 2.5936 D(x): 0.8680, D(G(z)): 0.1554 Epoch: [11/20], Batch Num: [332/600] Discriminator Loss: 0.8323, Generator Loss: 2.4503 D(x): 0.7557, D(G(z)): 0.1649 Epoch: [11/20], Batch Num: [333/600] Discriminator Loss: 0.5586, Generator Loss: 2.0840 D(x): 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1.8806 D(x): 0.8632, D(G(z)): 0.2421 Epoch: [11/20], Batch Num: [351/600] Discriminator Loss: 0.6049, Generator Loss: 1.9339 D(x): 0.8784, D(G(z)): 0.2669 Epoch: [11/20], Batch Num: [352/600] Discriminator Loss: 0.5464, Generator Loss: 2.5663 D(x): 0.8406, D(G(z)): 0.1988 Epoch: [11/20], Batch Num: [353/600] Discriminator Loss: 0.5801, Generator Loss: 2.9291 D(x): 0.7872, D(G(z)): 0.1272 Epoch: [11/20], Batch Num: [354/600] Discriminator Loss: 0.5198, Generator Loss: 2.7146 D(x): 0.8037, D(G(z)): 0.1177 Epoch: [11/20], Batch Num: [355/600] Discriminator Loss: 0.6145, Generator Loss: 2.3835 D(x): 0.7822, D(G(z)): 0.1614 Epoch: [11/20], Batch Num: [356/600] Discriminator Loss: 0.4326, Generator Loss: 2.3572 D(x): 0.8633, D(G(z)): 0.1553 Epoch: [11/20], Batch Num: [357/600] Discriminator Loss: 0.4404, Generator Loss: 2.3577 D(x): 0.9026, D(G(z)): 0.2193 Epoch: [11/20], Batch Num: [358/600] Discriminator Loss: 0.5564, Generator Loss: 2.5842 D(x): 0.8758, D(G(z)): 0.2251 Epoch: [11/20], Batch Num: [359/600] Discriminator Loss: 0.5225, Generator Loss: 3.2618 D(x): 0.8789, D(G(z)): 0.1954 Epoch: [11/20], Batch Num: [360/600] Discriminator Loss: 0.5728, Generator Loss: 3.7163 D(x): 0.7521, D(G(z)): 0.1018 Epoch: [11/20], Batch Num: [361/600] Discriminator Loss: 0.5507, Generator Loss: 3.0250 D(x): 0.7889, D(G(z)): 0.1290 Epoch: [11/20], Batch Num: [362/600] Discriminator Loss: 0.4604, Generator Loss: 2.3414 D(x): 0.8172, D(G(z)): 0.1002 Epoch: [11/20], Batch Num: [363/600] Discriminator Loss: 0.4487, Generator Loss: 2.3434 D(x): 0.8780, D(G(z)): 0.1818 Epoch: [11/20], Batch Num: [364/600] Discriminator Loss: 0.5010, Generator Loss: 2.1688 D(x): 0.8928, D(G(z)): 0.2242 Epoch: [11/20], Batch Num: [365/600] Discriminator Loss: 0.5663, Generator Loss: 2.7553 D(x): 0.8814, D(G(z)): 0.2372 Epoch: [11/20], Batch Num: [366/600] Discriminator Loss: 0.4812, Generator Loss: 2.9753 D(x): 0.8530, D(G(z)): 0.1682 Epoch: [11/20], Batch Num: [367/600] Discriminator Loss: 0.4841, Generator Loss: 2.8368 D(x): 0.7962, D(G(z)): 0.0977 Epoch: [11/20], Batch Num: [368/600] Discriminator Loss: 0.4900, Generator Loss: 2.6768 D(x): 0.8231, D(G(z)): 0.1302 Epoch: [11/20], Batch Num: [369/600] Discriminator Loss: 0.4880, Generator Loss: 2.6035 D(x): 0.8339, D(G(z)): 0.1628 Epoch: [11/20], Batch Num: [370/600] Discriminator Loss: 0.5337, Generator Loss: 2.5248 D(x): 0.8429, D(G(z)): 0.1960 Epoch: [11/20], Batch Num: [371/600] Discriminator Loss: 0.5244, Generator Loss: 2.6101 D(x): 0.8444, D(G(z)): 0.1828 Epoch: [11/20], Batch Num: [372/600] Discriminator Loss: 0.5475, Generator Loss: 2.6625 D(x): 0.8430, D(G(z)): 0.1849 Epoch: [11/20], Batch Num: [373/600] Discriminator Loss: 0.3923, Generator Loss: 2.7815 D(x): 0.8656, D(G(z)): 0.1168 Epoch: [11/20], Batch Num: [374/600] Discriminator Loss: 0.4719, Generator Loss: 2.5436 D(x): 0.8508, D(G(z)): 0.1468 Epoch: [11/20], Batch Num: [375/600] Discriminator Loss: 0.5388, Generator Loss: 2.5719 D(x): 0.8160, D(G(z)): 0.1381 Epoch: [11/20], Batch Num: [376/600] Discriminator Loss: 0.5077, Generator Loss: 2.6940 D(x): 0.8585, D(G(z)): 0.1561 Epoch: [11/20], Batch Num: [377/600] Discriminator Loss: 0.3699, Generator Loss: 2.5298 D(x): 0.8909, D(G(z)): 0.1561 Epoch: [11/20], Batch Num: [378/600] Discriminator Loss: 0.4037, Generator Loss: 2.8566 D(x): 0.8768, D(G(z)): 0.1387 Epoch: [11/20], Batch Num: [379/600] Discriminator Loss: 0.5902, Generator Loss: 2.4855 D(x): 0.8084, D(G(z)): 0.1604 Epoch: [11/20], Batch Num: [380/600] Discriminator Loss: 0.4025, Generator Loss: 2.8760 D(x): 0.8931, D(G(z)): 0.1669 Epoch: [11/20], Batch Num: [381/600] Discriminator Loss: 0.4684, Generator Loss: 3.0714 D(x): 0.8695, D(G(z)): 0.1748 Epoch: [11/20], Batch Num: [382/600] Discriminator Loss: 0.5987, Generator Loss: 3.1356 D(x): 0.7971, D(G(z)): 0.1339 Epoch: [11/20], Batch Num: [383/600] Discriminator Loss: 0.4775, Generator Loss: 2.9993 D(x): 0.8338, D(G(z)): 0.0919 Epoch: [11/20], Batch Num: [384/600] Discriminator Loss: 0.6695, Generator Loss: 2.7925 D(x): 0.7909, D(G(z)): 0.1432 Epoch: [11/20], Batch Num: [385/600] Discriminator Loss: 0.5356, Generator Loss: 2.3849 D(x): 0.8835, D(G(z)): 0.1764 Epoch: [11/20], Batch Num: [386/600] Discriminator Loss: 0.5297, Generator Loss: 2.5874 D(x): 0.8689, D(G(z)): 0.1959 Epoch: [11/20], Batch Num: [387/600] Discriminator Loss: 0.5409, Generator Loss: 2.1969 D(x): 0.8350, D(G(z)): 0.1687 Epoch: [11/20], Batch Num: [388/600] Discriminator Loss: 0.5042, Generator Loss: 2.7776 D(x): 0.8819, D(G(z)): 0.1996 Epoch: [11/20], Batch Num: [389/600] Discriminator Loss: 0.5098, Generator Loss: 3.1509 D(x): 0.8456, D(G(z)): 0.1550 Epoch: [11/20], Batch Num: [390/600] Discriminator Loss: 0.6047, Generator Loss: 2.5689 D(x): 0.7415, D(G(z)): 0.1087 Epoch: [11/20], Batch Num: [391/600] Discriminator Loss: 0.6374, Generator Loss: 2.3234 D(x): 0.8076, D(G(z)): 0.1621 Epoch: [11/20], Batch Num: [392/600] Discriminator Loss: 0.4378, Generator Loss: 2.3529 D(x): 0.9214, D(G(z)): 0.2255 Epoch: [11/20], Batch Num: [393/600] Discriminator Loss: 0.6166, Generator Loss: 2.7330 D(x): 0.8166, D(G(z)): 0.1858 Epoch: [11/20], Batch Num: [394/600] Discriminator Loss: 0.7089, Generator Loss: 2.8772 D(x): 0.7825, D(G(z)): 0.1340 Epoch: [11/20], Batch Num: [395/600] Discriminator Loss: 0.6790, Generator Loss: 2.3333 D(x): 0.7289, D(G(z)): 0.1279 Epoch: [11/20], Batch Num: [396/600] Discriminator Loss: 0.4407, Generator Loss: 2.3539 D(x): 0.8749, D(G(z)): 0.1717 Epoch: [11/20], Batch Num: [397/600] Discriminator Loss: 0.4862, Generator Loss: 2.5500 D(x): 0.9164, D(G(z)): 0.2200 Epoch: [11/20], Batch Num: [398/600] Discriminator Loss: 0.5841, Generator Loss: 2.9453 D(x): 0.8427, D(G(z)): 0.2137 Epoch: [11/20], Batch Num: [399/600] Discriminator Loss: 0.6098, Generator Loss: 2.9918 D(x): 0.8022, D(G(z)): 0.1578 Epoch: 11, Batch Num: [400/600]
Epoch: [11/20], Batch Num: [400/600] Discriminator Loss: 0.5843, Generator Loss: 2.6940 D(x): 0.7468, D(G(z)): 0.0895 Epoch: [11/20], Batch Num: [401/600] Discriminator Loss: 0.5665, Generator Loss: 2.2003 D(x): 0.8318, D(G(z)): 0.1571 Epoch: [11/20], Batch Num: [402/600] Discriminator Loss: 0.5087, Generator Loss: 2.0213 D(x): 0.8533, D(G(z)): 0.1994 Epoch: [11/20], Batch Num: [403/600] Discriminator Loss: 0.6708, Generator Loss: 2.6061 D(x): 0.9110, D(G(z)): 0.2982 Epoch: [11/20], Batch Num: [404/600] Discriminator Loss: 0.6468, Generator Loss: 3.3355 D(x): 0.8004, D(G(z)): 0.1599 Epoch: [11/20], Batch Num: [405/600] Discriminator Loss: 0.5679, Generator Loss: 3.0273 D(x): 0.8047, D(G(z)): 0.1133 Epoch: [11/20], Batch Num: [406/600] Discriminator Loss: 0.5723, Generator Loss: 3.1584 D(x): 0.8044, D(G(z)): 0.1082 Epoch: [11/20], Batch Num: [407/600] Discriminator Loss: 0.4225, Generator Loss: 2.2592 D(x): 0.8164, D(G(z)): 0.0922 Epoch: [11/20], Batch Num: [408/600] Discriminator Loss: 0.4114, Generator Loss: 2.1764 D(x): 0.9221, D(G(z)): 0.2073 Epoch: [11/20], Batch Num: [409/600] Discriminator Loss: 0.5155, Generator Loss: 2.0931 D(x): 0.8629, D(G(z)): 0.2233 Epoch: [11/20], Batch Num: [410/600] Discriminator Loss: 0.4527, Generator Loss: 2.5318 D(x): 0.9050, D(G(z)): 0.2166 Epoch: [11/20], Batch Num: [411/600] Discriminator Loss: 0.4199, Generator Loss: 3.0630 D(x): 0.8785, D(G(z)): 0.1607 Epoch: [11/20], Batch Num: [412/600] Discriminator Loss: 0.4528, Generator Loss: 3.2505 D(x): 0.8375, D(G(z)): 0.1236 Epoch: [11/20], Batch Num: [413/600] Discriminator Loss: 0.5760, Generator Loss: 3.0850 D(x): 0.7567, D(G(z)): 0.0757 Epoch: [11/20], Batch Num: [414/600] Discriminator Loss: 0.5448, Generator Loss: 2.4707 D(x): 0.7978, D(G(z)): 0.1270 Epoch: [11/20], Batch Num: [415/600] Discriminator Loss: 0.5799, Generator Loss: 1.9547 D(x): 0.7899, D(G(z)): 0.1433 Epoch: [11/20], Batch Num: [416/600] Discriminator Loss: 0.5428, Generator Loss: 1.6766 D(x): 0.8745, D(G(z)): 0.2263 Epoch: [11/20], Batch Num: [417/600] Discriminator Loss: 0.6286, Generator Loss: 2.1073 D(x): 0.9267, D(G(z)): 0.3042 Epoch: [11/20], Batch Num: [418/600] Discriminator Loss: 0.5759, Generator Loss: 2.9356 D(x): 0.8708, D(G(z)): 0.2095 Epoch: [11/20], Batch Num: [419/600] Discriminator Loss: 0.6363, Generator Loss: 3.0330 D(x): 0.7538, D(G(z)): 0.1283 Epoch: [11/20], Batch Num: [420/600] Discriminator Loss: 0.6089, Generator Loss: 2.8197 D(x): 0.7630, D(G(z)): 0.1079 Epoch: [11/20], Batch Num: [421/600] Discriminator Loss: 0.6444, Generator Loss: 2.5411 D(x): 0.7899, D(G(z)): 0.1393 Epoch: [11/20], Batch Num: [422/600] Discriminator Loss: 0.5319, Generator Loss: 2.3038 D(x): 0.8442, D(G(z)): 0.1706 Epoch: [11/20], Batch Num: [423/600] Discriminator Loss: 0.5375, Generator Loss: 1.9691 D(x): 0.8442, D(G(z)): 0.1788 Epoch: [11/20], Batch Num: [424/600] Discriminator Loss: 0.6473, Generator Loss: 2.1654 D(x): 0.8587, D(G(z)): 0.2450 Epoch: [11/20], Batch Num: [425/600] Discriminator Loss: 0.4914, Generator Loss: 2.5886 D(x): 0.8543, D(G(z)): 0.1697 Epoch: [11/20], Batch Num: [426/600] Discriminator Loss: 0.6084, Generator Loss: 2.4472 D(x): 0.8196, D(G(z)): 0.1751 Epoch: [11/20], Batch Num: [427/600] Discriminator Loss: 0.5237, Generator Loss: 2.6583 D(x): 0.8136, D(G(z)): 0.1542 Epoch: [11/20], Batch Num: [428/600] Discriminator Loss: 0.5784, Generator Loss: 2.6200 D(x): 0.7890, D(G(z)): 0.1563 Epoch: [11/20], Batch Num: [429/600] Discriminator Loss: 0.4575, Generator Loss: 2.5504 D(x): 0.8498, D(G(z)): 0.1617 Epoch: [11/20], Batch Num: [430/600] Discriminator Loss: 0.5007, Generator Loss: 2.2884 D(x): 0.8040, D(G(z)): 0.1211 Epoch: [11/20], Batch Num: [431/600] Discriminator Loss: 0.5151, Generator Loss: 2.2582 D(x): 0.8464, D(G(z)): 0.1990 Epoch: [11/20], Batch Num: [432/600] Discriminator Loss: 0.5073, Generator Loss: 2.2267 D(x): 0.8737, D(G(z)): 0.1938 Epoch: [11/20], Batch Num: [433/600] Discriminator Loss: 0.4500, Generator Loss: 2.5961 D(x): 0.8838, D(G(z)): 0.1831 Epoch: [11/20], Batch Num: [434/600] Discriminator Loss: 0.6643, Generator Loss: 2.5610 D(x): 0.7938, D(G(z)): 0.1610 Epoch: [11/20], Batch Num: [435/600] Discriminator Loss: 0.4237, Generator Loss: 2.5102 D(x): 0.8654, D(G(z)): 0.1434 Epoch: [11/20], Batch Num: [436/600] Discriminator Loss: 0.6788, Generator Loss: 2.3758 D(x): 0.7900, D(G(z)): 0.1617 Epoch: [11/20], Batch Num: [437/600] Discriminator Loss: 0.4563, Generator Loss: 2.5123 D(x): 0.8595, D(G(z)): 0.1426 Epoch: [11/20], Batch Num: [438/600] Discriminator Loss: 0.7062, Generator Loss: 2.3529 D(x): 0.7741, D(G(z)): 0.1777 Epoch: [11/20], Batch Num: [439/600] Discriminator Loss: 0.5857, Generator Loss: 1.8815 D(x): 0.8401, D(G(z)): 0.2027 Epoch: [11/20], Batch Num: [440/600] Discriminator Loss: 0.5975, Generator Loss: 2.5308 D(x): 0.8663, D(G(z)): 0.2193 Epoch: [11/20], Batch Num: [441/600] Discriminator Loss: 0.4840, Generator Loss: 2.8503 D(x): 0.8581, D(G(z)): 0.1810 Epoch: [11/20], Batch Num: 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2.6436 D(x): 0.8531, D(G(z)): 0.1167 Epoch: [11/20], Batch Num: [451/600] Discriminator Loss: 0.5282, Generator Loss: 2.7395 D(x): 0.8484, D(G(z)): 0.1737 Epoch: [11/20], Batch Num: [452/600] Discriminator Loss: 0.6030, Generator Loss: 2.7249 D(x): 0.8153, D(G(z)): 0.1886 Epoch: [11/20], Batch Num: [453/600] Discriminator Loss: 0.6513, Generator Loss: 2.7523 D(x): 0.8009, D(G(z)): 0.1612 Epoch: [11/20], Batch Num: [454/600] Discriminator Loss: 0.6847, Generator Loss: 2.3129 D(x): 0.8124, D(G(z)): 0.2072 Epoch: [11/20], Batch Num: [455/600] Discriminator Loss: 0.5295, Generator Loss: 2.3552 D(x): 0.8201, D(G(z)): 0.1723 Epoch: [11/20], Batch Num: [456/600] Discriminator Loss: 0.4257, Generator Loss: 2.2997 D(x): 0.8632, D(G(z)): 0.1582 Epoch: [11/20], Batch Num: [457/600] Discriminator Loss: 0.5696, Generator Loss: 2.7816 D(x): 0.8818, D(G(z)): 0.2139 Epoch: [11/20], Batch Num: [458/600] Discriminator Loss: 0.5761, Generator Loss: 3.3035 D(x): 0.8140, D(G(z)): 0.1808 Epoch: [11/20], 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Generator Loss: 2.4901 D(x): 0.8354, D(G(z)): 0.1554 Epoch: [11/20], Batch Num: [468/600] Discriminator Loss: 0.6386, Generator Loss: 2.2629 D(x): 0.8302, D(G(z)): 0.1884 Epoch: [11/20], Batch Num: [469/600] Discriminator Loss: 0.5838, Generator Loss: 2.2686 D(x): 0.8731, D(G(z)): 0.2362 Epoch: [11/20], Batch Num: [470/600] Discriminator Loss: 0.5519, Generator Loss: 2.3969 D(x): 0.8226, D(G(z)): 0.1823 Epoch: [11/20], Batch Num: [471/600] Discriminator Loss: 0.5598, Generator Loss: 2.1612 D(x): 0.8285, D(G(z)): 0.1646 Epoch: [11/20], Batch Num: [472/600] Discriminator Loss: 0.5855, Generator Loss: 2.5566 D(x): 0.8342, D(G(z)): 0.1916 Epoch: [11/20], Batch Num: [473/600] Discriminator Loss: 0.6138, Generator Loss: 2.4857 D(x): 0.8001, D(G(z)): 0.1830 Epoch: [11/20], Batch Num: [474/600] Discriminator Loss: 0.5638, Generator Loss: 2.4293 D(x): 0.8500, D(G(z)): 0.2111 Epoch: [11/20], Batch Num: [475/600] Discriminator Loss: 0.6948, Generator Loss: 2.4824 D(x): 0.7920, D(G(z)): 0.1679 Epoch: [11/20], Batch Num: [476/600] Discriminator Loss: 0.4855, Generator Loss: 2.6746 D(x): 0.8584, D(G(z)): 0.1740 Epoch: [11/20], Batch Num: [477/600] Discriminator Loss: 0.5180, Generator Loss: 2.5880 D(x): 0.8365, D(G(z)): 0.1640 Epoch: [11/20], Batch Num: [478/600] Discriminator Loss: 0.4875, Generator Loss: 2.4630 D(x): 0.8346, D(G(z)): 0.1765 Epoch: [11/20], Batch Num: [479/600] Discriminator Loss: 0.4431, Generator Loss: 2.6931 D(x): 0.8835, D(G(z)): 0.1873 Epoch: [11/20], Batch Num: [480/600] Discriminator Loss: 0.4310, Generator Loss: 3.1150 D(x): 0.8663, D(G(z)): 0.1675 Epoch: [11/20], Batch Num: [481/600] Discriminator Loss: 0.5420, Generator Loss: 2.8708 D(x): 0.8067, D(G(z)): 0.1307 Epoch: [11/20], Batch Num: [482/600] Discriminator Loss: 0.5228, Generator Loss: 2.7572 D(x): 0.8011, D(G(z)): 0.1136 Epoch: [11/20], Batch Num: [483/600] Discriminator Loss: 0.5673, Generator Loss: 2.4100 D(x): 0.8435, D(G(z)): 0.1878 Epoch: [11/20], Batch Num: [484/600] Discriminator Loss: 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0.1437 Epoch: [11/20], Batch Num: [493/600] Discriminator Loss: 0.4743, Generator Loss: 3.0044 D(x): 0.8523, D(G(z)): 0.1398 Epoch: [11/20], Batch Num: [494/600] Discriminator Loss: 0.3959, Generator Loss: 2.7681 D(x): 0.8664, D(G(z)): 0.0969 Epoch: [11/20], Batch Num: [495/600] Discriminator Loss: 0.4986, Generator Loss: 2.9148 D(x): 0.8941, D(G(z)): 0.1546 Epoch: [11/20], Batch Num: [496/600] Discriminator Loss: 0.4658, Generator Loss: 2.6556 D(x): 0.8635, D(G(z)): 0.1435 Epoch: [11/20], Batch Num: [497/600] Discriminator Loss: 0.5167, Generator Loss: 2.6995 D(x): 0.8442, D(G(z)): 0.1688 Epoch: [11/20], Batch Num: [498/600] Discriminator Loss: 0.4725, Generator Loss: 2.9432 D(x): 0.8943, D(G(z)): 0.1903 Epoch: [11/20], Batch Num: [499/600] Discriminator Loss: 0.4001, Generator Loss: 3.4381 D(x): 0.8667, D(G(z)): 0.1211 Epoch: 11, Batch Num: [500/600]
Epoch: [11/20], Batch Num: [500/600] Discriminator Loss: 0.6439, Generator Loss: 2.9388 D(x): 0.7670, D(G(z)): 0.1201 Epoch: [11/20], Batch Num: [501/600] Discriminator Loss: 0.6854, Generator Loss: 2.6282 D(x): 0.7922, D(G(z)): 0.1716 Epoch: [11/20], Batch Num: [502/600] Discriminator Loss: 0.5794, Generator Loss: 1.9663 D(x): 0.8484, D(G(z)): 0.1929 Epoch: [11/20], Batch Num: [503/600] Discriminator Loss: 0.6553, Generator Loss: 2.2467 D(x): 0.8371, D(G(z)): 0.2266 Epoch: [11/20], Batch Num: [504/600] Discriminator Loss: 0.4896, Generator Loss: 2.7806 D(x): 0.8456, D(G(z)): 0.1704 Epoch: [11/20], Batch Num: [505/600] Discriminator Loss: 0.5678, Generator Loss: 2.7947 D(x): 0.8417, D(G(z)): 0.1899 Epoch: [11/20], Batch Num: [506/600] Discriminator Loss: 0.3875, Generator Loss: 3.1256 D(x): 0.8516, D(G(z)): 0.1237 Epoch: [11/20], Batch Num: [507/600] Discriminator Loss: 0.5424, Generator Loss: 2.8619 D(x): 0.7907, D(G(z)): 0.1092 Epoch: [11/20], Batch Num: [508/600] Discriminator Loss: 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0.1907 Epoch: [11/20], Batch Num: [517/600] Discriminator Loss: 0.4952, Generator Loss: 2.8727 D(x): 0.8725, D(G(z)): 0.2006 Epoch: [11/20], Batch Num: [518/600] Discriminator Loss: 0.6079, Generator Loss: 2.9455 D(x): 0.8324, D(G(z)): 0.2005 Epoch: [11/20], Batch Num: [519/600] Discriminator Loss: 0.5447, Generator Loss: 3.3014 D(x): 0.8212, D(G(z)): 0.1602 Epoch: [11/20], Batch Num: [520/600] Discriminator Loss: 0.5437, Generator Loss: 3.2873 D(x): 0.7804, D(G(z)): 0.0949 Epoch: [11/20], Batch Num: [521/600] Discriminator Loss: 0.4628, Generator Loss: 2.5430 D(x): 0.8151, D(G(z)): 0.1093 Epoch: [11/20], Batch Num: [522/600] Discriminator Loss: 0.5497, Generator Loss: 1.8203 D(x): 0.8295, D(G(z)): 0.1796 Epoch: [11/20], Batch Num: [523/600] Discriminator Loss: 0.5336, Generator Loss: 1.9692 D(x): 0.9011, D(G(z)): 0.2466 Epoch: [11/20], Batch Num: [524/600] Discriminator Loss: 0.4791, Generator Loss: 2.8581 D(x): 0.9279, D(G(z)): 0.2351 Epoch: [11/20], Batch Num: [525/600] Discriminator Loss: 0.4693, Generator Loss: 3.6090 D(x): 0.8348, D(G(z)): 0.1518 Epoch: [11/20], Batch Num: [526/600] Discriminator Loss: 0.4890, Generator Loss: 3.5506 D(x): 0.8120, D(G(z)): 0.1105 Epoch: [11/20], Batch Num: [527/600] Discriminator Loss: 0.5732, Generator Loss: 2.8014 D(x): 0.7566, D(G(z)): 0.0948 Epoch: [11/20], Batch Num: [528/600] Discriminator Loss: 0.5798, Generator Loss: 2.4224 D(x): 0.8254, D(G(z)): 0.1661 Epoch: [11/20], Batch Num: [529/600] Discriminator Loss: 0.6899, Generator Loss: 2.2568 D(x): 0.8690, D(G(z)): 0.2610 Epoch: [11/20], Batch Num: [530/600] Discriminator Loss: 0.5359, Generator Loss: 2.7592 D(x): 0.8557, D(G(z)): 0.2181 Epoch: [11/20], Batch Num: [531/600] Discriminator Loss: 0.4450, Generator Loss: 3.2824 D(x): 0.8683, D(G(z)): 0.1576 Epoch: [11/20], Batch Num: [532/600] Discriminator Loss: 0.3927, Generator Loss: 3.3677 D(x): 0.8354, D(G(z)): 0.0938 Epoch: [11/20], Batch Num: [533/600] Discriminator Loss: 0.5548, Generator Loss: 2.7569 D(x): 0.7386, D(G(z)): 0.0890 Epoch: [11/20], Batch Num: [534/600] Discriminator Loss: 0.3960, Generator Loss: 2.2558 D(x): 0.8765, D(G(z)): 0.1562 Epoch: [11/20], Batch Num: [535/600] Discriminator Loss: 0.6066, Generator Loss: 1.9396 D(x): 0.8730, D(G(z)): 0.2311 Epoch: [11/20], Batch Num: [536/600] Discriminator Loss: 0.6837, Generator Loss: 3.1243 D(x): 0.9082, D(G(z)): 0.3012 Epoch: [11/20], Batch Num: [537/600] Discriminator Loss: 0.5965, Generator Loss: 3.8851 D(x): 0.8085, D(G(z)): 0.1436 Epoch: [11/20], Batch Num: [538/600] Discriminator Loss: 0.6508, Generator Loss: 3.1099 D(x): 0.7354, D(G(z)): 0.0630 Epoch: [11/20], Batch Num: [539/600] Discriminator Loss: 0.5157, Generator Loss: 2.8183 D(x): 0.8288, D(G(z)): 0.1281 Epoch: [11/20], Batch Num: [540/600] Discriminator Loss: 0.5785, Generator Loss: 1.8552 D(x): 0.8647, D(G(z)): 0.2070 Epoch: [11/20], Batch Num: [541/600] Discriminator Loss: 0.6626, Generator Loss: 2.4297 D(x): 0.8619, D(G(z)): 0.2520 Epoch: [11/20], Batch Num: [542/600] Discriminator Loss: 0.6677, Generator Loss: 3.0001 D(x): 0.8522, D(G(z)): 0.2428 Epoch: [11/20], Batch Num: [543/600] Discriminator Loss: 0.6310, Generator Loss: 3.1118 D(x): 0.7638, D(G(z)): 0.1249 Epoch: [11/20], Batch Num: [544/600] Discriminator Loss: 0.6046, Generator Loss: 2.7511 D(x): 0.7835, D(G(z)): 0.1102 Epoch: [11/20], Batch Num: [545/600] Discriminator Loss: 0.5832, Generator Loss: 2.4772 D(x): 0.7992, D(G(z)): 0.1343 Epoch: [11/20], Batch Num: [546/600] Discriminator Loss: 0.5742, Generator Loss: 2.0931 D(x): 0.7949, D(G(z)): 0.1670 Epoch: [11/20], Batch Num: [547/600] Discriminator Loss: 0.6700, Generator Loss: 1.9511 D(x): 0.8525, D(G(z)): 0.2480 Epoch: [11/20], Batch Num: [548/600] Discriminator Loss: 0.5081, Generator Loss: 2.2506 D(x): 0.8623, D(G(z)): 0.2229 Epoch: [11/20], Batch Num: [549/600] Discriminator Loss: 0.5737, Generator Loss: 2.4832 D(x): 0.8599, D(G(z)): 0.2146 Epoch: [11/20], Batch Num: [550/600] Discriminator Loss: 0.5060, Generator Loss: 2.6520 D(x): 0.8358, D(G(z)): 0.1431 Epoch: [11/20], Batch Num: [551/600] Discriminator Loss: 0.6441, Generator Loss: 3.0738 D(x): 0.7974, D(G(z)): 0.1611 Epoch: [11/20], Batch Num: [552/600] Discriminator Loss: 0.6006, Generator Loss: 2.8695 D(x): 0.7722, D(G(z)): 0.1159 Epoch: [11/20], Batch Num: [553/600] Discriminator Loss: 0.6609, Generator Loss: 2.4428 D(x): 0.7870, D(G(z)): 0.1334 Epoch: [11/20], Batch Num: [554/600] Discriminator Loss: 0.5089, Generator Loss: 2.3455 D(x): 0.8932, D(G(z)): 0.2017 Epoch: [11/20], Batch Num: [555/600] Discriminator Loss: 0.4296, Generator Loss: 2.4795 D(x): 0.8369, D(G(z)): 0.1529 Epoch: [11/20], Batch Num: [556/600] Discriminator Loss: 0.4777, Generator Loss: 2.2396 D(x): 0.8161, D(G(z)): 0.1549 Epoch: [11/20], Batch Num: [557/600] Discriminator Loss: 0.6590, Generator Loss: 2.5125 D(x): 0.8343, D(G(z)): 0.2500 Epoch: [11/20], Batch Num: [558/600] Discriminator Loss: 0.5659, Generator Loss: 2.7058 D(x): 0.8376, D(G(z)): 0.1797 Epoch: [11/20], Batch Num: [559/600] Discriminator Loss: 0.4614, Generator Loss: 2.8488 D(x): 0.8451, D(G(z)): 0.1547 Epoch: [11/20], Batch Num: [560/600] Discriminator Loss: 0.4276, Generator Loss: 3.0231 D(x): 0.8693, D(G(z)): 0.1600 Epoch: [11/20], Batch Num: [561/600] Discriminator Loss: 0.5293, Generator Loss: 3.2765 D(x): 0.8478, D(G(z)): 0.1494 Epoch: [11/20], Batch Num: [562/600] Discriminator Loss: 0.7244, Generator Loss: 2.7208 D(x): 0.7161, D(G(z)): 0.1251 Epoch: [11/20], Batch Num: [563/600] Discriminator Loss: 0.4641, Generator Loss: 2.7257 D(x): 0.8517, D(G(z)): 0.1369 Epoch: [11/20], Batch Num: [564/600] Discriminator Loss: 0.5168, Generator Loss: 2.1241 D(x): 0.8158, D(G(z)): 0.1457 Epoch: [11/20], Batch Num: [565/600] Discriminator Loss: 0.6535, Generator Loss: 2.0569 D(x): 0.8538, D(G(z)): 0.2227 Epoch: [11/20], Batch Num: [566/600] Discriminator Loss: 0.5895, Generator Loss: 2.4097 D(x): 0.8528, D(G(z)): 0.1999 Epoch: [11/20], Batch Num: [567/600] Discriminator Loss: 0.4627, Generator Loss: 2.2973 D(x): 0.8629, D(G(z)): 0.1930 Epoch: [11/20], Batch Num: [568/600] Discriminator Loss: 0.5555, Generator Loss: 2.7382 D(x): 0.8452, D(G(z)): 0.1836 Epoch: [11/20], Batch Num: [569/600] Discriminator Loss: 0.5963, Generator Loss: 2.9155 D(x): 0.7980, D(G(z)): 0.1209 Epoch: [11/20], Batch Num: [570/600] Discriminator Loss: 0.5500, Generator Loss: 2.6017 D(x): 0.8032, D(G(z)): 0.1171 Epoch: [11/20], Batch Num: [571/600] Discriminator Loss: 0.5776, Generator Loss: 2.6028 D(x): 0.8541, D(G(z)): 0.1905 Epoch: [11/20], Batch Num: [572/600] Discriminator Loss: 0.4949, Generator Loss: 2.6191 D(x): 0.8671, D(G(z)): 0.1746 Epoch: [11/20], Batch Num: [573/600] Discriminator Loss: 0.4964, Generator Loss: 2.6369 D(x): 0.8601, D(G(z)): 0.1662 Epoch: [11/20], Batch Num: [574/600] Discriminator Loss: 0.4845, Generator Loss: 2.6051 D(x): 0.8564, D(G(z)): 0.1690 Epoch: [11/20], Batch Num: [575/600] Discriminator Loss: 0.4629, Generator Loss: 2.9956 D(x): 0.8617, D(G(z)): 0.1659 Epoch: [11/20], Batch Num: [576/600] Discriminator Loss: 0.5329, Generator Loss: 3.2159 D(x): 0.8352, D(G(z)): 0.1563 Epoch: [11/20], Batch Num: [577/600] Discriminator Loss: 0.4902, Generator Loss: 2.6859 D(x): 0.8241, D(G(z)): 0.1153 Epoch: [11/20], Batch Num: [578/600] Discriminator Loss: 0.5898, Generator Loss: 2.6888 D(x): 0.8249, D(G(z)): 0.1634 Epoch: [11/20], Batch Num: [579/600] Discriminator Loss: 0.3854, Generator Loss: 2.7428 D(x): 0.8903, D(G(z)): 0.1616 Epoch: [11/20], Batch Num: [580/600] Discriminator Loss: 0.5069, Generator Loss: 2.5877 D(x): 0.8385, D(G(z)): 0.1499 Epoch: [11/20], Batch Num: [581/600] Discriminator Loss: 0.4666, Generator Loss: 2.8802 D(x): 0.8738, D(G(z)): 0.1518 Epoch: [11/20], Batch Num: [582/600] Discriminator Loss: 0.3916, Generator Loss: 3.3873 D(x): 0.8834, D(G(z)): 0.1326 Epoch: [11/20], Batch Num: [583/600] Discriminator Loss: 0.4278, Generator Loss: 3.0259 D(x): 0.8509, D(G(z)): 0.1322 Epoch: [11/20], Batch Num: [584/600] Discriminator Loss: 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0.1164 Epoch: [11/20], Batch Num: [593/600] Discriminator Loss: 0.4838, Generator Loss: 2.7274 D(x): 0.8516, D(G(z)): 0.1403 Epoch: [11/20], Batch Num: [594/600] Discriminator Loss: 0.5420, Generator Loss: 2.7220 D(x): 0.8556, D(G(z)): 0.1866 Epoch: [11/20], Batch Num: [595/600] Discriminator Loss: 0.4908, Generator Loss: 2.4481 D(x): 0.8368, D(G(z)): 0.1486 Epoch: [11/20], Batch Num: [596/600] Discriminator Loss: 0.4360, Generator Loss: 3.1092 D(x): 0.9164, D(G(z)): 0.1942 Epoch: [11/20], Batch Num: [597/600] Discriminator Loss: 0.4754, Generator Loss: 3.6923 D(x): 0.8779, D(G(z)): 0.1636 Epoch: [11/20], Batch Num: [598/600] Discriminator Loss: 0.6476, Generator Loss: 3.4709 D(x): 0.7724, D(G(z)): 0.0853 Epoch: [11/20], Batch Num: [599/600] Discriminator Loss: 0.5557, Generator Loss: 3.0996 D(x): 0.7785, D(G(z)): 0.0901 Epoch: 12, Batch Num: [0/600]
Epoch: [12/20], Batch Num: [0/600] Discriminator Loss: 0.4302, Generator Loss: 2.1562 D(x): 0.8771, D(G(z)): 0.1420 Epoch: [12/20], Batch Num: [1/600] Discriminator Loss: 0.5484, Generator Loss: 2.4132 D(x): 0.8746, D(G(z)): 0.2003 Epoch: [12/20], Batch Num: [2/600] Discriminator Loss: 0.6255, Generator Loss: 2.5167 D(x): 0.8399, D(G(z)): 0.2079 Epoch: [12/20], Batch Num: [3/600] Discriminator Loss: 0.6188, Generator Loss: 2.7232 D(x): 0.8816, D(G(z)): 0.2169 Epoch: [12/20], Batch Num: [4/600] Discriminator Loss: 0.6436, Generator Loss: 3.0095 D(x): 0.8169, D(G(z)): 0.1784 Epoch: [12/20], Batch Num: [5/600] Discriminator Loss: 0.7232, Generator Loss: 3.2854 D(x): 0.8188, D(G(z)): 0.1458 Epoch: [12/20], Batch Num: [6/600] Discriminator Loss: 0.6731, Generator Loss: 3.0502 D(x): 0.7764, D(G(z)): 0.1335 Epoch: [12/20], Batch Num: [7/600] Discriminator Loss: 0.8505, Generator Loss: 2.5886 D(x): 0.7467, D(G(z)): 0.1662 Epoch: [12/20], Batch Num: [8/600] Discriminator Loss: 0.5650, Generator 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Num: [17/600] Discriminator Loss: 0.5514, Generator Loss: 2.5745 D(x): 0.8263, D(G(z)): 0.1615 Epoch: [12/20], Batch Num: [18/600] Discriminator Loss: 0.5200, Generator Loss: 2.9667 D(x): 0.7968, D(G(z)): 0.1359 Epoch: [12/20], Batch Num: [19/600] Discriminator Loss: 0.6461, Generator Loss: 2.3976 D(x): 0.7291, D(G(z)): 0.1058 Epoch: [12/20], Batch Num: [20/600] Discriminator Loss: 0.5018, Generator Loss: 2.1198 D(x): 0.8103, D(G(z)): 0.1442 Epoch: [12/20], Batch Num: [21/600] Discriminator Loss: 0.4864, Generator Loss: 1.9516 D(x): 0.8830, D(G(z)): 0.2249 Epoch: [12/20], Batch Num: [22/600] Discriminator Loss: 0.5417, Generator Loss: 2.1600 D(x): 0.8664, D(G(z)): 0.2238 Epoch: [12/20], Batch Num: [23/600] Discriminator Loss: 0.8553, Generator Loss: 2.4196 D(x): 0.8286, D(G(z)): 0.2878 Epoch: [12/20], Batch Num: [24/600] Discriminator Loss: 0.5751, Generator Loss: 3.0211 D(x): 0.8301, D(G(z)): 0.1767 Epoch: [12/20], Batch Num: [25/600] Discriminator Loss: 0.5375, Generator Loss: 3.1413 D(x): 0.8070, D(G(z)): 0.1297 Epoch: [12/20], Batch Num: [26/600] Discriminator Loss: 0.6622, Generator Loss: 2.6852 D(x): 0.7400, D(G(z)): 0.1135 Epoch: [12/20], Batch Num: [27/600] Discriminator Loss: 0.4765, Generator Loss: 2.3803 D(x): 0.8079, D(G(z)): 0.1099 Epoch: [12/20], Batch Num: [28/600] Discriminator Loss: 0.4214, Generator Loss: 2.2014 D(x): 0.8994, D(G(z)): 0.1997 Epoch: [12/20], Batch Num: [29/600] Discriminator Loss: 0.6401, Generator Loss: 1.8866 D(x): 0.8296, D(G(z)): 0.2219 Epoch: [12/20], Batch Num: [30/600] Discriminator Loss: 0.4758, Generator Loss: 2.0401 D(x): 0.8708, D(G(z)): 0.1943 Epoch: [12/20], Batch Num: [31/600] Discriminator Loss: 0.5463, Generator Loss: 2.3356 D(x): 0.8702, D(G(z)): 0.2324 Epoch: [12/20], Batch Num: [32/600] Discriminator Loss: 0.5427, Generator Loss: 2.7189 D(x): 0.8050, D(G(z)): 0.1615 Epoch: [12/20], Batch Num: [33/600] Discriminator Loss: 0.5042, Generator Loss: 2.4812 D(x): 0.7992, D(G(z)): 0.1184 Epoch: [12/20], Batch Num: [34/600] Discriminator Loss: 0.6474, Generator Loss: 2.7487 D(x): 0.8006, D(G(z)): 0.1580 Epoch: [12/20], Batch Num: [35/600] Discriminator Loss: 0.4679, Generator Loss: 2.5942 D(x): 0.8456, D(G(z)): 0.1666 Epoch: [12/20], Batch Num: [36/600] Discriminator Loss: 0.5193, Generator Loss: 2.2396 D(x): 0.8453, D(G(z)): 0.1692 Epoch: [12/20], Batch Num: [37/600] Discriminator Loss: 0.6110, Generator Loss: 2.4799 D(x): 0.8676, D(G(z)): 0.2241 Epoch: [12/20], Batch Num: [38/600] Discriminator Loss: 0.5855, Generator Loss: 2.2725 D(x): 0.8075, D(G(z)): 0.1821 Epoch: [12/20], Batch Num: [39/600] Discriminator Loss: 0.6405, Generator Loss: 2.4457 D(x): 0.8233, D(G(z)): 0.2198 Epoch: [12/20], Batch Num: [40/600] Discriminator Loss: 0.4990, Generator Loss: 2.6252 D(x): 0.8241, D(G(z)): 0.1645 Epoch: [12/20], Batch Num: [41/600] Discriminator Loss: 0.4495, Generator Loss: 2.8504 D(x): 0.8786, D(G(z)): 0.2010 Epoch: [12/20], Batch Num: [42/600] Discriminator Loss: 0.6091, Generator Loss: 2.7324 D(x): 0.7913, D(G(z)): 0.1438 Epoch: [12/20], Batch Num: [43/600] Discriminator Loss: 0.6302, Generator Loss: 2.4906 D(x): 0.7992, D(G(z)): 0.1586 Epoch: [12/20], Batch Num: [44/600] Discriminator Loss: 0.6254, Generator Loss: 2.3338 D(x): 0.8117, D(G(z)): 0.1866 Epoch: [12/20], Batch Num: [45/600] Discriminator Loss: 0.6370, Generator Loss: 2.2359 D(x): 0.8209, D(G(z)): 0.2111 Epoch: [12/20], Batch Num: [46/600] Discriminator Loss: 0.5970, Generator Loss: 2.4249 D(x): 0.8031, D(G(z)): 0.2031 Epoch: [12/20], Batch Num: [47/600] Discriminator Loss: 0.6389, Generator Loss: 2.2169 D(x): 0.7927, D(G(z)): 0.1847 Epoch: [12/20], Batch Num: [48/600] Discriminator Loss: 0.6321, Generator Loss: 2.2914 D(x): 0.7970, D(G(z)): 0.1982 Epoch: [12/20], Batch Num: [49/600] Discriminator Loss: 0.5618, Generator Loss: 2.2732 D(x): 0.8741, D(G(z)): 0.2320 Epoch: [12/20], Batch Num: [50/600] Discriminator Loss: 0.5681, Generator Loss: 2.6512 D(x): 0.8281, D(G(z)): 0.2095 Epoch: [12/20], Batch Num: [51/600] Discriminator Loss: 0.4743, Generator Loss: 3.0695 D(x): 0.8440, D(G(z)): 0.1516 Epoch: [12/20], Batch Num: [52/600] Discriminator Loss: 0.5614, Generator Loss: 3.1974 D(x): 0.7764, D(G(z)): 0.1279 Epoch: [12/20], Batch Num: [53/600] Discriminator Loss: 0.6696, Generator Loss: 2.4750 D(x): 0.7517, D(G(z)): 0.1325 Epoch: [12/20], Batch Num: [54/600] Discriminator Loss: 0.6193, Generator Loss: 2.6755 D(x): 0.8450, D(G(z)): 0.2093 Epoch: [12/20], Batch Num: [55/600] Discriminator Loss: 0.6142, Generator Loss: 2.3218 D(x): 0.8586, D(G(z)): 0.2249 Epoch: [12/20], Batch Num: [56/600] Discriminator Loss: 0.6843, Generator Loss: 2.3961 D(x): 0.8000, D(G(z)): 0.2009 Epoch: [12/20], Batch Num: [57/600] Discriminator Loss: 0.6127, Generator Loss: 2.5235 D(x): 0.8413, D(G(z)): 0.2218 Epoch: [12/20], Batch Num: [58/600] Discriminator Loss: 0.7097, Generator Loss: 2.6766 D(x): 0.7845, D(G(z)): 0.1693 Epoch: [12/20], Batch Num: [59/600] Discriminator Loss: 0.5453, Generator Loss: 2.7555 D(x): 0.7995, D(G(z)): 0.1240 Epoch: [12/20], Batch Num: [60/600] Discriminator Loss: 0.6969, Generator Loss: 2.2063 D(x): 0.7378, D(G(z)): 0.1498 Epoch: [12/20], Batch Num: [61/600] Discriminator Loss: 0.4823, Generator Loss: 1.9592 D(x): 0.8112, D(G(z)): 0.1405 Epoch: [12/20], Batch Num: [62/600] Discriminator Loss: 0.6476, Generator Loss: 1.7242 D(x): 0.8093, D(G(z)): 0.2379 Epoch: [12/20], Batch Num: [63/600] Discriminator Loss: 0.7028, Generator Loss: 1.8263 D(x): 0.8268, D(G(z)): 0.2838 Epoch: [12/20], Batch Num: [64/600] Discriminator Loss: 0.7278, Generator Loss: 1.7661 D(x): 0.8389, D(G(z)): 0.2946 Epoch: [12/20], Batch Num: [65/600] Discriminator Loss: 0.6304, Generator Loss: 2.4944 D(x): 0.8592, D(G(z)): 0.2931 Epoch: [12/20], Batch Num: [66/600] Discriminator Loss: 0.5996, Generator Loss: 2.8480 D(x): 0.8000, D(G(z)): 0.1747 Epoch: [12/20], Batch Num: [67/600] Discriminator Loss: 0.6352, Generator Loss: 2.7814 D(x): 0.7493, D(G(z)): 0.0938 Epoch: [12/20], Batch Num: [68/600] Discriminator Loss: 0.6816, Generator Loss: 2.3400 D(x): 0.7340, D(G(z)): 0.1518 Epoch: [12/20], Batch Num: [69/600] Discriminator Loss: 0.6002, Generator Loss: 2.0635 D(x): 0.7637, D(G(z)): 0.1471 Epoch: [12/20], Batch Num: [70/600] Discriminator Loss: 0.5368, Generator Loss: 1.7617 D(x): 0.8258, D(G(z)): 0.1958 Epoch: [12/20], Batch Num: [71/600] Discriminator Loss: 0.6049, Generator Loss: 1.4954 D(x): 0.8860, D(G(z)): 0.2765 Epoch: [12/20], Batch Num: [72/600] Discriminator Loss: 0.5980, Generator Loss: 1.9657 D(x): 0.8862, D(G(z)): 0.2767 Epoch: [12/20], Batch Num: [73/600] Discriminator Loss: 0.5068, Generator Loss: 2.4095 D(x): 0.8714, D(G(z)): 0.2296 Epoch: [12/20], Batch Num: [74/600] Discriminator Loss: 0.6631, Generator Loss: 2.7650 D(x): 0.7760, D(G(z)): 0.1542 Epoch: [12/20], Batch Num: [75/600] Discriminator Loss: 0.6250, Generator Loss: 2.6920 D(x): 0.7713, D(G(z)): 0.1285 Epoch: [12/20], Batch Num: [76/600] Discriminator Loss: 0.5162, Generator Loss: 2.5914 D(x): 0.7967, D(G(z)): 0.1087 Epoch: [12/20], Batch Num: [77/600] Discriminator Loss: 0.6048, Generator Loss: 2.2092 D(x): 0.7667, D(G(z)): 0.1371 Epoch: [12/20], Batch Num: [78/600] Discriminator Loss: 0.5960, Generator Loss: 1.8156 D(x): 0.7872, D(G(z)): 0.1584 Epoch: [12/20], Batch Num: [79/600] Discriminator Loss: 0.6237, Generator Loss: 1.7746 D(x): 0.8706, D(G(z)): 0.2639 Epoch: [12/20], Batch Num: [80/600] Discriminator Loss: 0.6011, Generator Loss: 1.8599 D(x): 0.8942, D(G(z)): 0.2840 Epoch: [12/20], Batch Num: [81/600] Discriminator Loss: 0.5717, Generator Loss: 2.2852 D(x): 0.8365, D(G(z)): 0.2102 Epoch: [12/20], Batch Num: [82/600] Discriminator Loss: 0.5696, Generator Loss: 2.4996 D(x): 0.8363, D(G(z)): 0.1853 Epoch: [12/20], Batch Num: [83/600] Discriminator Loss: 0.6609, Generator Loss: 2.5002 D(x): 0.7261, D(G(z)): 0.1204 Epoch: [12/20], Batch Num: [84/600] Discriminator Loss: 0.5452, Generator Loss: 2.3607 D(x): 0.7858, D(G(z)): 0.1394 Epoch: [12/20], Batch Num: [85/600] Discriminator Loss: 0.6257, Generator Loss: 2.1163 D(x): 0.7772, D(G(z)): 0.1553 Epoch: [12/20], Batch Num: [86/600] Discriminator Loss: 0.6972, Generator Loss: 1.9582 D(x): 0.7989, D(G(z)): 0.2324 Epoch: [12/20], Batch Num: [87/600] Discriminator Loss: 0.6316, Generator Loss: 1.9881 D(x): 0.8484, D(G(z)): 0.2664 Epoch: [12/20], Batch Num: [88/600] Discriminator Loss: 0.6196, Generator Loss: 2.1776 D(x): 0.8472, D(G(z)): 0.2414 Epoch: [12/20], Batch Num: [89/600] Discriminator Loss: 0.5639, Generator Loss: 2.4856 D(x): 0.8435, D(G(z)): 0.2117 Epoch: [12/20], Batch Num: [90/600] Discriminator Loss: 0.5138, Generator Loss: 2.5407 D(x): 0.8062, D(G(z)): 0.1426 Epoch: [12/20], Batch Num: [91/600] Discriminator Loss: 0.5758, Generator Loss: 2.3265 D(x): 0.7702, D(G(z)): 0.1137 Epoch: [12/20], Batch Num: [92/600] Discriminator Loss: 0.6934, Generator Loss: 2.1590 D(x): 0.7326, D(G(z)): 0.1580 Epoch: [12/20], Batch Num: [93/600] Discriminator Loss: 0.6797, Generator Loss: 1.8205 D(x): 0.7749, D(G(z)): 0.1845 Epoch: [12/20], Batch Num: [94/600] Discriminator Loss: 0.5802, Generator Loss: 1.7618 D(x): 0.8443, D(G(z)): 0.2436 Epoch: [12/20], Batch Num: [95/600] Discriminator Loss: 0.4394, Generator Loss: 2.0726 D(x): 0.9073, D(G(z)): 0.2222 Epoch: [12/20], Batch Num: [96/600] Discriminator Loss: 0.4878, Generator Loss: 2.5861 D(x): 0.8833, D(G(z)): 0.2066 Epoch: [12/20], Batch Num: [97/600] Discriminator Loss: 0.4925, Generator Loss: 2.7682 D(x): 0.8112, D(G(z)): 0.1434 Epoch: [12/20], Batch Num: [98/600] Discriminator Loss: 0.4920, Generator Loss: 2.8600 D(x): 0.8106, D(G(z)): 0.1436 Epoch: [12/20], Batch Num: [99/600] Discriminator Loss: 0.6759, Generator Loss: 2.5253 D(x): 0.7150, D(G(z)): 0.1074 Epoch: 12, Batch Num: [100/600]
Epoch: [12/20], Batch Num: [100/600] Discriminator Loss: 0.5416, Generator Loss: 1.9459 D(x): 0.8072, D(G(z)): 0.1531 Epoch: [12/20], Batch Num: [101/600] Discriminator Loss: 0.5523, Generator Loss: 1.6780 D(x): 0.8789, D(G(z)): 0.2447 Epoch: [12/20], Batch Num: [102/600] Discriminator Loss: 0.4744, Generator Loss: 1.9944 D(x): 0.8928, D(G(z)): 0.2384 Epoch: [12/20], Batch Num: [103/600] Discriminator Loss: 0.5385, Generator Loss: 2.3871 D(x): 0.8632, D(G(z)): 0.2477 Epoch: [12/20], Batch Num: [104/600] Discriminator Loss: 0.3668, Generator Loss: 2.8496 D(x): 0.8808, D(G(z)): 0.1285 Epoch: [12/20], Batch Num: [105/600] Discriminator Loss: 0.4900, Generator Loss: 3.1071 D(x): 0.8277, D(G(z)): 0.1299 Epoch: [12/20], Batch Num: [106/600] Discriminator Loss: 0.4690, Generator Loss: 3.2645 D(x): 0.8285, D(G(z)): 0.1147 Epoch: [12/20], Batch Num: [107/600] Discriminator Loss: 0.6035, Generator Loss: 2.7695 D(x): 0.7738, D(G(z)): 0.1292 Epoch: [12/20], Batch Num: [108/600] Discriminator Loss: 0.5499, Generator Loss: 2.7309 D(x): 0.8370, D(G(z)): 0.1773 Epoch: [12/20], Batch Num: [109/600] Discriminator Loss: 0.5882, Generator Loss: 2.6265 D(x): 0.8235, D(G(z)): 0.1729 Epoch: [12/20], Batch Num: [110/600] Discriminator Loss: 0.6472, Generator Loss: 2.5333 D(x): 0.7794, D(G(z)): 0.1605 Epoch: [12/20], Batch Num: [111/600] Discriminator Loss: 0.5896, Generator Loss: 2.4774 D(x): 0.8299, D(G(z)): 0.2006 Epoch: [12/20], Batch Num: [112/600] Discriminator Loss: 0.5254, Generator Loss: 2.4746 D(x): 0.8784, D(G(z)): 0.2168 Epoch: [12/20], Batch Num: [113/600] Discriminator Loss: 0.4395, Generator Loss: 3.2429 D(x): 0.8709, D(G(z)): 0.1673 Epoch: [12/20], Batch Num: [114/600] Discriminator Loss: 0.3836, Generator Loss: 3.0626 D(x): 0.8519, D(G(z)): 0.1041 Epoch: [12/20], Batch Num: [115/600] Discriminator Loss: 0.6378, Generator Loss: 2.7108 D(x): 0.7858, D(G(z)): 0.1086 Epoch: [12/20], Batch Num: [116/600] Discriminator Loss: 0.6559, Generator Loss: 2.5335 D(x): 0.7513, D(G(z)): 0.1607 Epoch: [12/20], Batch Num: [117/600] Discriminator Loss: 0.4685, Generator Loss: 1.9361 D(x): 0.8707, D(G(z)): 0.1659 Epoch: [12/20], Batch Num: [118/600] Discriminator Loss: 0.7562, Generator Loss: 1.9705 D(x): 0.8055, D(G(z)): 0.2631 Epoch: [12/20], Batch Num: [119/600] Discriminator Loss: 0.5730, Generator Loss: 2.2375 D(x): 0.8673, D(G(z)): 0.2349 Epoch: [12/20], Batch Num: [120/600] Discriminator Loss: 0.4759, Generator Loss: 2.8325 D(x): 0.8636, D(G(z)): 0.1964 Epoch: [12/20], Batch Num: [121/600] Discriminator Loss: 0.6181, Generator Loss: 2.8434 D(x): 0.7711, D(G(z)): 0.1395 Epoch: [12/20], Batch Num: [122/600] Discriminator Loss: 0.6598, Generator Loss: 2.9402 D(x): 0.7847, D(G(z)): 0.1712 Epoch: [12/20], Batch Num: [123/600] Discriminator Loss: 0.5878, Generator Loss: 2.6773 D(x): 0.8448, D(G(z)): 0.1775 Epoch: [12/20], Batch Num: [124/600] Discriminator Loss: 0.6387, Generator Loss: 2.5809 D(x): 0.8054, D(G(z)): 0.1570 Epoch: [12/20], Batch Num: [125/600] Discriminator Loss: 0.5897, Generator Loss: 2.3558 D(x): 0.8200, D(G(z)): 0.2010 Epoch: [12/20], Batch Num: [126/600] Discriminator Loss: 0.5381, Generator Loss: 2.4826 D(x): 0.8610, D(G(z)): 0.1968 Epoch: [12/20], Batch Num: [127/600] Discriminator Loss: 0.5034, Generator Loss: 2.8230 D(x): 0.8575, D(G(z)): 0.1879 Epoch: [12/20], Batch Num: [128/600] Discriminator Loss: 0.4815, Generator Loss: 2.8761 D(x): 0.8467, D(G(z)): 0.1511 Epoch: [12/20], Batch Num: [129/600] Discriminator Loss: 0.4747, Generator Loss: 2.6278 D(x): 0.8032, D(G(z)): 0.1187 Epoch: [12/20], Batch Num: [130/600] Discriminator Loss: 0.5745, Generator Loss: 2.7182 D(x): 0.8118, D(G(z)): 0.1620 Epoch: [12/20], Batch Num: [131/600] Discriminator Loss: 0.5342, Generator Loss: 1.9917 D(x): 0.8070, D(G(z)): 0.1705 Epoch: [12/20], Batch Num: [132/600] Discriminator Loss: 0.5339, Generator Loss: 2.3923 D(x): 0.8561, D(G(z)): 0.2073 Epoch: [12/20], Batch Num: [133/600] Discriminator Loss: 0.7635, Generator Loss: 2.5339 D(x): 0.8395, D(G(z)): 0.2814 Epoch: [12/20], Batch Num: [134/600] Discriminator Loss: 0.5866, Generator Loss: 3.2629 D(x): 0.8287, D(G(z)): 0.2059 Epoch: [12/20], Batch Num: [135/600] Discriminator Loss: 0.8264, Generator Loss: 2.7767 D(x): 0.6695, D(G(z)): 0.1435 Epoch: [12/20], Batch Num: [136/600] Discriminator Loss: 0.7061, Generator Loss: 2.4397 D(x): 0.7353, D(G(z)): 0.1678 Epoch: [12/20], Batch Num: [137/600] Discriminator Loss: 0.7879, Generator Loss: 1.9232 D(x): 0.7267, D(G(z)): 0.2298 Epoch: [12/20], Batch Num: [138/600] Discriminator Loss: 0.6643, Generator Loss: 1.8358 D(x): 0.8758, D(G(z)): 0.2943 Epoch: [12/20], Batch Num: [139/600] Discriminator Loss: 0.5360, Generator Loss: 2.3898 D(x): 0.8442, D(G(z)): 0.2224 Epoch: [12/20], Batch Num: [140/600] Discriminator Loss: 0.7735, Generator Loss: 2.5507 D(x): 0.7894, D(G(z)): 0.2522 Epoch: [12/20], Batch Num: [141/600] Discriminator Loss: 0.7553, Generator Loss: 2.5895 D(x): 0.7257, D(G(z)): 0.1629 Epoch: [12/20], Batch Num: [142/600] Discriminator Loss: 0.5984, Generator Loss: 2.3283 D(x): 0.7969, D(G(z)): 0.1575 Epoch: [12/20], Batch Num: [143/600] Discriminator Loss: 0.8422, Generator Loss: 2.1799 D(x): 0.7753, D(G(z)): 0.2418 Epoch: [12/20], Batch Num: [144/600] Discriminator Loss: 0.7226, Generator Loss: 1.8493 D(x): 0.7950, D(G(z)): 0.2072 Epoch: [12/20], Batch Num: [145/600] Discriminator Loss: 0.7049, Generator Loss: 2.0198 D(x): 0.7994, D(G(z)): 0.2318 Epoch: [12/20], Batch Num: [146/600] Discriminator Loss: 0.4875, Generator Loss: 2.1200 D(x): 0.8253, D(G(z)): 0.1805 Epoch: [12/20], Batch Num: [147/600] Discriminator Loss: 0.5811, Generator Loss: 2.3159 D(x): 0.8249, D(G(z)): 0.2220 Epoch: [12/20], Batch Num: [148/600] Discriminator Loss: 0.5992, Generator Loss: 2.4566 D(x): 0.8030, D(G(z)): 0.2123 Epoch: [12/20], Batch Num: [149/600] Discriminator Loss: 0.7062, Generator Loss: 2.2648 D(x): 0.7695, D(G(z)): 0.1922 Epoch: [12/20], Batch Num: [150/600] Discriminator Loss: 0.6903, Generator Loss: 2.0215 D(x): 0.7870, D(G(z)): 0.2075 Epoch: [12/20], Batch Num: [151/600] Discriminator Loss: 0.4957, Generator Loss: 2.0649 D(x): 0.8529, D(G(z)): 0.1974 Epoch: [12/20], Batch Num: [152/600] Discriminator Loss: 0.5676, Generator Loss: 2.4743 D(x): 0.8246, D(G(z)): 0.1848 Epoch: [12/20], Batch Num: [153/600] Discriminator Loss: 0.4936, Generator Loss: 2.5798 D(x): 0.8402, D(G(z)): 0.1574 Epoch: [12/20], Batch Num: [154/600] Discriminator Loss: 0.5931, Generator Loss: 2.2854 D(x): 0.8212, D(G(z)): 0.1737 Epoch: [12/20], Batch Num: [155/600] Discriminator Loss: 0.4990, Generator Loss: 2.6952 D(x): 0.8365, D(G(z)): 0.1675 Epoch: [12/20], Batch Num: [156/600] Discriminator Loss: 0.5380, Generator Loss: 2.6239 D(x): 0.8129, D(G(z)): 0.1451 Epoch: [12/20], Batch Num: [157/600] Discriminator Loss: 0.6072, Generator Loss: 2.6423 D(x): 0.7996, D(G(z)): 0.1799 Epoch: [12/20], Batch Num: [158/600] Discriminator Loss: 0.4767, Generator Loss: 2.6736 D(x): 0.8188, D(G(z)): 0.1492 Epoch: [12/20], Batch Num: [159/600] Discriminator Loss: 0.6463, Generator Loss: 2.3332 D(x): 0.8061, D(G(z)): 0.1694 Epoch: [12/20], Batch Num: [160/600] Discriminator Loss: 0.5350, Generator Loss: 2.5955 D(x): 0.8557, D(G(z)): 0.1571 Epoch: [12/20], Batch Num: [161/600] Discriminator Loss: 0.7329, Generator Loss: 2.7191 D(x): 0.8491, D(G(z)): 0.2527 Epoch: [12/20], Batch Num: [162/600] Discriminator Loss: 0.4432, Generator Loss: 3.0536 D(x): 0.8535, D(G(z)): 0.1242 Epoch: [12/20], Batch Num: [163/600] Discriminator Loss: 0.6379, Generator Loss: 3.0663 D(x): 0.8062, D(G(z)): 0.1346 Epoch: [12/20], Batch Num: [164/600] Discriminator Loss: 0.5033, Generator Loss: 2.6842 D(x): 0.8141, D(G(z)): 0.1138 Epoch: [12/20], Batch Num: [165/600] Discriminator Loss: 0.6282, Generator Loss: 2.3961 D(x): 0.8087, D(G(z)): 0.1610 Epoch: [12/20], Batch Num: [166/600] Discriminator Loss: 0.5717, Generator Loss: 1.9669 D(x): 0.8399, D(G(z)): 0.1864 Epoch: [12/20], Batch Num: [167/600] Discriminator Loss: 0.7240, Generator Loss: 2.1179 D(x): 0.8314, D(G(z)): 0.2535 Epoch: [12/20], Batch Num: [168/600] Discriminator Loss: 0.7253, Generator Loss: 2.5601 D(x): 0.8518, D(G(z)): 0.2700 Epoch: [12/20], Batch Num: [169/600] Discriminator Loss: 0.4987, Generator Loss: 2.8630 D(x): 0.8387, D(G(z)): 0.1890 Epoch: [12/20], Batch Num: [170/600] Discriminator Loss: 0.6304, Generator Loss: 3.1829 D(x): 0.7678, D(G(z)): 0.1224 Epoch: [12/20], Batch Num: [171/600] Discriminator Loss: 0.7189, Generator Loss: 2.6924 D(x): 0.7232, D(G(z)): 0.1138 Epoch: [12/20], Batch Num: [172/600] Discriminator Loss: 0.5931, Generator Loss: 2.3399 D(x): 0.7630, D(G(z)): 0.1333 Epoch: [12/20], Batch Num: [173/600] Discriminator Loss: 0.5259, Generator Loss: 1.6180 D(x): 0.8582, D(G(z)): 0.2067 Epoch: [12/20], Batch Num: [174/600] Discriminator Loss: 0.7105, Generator Loss: 1.5861 D(x): 0.8651, D(G(z)): 0.3018 Epoch: [12/20], Batch Num: [175/600] Discriminator Loss: 0.5851, Generator Loss: 2.1379 D(x): 0.8707, D(G(z)): 0.2680 Epoch: [12/20], Batch Num: [176/600] Discriminator Loss: 0.6862, Generator Loss: 2.5424 D(x): 0.8006, D(G(z)): 0.2206 Epoch: [12/20], Batch Num: [177/600] Discriminator Loss: 0.6362, Generator Loss: 2.3184 D(x): 0.7496, D(G(z)): 0.1435 Epoch: [12/20], Batch Num: [178/600] Discriminator Loss: 0.4827, Generator Loss: 2.7320 D(x): 0.8163, D(G(z)): 0.1225 Epoch: [12/20], Batch Num: [179/600] Discriminator Loss: 0.6415, Generator Loss: 2.5642 D(x): 0.7792, D(G(z)): 0.1529 Epoch: [12/20], Batch Num: [180/600] Discriminator Loss: 0.5199, Generator Loss: 2.1893 D(x): 0.8514, D(G(z)): 0.1745 Epoch: [12/20], Batch Num: [181/600] Discriminator Loss: 0.6205, Generator Loss: 2.3677 D(x): 0.8531, D(G(z)): 0.2233 Epoch: [12/20], Batch Num: [182/600] Discriminator Loss: 0.4935, Generator Loss: 2.4155 D(x): 0.8625, D(G(z)): 0.2040 Epoch: [12/20], Batch Num: [183/600] Discriminator Loss: 0.4952, Generator Loss: 2.5896 D(x): 0.8448, D(G(z)): 0.1555 Epoch: [12/20], Batch Num: [184/600] Discriminator Loss: 0.5000, Generator Loss: 2.8423 D(x): 0.8135, D(G(z)): 0.1290 Epoch: [12/20], Batch Num: [185/600] Discriminator Loss: 0.5407, Generator Loss: 2.8539 D(x): 0.8021, D(G(z)): 0.1204 Epoch: [12/20], Batch Num: [186/600] Discriminator Loss: 0.7480, Generator Loss: 2.2822 D(x): 0.7642, D(G(z)): 0.1739 Epoch: [12/20], Batch Num: [187/600] Discriminator Loss: 0.5037, Generator Loss: 2.4211 D(x): 0.8787, D(G(z)): 0.2155 Epoch: [12/20], Batch Num: [188/600] Discriminator Loss: 0.7626, Generator Loss: 2.4175 D(x): 0.7929, D(G(z)): 0.2321 Epoch: [12/20], Batch Num: [189/600] Discriminator Loss: 0.7030, Generator Loss: 2.5747 D(x): 0.8000, D(G(z)): 0.2103 Epoch: [12/20], Batch Num: [190/600] Discriminator Loss: 0.4720, Generator Loss: 2.7896 D(x): 0.8461, D(G(z)): 0.1610 Epoch: [12/20], Batch Num: [191/600] Discriminator Loss: 0.5372, Generator Loss: 2.2875 D(x): 0.7980, D(G(z)): 0.1240 Epoch: [12/20], Batch Num: [192/600] Discriminator Loss: 0.5722, Generator Loss: 2.1153 D(x): 0.7950, D(G(z)): 0.1618 Epoch: [12/20], Batch Num: [193/600] Discriminator Loss: 0.5268, Generator Loss: 1.9827 D(x): 0.8783, D(G(z)): 0.2392 Epoch: [12/20], Batch Num: [194/600] Discriminator Loss: 0.6120, Generator Loss: 2.1951 D(x): 0.8656, D(G(z)): 0.2419 Epoch: [12/20], Batch Num: [195/600] Discriminator Loss: 0.5282, Generator Loss: 2.4495 D(x): 0.8320, D(G(z)): 0.1781 Epoch: [12/20], Batch Num: [196/600] Discriminator Loss: 0.5516, Generator Loss: 2.7830 D(x): 0.8613, D(G(z)): 0.1988 Epoch: [12/20], Batch Num: [197/600] Discriminator Loss: 0.3755, Generator Loss: 2.8264 D(x): 0.8483, D(G(z)): 0.0988 Epoch: [12/20], Batch Num: [198/600] Discriminator Loss: 0.4784, Generator Loss: 2.8696 D(x): 0.8056, D(G(z)): 0.0909 Epoch: [12/20], Batch Num: [199/600] Discriminator Loss: 0.5477, Generator Loss: 2.2354 D(x): 0.7872, D(G(z)): 0.1118 Epoch: 12, Batch Num: [200/600]
Epoch: [12/20], Batch Num: [200/600] Discriminator Loss: 0.5028, Generator Loss: 2.0293 D(x): 0.8348, D(G(z)): 0.1793 Epoch: [12/20], Batch Num: [201/600] Discriminator Loss: 0.6465, Generator Loss: 2.1532 D(x): 0.8525, D(G(z)): 0.2420 Epoch: [12/20], Batch Num: [202/600] Discriminator Loss: 0.8501, Generator Loss: 2.3776 D(x): 0.8477, D(G(z)): 0.2920 Epoch: [12/20], Batch Num: [203/600] Discriminator Loss: 0.6106, Generator Loss: 2.7935 D(x): 0.7841, D(G(z)): 0.1392 Epoch: [12/20], Batch Num: [204/600] Discriminator Loss: 0.5223, Generator Loss: 2.5371 D(x): 0.8251, D(G(z)): 0.1263 Epoch: [12/20], Batch Num: [205/600] Discriminator Loss: 0.5734, Generator Loss: 2.3727 D(x): 0.7941, D(G(z)): 0.1496 Epoch: [12/20], Batch Num: [206/600] Discriminator Loss: 0.5125, Generator Loss: 2.4565 D(x): 0.8522, D(G(z)): 0.1633 Epoch: [12/20], Batch Num: [207/600] Discriminator Loss: 0.4243, Generator Loss: 2.2022 D(x): 0.8687, D(G(z)): 0.1633 Epoch: [12/20], Batch Num: [208/600] Discriminator Loss: 0.4460, Generator Loss: 2.1887 D(x): 0.8665, D(G(z)): 0.1716 Epoch: [12/20], Batch Num: [209/600] Discriminator Loss: 0.6051, Generator Loss: 2.2951 D(x): 0.7826, D(G(z)): 0.1649 Epoch: [12/20], Batch Num: [210/600] Discriminator Loss: 0.4590, Generator Loss: 2.1911 D(x): 0.8931, D(G(z)): 0.1937 Epoch: [12/20], Batch Num: [211/600] Discriminator Loss: 0.4153, Generator Loss: 2.5383 D(x): 0.8629, D(G(z)): 0.1293 Epoch: [12/20], Batch Num: [212/600] Discriminator Loss: 0.4763, Generator Loss: 2.5613 D(x): 0.8368, D(G(z)): 0.1451 Epoch: [12/20], Batch Num: [213/600] Discriminator Loss: 0.6387, Generator Loss: 2.4498 D(x): 0.7708, D(G(z)): 0.1272 Epoch: [12/20], Batch Num: [214/600] Discriminator Loss: 0.5164, Generator Loss: 2.4153 D(x): 0.8569, D(G(z)): 0.1762 Epoch: [12/20], Batch Num: [215/600] Discriminator Loss: 0.5536, Generator Loss: 2.2843 D(x): 0.7765, D(G(z)): 0.1370 Epoch: [12/20], Batch Num: [216/600] Discriminator Loss: 0.5263, Generator Loss: 2.2676 D(x): 0.8974, D(G(z)): 0.2241 Epoch: [12/20], Batch Num: [217/600] Discriminator Loss: 0.5709, Generator Loss: 2.5185 D(x): 0.8537, D(G(z)): 0.1998 Epoch: [12/20], Batch Num: [218/600] Discriminator Loss: 0.5269, Generator Loss: 2.8672 D(x): 0.8436, D(G(z)): 0.1737 Epoch: [12/20], Batch Num: [219/600] Discriminator Loss: 0.4985, Generator Loss: 2.7323 D(x): 0.8124, D(G(z)): 0.1101 Epoch: [12/20], Batch Num: [220/600] Discriminator Loss: 0.4609, Generator Loss: 2.6551 D(x): 0.8393, D(G(z)): 0.1123 Epoch: [12/20], Batch Num: [221/600] Discriminator Loss: 0.5661, Generator Loss: 2.5531 D(x): 0.8568, D(G(z)): 0.1940 Epoch: [12/20], Batch Num: [222/600] Discriminator Loss: 0.8006, Generator Loss: 2.7735 D(x): 0.8258, D(G(z)): 0.2292 Epoch: [12/20], Batch Num: [223/600] Discriminator Loss: 0.6347, Generator Loss: 2.7091 D(x): 0.8115, D(G(z)): 0.1448 Epoch: [12/20], Batch Num: [224/600] Discriminator Loss: 0.6024, Generator Loss: 2.6087 D(x): 0.7896, D(G(z)): 0.1461 Epoch: [12/20], Batch Num: [225/600] Discriminator Loss: 0.6511, Generator Loss: 2.6078 D(x): 0.7958, D(G(z)): 0.1767 Epoch: [12/20], Batch Num: [226/600] Discriminator Loss: 0.6023, Generator Loss: 2.2895 D(x): 0.7901, D(G(z)): 0.1643 Epoch: [12/20], Batch Num: [227/600] Discriminator Loss: 0.4995, Generator Loss: 2.4181 D(x): 0.8753, D(G(z)): 0.2197 Epoch: [12/20], Batch Num: [228/600] Discriminator Loss: 0.5723, Generator Loss: 2.4479 D(x): 0.8240, D(G(z)): 0.2022 Epoch: [12/20], Batch Num: [229/600] Discriminator Loss: 0.7433, Generator Loss: 2.3508 D(x): 0.7208, D(G(z)): 0.1798 Epoch: [12/20], Batch Num: [230/600] Discriminator Loss: 0.4398, Generator Loss: 2.1705 D(x): 0.8491, D(G(z)): 0.1528 Epoch: [12/20], Batch Num: [231/600] Discriminator Loss: 0.6424, Generator Loss: 2.0023 D(x): 0.8078, D(G(z)): 0.2055 Epoch: [12/20], Batch Num: [232/600] Discriminator Loss: 0.6542, Generator Loss: 2.0342 D(x): 0.8192, D(G(z)): 0.2259 Epoch: [12/20], Batch Num: [233/600] Discriminator Loss: 0.7370, Generator Loss: 2.0994 D(x): 0.7880, D(G(z)): 0.2168 Epoch: [12/20], Batch Num: [234/600] Discriminator Loss: 0.7225, Generator Loss: 2.1838 D(x): 0.8049, D(G(z)): 0.2208 Epoch: [12/20], Batch Num: [235/600] Discriminator Loss: 0.5568, Generator Loss: 2.5631 D(x): 0.8220, D(G(z)): 0.1855 Epoch: [12/20], Batch Num: [236/600] Discriminator Loss: 0.6515, Generator Loss: 2.4788 D(x): 0.7634, D(G(z)): 0.1676 Epoch: [12/20], Batch Num: [237/600] Discriminator Loss: 0.6396, Generator Loss: 2.1609 D(x): 0.7518, D(G(z)): 0.1363 Epoch: [12/20], Batch Num: [238/600] Discriminator Loss: 0.6219, Generator Loss: 1.8282 D(x): 0.7949, D(G(z)): 0.1946 Epoch: [12/20], Batch Num: [239/600] Discriminator Loss: 0.5231, Generator Loss: 1.6259 D(x): 0.8447, D(G(z)): 0.2094 Epoch: [12/20], Batch Num: [240/600] Discriminator Loss: 0.6908, Generator Loss: 1.8564 D(x): 0.8781, D(G(z)): 0.2945 Epoch: [12/20], Batch Num: [241/600] Discriminator Loss: 0.6074, Generator Loss: 2.4108 D(x): 0.8603, D(G(z)): 0.2408 Epoch: [12/20], Batch Num: 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2.6767 D(x): 0.7571, D(G(z)): 0.1101 Epoch: [12/20], Batch Num: [251/600] Discriminator Loss: 0.6660, Generator Loss: 2.4105 D(x): 0.7915, D(G(z)): 0.1738 Epoch: [12/20], Batch Num: [252/600] Discriminator Loss: 0.6276, Generator Loss: 2.0303 D(x): 0.7739, D(G(z)): 0.1636 Epoch: [12/20], Batch Num: [253/600] Discriminator Loss: 0.6185, Generator Loss: 2.1398 D(x): 0.8564, D(G(z)): 0.2362 Epoch: [12/20], Batch Num: [254/600] Discriminator Loss: 0.6211, Generator Loss: 2.3332 D(x): 0.8499, D(G(z)): 0.2521 Epoch: [12/20], Batch Num: [255/600] Discriminator Loss: 0.4475, Generator Loss: 2.7470 D(x): 0.8544, D(G(z)): 0.1621 Epoch: [12/20], Batch Num: [256/600] Discriminator Loss: 0.7188, Generator Loss: 2.7072 D(x): 0.7615, D(G(z)): 0.1727 Epoch: [12/20], Batch Num: [257/600] Discriminator Loss: 0.5277, Generator Loss: 2.3483 D(x): 0.7991, D(G(z)): 0.1161 Epoch: [12/20], Batch Num: [258/600] Discriminator Loss: 0.5129, Generator Loss: 2.0403 D(x): 0.8025, D(G(z)): 0.1539 Epoch: [12/20], Batch Num: [259/600] Discriminator Loss: 0.4332, Generator Loss: 1.9061 D(x): 0.8675, D(G(z)): 0.1898 Epoch: [12/20], Batch Num: [260/600] Discriminator Loss: 0.4489, Generator Loss: 2.0900 D(x): 0.8748, D(G(z)): 0.2100 Epoch: [12/20], Batch Num: [261/600] Discriminator Loss: 0.5788, Generator Loss: 2.4238 D(x): 0.8551, D(G(z)): 0.2327 Epoch: [12/20], Batch Num: [262/600] Discriminator Loss: 0.4869, Generator Loss: 2.6941 D(x): 0.8313, D(G(z)): 0.1536 Epoch: [12/20], Batch Num: [263/600] Discriminator Loss: 0.5530, Generator Loss: 2.8150 D(x): 0.8168, D(G(z)): 0.1531 Epoch: [12/20], Batch Num: [264/600] Discriminator Loss: 0.4817, Generator Loss: 2.9578 D(x): 0.8144, D(G(z)): 0.1362 Epoch: [12/20], Batch Num: [265/600] Discriminator Loss: 0.5559, Generator Loss: 2.4345 D(x): 0.7967, D(G(z)): 0.1327 Epoch: [12/20], Batch Num: [266/600] Discriminator Loss: 0.6778, Generator Loss: 2.1199 D(x): 0.8005, D(G(z)): 0.2139 Epoch: [12/20], Batch Num: [267/600] Discriminator Loss: 0.5261, Generator Loss: 2.2244 D(x): 0.8931, D(G(z)): 0.2268 Epoch: [12/20], Batch Num: [268/600] Discriminator Loss: 0.5513, Generator Loss: 2.4857 D(x): 0.8472, D(G(z)): 0.1905 Epoch: [12/20], Batch Num: [269/600] Discriminator Loss: 0.5100, Generator Loss: 2.5163 D(x): 0.8250, D(G(z)): 0.1564 Epoch: [12/20], Batch Num: [270/600] Discriminator Loss: 0.4885, Generator Loss: 2.7721 D(x): 0.8019, D(G(z)): 0.1222 Epoch: [12/20], Batch Num: [271/600] Discriminator Loss: 0.7412, Generator Loss: 2.4422 D(x): 0.7863, D(G(z)): 0.1594 Epoch: [12/20], Batch Num: [272/600] Discriminator Loss: 0.5840, Generator Loss: 2.2174 D(x): 0.8230, D(G(z)): 0.2053 Epoch: [12/20], Batch Num: [273/600] Discriminator Loss: 0.6552, Generator Loss: 2.2694 D(x): 0.8083, D(G(z)): 0.1991 Epoch: [12/20], Batch Num: [274/600] Discriminator Loss: 0.5417, Generator Loss: 2.2954 D(x): 0.8443, D(G(z)): 0.2217 Epoch: [12/20], Batch Num: [275/600] Discriminator Loss: 0.5856, Generator Loss: 2.5651 D(x): 0.8356, D(G(z)): 0.1987 Epoch: [12/20], Batch Num: [276/600] Discriminator Loss: 0.4741, Generator Loss: 3.2392 D(x): 0.8338, D(G(z)): 0.1501 Epoch: [12/20], Batch Num: [277/600] Discriminator Loss: 0.4891, Generator Loss: 2.8062 D(x): 0.7900, D(G(z)): 0.1211 Epoch: [12/20], Batch Num: [278/600] Discriminator Loss: 0.4925, Generator Loss: 2.2749 D(x): 0.7996, D(G(z)): 0.1003 Epoch: [12/20], Batch Num: [279/600] Discriminator Loss: 0.6022, Generator Loss: 1.9204 D(x): 0.8271, D(G(z)): 0.1960 Epoch: [12/20], Batch Num: [280/600] Discriminator Loss: 0.6028, Generator Loss: 2.1023 D(x): 0.8759, D(G(z)): 0.2498 Epoch: [12/20], Batch Num: [281/600] Discriminator Loss: 0.5375, Generator Loss: 2.6085 D(x): 0.8982, D(G(z)): 0.2409 Epoch: [12/20], Batch Num: [282/600] Discriminator Loss: 0.5848, Generator Loss: 3.1074 D(x): 0.7915, D(G(z)): 0.1432 Epoch: [12/20], Batch Num: [283/600] Discriminator Loss: 0.5004, Generator Loss: 3.1152 D(x): 0.8474, D(G(z)): 0.1381 Epoch: [12/20], Batch Num: [284/600] Discriminator Loss: 0.6818, Generator Loss: 2.7665 D(x): 0.7454, D(G(z)): 0.1022 Epoch: [12/20], Batch Num: [285/600] Discriminator Loss: 0.5968, Generator Loss: 2.1316 D(x): 0.7774, D(G(z)): 0.1254 Epoch: [12/20], Batch Num: [286/600] Discriminator Loss: 0.6320, Generator Loss: 2.1192 D(x): 0.8574, D(G(z)): 0.2584 Epoch: [12/20], Batch Num: [287/600] Discriminator Loss: 0.6048, Generator Loss: 2.6987 D(x): 0.8613, D(G(z)): 0.2453 Epoch: [12/20], Batch Num: [288/600] Discriminator Loss: 0.4947, Generator Loss: 3.0699 D(x): 0.8815, D(G(z)): 0.2046 Epoch: [12/20], Batch Num: [289/600] Discriminator Loss: 0.6096, Generator Loss: 3.2831 D(x): 0.7595, D(G(z)): 0.0976 Epoch: [12/20], Batch Num: [290/600] Discriminator Loss: 0.6726, Generator Loss: 3.0427 D(x): 0.7874, D(G(z)): 0.1474 Epoch: [12/20], Batch Num: [291/600] Discriminator Loss: 0.5829, Generator Loss: 2.2304 D(x): 0.7462, D(G(z)): 0.0925 Epoch: [12/20], Batch Num: [292/600] Discriminator Loss: 0.4914, Generator Loss: 2.0969 D(x): 0.8906, D(G(z)): 0.2088 Epoch: [12/20], Batch Num: [293/600] Discriminator Loss: 0.7327, Generator Loss: 1.9954 D(x): 0.8719, D(G(z)): 0.2976 Epoch: [12/20], Batch Num: [294/600] Discriminator Loss: 0.6324, Generator Loss: 2.5441 D(x): 0.8308, D(G(z)): 0.2241 Epoch: [12/20], Batch Num: [295/600] Discriminator Loss: 0.5974, Generator Loss: 2.9111 D(x): 0.8130, D(G(z)): 0.1730 Epoch: [12/20], Batch Num: [296/600] Discriminator Loss: 0.7882, Generator Loss: 2.5959 D(x): 0.7063, D(G(z)): 0.1119 Epoch: [12/20], Batch Num: [297/600] Discriminator Loss: 0.5955, Generator Loss: 2.1023 D(x): 0.7839, D(G(z)): 0.1411 Epoch: [12/20], Batch Num: [298/600] Discriminator Loss: 0.7624, Generator Loss: 1.6646 D(x): 0.8136, D(G(z)): 0.2268 Epoch: [12/20], Batch Num: [299/600] Discriminator Loss: 0.6066, Generator Loss: 1.8659 D(x): 0.8888, D(G(z)): 0.2811 Epoch: 12, Batch Num: [300/600]
Epoch: [12/20], Batch Num: [300/600] Discriminator Loss: 0.6422, Generator Loss: 2.6106 D(x): 0.8727, D(G(z)): 0.2772 Epoch: [12/20], Batch Num: [301/600] Discriminator Loss: 0.5995, Generator Loss: 3.0624 D(x): 0.8183, D(G(z)): 0.1915 Epoch: [12/20], Batch Num: [302/600] Discriminator Loss: 0.5426, Generator Loss: 3.2063 D(x): 0.7936, D(G(z)): 0.1127 Epoch: [12/20], Batch Num: [303/600] Discriminator Loss: 0.6785, Generator Loss: 2.7433 D(x): 0.7330, D(G(z)): 0.1048 Epoch: [12/20], Batch Num: [304/600] Discriminator Loss: 0.7127, Generator Loss: 2.1535 D(x): 0.7739, D(G(z)): 0.1525 Epoch: [12/20], Batch Num: [305/600] Discriminator Loss: 0.5352, Generator Loss: 1.6882 D(x): 0.8321, D(G(z)): 0.2001 Epoch: [12/20], Batch Num: [306/600] Discriminator Loss: 0.7548, Generator Loss: 1.5546 D(x): 0.8631, D(G(z)): 0.3095 Epoch: [12/20], Batch Num: [307/600] Discriminator Loss: 0.5562, Generator Loss: 1.9218 D(x): 0.8870, D(G(z)): 0.2665 Epoch: [12/20], Batch Num: [308/600] Discriminator Loss: 0.6333, Generator Loss: 2.2883 D(x): 0.8015, D(G(z)): 0.2152 Epoch: [12/20], Batch Num: [309/600] Discriminator Loss: 0.7537, Generator Loss: 2.4295 D(x): 0.7610, D(G(z)): 0.1795 Epoch: [12/20], Batch Num: [310/600] Discriminator Loss: 0.6440, Generator Loss: 2.2215 D(x): 0.7703, D(G(z)): 0.1574 Epoch: [12/20], Batch Num: [311/600] Discriminator Loss: 0.6091, Generator Loss: 2.0106 D(x): 0.7959, D(G(z)): 0.1780 Epoch: [12/20], Batch Num: [312/600] Discriminator Loss: 0.4826, Generator Loss: 2.2360 D(x): 0.8448, D(G(z)): 0.1680 Epoch: [12/20], Batch Num: [313/600] Discriminator Loss: 0.7193, Generator Loss: 2.0442 D(x): 0.8003, D(G(z)): 0.2245 Epoch: [12/20], Batch Num: [314/600] Discriminator Loss: 0.6282, Generator Loss: 2.1137 D(x): 0.7921, D(G(z)): 0.1902 Epoch: [12/20], Batch Num: [315/600] Discriminator Loss: 0.5251, Generator Loss: 2.1353 D(x): 0.8238, D(G(z)): 0.1963 Epoch: [12/20], Batch Num: [316/600] Discriminator Loss: 0.5461, Generator Loss: 2.1617 D(x): 0.8496, D(G(z)): 0.2202 Epoch: [12/20], Batch Num: [317/600] Discriminator Loss: 0.6291, Generator Loss: 2.0980 D(x): 0.7921, D(G(z)): 0.1915 Epoch: [12/20], Batch Num: [318/600] Discriminator Loss: 0.7386, Generator Loss: 2.0450 D(x): 0.7333, D(G(z)): 0.1794 Epoch: [12/20], Batch Num: [319/600] Discriminator Loss: 0.4204, Generator Loss: 2.3046 D(x): 0.9023, D(G(z)): 0.2191 Epoch: [12/20], Batch Num: [320/600] Discriminator Loss: 0.6182, Generator Loss: 2.4887 D(x): 0.8473, D(G(z)): 0.2096 Epoch: [12/20], Batch Num: [321/600] Discriminator Loss: 0.5261, Generator Loss: 2.4378 D(x): 0.8111, D(G(z)): 0.1695 Epoch: [12/20], Batch Num: [322/600] Discriminator Loss: 0.4970, Generator Loss: 2.6483 D(x): 0.8294, D(G(z)): 0.1590 Epoch: [12/20], Batch Num: [323/600] Discriminator Loss: 0.7267, Generator Loss: 2.1646 D(x): 0.7372, D(G(z)): 0.1484 Epoch: [12/20], Batch Num: [324/600] Discriminator Loss: 0.6806, Generator Loss: 1.8834 D(x): 0.7696, D(G(z)): 0.1834 Epoch: [12/20], Batch Num: [325/600] Discriminator Loss: 0.7351, Generator Loss: 1.8305 D(x): 0.8282, D(G(z)): 0.2384 Epoch: [12/20], Batch Num: [326/600] Discriminator Loss: 0.5234, Generator Loss: 2.1616 D(x): 0.8966, D(G(z)): 0.2347 Epoch: [12/20], Batch Num: [327/600] Discriminator Loss: 0.6800, Generator Loss: 2.4945 D(x): 0.8325, D(G(z)): 0.2358 Epoch: [12/20], Batch Num: [328/600] Discriminator Loss: 0.5766, Generator Loss: 2.7776 D(x): 0.7914, D(G(z)): 0.1543 Epoch: [12/20], Batch Num: [329/600] Discriminator Loss: 0.6908, Generator Loss: 2.7038 D(x): 0.7888, D(G(z)): 0.1934 Epoch: [12/20], Batch Num: [330/600] Discriminator Loss: 0.5599, Generator Loss: 2.5138 D(x): 0.8028, D(G(z)): 0.1488 Epoch: [12/20], Batch Num: [331/600] Discriminator Loss: 0.6488, Generator Loss: 2.2419 D(x): 0.7656, D(G(z)): 0.1449 Epoch: [12/20], Batch Num: [332/600] Discriminator Loss: 0.6056, Generator Loss: 2.2033 D(x): 0.8219, D(G(z)): 0.2049 Epoch: [12/20], Batch Num: [333/600] Discriminator Loss: 0.7247, Generator Loss: 2.0053 D(x): 0.7731, D(G(z)): 0.1872 Epoch: [12/20], Batch Num: [334/600] Discriminator Loss: 0.6819, Generator Loss: 1.8346 D(x): 0.8029, D(G(z)): 0.2332 Epoch: [12/20], Batch Num: [335/600] Discriminator Loss: 0.6344, Generator Loss: 2.0267 D(x): 0.8437, D(G(z)): 0.2452 Epoch: [12/20], Batch Num: [336/600] Discriminator Loss: 0.6329, Generator Loss: 2.2766 D(x): 0.8813, D(G(z)): 0.2632 Epoch: [12/20], Batch Num: [337/600] Discriminator Loss: 0.5963, Generator Loss: 2.6105 D(x): 0.8002, D(G(z)): 0.1893 Epoch: [12/20], Batch Num: [338/600] Discriminator Loss: 0.7589, Generator Loss: 2.6332 D(x): 0.7180, D(G(z)): 0.1463 Epoch: [12/20], Batch Num: [339/600] Discriminator Loss: 0.7492, Generator Loss: 2.2683 D(x): 0.7115, D(G(z)): 0.1598 Epoch: [12/20], Batch Num: [340/600] Discriminator Loss: 0.6609, Generator Loss: 2.2601 D(x): 0.8129, D(G(z)): 0.2315 Epoch: [12/20], Batch Num: [341/600] Discriminator Loss: 0.8541, Generator Loss: 1.9380 D(x): 0.7357, D(G(z)): 0.2011 Epoch: [12/20], Batch Num: [342/600] Discriminator Loss: 0.6223, Generator Loss: 2.0620 D(x): 0.8385, D(G(z)): 0.2561 Epoch: [12/20], Batch Num: [343/600] Discriminator Loss: 0.7268, Generator Loss: 2.2327 D(x): 0.8319, D(G(z)): 0.2709 Epoch: [12/20], Batch Num: [344/600] Discriminator Loss: 0.6764, Generator Loss: 2.4158 D(x): 0.7675, D(G(z)): 0.1835 Epoch: [12/20], Batch Num: [345/600] Discriminator Loss: 0.5974, Generator Loss: 2.2496 D(x): 0.7767, D(G(z)): 0.1748 Epoch: [12/20], Batch Num: [346/600] Discriminator Loss: 0.6533, Generator Loss: 2.0196 D(x): 0.7756, D(G(z)): 0.1765 Epoch: [12/20], Batch Num: [347/600] Discriminator Loss: 0.6408, Generator Loss: 1.9676 D(x): 0.8390, D(G(z)): 0.2449 Epoch: [12/20], Batch Num: [348/600] Discriminator Loss: 0.6986, Generator Loss: 2.4598 D(x): 0.8214, D(G(z)): 0.2530 Epoch: [12/20], Batch Num: [349/600] Discriminator Loss: 0.6651, Generator Loss: 2.5255 D(x): 0.7861, D(G(z)): 0.1998 Epoch: [12/20], Batch Num: [350/600] Discriminator Loss: 0.5373, Generator Loss: 2.3412 D(x): 0.7829, D(G(z)): 0.1281 Epoch: [12/20], Batch Num: [351/600] Discriminator Loss: 0.6177, Generator Loss: 2.1128 D(x): 0.7971, D(G(z)): 0.1881 Epoch: [12/20], Batch Num: [352/600] Discriminator Loss: 0.6306, Generator Loss: 1.8738 D(x): 0.7859, D(G(z)): 0.2189 Epoch: [12/20], Batch Num: [353/600] Discriminator Loss: 0.4830, Generator Loss: 1.8932 D(x): 0.8585, D(G(z)): 0.2045 Epoch: [12/20], Batch Num: [354/600] Discriminator Loss: 0.6004, Generator Loss: 2.1942 D(x): 0.8538, D(G(z)): 0.2544 Epoch: [12/20], Batch Num: [355/600] Discriminator Loss: 0.6517, Generator Loss: 2.1867 D(x): 0.7958, D(G(z)): 0.2084 Epoch: [12/20], Batch Num: [356/600] Discriminator Loss: 0.6549, Generator Loss: 2.5724 D(x): 0.7952, D(G(z)): 0.2010 Epoch: [12/20], Batch Num: [357/600] Discriminator Loss: 0.6845, Generator Loss: 2.1496 D(x): 0.7294, D(G(z)): 0.1326 Epoch: [12/20], Batch Num: [358/600] Discriminator Loss: 0.5006, Generator Loss: 2.2104 D(x): 0.8396, D(G(z)): 0.1802 Epoch: [12/20], Batch Num: [359/600] Discriminator Loss: 0.5377, Generator Loss: 2.0013 D(x): 0.8347, D(G(z)): 0.1861 Epoch: [12/20], Batch Num: [360/600] Discriminator Loss: 0.5958, Generator Loss: 2.1950 D(x): 0.8415, D(G(z)): 0.2375 Epoch: [12/20], Batch Num: [361/600] Discriminator Loss: 0.5353, Generator Loss: 2.1135 D(x): 0.8615, D(G(z)): 0.2108 Epoch: [12/20], Batch Num: [362/600] Discriminator Loss: 0.4741, Generator Loss: 2.3512 D(x): 0.8505, D(G(z)): 0.1785 Epoch: [12/20], Batch Num: [363/600] Discriminator Loss: 0.5529, Generator Loss: 2.4552 D(x): 0.8179, D(G(z)): 0.1690 Epoch: [12/20], Batch Num: [364/600] Discriminator Loss: 0.6429, Generator Loss: 2.4246 D(x): 0.7761, D(G(z)): 0.1565 Epoch: [12/20], Batch Num: [365/600] Discriminator Loss: 0.5362, Generator Loss: 2.4063 D(x): 0.8397, D(G(z)): 0.1718 Epoch: [12/20], Batch Num: [366/600] Discriminator Loss: 0.6637, Generator Loss: 2.2096 D(x): 0.7735, D(G(z)): 0.1623 Epoch: [12/20], Batch Num: [367/600] Discriminator Loss: 0.6992, Generator Loss: 2.2285 D(x): 0.8312, D(G(z)): 0.2466 Epoch: [12/20], Batch Num: [368/600] Discriminator Loss: 0.7129, Generator Loss: 2.4780 D(x): 0.8285, D(G(z)): 0.2474 Epoch: [12/20], Batch Num: [369/600] Discriminator Loss: 0.7363, Generator Loss: 2.4111 D(x): 0.7672, D(G(z)): 0.1821 Epoch: [12/20], Batch Num: [370/600] Discriminator Loss: 0.5620, Generator Loss: 2.2947 D(x): 0.8190, D(G(z)): 0.1489 Epoch: [12/20], Batch Num: [371/600] Discriminator Loss: 0.5599, Generator Loss: 2.2252 D(x): 0.7757, D(G(z)): 0.1203 Epoch: [12/20], Batch Num: [372/600] Discriminator Loss: 0.5060, Generator Loss: 2.1583 D(x): 0.8422, D(G(z)): 0.1799 Epoch: [12/20], Batch Num: [373/600] Discriminator Loss: 0.4248, Generator Loss: 2.0095 D(x): 0.8966, D(G(z)): 0.2007 Epoch: [12/20], Batch Num: [374/600] Discriminator Loss: 0.5655, Generator Loss: 2.1661 D(x): 0.8465, D(G(z)): 0.2094 Epoch: [12/20], Batch Num: [375/600] Discriminator Loss: 0.6222, Generator Loss: 2.3265 D(x): 0.7832, D(G(z)): 0.1878 Epoch: [12/20], Batch Num: [376/600] Discriminator Loss: 0.6937, Generator Loss: 2.3808 D(x): 0.7744, D(G(z)): 0.1966 Epoch: [12/20], Batch Num: [377/600] Discriminator Loss: 0.6230, Generator Loss: 2.3272 D(x): 0.8073, D(G(z)): 0.1853 Epoch: [12/20], Batch Num: [378/600] Discriminator Loss: 0.5807, Generator Loss: 2.2468 D(x): 0.7802, D(G(z)): 0.1422 Epoch: [12/20], Batch Num: [379/600] Discriminator Loss: 0.5078, Generator Loss: 2.2103 D(x): 0.8584, D(G(z)): 0.1872 Epoch: [12/20], Batch Num: [380/600] Discriminator Loss: 0.5681, Generator Loss: 2.0792 D(x): 0.8627, D(G(z)): 0.2164 Epoch: [12/20], Batch Num: [381/600] Discriminator Loss: 0.5229, Generator Loss: 2.3127 D(x): 0.8376, D(G(z)): 0.1851 Epoch: [12/20], Batch Num: [382/600] Discriminator Loss: 0.6450, Generator Loss: 2.4455 D(x): 0.8334, D(G(z)): 0.2268 Epoch: [12/20], Batch Num: [383/600] Discriminator Loss: 0.6402, Generator Loss: 2.6806 D(x): 0.7889, D(G(z)): 0.1639 Epoch: [12/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [12/20], Batch Num: [400/600] Discriminator Loss: 0.5514, Generator Loss: 2.4491 D(x): 0.8342, D(G(z)): 0.2297 Epoch: [12/20], Batch Num: [401/600] Discriminator Loss: 0.6176, Generator Loss: 2.7923 D(x): 0.7943, D(G(z)): 0.1810 Epoch: [12/20], Batch Num: [402/600] Discriminator Loss: 0.6538, Generator Loss: 3.0704 D(x): 0.7401, D(G(z)): 0.1264 Epoch: [12/20], Batch Num: [403/600] Discriminator Loss: 0.7512, Generator Loss: 2.3509 D(x): 0.7213, D(G(z)): 0.1350 Epoch: [12/20], Batch Num: [404/600] Discriminator Loss: 0.5485, Generator Loss: 1.9135 D(x): 0.7924, D(G(z)): 0.1253 Epoch: [12/20], Batch Num: [405/600] Discriminator Loss: 0.6357, Generator Loss: 1.8669 D(x): 0.8714, D(G(z)): 0.2442 Epoch: [12/20], Batch Num: [406/600] Discriminator Loss: 0.7991, Generator Loss: 2.3034 D(x): 0.8336, D(G(z)): 0.2877 Epoch: [12/20], Batch Num: [407/600] Discriminator Loss: 0.6169, Generator Loss: 2.7399 D(x): 0.8608, D(G(z)): 0.2339 Epoch: [12/20], Batch Num: [408/600] Discriminator Loss: 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Discriminator Loss: 0.5474, Generator Loss: 2.3669 D(x): 0.7772, D(G(z)): 0.1004 Epoch: [12/20], Batch Num: [426/600] Discriminator Loss: 0.5492, Generator Loss: 2.1943 D(x): 0.7939, D(G(z)): 0.1612 Epoch: [12/20], Batch Num: [427/600] Discriminator Loss: 0.5502, Generator Loss: 1.9830 D(x): 0.8742, D(G(z)): 0.2576 Epoch: [12/20], Batch Num: [428/600] Discriminator Loss: 0.6694, Generator Loss: 2.0931 D(x): 0.8351, D(G(z)): 0.2371 Epoch: [12/20], Batch Num: [429/600] Discriminator Loss: 0.4771, Generator Loss: 2.2618 D(x): 0.8409, D(G(z)): 0.1830 Epoch: [12/20], Batch Num: [430/600] Discriminator Loss: 0.5588, Generator Loss: 2.5088 D(x): 0.8206, D(G(z)): 0.1686 Epoch: [12/20], Batch Num: [431/600] Discriminator Loss: 0.5091, Generator Loss: 2.4695 D(x): 0.7887, D(G(z)): 0.1424 Epoch: [12/20], Batch Num: [432/600] Discriminator Loss: 0.5090, Generator Loss: 2.2398 D(x): 0.8136, D(G(z)): 0.1625 Epoch: [12/20], Batch Num: [433/600] Discriminator Loss: 0.6200, Generator Loss: 1.7953 D(x): 0.7988, D(G(z)): 0.1649 Epoch: [12/20], Batch Num: [434/600] Discriminator Loss: 0.6630, Generator Loss: 1.9365 D(x): 0.8727, D(G(z)): 0.3039 Epoch: [12/20], Batch Num: [435/600] Discriminator Loss: 0.5957, Generator Loss: 2.5221 D(x): 0.8957, D(G(z)): 0.2747 Epoch: [12/20], Batch Num: [436/600] Discriminator Loss: 0.5748, Generator Loss: 2.8353 D(x): 0.8140, D(G(z)): 0.1644 Epoch: [12/20], Batch Num: [437/600] Discriminator Loss: 0.6536, Generator Loss: 2.6696 D(x): 0.7579, D(G(z)): 0.1155 Epoch: [12/20], Batch Num: [438/600] Discriminator Loss: 0.6770, Generator Loss: 2.4499 D(x): 0.7402, D(G(z)): 0.1136 Epoch: [12/20], Batch Num: [439/600] Discriminator Loss: 0.4133, Generator Loss: 2.0750 D(x): 0.8829, D(G(z)): 0.1739 Epoch: [12/20], Batch Num: [440/600] Discriminator Loss: 0.6198, Generator Loss: 1.9075 D(x): 0.8880, D(G(z)): 0.2777 Epoch: [12/20], Batch Num: [441/600] Discriminator Loss: 0.5330, Generator Loss: 2.4173 D(x): 0.8518, D(G(z)): 0.1980 Epoch: [12/20], Batch Num: [442/600] Discriminator Loss: 0.5033, Generator Loss: 2.5912 D(x): 0.8387, D(G(z)): 0.1746 Epoch: [12/20], Batch Num: [443/600] Discriminator Loss: 0.4496, Generator Loss: 2.7919 D(x): 0.8377, D(G(z)): 0.1339 Epoch: [12/20], Batch Num: [444/600] Discriminator Loss: 0.5560, Generator Loss: 3.0116 D(x): 0.7979, D(G(z)): 0.1384 Epoch: [12/20], Batch Num: [445/600] Discriminator Loss: 0.5097, Generator Loss: 2.9356 D(x): 0.8256, D(G(z)): 0.1604 Epoch: [12/20], Batch Num: [446/600] Discriminator Loss: 0.5089, Generator Loss: 2.1386 D(x): 0.8159, D(G(z)): 0.1331 Epoch: [12/20], Batch Num: [447/600] Discriminator Loss: 0.6309, Generator Loss: 2.3428 D(x): 0.8316, D(G(z)): 0.1885 Epoch: [12/20], Batch Num: [448/600] Discriminator Loss: 0.5763, Generator Loss: 2.1429 D(x): 0.8381, D(G(z)): 0.1920 Epoch: [12/20], Batch Num: [449/600] Discriminator Loss: 0.4641, Generator Loss: 2.2867 D(x): 0.9008, D(G(z)): 0.2076 Epoch: [12/20], Batch Num: [450/600] Discriminator Loss: 0.5199, Generator Loss: 2.7641 D(x): 0.8499, D(G(z)): 0.1950 Epoch: [12/20], Batch Num: [451/600] Discriminator Loss: 0.5139, Generator Loss: 3.1600 D(x): 0.7919, D(G(z)): 0.1063 Epoch: [12/20], Batch Num: [452/600] Discriminator Loss: 0.5125, Generator Loss: 2.6188 D(x): 0.8250, D(G(z)): 0.1541 Epoch: [12/20], Batch Num: [453/600] Discriminator Loss: 0.5380, Generator Loss: 2.6925 D(x): 0.8193, D(G(z)): 0.1357 Epoch: [12/20], Batch Num: [454/600] Discriminator Loss: 0.5355, Generator Loss: 2.5930 D(x): 0.8558, D(G(z)): 0.1797 Epoch: [12/20], Batch Num: [455/600] Discriminator Loss: 0.5799, Generator Loss: 2.3193 D(x): 0.7958, D(G(z)): 0.1653 Epoch: [12/20], Batch Num: [456/600] Discriminator Loss: 0.4832, Generator Loss: 1.9713 D(x): 0.8163, D(G(z)): 0.1654 Epoch: [12/20], Batch Num: [457/600] Discriminator Loss: 0.5831, Generator Loss: 2.1612 D(x): 0.8789, D(G(z)): 0.2286 Epoch: [12/20], Batch Num: [458/600] Discriminator Loss: 0.4716, Generator Loss: 2.8000 D(x): 0.9000, D(G(z)): 0.2179 Epoch: [12/20], Batch Num: [459/600] Discriminator Loss: 0.5579, Generator Loss: 2.9212 D(x): 0.8185, D(G(z)): 0.1421 Epoch: [12/20], Batch Num: [460/600] Discriminator Loss: 0.5760, Generator Loss: 2.9629 D(x): 0.8198, D(G(z)): 0.1411 Epoch: [12/20], Batch Num: [461/600] Discriminator Loss: 0.6912, Generator Loss: 2.5836 D(x): 0.8037, D(G(z)): 0.1740 Epoch: [12/20], Batch Num: [462/600] Discriminator Loss: 0.6369, Generator Loss: 2.6184 D(x): 0.8244, D(G(z)): 0.1690 Epoch: [12/20], Batch Num: [463/600] Discriminator Loss: 0.4993, Generator Loss: 2.2794 D(x): 0.8615, D(G(z)): 0.1838 Epoch: [12/20], Batch Num: [464/600] Discriminator Loss: 0.6826, Generator Loss: 2.1705 D(x): 0.8264, D(G(z)): 0.1906 Epoch: [12/20], Batch Num: [465/600] Discriminator Loss: 0.6696, Generator Loss: 2.5498 D(x): 0.7885, D(G(z)): 0.1623 Epoch: [12/20], Batch Num: [466/600] Discriminator Loss: 0.5960, Generator Loss: 2.1111 D(x): 0.7914, D(G(z)): 0.1353 Epoch: [12/20], Batch Num: [467/600] Discriminator Loss: 0.6240, Generator Loss: 1.9754 D(x): 0.8075, D(G(z)): 0.1903 Epoch: [12/20], Batch Num: [468/600] Discriminator Loss: 0.6213, Generator Loss: 2.1734 D(x): 0.8405, D(G(z)): 0.2308 Epoch: [12/20], Batch Num: [469/600] Discriminator Loss: 0.6279, Generator Loss: 2.3807 D(x): 0.8421, D(G(z)): 0.2331 Epoch: [12/20], Batch Num: [470/600] Discriminator Loss: 0.4892, Generator Loss: 2.5222 D(x): 0.8250, D(G(z)): 0.1465 Epoch: [12/20], Batch Num: [471/600] Discriminator Loss: 0.5660, Generator Loss: 2.4818 D(x): 0.7839, D(G(z)): 0.1543 Epoch: [12/20], Batch Num: [472/600] Discriminator Loss: 0.5299, Generator Loss: 2.3813 D(x): 0.8226, D(G(z)): 0.1774 Epoch: [12/20], Batch Num: [473/600] Discriminator Loss: 0.5330, Generator Loss: 2.1258 D(x): 0.8203, D(G(z)): 0.1710 Epoch: [12/20], Batch Num: [474/600] Discriminator Loss: 0.5442, Generator Loss: 2.0068 D(x): 0.8115, D(G(z)): 0.1635 Epoch: [12/20], Batch Num: [475/600] Discriminator Loss: 0.6284, Generator Loss: 2.0643 D(x): 0.8319, D(G(z)): 0.2268 Epoch: [12/20], Batch Num: [476/600] Discriminator Loss: 0.5870, Generator Loss: 2.3278 D(x): 0.8795, D(G(z)): 0.2333 Epoch: [12/20], Batch Num: [477/600] Discriminator Loss: 0.6730, Generator Loss: 2.7909 D(x): 0.7870, D(G(z)): 0.1781 Epoch: [12/20], Batch Num: [478/600] Discriminator Loss: 0.5030, Generator Loss: 2.7917 D(x): 0.8282, D(G(z)): 0.1536 Epoch: [12/20], Batch Num: [479/600] Discriminator Loss: 0.4619, Generator Loss: 2.5148 D(x): 0.8315, D(G(z)): 0.1292 Epoch: [12/20], Batch Num: [480/600] Discriminator Loss: 0.6235, Generator Loss: 1.9603 D(x): 0.7619, D(G(z)): 0.1201 Epoch: [12/20], Batch Num: [481/600] Discriminator Loss: 0.5920, Generator Loss: 1.8618 D(x): 0.8610, D(G(z)): 0.2321 Epoch: [12/20], Batch Num: [482/600] Discriminator Loss: 0.4805, Generator Loss: 2.0808 D(x): 0.8733, D(G(z)): 0.2050 Epoch: [12/20], Batch Num: [483/600] Discriminator Loss: 0.7332, Generator Loss: 2.5654 D(x): 0.8782, D(G(z)): 0.2987 Epoch: [12/20], Batch Num: [484/600] Discriminator Loss: 0.5836, Generator Loss: 2.9702 D(x): 0.8072, D(G(z)): 0.1393 Epoch: [12/20], Batch Num: [485/600] Discriminator Loss: 0.7713, Generator Loss: 2.8291 D(x): 0.7337, D(G(z)): 0.1418 Epoch: [12/20], Batch Num: [486/600] Discriminator Loss: 0.7126, Generator Loss: 2.2522 D(x): 0.7348, D(G(z)): 0.1413 Epoch: [12/20], Batch Num: [487/600] Discriminator Loss: 0.6213, Generator Loss: 1.8453 D(x): 0.8257, D(G(z)): 0.1814 Epoch: [12/20], Batch Num: [488/600] Discriminator Loss: 0.5400, Generator Loss: 1.7785 D(x): 0.8950, D(G(z)): 0.2580 Epoch: [12/20], Batch Num: [489/600] Discriminator Loss: 0.4485, Generator Loss: 2.0775 D(x): 0.8809, D(G(z)): 0.2148 Epoch: [12/20], Batch Num: [490/600] Discriminator Loss: 0.5186, Generator Loss: 2.1777 D(x): 0.8275, D(G(z)): 0.1562 Epoch: [12/20], Batch Num: [491/600] Discriminator Loss: 0.6632, Generator Loss: 2.2544 D(x): 0.8245, D(G(z)): 0.2357 Epoch: [12/20], Batch Num: [492/600] Discriminator Loss: 0.5983, Generator Loss: 2.2029 D(x): 0.7578, D(G(z)): 0.1351 Epoch: [12/20], Batch Num: [493/600] Discriminator Loss: 0.5043, Generator Loss: 2.2152 D(x): 0.8509, D(G(z)): 0.1877 Epoch: [12/20], Batch Num: [494/600] Discriminator Loss: 0.6319, Generator Loss: 2.2399 D(x): 0.8036, D(G(z)): 0.1857 Epoch: [12/20], Batch Num: [495/600] Discriminator Loss: 0.5725, Generator Loss: 2.3374 D(x): 0.8344, D(G(z)): 0.2055 Epoch: [12/20], Batch Num: [496/600] Discriminator Loss: 0.6857, Generator Loss: 1.9240 D(x): 0.7657, D(G(z)): 0.1732 Epoch: [12/20], Batch Num: [497/600] Discriminator Loss: 0.6514, Generator Loss: 2.0629 D(x): 0.8439, D(G(z)): 0.2302 Epoch: [12/20], Batch Num: [498/600] Discriminator Loss: 0.7080, Generator Loss: 2.2028 D(x): 0.7972, D(G(z)): 0.1987 Epoch: [12/20], Batch Num: [499/600] Discriminator Loss: 0.8352, Generator Loss: 2.2817 D(x): 0.7190, D(G(z)): 0.1859 Epoch: 12, Batch Num: [500/600]
Epoch: [12/20], Batch Num: [500/600] Discriminator Loss: 0.5712, Generator Loss: 2.2699 D(x): 0.8363, D(G(z)): 0.1836 Epoch: [12/20], Batch Num: [501/600] Discriminator Loss: 0.5079, Generator Loss: 2.1068 D(x): 0.8264, D(G(z)): 0.1812 Epoch: [12/20], Batch Num: [502/600] Discriminator Loss: 0.7289, Generator Loss: 2.3056 D(x): 0.8204, D(G(z)): 0.2551 Epoch: [12/20], Batch Num: [503/600] Discriminator Loss: 0.6767, Generator Loss: 2.4746 D(x): 0.7737, D(G(z)): 0.2307 Epoch: [12/20], Batch Num: [504/600] Discriminator Loss: 0.6958, Generator Loss: 2.2916 D(x): 0.7755, D(G(z)): 0.1717 Epoch: [12/20], Batch Num: [505/600] Discriminator Loss: 0.6310, Generator Loss: 2.1828 D(x): 0.7801, D(G(z)): 0.1766 Epoch: [12/20], Batch Num: [506/600] Discriminator Loss: 0.5980, Generator Loss: 1.9563 D(x): 0.8022, D(G(z)): 0.1781 Epoch: [12/20], Batch Num: [507/600] Discriminator Loss: 0.6436, Generator Loss: 1.9855 D(x): 0.8480, D(G(z)): 0.2706 Epoch: [12/20], Batch Num: [508/600] Discriminator Loss: 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0.2324 Epoch: [12/20], Batch Num: [517/600] Discriminator Loss: 0.5171, Generator Loss: 2.5322 D(x): 0.8344, D(G(z)): 0.1704 Epoch: [12/20], Batch Num: [518/600] Discriminator Loss: 0.7522, Generator Loss: 2.3327 D(x): 0.7066, D(G(z)): 0.1616 Epoch: [12/20], Batch Num: [519/600] Discriminator Loss: 0.7372, Generator Loss: 2.2529 D(x): 0.7303, D(G(z)): 0.1583 Epoch: [12/20], Batch Num: [520/600] Discriminator Loss: 0.5660, Generator Loss: 2.0894 D(x): 0.8156, D(G(z)): 0.1888 Epoch: [12/20], Batch Num: [521/600] Discriminator Loss: 0.5594, Generator Loss: 2.0162 D(x): 0.8119, D(G(z)): 0.1820 Epoch: [12/20], Batch Num: [522/600] Discriminator Loss: 0.6058, Generator Loss: 1.8984 D(x): 0.8224, D(G(z)): 0.2204 Epoch: [12/20], Batch Num: [523/600] Discriminator Loss: 0.5890, Generator Loss: 2.2618 D(x): 0.8205, D(G(z)): 0.2176 Epoch: [12/20], Batch Num: [524/600] Discriminator Loss: 0.5662, Generator Loss: 2.3288 D(x): 0.8277, D(G(z)): 0.1899 Epoch: [12/20], Batch Num: [525/600] Discriminator Loss: 0.5104, Generator Loss: 2.3846 D(x): 0.8120, D(G(z)): 0.1776 Epoch: [12/20], Batch Num: [526/600] Discriminator Loss: 0.4644, Generator Loss: 2.4507 D(x): 0.8200, D(G(z)): 0.1470 Epoch: [12/20], Batch Num: [527/600] Discriminator Loss: 0.6156, Generator Loss: 2.0594 D(x): 0.7906, D(G(z)): 0.1720 Epoch: [12/20], Batch Num: [528/600] Discriminator Loss: 0.4485, Generator Loss: 2.3677 D(x): 0.8832, D(G(z)): 0.1809 Epoch: [12/20], Batch Num: [529/600] Discriminator Loss: 0.4298, Generator Loss: 2.3555 D(x): 0.8404, D(G(z)): 0.1268 Epoch: [12/20], Batch Num: [530/600] Discriminator Loss: 0.5139, Generator Loss: 2.4501 D(x): 0.8514, D(G(z)): 0.1729 Epoch: [12/20], Batch Num: [531/600] Discriminator Loss: 0.4384, Generator Loss: 2.6083 D(x): 0.8505, D(G(z)): 0.1456 Epoch: [12/20], Batch Num: [532/600] Discriminator Loss: 0.3966, Generator Loss: 2.5649 D(x): 0.8859, D(G(z)): 0.1389 Epoch: [12/20], Batch Num: [533/600] Discriminator Loss: 0.4722, Generator Loss: 2.8451 D(x): 0.8538, D(G(z)): 0.1641 Epoch: [12/20], Batch Num: [534/600] Discriminator Loss: 0.6243, Generator Loss: 3.0928 D(x): 0.8540, D(G(z)): 0.1892 Epoch: [12/20], Batch Num: [535/600] Discriminator Loss: 0.5474, Generator Loss: 3.0995 D(x): 0.7980, D(G(z)): 0.0879 Epoch: [12/20], Batch Num: [536/600] Discriminator Loss: 0.4952, Generator Loss: 2.9991 D(x): 0.8670, D(G(z)): 0.1360 Epoch: [12/20], Batch Num: [537/600] Discriminator Loss: 0.5254, Generator Loss: 3.0883 D(x): 0.8670, D(G(z)): 0.1576 Epoch: [12/20], Batch Num: [538/600] Discriminator Loss: 0.6555, Generator Loss: 2.2961 D(x): 0.7779, D(G(z)): 0.1432 Epoch: [12/20], Batch Num: [539/600] Discriminator Loss: 0.6026, Generator Loss: 2.1402 D(x): 0.8452, D(G(z)): 0.1716 Epoch: [12/20], Batch Num: [540/600] Discriminator Loss: 0.5312, Generator Loss: 2.3519 D(x): 0.8868, D(G(z)): 0.1971 Epoch: [12/20], Batch Num: [541/600] Discriminator Loss: 0.7663, Generator Loss: 2.7980 D(x): 0.7824, D(G(z)): 0.1888 Epoch: [12/20], Batch Num: 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2.4421 D(x): 0.8541, D(G(z)): 0.2169 Epoch: [12/20], Batch Num: [551/600] Discriminator Loss: 0.4834, Generator Loss: 2.3789 D(x): 0.8416, D(G(z)): 0.1584 Epoch: [12/20], Batch Num: [552/600] Discriminator Loss: 0.5406, Generator Loss: 2.3815 D(x): 0.8179, D(G(z)): 0.1446 Epoch: [12/20], Batch Num: [553/600] Discriminator Loss: 0.6043, Generator Loss: 2.3205 D(x): 0.8262, D(G(z)): 0.1791 Epoch: [12/20], Batch Num: [554/600] Discriminator Loss: 0.5567, Generator Loss: 2.2824 D(x): 0.8321, D(G(z)): 0.1575 Epoch: [12/20], Batch Num: [555/600] Discriminator Loss: 0.4781, Generator Loss: 2.3393 D(x): 0.8351, D(G(z)): 0.1651 Epoch: [12/20], Batch Num: [556/600] Discriminator Loss: 0.4228, Generator Loss: 2.3647 D(x): 0.8608, D(G(z)): 0.1404 Epoch: [12/20], Batch Num: [557/600] Discriminator Loss: 0.6433, Generator Loss: 2.5026 D(x): 0.8175, D(G(z)): 0.1826 Epoch: [12/20], Batch Num: [558/600] Discriminator Loss: 0.5039, Generator Loss: 2.1751 D(x): 0.8317, D(G(z)): 0.1558 Epoch: [12/20], Batch Num: [559/600] Discriminator Loss: 0.6102, Generator Loss: 2.1713 D(x): 0.8493, D(G(z)): 0.2343 Epoch: [12/20], Batch Num: [560/600] Discriminator Loss: 0.7563, Generator Loss: 2.4980 D(x): 0.7834, D(G(z)): 0.2027 Epoch: [12/20], Batch Num: [561/600] Discriminator Loss: 0.5010, Generator Loss: 2.5746 D(x): 0.8748, D(G(z)): 0.1913 Epoch: [12/20], Batch Num: [562/600] Discriminator Loss: 0.5301, Generator Loss: 2.7076 D(x): 0.8125, D(G(z)): 0.1576 Epoch: [12/20], Batch Num: [563/600] Discriminator Loss: 0.6202, Generator Loss: 2.5641 D(x): 0.7697, D(G(z)): 0.1311 Epoch: [12/20], Batch Num: [564/600] Discriminator Loss: 0.7861, Generator Loss: 2.1057 D(x): 0.7933, D(G(z)): 0.2125 Epoch: [12/20], Batch Num: [565/600] Discriminator Loss: 0.3555, Generator Loss: 1.9971 D(x): 0.9126, D(G(z)): 0.1848 Epoch: [12/20], Batch Num: [566/600] Discriminator Loss: 0.4948, Generator Loss: 2.1909 D(x): 0.8840, D(G(z)): 0.1929 Epoch: [12/20], Batch Num: [567/600] Discriminator Loss: 0.6172, Generator Loss: 2.5457 D(x): 0.8543, D(G(z)): 0.2307 Epoch: [12/20], Batch Num: [568/600] Discriminator Loss: 0.5615, Generator Loss: 2.8016 D(x): 0.8008, D(G(z)): 0.1661 Epoch: [12/20], Batch Num: [569/600] Discriminator Loss: 0.7370, Generator Loss: 2.7904 D(x): 0.7667, D(G(z)): 0.1600 Epoch: [12/20], Batch Num: [570/600] Discriminator Loss: 0.6233, Generator Loss: 2.5348 D(x): 0.7531, D(G(z)): 0.1037 Epoch: [12/20], Batch Num: [571/600] Discriminator Loss: 0.8345, Generator Loss: 2.0581 D(x): 0.7536, D(G(z)): 0.1761 Epoch: [12/20], Batch Num: [572/600] Discriminator Loss: 0.5895, Generator Loss: 1.7238 D(x): 0.8245, D(G(z)): 0.2220 Epoch: [12/20], Batch Num: [573/600] Discriminator Loss: 0.8761, Generator Loss: 1.8905 D(x): 0.8285, D(G(z)): 0.3051 Epoch: [12/20], Batch Num: [574/600] Discriminator Loss: 0.6592, Generator Loss: 2.4082 D(x): 0.8750, D(G(z)): 0.2934 Epoch: [12/20], Batch Num: [575/600] Discriminator Loss: 0.5148, Generator Loss: 2.8951 D(x): 0.8459, D(G(z)): 0.1918 Epoch: [12/20], Batch Num: [576/600] Discriminator Loss: 0.8070, Generator Loss: 3.3709 D(x): 0.6919, D(G(z)): 0.1372 Epoch: [12/20], Batch Num: [577/600] Discriminator Loss: 0.6468, Generator Loss: 2.7658 D(x): 0.7607, D(G(z)): 0.1286 Epoch: [12/20], Batch Num: [578/600] Discriminator Loss: 0.5670, Generator Loss: 2.1587 D(x): 0.7456, D(G(z)): 0.1189 Epoch: [12/20], Batch Num: [579/600] Discriminator Loss: 0.6606, Generator Loss: 1.8888 D(x): 0.7877, D(G(z)): 0.2303 Epoch: [12/20], Batch Num: [580/600] Discriminator Loss: 0.7187, Generator Loss: 1.7797 D(x): 0.8523, D(G(z)): 0.2975 Epoch: [12/20], Batch Num: [581/600] Discriminator Loss: 0.5537, Generator Loss: 1.9540 D(x): 0.8589, D(G(z)): 0.2215 Epoch: [12/20], Batch Num: [582/600] Discriminator Loss: 0.5133, Generator Loss: 2.1967 D(x): 0.8518, D(G(z)): 0.2175 Epoch: [12/20], Batch Num: [583/600] Discriminator Loss: 0.5833, Generator Loss: 2.4560 D(x): 0.7954, D(G(z)): 0.1865 Epoch: [12/20], Batch Num: [584/600] Discriminator Loss: 0.5446, Generator Loss: 2.3553 D(x): 0.8211, D(G(z)): 0.1677 Epoch: [12/20], Batch Num: [585/600] Discriminator Loss: 0.6022, Generator Loss: 2.5323 D(x): 0.7621, D(G(z)): 0.1243 Epoch: [12/20], Batch Num: [586/600] Discriminator Loss: 0.4601, Generator Loss: 2.2004 D(x): 0.8482, D(G(z)): 0.1335 Epoch: [12/20], Batch Num: [587/600] Discriminator Loss: 0.6607, Generator Loss: 2.1930 D(x): 0.8077, D(G(z)): 0.1987 Epoch: [12/20], Batch Num: [588/600] Discriminator Loss: 0.6179, Generator Loss: 2.2425 D(x): 0.8411, D(G(z)): 0.1944 Epoch: [12/20], Batch Num: [589/600] Discriminator Loss: 0.6001, Generator Loss: 2.3045 D(x): 0.8099, D(G(z)): 0.1694 Epoch: [12/20], Batch Num: [590/600] Discriminator Loss: 0.5393, Generator Loss: 2.3019 D(x): 0.8210, D(G(z)): 0.1631 Epoch: [12/20], Batch Num: [591/600] Discriminator Loss: 0.6411, Generator Loss: 2.2765 D(x): 0.7882, D(G(z)): 0.1952 Epoch: [12/20], Batch Num: [592/600] Discriminator Loss: 0.6504, Generator Loss: 2.4862 D(x): 0.8389, D(G(z)): 0.2248 Epoch: [12/20], Batch Num: [593/600] Discriminator Loss: 0.6157, Generator Loss: 2.1527 D(x): 0.8257, D(G(z)): 0.2274 Epoch: [12/20], Batch Num: [594/600] Discriminator Loss: 0.5285, Generator Loss: 2.4872 D(x): 0.8198, D(G(z)): 0.1685 Epoch: [12/20], Batch Num: [595/600] Discriminator Loss: 0.6265, Generator Loss: 2.2063 D(x): 0.7774, D(G(z)): 0.1821 Epoch: [12/20], Batch Num: [596/600] Discriminator Loss: 0.5637, Generator Loss: 2.3757 D(x): 0.8087, D(G(z)): 0.1680 Epoch: [12/20], Batch Num: [597/600] Discriminator Loss: 0.5090, Generator Loss: 1.9554 D(x): 0.8227, D(G(z)): 0.1594 Epoch: [12/20], Batch Num: [598/600] Discriminator Loss: 0.6003, Generator Loss: 2.1097 D(x): 0.8334, D(G(z)): 0.2267 Epoch: [12/20], Batch Num: [599/600] Discriminator Loss: 0.6101, Generator Loss: 2.2867 D(x): 0.7973, D(G(z)): 0.1864 Epoch: 13, Batch Num: [0/600]
Epoch: [13/20], Batch Num: [0/600] Discriminator Loss: 0.4899, Generator Loss: 2.4791 D(x): 0.8665, D(G(z)): 0.1806 Epoch: [13/20], Batch Num: [1/600] Discriminator Loss: 0.4100, Generator Loss: 2.6025 D(x): 0.8774, D(G(z)): 0.1729 Epoch: [13/20], Batch Num: [2/600] Discriminator Loss: 0.5587, Generator Loss: 2.7043 D(x): 0.8083, D(G(z)): 0.1562 Epoch: [13/20], Batch Num: [3/600] Discriminator Loss: 0.6290, Generator Loss: 2.7868 D(x): 0.8003, D(G(z)): 0.1474 Epoch: [13/20], Batch Num: [4/600] Discriminator Loss: 0.5280, Generator Loss: 2.4884 D(x): 0.7950, D(G(z)): 0.1283 Epoch: [13/20], Batch Num: [5/600] Discriminator Loss: 0.5524, Generator Loss: 2.4835 D(x): 0.8651, D(G(z)): 0.2019 Epoch: [13/20], Batch Num: [6/600] Discriminator Loss: 0.5697, Generator Loss: 2.4800 D(x): 0.8862, D(G(z)): 0.2223 Epoch: [13/20], Batch Num: [7/600] Discriminator Loss: 0.5390, Generator Loss: 3.0167 D(x): 0.8299, D(G(z)): 0.1613 Epoch: [13/20], Batch Num: [8/600] Discriminator Loss: 0.5117, Generator Loss: 2.7758 D(x): 0.7904, D(G(z)): 0.1005 Epoch: [13/20], Batch Num: [9/600] Discriminator Loss: 0.5633, Generator Loss: 2.6299 D(x): 0.7738, D(G(z)): 0.1211 Epoch: [13/20], Batch Num: [10/600] Discriminator Loss: 0.5175, Generator Loss: 2.0259 D(x): 0.8423, D(G(z)): 0.1498 Epoch: [13/20], Batch Num: [11/600] Discriminator Loss: 0.5233, Generator Loss: 1.8380 D(x): 0.8509, D(G(z)): 0.2075 Epoch: [13/20], Batch Num: [12/600] Discriminator Loss: 0.6440, Generator Loss: 2.1186 D(x): 0.8479, D(G(z)): 0.2536 Epoch: [13/20], Batch Num: [13/600] Discriminator Loss: 0.6092, Generator Loss: 2.4909 D(x): 0.8161, D(G(z)): 0.2113 Epoch: [13/20], Batch Num: [14/600] Discriminator Loss: 0.6307, Generator Loss: 2.5870 D(x): 0.7860, D(G(z)): 0.1715 Epoch: [13/20], Batch Num: [15/600] Discriminator Loss: 0.6499, Generator Loss: 2.6586 D(x): 0.8049, D(G(z)): 0.2103 Epoch: [13/20], Batch Num: [16/600] Discriminator Loss: 0.5579, Generator Loss: 2.7844 D(x): 0.8244, D(G(z)): 0.1364 Epoch: [13/20], Batch Num: [17/600] Discriminator Loss: 0.5586, Generator Loss: 2.3441 D(x): 0.8117, D(G(z)): 0.1345 Epoch: [13/20], Batch Num: [18/600] Discriminator Loss: 0.5603, Generator Loss: 2.0572 D(x): 0.7973, D(G(z)): 0.1611 Epoch: [13/20], Batch Num: [19/600] Discriminator Loss: 0.6013, Generator Loss: 1.8166 D(x): 0.8310, D(G(z)): 0.1992 Epoch: [13/20], Batch Num: [20/600] Discriminator Loss: 0.4543, Generator Loss: 2.0335 D(x): 0.8891, D(G(z)): 0.2114 Epoch: [13/20], Batch Num: [21/600] Discriminator Loss: 0.6713, Generator Loss: 2.4576 D(x): 0.8365, D(G(z)): 0.2492 Epoch: [13/20], Batch Num: [22/600] Discriminator Loss: 0.6163, Generator Loss: 2.8851 D(x): 0.7821, D(G(z)): 0.1582 Epoch: [13/20], Batch Num: [23/600] Discriminator Loss: 0.5660, Generator Loss: 2.7670 D(x): 0.7935, D(G(z)): 0.1590 Epoch: [13/20], Batch Num: [24/600] Discriminator Loss: 0.6573, Generator Loss: 2.4704 D(x): 0.7846, D(G(z)): 0.1567 Epoch: [13/20], Batch Num: [25/600] Discriminator Loss: 0.7758, Generator Loss: 2.0866 D(x): 0.7357, D(G(z)): 0.1711 Epoch: [13/20], Batch Num: [26/600] Discriminator Loss: 0.5126, Generator Loss: 1.9343 D(x): 0.8280, D(G(z)): 0.1708 Epoch: [13/20], Batch Num: [27/600] Discriminator Loss: 0.6196, Generator Loss: 1.5461 D(x): 0.8303, D(G(z)): 0.2519 Epoch: [13/20], Batch Num: [28/600] Discriminator Loss: 0.6639, Generator Loss: 2.2596 D(x): 0.8618, D(G(z)): 0.2711 Epoch: [13/20], Batch Num: [29/600] Discriminator Loss: 0.6014, Generator Loss: 2.7179 D(x): 0.8403, D(G(z)): 0.1996 Epoch: [13/20], Batch Num: [30/600] Discriminator Loss: 0.6208, Generator Loss: 2.8972 D(x): 0.7755, D(G(z)): 0.1322 Epoch: [13/20], Batch Num: [31/600] Discriminator Loss: 0.5061, Generator Loss: 3.0890 D(x): 0.8012, D(G(z)): 0.1230 Epoch: [13/20], Batch Num: [32/600] Discriminator Loss: 0.7064, Generator Loss: 2.1357 D(x): 0.7350, D(G(z)): 0.1267 Epoch: [13/20], Batch Num: [33/600] Discriminator Loss: 0.5873, Generator Loss: 1.7597 D(x): 0.8397, D(G(z)): 0.2001 Epoch: [13/20], Batch Num: [34/600] Discriminator Loss: 0.7401, Generator Loss: 1.8041 D(x): 0.8732, D(G(z)): 0.3148 Epoch: [13/20], Batch Num: [35/600] Discriminator Loss: 0.5878, Generator Loss: 2.3938 D(x): 0.8311, D(G(z)): 0.2150 Epoch: [13/20], Batch Num: [36/600] Discriminator Loss: 0.5305, Generator Loss: 3.1478 D(x): 0.8298, D(G(z)): 0.1656 Epoch: [13/20], Batch Num: [37/600] Discriminator Loss: 0.6354, Generator Loss: 2.8490 D(x): 0.7352, D(G(z)): 0.1027 Epoch: [13/20], Batch Num: [38/600] Discriminator Loss: 0.5727, Generator Loss: 2.3428 D(x): 0.8074, D(G(z)): 0.1351 Epoch: [13/20], Batch Num: [39/600] Discriminator Loss: 0.5544, Generator Loss: 2.1038 D(x): 0.8235, D(G(z)): 0.1787 Epoch: [13/20], Batch Num: [40/600] Discriminator Loss: 0.5834, Generator Loss: 2.0737 D(x): 0.8456, D(G(z)): 0.2164 Epoch: [13/20], Batch Num: [41/600] Discriminator Loss: 0.6771, Generator Loss: 2.2534 D(x): 0.8416, D(G(z)): 0.2444 Epoch: [13/20], Batch Num: [42/600] Discriminator Loss: 0.5152, Generator Loss: 2.3571 D(x): 0.8494, D(G(z)): 0.1961 Epoch: [13/20], Batch Num: [43/600] Discriminator Loss: 0.6466, Generator Loss: 2.6492 D(x): 0.7704, D(G(z)): 0.1631 Epoch: [13/20], Batch Num: [44/600] Discriminator Loss: 0.6359, Generator Loss: 2.4419 D(x): 0.7676, D(G(z)): 0.1528 Epoch: [13/20], Batch Num: [45/600] Discriminator Loss: 0.6331, Generator Loss: 2.2079 D(x): 0.7979, D(G(z)): 0.1754 Epoch: [13/20], Batch Num: [46/600] Discriminator Loss: 0.5159, Generator Loss: 1.9334 D(x): 0.8192, D(G(z)): 0.1828 Epoch: [13/20], Batch Num: [47/600] Discriminator Loss: 0.5329, Generator Loss: 1.9678 D(x): 0.8458, D(G(z)): 0.2140 Epoch: [13/20], Batch Num: [48/600] Discriminator Loss: 0.6339, Generator Loss: 2.1017 D(x): 0.8365, D(G(z)): 0.2452 Epoch: [13/20], Batch Num: [49/600] Discriminator Loss: 0.5953, Generator Loss: 2.3355 D(x): 0.8369, D(G(z)): 0.2271 Epoch: [13/20], Batch Num: [50/600] Discriminator Loss: 0.4837, Generator Loss: 2.7721 D(x): 0.8397, D(G(z)): 0.1650 Epoch: [13/20], Batch Num: 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D(x): 0.8090, D(G(z)): 0.1841 Epoch: [13/20], Batch Num: [60/600] Discriminator Loss: 0.5473, Generator Loss: 2.5499 D(x): 0.8371, D(G(z)): 0.1467 Epoch: [13/20], Batch Num: [61/600] Discriminator Loss: 0.4034, Generator Loss: 2.3503 D(x): 0.8480, D(G(z)): 0.1135 Epoch: [13/20], Batch Num: [62/600] Discriminator Loss: 0.6068, Generator Loss: 2.1806 D(x): 0.7867, D(G(z)): 0.1266 Epoch: [13/20], Batch Num: [63/600] Discriminator Loss: 0.5639, Generator Loss: 1.9070 D(x): 0.8428, D(G(z)): 0.1967 Epoch: [13/20], Batch Num: [64/600] Discriminator Loss: 0.6241, Generator Loss: 2.0572 D(x): 0.8707, D(G(z)): 0.2353 Epoch: [13/20], Batch Num: [65/600] Discriminator Loss: 0.7544, Generator Loss: 3.0172 D(x): 0.8790, D(G(z)): 0.2838 Epoch: [13/20], Batch Num: [66/600] Discriminator Loss: 0.7737, Generator Loss: 3.4122 D(x): 0.7300, D(G(z)): 0.1490 Epoch: [13/20], Batch Num: [67/600] Discriminator Loss: 0.5632, Generator Loss: 3.2740 D(x): 0.7977, D(G(z)): 0.0950 Epoch: [13/20], Batch Num: 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D(x): 0.7343, D(G(z)): 0.1135 Epoch: [13/20], Batch Num: [77/600] Discriminator Loss: 0.5689, Generator Loss: 1.8943 D(x): 0.8428, D(G(z)): 0.2193 Epoch: [13/20], Batch Num: [78/600] Discriminator Loss: 0.6270, Generator Loss: 1.6549 D(x): 0.8213, D(G(z)): 0.2390 Epoch: [13/20], Batch Num: [79/600] Discriminator Loss: 0.6517, Generator Loss: 1.8306 D(x): 0.8718, D(G(z)): 0.2977 Epoch: [13/20], Batch Num: [80/600] Discriminator Loss: 0.5788, Generator Loss: 2.3766 D(x): 0.8595, D(G(z)): 0.2459 Epoch: [13/20], Batch Num: [81/600] Discriminator Loss: 0.4877, Generator Loss: 2.8800 D(x): 0.8458, D(G(z)): 0.1654 Epoch: [13/20], Batch Num: [82/600] Discriminator Loss: 0.5763, Generator Loss: 2.7096 D(x): 0.7929, D(G(z)): 0.1374 Epoch: [13/20], Batch Num: [83/600] Discriminator Loss: 0.7006, Generator Loss: 2.4293 D(x): 0.7441, D(G(z)): 0.1225 Epoch: [13/20], Batch Num: [84/600] Discriminator Loss: 0.5489, Generator Loss: 2.1307 D(x): 0.8085, D(G(z)): 0.1621 Epoch: [13/20], Batch Num: 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D(x): 0.8500, D(G(z)): 0.1423 Epoch: [13/20], Batch Num: [94/600] Discriminator Loss: 0.5167, Generator Loss: 2.0959 D(x): 0.8140, D(G(z)): 0.1534 Epoch: [13/20], Batch Num: [95/600] Discriminator Loss: 0.5579, Generator Loss: 2.1941 D(x): 0.8754, D(G(z)): 0.2517 Epoch: [13/20], Batch Num: [96/600] Discriminator Loss: 0.5471, Generator Loss: 2.3633 D(x): 0.8261, D(G(z)): 0.1984 Epoch: [13/20], Batch Num: [97/600] Discriminator Loss: 0.5992, Generator Loss: 2.4929 D(x): 0.7892, D(G(z)): 0.1623 Epoch: [13/20], Batch Num: [98/600] Discriminator Loss: 0.4998, Generator Loss: 2.3012 D(x): 0.8541, D(G(z)): 0.1790 Epoch: [13/20], Batch Num: [99/600] Discriminator Loss: 0.5644, Generator Loss: 2.4409 D(x): 0.8274, D(G(z)): 0.1750 Epoch: 13, Batch Num: [100/600]
Epoch: [13/20], Batch Num: [100/600] Discriminator Loss: 0.5703, Generator Loss: 2.4170 D(x): 0.8404, D(G(z)): 0.1879 Epoch: [13/20], Batch Num: [101/600] Discriminator Loss: 0.4511, Generator Loss: 2.6705 D(x): 0.8906, D(G(z)): 0.1728 Epoch: [13/20], Batch Num: [102/600] Discriminator Loss: 0.5187, Generator Loss: 2.7713 D(x): 0.8265, D(G(z)): 0.1574 Epoch: [13/20], Batch Num: [103/600] Discriminator Loss: 0.4353, Generator Loss: 2.6252 D(x): 0.8268, D(G(z)): 0.1236 Epoch: [13/20], Batch Num: [104/600] Discriminator Loss: 0.7233, Generator Loss: 2.1820 D(x): 0.7570, D(G(z)): 0.1601 Epoch: [13/20], Batch Num: [105/600] Discriminator Loss: 0.5065, Generator Loss: 2.2235 D(x): 0.8707, D(G(z)): 0.1834 Epoch: [13/20], Batch Num: [106/600] Discriminator Loss: 0.5377, Generator Loss: 2.2846 D(x): 0.8814, D(G(z)): 0.2203 Epoch: [13/20], Batch Num: [107/600] Discriminator Loss: 0.5981, Generator Loss: 2.5987 D(x): 0.8111, D(G(z)): 0.1914 Epoch: [13/20], Batch Num: [108/600] Discriminator Loss: 0.5151, Generator Loss: 2.5674 D(x): 0.8079, D(G(z)): 0.1414 Epoch: [13/20], Batch Num: [109/600] Discriminator Loss: 0.6012, Generator Loss: 2.5825 D(x): 0.8090, D(G(z)): 0.1637 Epoch: [13/20], Batch Num: [110/600] Discriminator Loss: 0.6213, Generator Loss: 2.5208 D(x): 0.8345, D(G(z)): 0.1898 Epoch: [13/20], Batch Num: [111/600] Discriminator Loss: 0.5798, Generator Loss: 2.4274 D(x): 0.8173, D(G(z)): 0.1609 Epoch: [13/20], Batch Num: [112/600] Discriminator Loss: 0.5613, Generator Loss: 2.4349 D(x): 0.8307, D(G(z)): 0.1658 Epoch: [13/20], Batch Num: [113/600] Discriminator Loss: 0.6084, Generator Loss: 2.1333 D(x): 0.8155, D(G(z)): 0.1559 Epoch: [13/20], Batch Num: [114/600] Discriminator Loss: 0.7712, Generator Loss: 2.2194 D(x): 0.7889, D(G(z)): 0.2144 Epoch: [13/20], Batch Num: [115/600] Discriminator Loss: 0.5165, Generator Loss: 2.3631 D(x): 0.8648, D(G(z)): 0.1925 Epoch: [13/20], Batch Num: [116/600] Discriminator Loss: 0.6996, Generator Loss: 2.4479 D(x): 0.7982, D(G(z)): 0.2269 Epoch: [13/20], Batch Num: [117/600] Discriminator Loss: 0.7277, Generator Loss: 2.0690 D(x): 0.7278, D(G(z)): 0.1493 Epoch: [13/20], Batch Num: [118/600] Discriminator Loss: 0.7331, Generator Loss: 1.7994 D(x): 0.7777, D(G(z)): 0.2032 Epoch: [13/20], Batch Num: [119/600] Discriminator Loss: 0.6177, Generator Loss: 1.6770 D(x): 0.8047, D(G(z)): 0.2075 Epoch: [13/20], Batch Num: [120/600] Discriminator Loss: 0.5812, Generator Loss: 1.9363 D(x): 0.9181, D(G(z)): 0.3039 Epoch: [13/20], Batch Num: [121/600] Discriminator Loss: 0.6801, Generator Loss: 2.5010 D(x): 0.8695, D(G(z)): 0.2911 Epoch: [13/20], Batch Num: [122/600] Discriminator Loss: 0.4861, Generator Loss: 2.6937 D(x): 0.8254, D(G(z)): 0.1426 Epoch: [13/20], Batch Num: [123/600] Discriminator Loss: 0.6954, Generator Loss: 2.8764 D(x): 0.7295, D(G(z)): 0.1195 Epoch: [13/20], Batch Num: [124/600] Discriminator Loss: 0.5388, Generator Loss: 2.7301 D(x): 0.7886, D(G(z)): 0.1252 Epoch: [13/20], Batch Num: [125/600] Discriminator Loss: 0.8082, Generator Loss: 2.1331 D(x): 0.7125, D(G(z)): 0.1314 Epoch: [13/20], Batch Num: [126/600] Discriminator Loss: 0.6652, Generator Loss: 1.4717 D(x): 0.8143, D(G(z)): 0.2171 Epoch: [13/20], Batch Num: [127/600] Discriminator Loss: 0.5854, Generator Loss: 1.7219 D(x): 0.8508, D(G(z)): 0.2454 Epoch: [13/20], Batch Num: [128/600] Discriminator Loss: 0.6151, Generator Loss: 1.6242 D(x): 0.8640, D(G(z)): 0.2601 Epoch: [13/20], Batch Num: [129/600] Discriminator Loss: 0.6149, Generator Loss: 1.9698 D(x): 0.8552, D(G(z)): 0.2646 Epoch: [13/20], Batch Num: [130/600] Discriminator Loss: 0.5386, Generator Loss: 2.2908 D(x): 0.8577, D(G(z)): 0.2240 Epoch: [13/20], Batch Num: [131/600] Discriminator Loss: 0.5528, Generator Loss: 2.4526 D(x): 0.8192, D(G(z)): 0.1773 Epoch: [13/20], Batch Num: [132/600] Discriminator Loss: 0.5914, Generator Loss: 2.8531 D(x): 0.8004, D(G(z)): 0.1746 Epoch: [13/20], Batch Num: [133/600] Discriminator Loss: 0.6254, Generator Loss: 2.6901 D(x): 0.7565, D(G(z)): 0.0894 Epoch: [13/20], Batch Num: [134/600] Discriminator Loss: 0.5746, Generator Loss: 2.4554 D(x): 0.7663, D(G(z)): 0.1304 Epoch: [13/20], Batch Num: [135/600] Discriminator Loss: 0.8058, Generator Loss: 2.0544 D(x): 0.7221, D(G(z)): 0.1519 Epoch: [13/20], Batch Num: [136/600] Discriminator Loss: 0.5593, Generator Loss: 1.7848 D(x): 0.8159, D(G(z)): 0.1834 Epoch: [13/20], Batch Num: [137/600] Discriminator Loss: 0.4483, Generator Loss: 1.5244 D(x): 0.9039, D(G(z)): 0.2384 Epoch: [13/20], Batch Num: [138/600] Discriminator Loss: 0.6418, Generator Loss: 2.0519 D(x): 0.8633, D(G(z)): 0.2766 Epoch: [13/20], Batch Num: [139/600] Discriminator Loss: 0.5188, Generator Loss: 2.3860 D(x): 0.9065, D(G(z)): 0.2443 Epoch: [13/20], Batch Num: [140/600] Discriminator Loss: 0.4748, Generator Loss: 2.7921 D(x): 0.8497, D(G(z)): 0.1706 Epoch: [13/20], Batch Num: [141/600] Discriminator Loss: 0.5489, Generator Loss: 3.2263 D(x): 0.7713, D(G(z)): 0.1046 Epoch: [13/20], Batch Num: [142/600] Discriminator Loss: 0.4742, Generator Loss: 2.9008 D(x): 0.8174, D(G(z)): 0.1212 Epoch: [13/20], Batch Num: [143/600] Discriminator Loss: 0.6687, Generator Loss: 2.5210 D(x): 0.7109, D(G(z)): 0.1107 Epoch: [13/20], Batch Num: [144/600] Discriminator Loss: 0.5935, Generator Loss: 2.2177 D(x): 0.8148, D(G(z)): 0.1584 Epoch: [13/20], Batch Num: [145/600] Discriminator Loss: 0.5029, Generator Loss: 2.1085 D(x): 0.8869, D(G(z)): 0.2107 Epoch: [13/20], Batch Num: [146/600] Discriminator Loss: 0.6046, Generator Loss: 2.2085 D(x): 0.8631, D(G(z)): 0.2136 Epoch: [13/20], Batch Num: [147/600] Discriminator Loss: 0.6148, Generator Loss: 2.7303 D(x): 0.8589, D(G(z)): 0.2128 Epoch: [13/20], Batch Num: [148/600] Discriminator Loss: 0.6202, Generator Loss: 3.1023 D(x): 0.8178, D(G(z)): 0.1546 Epoch: [13/20], Batch Num: [149/600] Discriminator Loss: 0.6660, Generator Loss: 2.9200 D(x): 0.7529, D(G(z)): 0.0914 Epoch: [13/20], Batch Num: [150/600] Discriminator Loss: 0.5731, Generator Loss: 2.5698 D(x): 0.7652, D(G(z)): 0.1150 Epoch: [13/20], Batch Num: [151/600] Discriminator Loss: 0.4793, Generator Loss: 2.0196 D(x): 0.8415, D(G(z)): 0.1474 Epoch: [13/20], Batch Num: [152/600] Discriminator Loss: 0.6787, Generator Loss: 1.9157 D(x): 0.7765, D(G(z)): 0.2104 Epoch: [13/20], Batch Num: [153/600] Discriminator Loss: 0.5352, Generator Loss: 2.0164 D(x): 0.8628, D(G(z)): 0.2351 Epoch: [13/20], Batch Num: [154/600] Discriminator Loss: 0.6776, Generator Loss: 2.2369 D(x): 0.9039, D(G(z)): 0.2994 Epoch: [13/20], Batch Num: [155/600] Discriminator Loss: 0.8246, Generator Loss: 3.0038 D(x): 0.8008, D(G(z)): 0.2424 Epoch: [13/20], Batch Num: [156/600] Discriminator Loss: 0.6273, Generator Loss: 2.7154 D(x): 0.7588, D(G(z)): 0.1122 Epoch: [13/20], Batch Num: [157/600] Discriminator Loss: 0.5395, Generator Loss: 2.8676 D(x): 0.7863, D(G(z)): 0.0989 Epoch: [13/20], Batch Num: [158/600] Discriminator Loss: 0.4622, Generator Loss: 2.5104 D(x): 0.8397, D(G(z)): 0.1419 Epoch: [13/20], Batch Num: [159/600] Discriminator Loss: 0.5656, Generator Loss: 2.0919 D(x): 0.8155, D(G(z)): 0.1516 Epoch: [13/20], Batch Num: [160/600] Discriminator Loss: 0.4238, Generator Loss: 1.8990 D(x): 0.8979, D(G(z)): 0.1757 Epoch: [13/20], Batch Num: [161/600] Discriminator Loss: 0.7872, Generator Loss: 2.0958 D(x): 0.8404, D(G(z)): 0.2820 Epoch: [13/20], Batch Num: [162/600] Discriminator Loss: 0.7444, Generator Loss: 2.3835 D(x): 0.8590, D(G(z)): 0.2714 Epoch: [13/20], Batch Num: [163/600] Discriminator Loss: 0.6620, Generator Loss: 2.6505 D(x): 0.7741, D(G(z)): 0.1533 Epoch: [13/20], Batch Num: [164/600] Discriminator Loss: 0.5257, Generator Loss: 2.4857 D(x): 0.7910, D(G(z)): 0.1297 Epoch: [13/20], Batch Num: [165/600] Discriminator Loss: 0.7270, Generator Loss: 2.1013 D(x): 0.7069, D(G(z)): 0.1464 Epoch: [13/20], Batch Num: [166/600] Discriminator Loss: 0.5110, Generator Loss: 1.7916 D(x): 0.8609, D(G(z)): 0.1969 Epoch: [13/20], Batch Num: [167/600] Discriminator Loss: 0.6749, Generator Loss: 1.5499 D(x): 0.8217, D(G(z)): 0.2651 Epoch: [13/20], Batch Num: [168/600] Discriminator Loss: 0.6403, Generator Loss: 2.0600 D(x): 0.8824, D(G(z)): 0.2895 Epoch: [13/20], Batch Num: [169/600] Discriminator Loss: 0.6509, Generator Loss: 2.6327 D(x): 0.8498, D(G(z)): 0.2459 Epoch: [13/20], Batch Num: [170/600] Discriminator Loss: 0.7556, Generator Loss: 2.7435 D(x): 0.7265, D(G(z)): 0.1726 Epoch: [13/20], Batch Num: [171/600] Discriminator Loss: 1.0344, Generator Loss: 2.7502 D(x): 0.6354, D(G(z)): 0.1589 Epoch: [13/20], Batch Num: [172/600] Discriminator Loss: 0.8529, Generator Loss: 2.1588 D(x): 0.6986, D(G(z)): 0.1799 Epoch: [13/20], Batch Num: [173/600] Discriminator Loss: 0.6795, Generator Loss: 1.7062 D(x): 0.7629, D(G(z)): 0.1538 Epoch: [13/20], Batch Num: [174/600] Discriminator Loss: 0.5631, Generator Loss: 1.4760 D(x): 0.8442, D(G(z)): 0.2298 Epoch: [13/20], Batch Num: [175/600] Discriminator Loss: 0.8921, Generator Loss: 1.7650 D(x): 0.8716, D(G(z)): 0.3750 Epoch: [13/20], Batch Num: [176/600] Discriminator Loss: 0.7186, Generator Loss: 2.2681 D(x): 0.8487, D(G(z)): 0.3054 Epoch: [13/20], Batch Num: [177/600] Discriminator Loss: 0.6094, Generator Loss: 2.7120 D(x): 0.7767, D(G(z)): 0.1644 Epoch: [13/20], Batch Num: [178/600] Discriminator Loss: 0.7843, Generator Loss: 2.8003 D(x): 0.7033, D(G(z)): 0.1521 Epoch: [13/20], Batch Num: [179/600] Discriminator Loss: 0.6735, Generator Loss: 2.8947 D(x): 0.7026, D(G(z)): 0.1084 Epoch: [13/20], Batch Num: [180/600] Discriminator Loss: 0.8196, Generator Loss: 1.9157 D(x): 0.6702, D(G(z)): 0.1860 Epoch: [13/20], Batch Num: [181/600] Discriminator Loss: 0.5923, Generator Loss: 1.6120 D(x): 0.7925, D(G(z)): 0.1987 Epoch: [13/20], Batch Num: [182/600] Discriminator Loss: 0.7191, Generator Loss: 1.5365 D(x): 0.8174, D(G(z)): 0.3002 Epoch: [13/20], Batch Num: [183/600] Discriminator Loss: 0.6070, Generator Loss: 1.5475 D(x): 0.8859, D(G(z)): 0.2959 Epoch: [13/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [13/20], Batch Num: [200/600] Discriminator Loss: 0.7456, Generator Loss: 1.9294 D(x): 0.8195, D(G(z)): 0.2781 Epoch: [13/20], Batch Num: [201/600] Discriminator Loss: 0.7197, Generator Loss: 2.0571 D(x): 0.7940, D(G(z)): 0.2543 Epoch: [13/20], Batch Num: [202/600] Discriminator Loss: 0.6119, Generator Loss: 2.4734 D(x): 0.7809, D(G(z)): 0.1936 Epoch: [13/20], Batch Num: [203/600] Discriminator Loss: 0.7258, Generator Loss: 2.3288 D(x): 0.7341, D(G(z)): 0.1825 Epoch: [13/20], Batch Num: [204/600] Discriminator Loss: 0.7416, Generator Loss: 2.1068 D(x): 0.7534, D(G(z)): 0.2097 Epoch: [13/20], Batch Num: [205/600] Discriminator Loss: 0.6961, Generator Loss: 1.9263 D(x): 0.7490, D(G(z)): 0.2160 Epoch: [13/20], Batch Num: [206/600] Discriminator Loss: 0.6339, Generator Loss: 1.8688 D(x): 0.8080, D(G(z)): 0.2469 Epoch: [13/20], Batch Num: [207/600] Discriminator Loss: 0.6063, Generator Loss: 1.9912 D(x): 0.8175, D(G(z)): 0.2369 Epoch: [13/20], Batch Num: [208/600] Discriminator Loss: 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0.2003 Epoch: [13/20], Batch Num: [217/600] Discriminator Loss: 0.5643, Generator Loss: 2.1998 D(x): 0.8261, D(G(z)): 0.2261 Epoch: [13/20], Batch Num: [218/600] Discriminator Loss: 0.5021, Generator Loss: 2.3165 D(x): 0.8230, D(G(z)): 0.1711 Epoch: [13/20], Batch Num: [219/600] Discriminator Loss: 0.5596, Generator Loss: 2.9939 D(x): 0.8079, D(G(z)): 0.1563 Epoch: [13/20], Batch Num: [220/600] Discriminator Loss: 0.7809, Generator Loss: 2.5723 D(x): 0.7434, D(G(z)): 0.1696 Epoch: [13/20], Batch Num: [221/600] Discriminator Loss: 0.5322, Generator Loss: 2.1860 D(x): 0.7883, D(G(z)): 0.1284 Epoch: [13/20], Batch Num: [222/600] Discriminator Loss: 0.5485, Generator Loss: 1.7964 D(x): 0.8369, D(G(z)): 0.1788 Epoch: [13/20], Batch Num: [223/600] Discriminator Loss: 0.6098, Generator Loss: 2.1829 D(x): 0.8348, D(G(z)): 0.2125 Epoch: [13/20], Batch Num: [224/600] Discriminator Loss: 0.6954, Generator Loss: 1.8436 D(x): 0.8193, D(G(z)): 0.2542 Epoch: [13/20], Batch Num: [225/600] Discriminator Loss: 0.5699, Generator Loss: 2.0799 D(x): 0.8417, D(G(z)): 0.2190 Epoch: [13/20], Batch Num: [226/600] Discriminator Loss: 0.6021, Generator Loss: 2.3357 D(x): 0.7844, D(G(z)): 0.1743 Epoch: [13/20], Batch Num: [227/600] Discriminator Loss: 0.5801, Generator Loss: 2.4310 D(x): 0.7954, D(G(z)): 0.1487 Epoch: [13/20], Batch Num: [228/600] Discriminator Loss: 0.5967, Generator Loss: 2.3376 D(x): 0.7852, D(G(z)): 0.1726 Epoch: [13/20], Batch Num: [229/600] Discriminator Loss: 0.6048, Generator Loss: 1.9673 D(x): 0.8115, D(G(z)): 0.2072 Epoch: [13/20], Batch Num: [230/600] Discriminator Loss: 0.5916, Generator Loss: 1.9344 D(x): 0.8132, D(G(z)): 0.2056 Epoch: [13/20], Batch Num: [231/600] Discriminator Loss: 0.5692, Generator Loss: 1.9081 D(x): 0.8353, D(G(z)): 0.2358 Epoch: [13/20], Batch Num: [232/600] Discriminator Loss: 0.6775, Generator Loss: 2.2378 D(x): 0.8279, D(G(z)): 0.2240 Epoch: [13/20], Batch Num: [233/600] Discriminator Loss: 0.5703, Generator Loss: 2.7854 D(x): 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2.1678 D(x): 0.7330, D(G(z)): 0.1540 Epoch: [13/20], Batch Num: [251/600] Discriminator Loss: 0.6859, Generator Loss: 2.3594 D(x): 0.7822, D(G(z)): 0.1980 Epoch: [13/20], Batch Num: [252/600] Discriminator Loss: 0.7069, Generator Loss: 1.9671 D(x): 0.7897, D(G(z)): 0.2086 Epoch: [13/20], Batch Num: [253/600] Discriminator Loss: 0.7026, Generator Loss: 1.9819 D(x): 0.7547, D(G(z)): 0.1922 Epoch: [13/20], Batch Num: [254/600] Discriminator Loss: 0.7192, Generator Loss: 2.3100 D(x): 0.8683, D(G(z)): 0.2946 Epoch: [13/20], Batch Num: [255/600] Discriminator Loss: 0.8412, Generator Loss: 2.3380 D(x): 0.7217, D(G(z)): 0.2035 Epoch: [13/20], Batch Num: [256/600] Discriminator Loss: 0.7375, Generator Loss: 2.0758 D(x): 0.7270, D(G(z)): 0.1595 Epoch: [13/20], Batch Num: [257/600] Discriminator Loss: 0.7210, Generator Loss: 1.8275 D(x): 0.7500, D(G(z)): 0.1791 Epoch: [13/20], Batch Num: [258/600] Discriminator Loss: 0.7782, Generator Loss: 1.7501 D(x): 0.7606, D(G(z)): 0.2161 Epoch: [13/20], 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Generator Loss: 1.7593 D(x): 0.7667, D(G(z)): 0.2608 Epoch: [13/20], Batch Num: [268/600] Discriminator Loss: 0.6038, Generator Loss: 1.7606 D(x): 0.8012, D(G(z)): 0.2334 Epoch: [13/20], Batch Num: [269/600] Discriminator Loss: 0.6623, Generator Loss: 1.5057 D(x): 0.8054, D(G(z)): 0.2435 Epoch: [13/20], Batch Num: [270/600] Discriminator Loss: 0.6584, Generator Loss: 1.7678 D(x): 0.7936, D(G(z)): 0.2448 Epoch: [13/20], Batch Num: [271/600] Discriminator Loss: 0.7824, Generator Loss: 1.9758 D(x): 0.7294, D(G(z)): 0.2333 Epoch: [13/20], Batch Num: [272/600] Discriminator Loss: 0.7448, Generator Loss: 1.9828 D(x): 0.8125, D(G(z)): 0.2772 Epoch: [13/20], Batch Num: [273/600] Discriminator Loss: 0.6182, Generator Loss: 2.1681 D(x): 0.8029, D(G(z)): 0.1998 Epoch: [13/20], Batch Num: [274/600] Discriminator Loss: 0.7930, Generator Loss: 2.0408 D(x): 0.6883, D(G(z)): 0.1728 Epoch: [13/20], Batch Num: [275/600] Discriminator Loss: 0.6119, Generator Loss: 1.9341 D(x): 0.8014, D(G(z)): 0.1821 Epoch: [13/20], Batch Num: [276/600] Discriminator Loss: 0.6751, Generator Loss: 1.7800 D(x): 0.7780, D(G(z)): 0.2164 Epoch: [13/20], Batch Num: [277/600] Discriminator Loss: 0.6421, Generator Loss: 2.0231 D(x): 0.8105, D(G(z)): 0.2483 Epoch: [13/20], Batch Num: [278/600] Discriminator Loss: 0.6692, Generator Loss: 2.0588 D(x): 0.7891, D(G(z)): 0.2324 Epoch: [13/20], Batch Num: [279/600] Discriminator Loss: 0.5857, Generator Loss: 2.0684 D(x): 0.7699, D(G(z)): 0.1698 Epoch: [13/20], Batch Num: [280/600] Discriminator Loss: 0.6174, Generator Loss: 1.9103 D(x): 0.7729, D(G(z)): 0.2022 Epoch: [13/20], Batch Num: [281/600] Discriminator Loss: 0.5074, Generator Loss: 1.9003 D(x): 0.8315, D(G(z)): 0.1910 Epoch: [13/20], Batch Num: [282/600] Discriminator Loss: 0.5681, Generator Loss: 1.9546 D(x): 0.8183, D(G(z)): 0.2037 Epoch: [13/20], Batch Num: [283/600] Discriminator Loss: 0.6741, Generator Loss: 1.8656 D(x): 0.7753, D(G(z)): 0.2151 Epoch: [13/20], Batch Num: [284/600] Discriminator Loss: 0.6813, Generator Loss: 1.8212 D(x): 0.7602, D(G(z)): 0.2168 Epoch: [13/20], Batch Num: [285/600] Discriminator Loss: 0.6289, Generator Loss: 1.8928 D(x): 0.8066, D(G(z)): 0.2175 Epoch: [13/20], Batch Num: [286/600] Discriminator Loss: 0.6332, Generator Loss: 2.1819 D(x): 0.8164, D(G(z)): 0.2173 Epoch: [13/20], Batch Num: [287/600] Discriminator Loss: 0.7200, Generator Loss: 2.3339 D(x): 0.7839, D(G(z)): 0.2322 Epoch: [13/20], Batch Num: [288/600] Discriminator Loss: 0.6827, Generator Loss: 2.3405 D(x): 0.7848, D(G(z)): 0.2201 Epoch: [13/20], Batch Num: [289/600] Discriminator Loss: 0.6357, Generator Loss: 2.2417 D(x): 0.7516, D(G(z)): 0.1471 Epoch: [13/20], Batch Num: [290/600] Discriminator Loss: 0.6367, Generator Loss: 2.0226 D(x): 0.7654, D(G(z)): 0.1787 Epoch: [13/20], Batch Num: [291/600] Discriminator Loss: 0.6212, Generator Loss: 1.8341 D(x): 0.8346, D(G(z)): 0.2357 Epoch: [13/20], Batch Num: [292/600] Discriminator Loss: 0.7187, Generator Loss: 1.8416 D(x): 0.8063, D(G(z)): 0.2554 Epoch: [13/20], Batch Num: [293/600] Discriminator Loss: 0.7727, Generator Loss: 2.1797 D(x): 0.8179, D(G(z)): 0.2921 Epoch: [13/20], Batch Num: [294/600] Discriminator Loss: 0.7863, Generator Loss: 2.4651 D(x): 0.7320, D(G(z)): 0.2057 Epoch: [13/20], Batch Num: [295/600] Discriminator Loss: 0.5094, Generator Loss: 2.4313 D(x): 0.8055, D(G(z)): 0.1427 Epoch: [13/20], Batch Num: [296/600] Discriminator Loss: 0.5719, Generator Loss: 2.1930 D(x): 0.7749, D(G(z)): 0.1420 Epoch: [13/20], Batch Num: [297/600] Discriminator Loss: 0.7907, Generator Loss: 1.8346 D(x): 0.7685, D(G(z)): 0.2188 Epoch: [13/20], Batch Num: [298/600] Discriminator Loss: 0.6419, Generator Loss: 1.7761 D(x): 0.8116, D(G(z)): 0.2167 Epoch: [13/20], Batch Num: [299/600] Discriminator Loss: 0.7079, Generator Loss: 1.7783 D(x): 0.8005, D(G(z)): 0.2612 Epoch: 13, Batch Num: [300/600]
Epoch: [13/20], Batch Num: [300/600] Discriminator Loss: 0.7098, Generator Loss: 2.0191 D(x): 0.7897, D(G(z)): 0.2324 Epoch: [13/20], Batch Num: [301/600] Discriminator Loss: 0.6443, Generator Loss: 2.0973 D(x): 0.7860, D(G(z)): 0.2201 Epoch: [13/20], Batch Num: [302/600] Discriminator Loss: 0.7929, Generator Loss: 2.2322 D(x): 0.7897, D(G(z)): 0.2645 Epoch: [13/20], Batch Num: [303/600] Discriminator Loss: 0.7626, Generator Loss: 2.4867 D(x): 0.7800, D(G(z)): 0.2165 Epoch: [13/20], Batch Num: [304/600] Discriminator Loss: 0.7113, Generator Loss: 2.8939 D(x): 0.7463, D(G(z)): 0.1834 Epoch: [13/20], Batch Num: [305/600] Discriminator Loss: 0.7423, Generator Loss: 1.9699 D(x): 0.6867, D(G(z)): 0.1371 Epoch: [13/20], Batch Num: [306/600] Discriminator Loss: 0.6770, Generator Loss: 1.8622 D(x): 0.7705, D(G(z)): 0.1860 Epoch: [13/20], Batch Num: [307/600] Discriminator Loss: 0.6228, Generator Loss: 1.7362 D(x): 0.8246, D(G(z)): 0.2193 Epoch: [13/20], Batch Num: [308/600] Discriminator Loss: 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0.1957 Epoch: [13/20], Batch Num: [317/600] Discriminator Loss: 0.5425, Generator Loss: 1.9387 D(x): 0.8105, D(G(z)): 0.1802 Epoch: [13/20], Batch Num: [318/600] Discriminator Loss: 0.7212, Generator Loss: 2.3589 D(x): 0.8055, D(G(z)): 0.2375 Epoch: [13/20], Batch Num: [319/600] Discriminator Loss: 0.6479, Generator Loss: 2.0873 D(x): 0.8012, D(G(z)): 0.2160 Epoch: [13/20], Batch Num: [320/600] Discriminator Loss: 0.6784, Generator Loss: 2.3091 D(x): 0.7625, D(G(z)): 0.1963 Epoch: [13/20], Batch Num: [321/600] Discriminator Loss: 0.5399, Generator Loss: 2.3413 D(x): 0.8384, D(G(z)): 0.1865 Epoch: [13/20], Batch Num: [322/600] Discriminator Loss: 0.5660, Generator Loss: 2.4413 D(x): 0.8216, D(G(z)): 0.1942 Epoch: [13/20], Batch Num: [323/600] Discriminator Loss: 0.4863, Generator Loss: 2.5623 D(x): 0.8300, D(G(z)): 0.1499 Epoch: [13/20], Batch Num: [324/600] Discriminator Loss: 0.5431, Generator Loss: 2.4540 D(x): 0.7841, D(G(z)): 0.1314 Epoch: [13/20], Batch Num: [325/600] Discriminator Loss: 0.5151, Generator Loss: 1.9773 D(x): 0.8171, D(G(z)): 0.1635 Epoch: [13/20], Batch Num: [326/600] Discriminator Loss: 0.5644, Generator Loss: 2.2404 D(x): 0.8710, D(G(z)): 0.2385 Epoch: [13/20], Batch Num: [327/600] Discriminator Loss: 0.5579, Generator Loss: 2.2908 D(x): 0.8502, D(G(z)): 0.2159 Epoch: [13/20], Batch Num: [328/600] Discriminator Loss: 0.5046, Generator Loss: 2.8025 D(x): 0.8543, D(G(z)): 0.1921 Epoch: [13/20], Batch Num: [329/600] Discriminator Loss: 0.4921, Generator Loss: 2.7125 D(x): 0.8103, D(G(z)): 0.1211 Epoch: [13/20], Batch Num: [330/600] Discriminator Loss: 0.6151, Generator Loss: 2.6583 D(x): 0.8170, D(G(z)): 0.1630 Epoch: [13/20], Batch Num: [331/600] Discriminator Loss: 0.3991, Generator Loss: 3.0262 D(x): 0.8571, D(G(z)): 0.1262 Epoch: [13/20], Batch Num: [332/600] Discriminator Loss: 0.6214, Generator Loss: 2.4649 D(x): 0.8083, D(G(z)): 0.2008 Epoch: [13/20], Batch Num: [333/600] Discriminator Loss: 0.5751, Generator Loss: 2.5046 D(x): 0.8260, D(G(z)): 0.1936 Epoch: [13/20], Batch Num: [334/600] Discriminator Loss: 0.5133, Generator Loss: 2.7386 D(x): 0.8306, D(G(z)): 0.1719 Epoch: [13/20], Batch Num: [335/600] Discriminator Loss: 0.6088, Generator Loss: 2.5529 D(x): 0.8335, D(G(z)): 0.1906 Epoch: [13/20], Batch Num: [336/600] Discriminator Loss: 0.6809, Generator Loss: 2.6342 D(x): 0.7827, D(G(z)): 0.1631 Epoch: [13/20], Batch Num: [337/600] Discriminator Loss: 0.6500, Generator Loss: 2.5469 D(x): 0.7858, D(G(z)): 0.1498 Epoch: [13/20], Batch Num: [338/600] Discriminator Loss: 0.5727, Generator Loss: 2.2990 D(x): 0.8001, D(G(z)): 0.1684 Epoch: [13/20], Batch Num: [339/600] Discriminator Loss: 0.7693, Generator Loss: 2.0251 D(x): 0.8098, D(G(z)): 0.2330 Epoch: [13/20], Batch Num: [340/600] Discriminator Loss: 0.5354, Generator Loss: 1.8213 D(x): 0.8643, D(G(z)): 0.2103 Epoch: [13/20], Batch Num: [341/600] Discriminator Loss: 0.5054, Generator Loss: 2.3066 D(x): 0.8766, D(G(z)): 0.2194 Epoch: [13/20], Batch Num: 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1.8963 D(x): 0.7700, D(G(z)): 0.1746 Epoch: [13/20], Batch Num: [351/600] Discriminator Loss: 0.6689, Generator Loss: 1.5595 D(x): 0.7839, D(G(z)): 0.2299 Epoch: [13/20], Batch Num: [352/600] Discriminator Loss: 0.5047, Generator Loss: 1.5342 D(x): 0.8674, D(G(z)): 0.2248 Epoch: [13/20], Batch Num: [353/600] Discriminator Loss: 0.6275, Generator Loss: 1.8721 D(x): 0.8564, D(G(z)): 0.2686 Epoch: [13/20], Batch Num: [354/600] Discriminator Loss: 0.6805, Generator Loss: 1.8741 D(x): 0.7841, D(G(z)): 0.2012 Epoch: [13/20], Batch Num: [355/600] Discriminator Loss: 0.6322, Generator Loss: 2.0431 D(x): 0.7855, D(G(z)): 0.1867 Epoch: [13/20], Batch Num: [356/600] Discriminator Loss: 0.6890, Generator Loss: 2.0246 D(x): 0.7807, D(G(z)): 0.2036 Epoch: [13/20], Batch Num: [357/600] Discriminator Loss: 0.6881, Generator Loss: 2.0998 D(x): 0.7841, D(G(z)): 0.1810 Epoch: [13/20], Batch Num: [358/600] Discriminator Loss: 0.4911, Generator Loss: 2.1718 D(x): 0.8460, D(G(z)): 0.1724 Epoch: [13/20], 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Generator Loss: 2.1161 D(x): 0.8401, D(G(z)): 0.2025 Epoch: [13/20], Batch Num: [368/600] Discriminator Loss: 0.7711, Generator Loss: 2.0448 D(x): 0.7555, D(G(z)): 0.2165 Epoch: [13/20], Batch Num: [369/600] Discriminator Loss: 0.5091, Generator Loss: 2.1241 D(x): 0.8132, D(G(z)): 0.1567 Epoch: [13/20], Batch Num: [370/600] Discriminator Loss: 0.4801, Generator Loss: 1.9220 D(x): 0.8187, D(G(z)): 0.1552 Epoch: [13/20], Batch Num: [371/600] Discriminator Loss: 0.5428, Generator Loss: 1.9833 D(x): 0.8538, D(G(z)): 0.2203 Epoch: [13/20], Batch Num: [372/600] Discriminator Loss: 0.5360, Generator Loss: 2.0649 D(x): 0.8685, D(G(z)): 0.2080 Epoch: [13/20], Batch Num: [373/600] Discriminator Loss: 0.5090, Generator Loss: 2.3565 D(x): 0.8538, D(G(z)): 0.1894 Epoch: [13/20], Batch Num: [374/600] Discriminator Loss: 0.5543, Generator Loss: 2.6746 D(x): 0.8132, D(G(z)): 0.1516 Epoch: [13/20], Batch Num: [375/600] Discriminator Loss: 0.6821, Generator Loss: 2.3892 D(x): 0.7430, D(G(z)): 0.1256 Epoch: [13/20], Batch Num: [376/600] Discriminator Loss: 0.6176, Generator Loss: 2.1147 D(x): 0.7816, D(G(z)): 0.1529 Epoch: [13/20], Batch Num: [377/600] Discriminator Loss: 0.6636, Generator Loss: 1.9673 D(x): 0.8291, D(G(z)): 0.2115 Epoch: [13/20], Batch Num: [378/600] Discriminator Loss: 0.6489, Generator Loss: 1.9873 D(x): 0.8305, D(G(z)): 0.2436 Epoch: [13/20], Batch Num: [379/600] Discriminator Loss: 0.5278, Generator Loss: 2.2628 D(x): 0.8345, D(G(z)): 0.1862 Epoch: [13/20], Batch Num: [380/600] Discriminator Loss: 0.6227, Generator Loss: 2.2536 D(x): 0.7915, D(G(z)): 0.1781 Epoch: [13/20], Batch Num: [381/600] Discriminator Loss: 0.4422, Generator Loss: 2.2816 D(x): 0.8916, D(G(z)): 0.1939 Epoch: [13/20], Batch Num: [382/600] Discriminator Loss: 0.5860, Generator Loss: 2.4944 D(x): 0.7971, D(G(z)): 0.1569 Epoch: [13/20], Batch Num: [383/600] Discriminator Loss: 0.6922, Generator Loss: 2.6198 D(x): 0.7503, D(G(z)): 0.1357 Epoch: [13/20], Batch Num: [384/600] Discriminator Loss: 0.5447, Generator Loss: 2.1465 D(x): 0.8308, D(G(z)): 0.1546 Epoch: [13/20], Batch Num: [385/600] Discriminator Loss: 0.5074, Generator Loss: 2.0441 D(x): 0.8169, D(G(z)): 0.1571 Epoch: [13/20], Batch Num: [386/600] Discriminator Loss: 0.5962, Generator Loss: 2.3197 D(x): 0.8598, D(G(z)): 0.2538 Epoch: [13/20], Batch Num: [387/600] Discriminator Loss: 0.5032, Generator Loss: 2.4076 D(x): 0.8536, D(G(z)): 0.1973 Epoch: [13/20], Batch Num: [388/600] Discriminator Loss: 0.6135, Generator Loss: 2.7471 D(x): 0.8018, D(G(z)): 0.1843 Epoch: [13/20], Batch Num: [389/600] Discriminator Loss: 0.7473, Generator Loss: 2.5616 D(x): 0.7479, D(G(z)): 0.1499 Epoch: [13/20], Batch Num: [390/600] Discriminator Loss: 0.5625, Generator Loss: 2.6491 D(x): 0.8056, D(G(z)): 0.1305 Epoch: [13/20], Batch Num: [391/600] Discriminator Loss: 0.5506, Generator Loss: 2.3525 D(x): 0.8101, D(G(z)): 0.1413 Epoch: [13/20], Batch Num: [392/600] Discriminator Loss: 0.7075, Generator Loss: 2.2281 D(x): 0.8008, D(G(z)): 0.2176 Epoch: [13/20], Batch Num: [393/600] Discriminator Loss: 0.5643, Generator Loss: 2.0017 D(x): 0.8383, D(G(z)): 0.2009 Epoch: [13/20], Batch Num: [394/600] Discriminator Loss: 0.5081, Generator Loss: 2.2496 D(x): 0.8585, D(G(z)): 0.1807 Epoch: [13/20], Batch Num: [395/600] Discriminator Loss: 0.5954, Generator Loss: 2.4910 D(x): 0.8222, D(G(z)): 0.2123 Epoch: [13/20], Batch Num: [396/600] Discriminator Loss: 0.4841, Generator Loss: 2.5824 D(x): 0.8482, D(G(z)): 0.1616 Epoch: [13/20], Batch Num: [397/600] Discriminator Loss: 0.7202, Generator Loss: 2.8537 D(x): 0.7564, D(G(z)): 0.1586 Epoch: [13/20], Batch Num: [398/600] Discriminator Loss: 0.5972, Generator Loss: 2.4154 D(x): 0.7991, D(G(z)): 0.1385 Epoch: [13/20], Batch Num: [399/600] Discriminator Loss: 0.5890, Generator Loss: 1.9248 D(x): 0.7759, D(G(z)): 0.1372 Epoch: 13, Batch Num: [400/600]
Epoch: [13/20], Batch Num: [400/600] Discriminator Loss: 0.5978, Generator Loss: 1.4878 D(x): 0.8219, D(G(z)): 0.2061 Epoch: [13/20], Batch Num: [401/600] Discriminator Loss: 0.6454, Generator Loss: 2.0366 D(x): 0.8741, D(G(z)): 0.2833 Epoch: [13/20], Batch Num: [402/600] Discriminator Loss: 0.6785, Generator Loss: 2.2263 D(x): 0.8183, D(G(z)): 0.2476 Epoch: [13/20], Batch Num: [403/600] Discriminator Loss: 0.4860, Generator Loss: 2.6473 D(x): 0.8778, D(G(z)): 0.2021 Epoch: [13/20], Batch Num: [404/600] Discriminator Loss: 0.5716, Generator Loss: 3.0010 D(x): 0.8081, D(G(z)): 0.1182 Epoch: [13/20], Batch Num: [405/600] Discriminator Loss: 0.6452, Generator Loss: 2.7796 D(x): 0.7408, D(G(z)): 0.0970 Epoch: [13/20], Batch Num: [406/600] Discriminator Loss: 0.6930, Generator Loss: 2.3306 D(x): 0.7458, D(G(z)): 0.1060 Epoch: [13/20], Batch Num: [407/600] Discriminator Loss: 0.5671, Generator Loss: 1.5926 D(x): 0.8085, D(G(z)): 0.1632 Epoch: [13/20], Batch Num: [408/600] Discriminator Loss: 0.6604, Generator Loss: 1.6421 D(x): 0.8626, D(G(z)): 0.2696 Epoch: [13/20], Batch Num: [409/600] Discriminator Loss: 0.6963, Generator Loss: 2.0615 D(x): 0.8658, D(G(z)): 0.2895 Epoch: [13/20], Batch Num: [410/600] Discriminator Loss: 0.5649, Generator Loss: 2.6706 D(x): 0.8931, D(G(z)): 0.2681 Epoch: [13/20], Batch Num: [411/600] Discriminator Loss: 0.5975, Generator Loss: 2.4722 D(x): 0.7589, D(G(z)): 0.1318 Epoch: [13/20], Batch Num: [412/600] Discriminator Loss: 0.5788, Generator Loss: 2.3994 D(x): 0.7419, D(G(z)): 0.1107 Epoch: [13/20], Batch Num: [413/600] Discriminator Loss: 0.7061, Generator Loss: 2.1705 D(x): 0.7137, D(G(z)): 0.1368 Epoch: [13/20], Batch Num: [414/600] Discriminator Loss: 0.6766, Generator Loss: 1.5957 D(x): 0.7662, D(G(z)): 0.1772 Epoch: [13/20], Batch Num: [415/600] Discriminator Loss: 0.6231, Generator Loss: 1.6055 D(x): 0.8669, D(G(z)): 0.2710 Epoch: [13/20], Batch Num: [416/600] Discriminator Loss: 0.5039, Generator Loss: 1.8739 D(x): 0.8586, D(G(z)): 0.2134 Epoch: [13/20], Batch Num: [417/600] Discriminator Loss: 0.6134, Generator Loss: 1.9941 D(x): 0.8456, D(G(z)): 0.2452 Epoch: [13/20], Batch Num: [418/600] Discriminator Loss: 0.4760, Generator Loss: 2.1212 D(x): 0.8765, D(G(z)): 0.2010 Epoch: [13/20], Batch Num: [419/600] Discriminator Loss: 0.6042, Generator Loss: 2.3798 D(x): 0.7644, D(G(z)): 0.1508 Epoch: [13/20], Batch Num: [420/600] Discriminator Loss: 0.5346, Generator Loss: 2.1959 D(x): 0.7864, D(G(z)): 0.1503 Epoch: [13/20], Batch Num: [421/600] Discriminator Loss: 0.6392, Generator Loss: 2.1617 D(x): 0.7690, D(G(z)): 0.1531 Epoch: [13/20], Batch Num: [422/600] Discriminator Loss: 0.6109, Generator Loss: 1.9037 D(x): 0.7695, D(G(z)): 0.1530 Epoch: [13/20], Batch Num: [423/600] Discriminator Loss: 0.6355, Generator Loss: 1.8062 D(x): 0.8175, D(G(z)): 0.2140 Epoch: [13/20], Batch Num: [424/600] Discriminator Loss: 0.5091, Generator Loss: 2.0399 D(x): 0.9178, D(G(z)): 0.2398 Epoch: [13/20], Batch Num: [425/600] Discriminator Loss: 0.6631, Generator Loss: 2.2599 D(x): 0.8326, D(G(z)): 0.2108 Epoch: [13/20], Batch Num: [426/600] Discriminator Loss: 0.5307, Generator Loss: 2.5066 D(x): 0.8192, D(G(z)): 0.1402 Epoch: [13/20], Batch Num: [427/600] Discriminator Loss: 0.7908, Generator Loss: 2.2904 D(x): 0.7201, D(G(z)): 0.1560 Epoch: [13/20], Batch Num: [428/600] Discriminator Loss: 0.8026, Generator Loss: 1.8808 D(x): 0.7301, D(G(z)): 0.1748 Epoch: [13/20], Batch Num: [429/600] Discriminator Loss: 0.5836, Generator Loss: 1.6970 D(x): 0.8371, D(G(z)): 0.2145 Epoch: [13/20], Batch Num: [430/600] Discriminator Loss: 0.5587, Generator Loss: 1.9384 D(x): 0.8854, D(G(z)): 0.2576 Epoch: [13/20], Batch Num: [431/600] Discriminator Loss: 0.7221, Generator Loss: 2.1579 D(x): 0.8354, D(G(z)): 0.2601 Epoch: [13/20], Batch Num: [432/600] Discriminator Loss: 0.7086, Generator Loss: 2.5116 D(x): 0.8080, D(G(z)): 0.2223 Epoch: [13/20], Batch Num: [433/600] Discriminator Loss: 0.7067, Generator Loss: 2.4404 D(x): 0.7004, D(G(z)): 0.1434 Epoch: [13/20], Batch Num: [434/600] Discriminator Loss: 0.5629, Generator Loss: 2.4444 D(x): 0.7911, D(G(z)): 0.1206 Epoch: [13/20], Batch Num: [435/600] Discriminator Loss: 0.7050, Generator Loss: 2.2378 D(x): 0.7494, D(G(z)): 0.1583 Epoch: [13/20], Batch Num: [436/600] Discriminator Loss: 0.5524, Generator Loss: 1.7053 D(x): 0.8355, D(G(z)): 0.1969 Epoch: [13/20], Batch Num: [437/600] Discriminator Loss: 0.7023, Generator Loss: 1.7653 D(x): 0.8015, D(G(z)): 0.2297 Epoch: [13/20], Batch Num: [438/600] Discriminator Loss: 0.7466, Generator Loss: 1.8384 D(x): 0.8503, D(G(z)): 0.2699 Epoch: [13/20], Batch Num: [439/600] Discriminator Loss: 0.6984, Generator Loss: 2.0506 D(x): 0.8134, D(G(z)): 0.2307 Epoch: [13/20], Batch Num: [440/600] Discriminator Loss: 0.6085, Generator Loss: 2.3574 D(x): 0.7994, D(G(z)): 0.1888 Epoch: [13/20], Batch Num: [441/600] Discriminator Loss: 0.4898, Generator Loss: 2.4231 D(x): 0.8466, D(G(z)): 0.1722 Epoch: [13/20], Batch Num: 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2.0906 D(x): 0.8165, D(G(z)): 0.1610 Epoch: [13/20], Batch Num: [451/600] Discriminator Loss: 0.5848, Generator Loss: 1.9615 D(x): 0.8203, D(G(z)): 0.1891 Epoch: [13/20], Batch Num: [452/600] Discriminator Loss: 0.6062, Generator Loss: 2.0178 D(x): 0.8455, D(G(z)): 0.2406 Epoch: [13/20], Batch Num: [453/600] Discriminator Loss: 0.5804, Generator Loss: 2.2761 D(x): 0.7961, D(G(z)): 0.1667 Epoch: [13/20], Batch Num: [454/600] Discriminator Loss: 0.4933, Generator Loss: 2.4135 D(x): 0.8489, D(G(z)): 0.1775 Epoch: [13/20], Batch Num: [455/600] Discriminator Loss: 0.5625, Generator Loss: 2.3944 D(x): 0.8372, D(G(z)): 0.1872 Epoch: [13/20], Batch Num: [456/600] Discriminator Loss: 0.6398, Generator Loss: 2.5604 D(x): 0.8036, D(G(z)): 0.1860 Epoch: [13/20], Batch Num: [457/600] Discriminator Loss: 0.5427, Generator Loss: 2.4956 D(x): 0.8032, D(G(z)): 0.1431 Epoch: [13/20], Batch Num: [458/600] Discriminator Loss: 0.4899, Generator Loss: 2.2975 D(x): 0.8450, D(G(z)): 0.1674 Epoch: [13/20], 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Generator Loss: 2.4517 D(x): 0.7889, D(G(z)): 0.1573 Epoch: [13/20], Batch Num: [468/600] Discriminator Loss: 0.5046, Generator Loss: 2.0726 D(x): 0.8153, D(G(z)): 0.1601 Epoch: [13/20], Batch Num: [469/600] Discriminator Loss: 0.6122, Generator Loss: 1.9644 D(x): 0.8173, D(G(z)): 0.2057 Epoch: [13/20], Batch Num: [470/600] Discriminator Loss: 0.6305, Generator Loss: 2.0427 D(x): 0.8303, D(G(z)): 0.2228 Epoch: [13/20], Batch Num: [471/600] Discriminator Loss: 0.7928, Generator Loss: 2.3305 D(x): 0.8028, D(G(z)): 0.2349 Epoch: [13/20], Batch Num: [472/600] Discriminator Loss: 0.4663, Generator Loss: 2.4131 D(x): 0.8255, D(G(z)): 0.1377 Epoch: [13/20], Batch Num: [473/600] Discriminator Loss: 0.4894, Generator Loss: 2.1852 D(x): 0.8282, D(G(z)): 0.1483 Epoch: [13/20], Batch Num: [474/600] Discriminator Loss: 0.4482, Generator Loss: 2.2589 D(x): 0.8312, D(G(z)): 0.1394 Epoch: [13/20], Batch Num: [475/600] Discriminator Loss: 0.5481, Generator Loss: 2.3933 D(x): 0.8530, D(G(z)): 0.1926 Epoch: [13/20], Batch Num: [476/600] Discriminator Loss: 0.5590, Generator Loss: 2.5272 D(x): 0.8284, D(G(z)): 0.1768 Epoch: [13/20], Batch Num: [477/600] Discriminator Loss: 0.6095, Generator Loss: 2.5147 D(x): 0.8117, D(G(z)): 0.1580 Epoch: [13/20], Batch Num: [478/600] Discriminator Loss: 0.5840, Generator Loss: 2.5359 D(x): 0.8325, D(G(z)): 0.1907 Epoch: [13/20], Batch Num: [479/600] Discriminator Loss: 0.5846, Generator Loss: 2.4253 D(x): 0.8110, D(G(z)): 0.1708 Epoch: [13/20], Batch Num: [480/600] Discriminator Loss: 0.8005, Generator Loss: 2.3773 D(x): 0.7362, D(G(z)): 0.1942 Epoch: [13/20], Batch Num: [481/600] Discriminator Loss: 0.5736, Generator Loss: 1.8396 D(x): 0.7911, D(G(z)): 0.1617 Epoch: [13/20], Batch Num: [482/600] Discriminator Loss: 0.7024, Generator Loss: 1.9802 D(x): 0.8019, D(G(z)): 0.2259 Epoch: [13/20], Batch Num: [483/600] Discriminator Loss: 0.6117, Generator Loss: 1.9017 D(x): 0.8100, D(G(z)): 0.2225 Epoch: [13/20], Batch Num: [484/600] Discriminator Loss: 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0.1487 Epoch: [13/20], Batch Num: [493/600] Discriminator Loss: 0.8986, Generator Loss: 2.3414 D(x): 0.6872, D(G(z)): 0.1722 Epoch: [13/20], Batch Num: [494/600] Discriminator Loss: 0.5644, Generator Loss: 2.2903 D(x): 0.8489, D(G(z)): 0.2047 Epoch: [13/20], Batch Num: [495/600] Discriminator Loss: 0.6296, Generator Loss: 2.3533 D(x): 0.8409, D(G(z)): 0.2148 Epoch: [13/20], Batch Num: [496/600] Discriminator Loss: 0.6908, Generator Loss: 2.6357 D(x): 0.8017, D(G(z)): 0.2189 Epoch: [13/20], Batch Num: [497/600] Discriminator Loss: 0.6630, Generator Loss: 2.3145 D(x): 0.8005, D(G(z)): 0.2140 Epoch: [13/20], Batch Num: [498/600] Discriminator Loss: 0.7402, Generator Loss: 2.5777 D(x): 0.7565, D(G(z)): 0.1822 Epoch: [13/20], Batch Num: [499/600] Discriminator Loss: 0.5907, Generator Loss: 2.2053 D(x): 0.7723, D(G(z)): 0.1690 Epoch: 13, Batch Num: [500/600]
Epoch: [13/20], Batch Num: [500/600] Discriminator Loss: 0.5850, Generator Loss: 1.8775 D(x): 0.8118, D(G(z)): 0.1905 Epoch: [13/20], Batch Num: [501/600] Discriminator Loss: 0.7077, Generator Loss: 1.9589 D(x): 0.7989, D(G(z)): 0.2518 Epoch: [13/20], Batch Num: [502/600] Discriminator Loss: 0.6120, Generator Loss: 2.0870 D(x): 0.8073, D(G(z)): 0.2269 Epoch: [13/20], Batch Num: [503/600] Discriminator Loss: 0.5954, Generator Loss: 2.1564 D(x): 0.8572, D(G(z)): 0.2475 Epoch: [13/20], Batch Num: [504/600] Discriminator Loss: 0.6532, Generator Loss: 2.2654 D(x): 0.7750, D(G(z)): 0.1702 Epoch: [13/20], Batch Num: [505/600] Discriminator Loss: 0.8742, Generator Loss: 2.3437 D(x): 0.7513, D(G(z)): 0.2392 Epoch: [13/20], Batch Num: [506/600] Discriminator Loss: 0.7698, Generator Loss: 2.2162 D(x): 0.6940, D(G(z)): 0.1578 Epoch: [13/20], Batch Num: [507/600] Discriminator Loss: 0.6335, Generator Loss: 1.8311 D(x): 0.7833, D(G(z)): 0.1969 Epoch: [13/20], Batch Num: [508/600] Discriminator Loss: 0.8085, Generator Loss: 1.6952 D(x): 0.7898, D(G(z)): 0.2570 Epoch: [13/20], Batch Num: [509/600] Discriminator Loss: 0.8841, Generator Loss: 1.8263 D(x): 0.8314, D(G(z)): 0.3449 Epoch: [13/20], Batch Num: [510/600] Discriminator Loss: 0.6751, Generator Loss: 2.3197 D(x): 0.8138, D(G(z)): 0.2519 Epoch: [13/20], Batch Num: [511/600] Discriminator Loss: 0.5131, Generator Loss: 2.3919 D(x): 0.7990, D(G(z)): 0.1497 Epoch: [13/20], Batch Num: [512/600] Discriminator Loss: 0.8316, Generator Loss: 2.3040 D(x): 0.6886, D(G(z)): 0.1485 Epoch: [13/20], Batch Num: [513/600] Discriminator Loss: 0.9303, Generator Loss: 1.6881 D(x): 0.6377, D(G(z)): 0.1705 Epoch: [13/20], Batch Num: [514/600] Discriminator Loss: 0.7753, Generator Loss: 1.3539 D(x): 0.7826, D(G(z)): 0.2628 Epoch: [13/20], Batch Num: [515/600] Discriminator Loss: 0.7589, Generator Loss: 1.3571 D(x): 0.8509, D(G(z)): 0.3223 Epoch: [13/20], Batch Num: [516/600] Discriminator Loss: 0.7739, Generator Loss: 1.7487 D(x): 0.8428, D(G(z)): 0.3387 Epoch: [13/20], Batch Num: [517/600] Discriminator Loss: 0.7558, Generator Loss: 1.8614 D(x): 0.7623, D(G(z)): 0.2572 Epoch: [13/20], Batch Num: [518/600] Discriminator Loss: 0.6870, Generator Loss: 2.1007 D(x): 0.7676, D(G(z)): 0.2222 Epoch: [13/20], Batch Num: [519/600] Discriminator Loss: 0.6617, Generator Loss: 1.9785 D(x): 0.7315, D(G(z)): 0.1643 Epoch: [13/20], Batch Num: [520/600] Discriminator Loss: 0.6430, Generator Loss: 2.0968 D(x): 0.7437, D(G(z)): 0.1660 Epoch: [13/20], Batch Num: [521/600] Discriminator Loss: 0.6710, Generator Loss: 1.8264 D(x): 0.7929, D(G(z)): 0.2289 Epoch: [13/20], Batch Num: [522/600] Discriminator Loss: 0.8417, Generator Loss: 2.0038 D(x): 0.7630, D(G(z)): 0.2771 Epoch: [13/20], Batch Num: [523/600] Discriminator Loss: 0.5475, Generator Loss: 1.9821 D(x): 0.8538, D(G(z)): 0.2352 Epoch: [13/20], Batch Num: [524/600] Discriminator Loss: 0.6901, Generator Loss: 1.9642 D(x): 0.7576, D(G(z)): 0.2268 Epoch: [13/20], Batch Num: [525/600] Discriminator Loss: 0.6957, Generator Loss: 1.9420 D(x): 0.7504, D(G(z)): 0.2169 Epoch: [13/20], Batch Num: [526/600] Discriminator Loss: 0.8315, Generator Loss: 1.9653 D(x): 0.7624, D(G(z)): 0.2644 Epoch: [13/20], Batch Num: [527/600] Discriminator Loss: 0.6116, Generator Loss: 1.9000 D(x): 0.7930, D(G(z)): 0.2011 Epoch: [13/20], Batch Num: [528/600] Discriminator Loss: 0.5170, Generator Loss: 2.0199 D(x): 0.8578, D(G(z)): 0.2095 Epoch: [13/20], Batch Num: [529/600] Discriminator Loss: 0.6106, Generator Loss: 2.5843 D(x): 0.8159, D(G(z)): 0.2173 Epoch: [13/20], Batch Num: [530/600] Discriminator Loss: 0.5654, Generator Loss: 2.2342 D(x): 0.7906, D(G(z)): 0.1605 Epoch: [13/20], Batch Num: [531/600] Discriminator Loss: 0.6235, Generator Loss: 2.2870 D(x): 0.7386, D(G(z)): 0.1267 Epoch: [13/20], Batch Num: [532/600] Discriminator Loss: 0.5651, Generator Loss: 1.9159 D(x): 0.8289, D(G(z)): 0.2001 Epoch: [13/20], Batch Num: [533/600] Discriminator Loss: 0.7094, Generator Loss: 1.9487 D(x): 0.7982, D(G(z)): 0.2332 Epoch: [13/20], Batch Num: [534/600] Discriminator Loss: 0.5960, Generator Loss: 2.1602 D(x): 0.8387, D(G(z)): 0.2340 Epoch: [13/20], Batch Num: [535/600] Discriminator Loss: 0.5852, Generator Loss: 2.3418 D(x): 0.7988, D(G(z)): 0.1908 Epoch: [13/20], Batch Num: [536/600] Discriminator Loss: 0.7285, Generator Loss: 2.2828 D(x): 0.7397, D(G(z)): 0.1920 Epoch: [13/20], Batch Num: [537/600] Discriminator Loss: 0.5546, Generator Loss: 2.2392 D(x): 0.8504, D(G(z)): 0.1972 Epoch: [13/20], Batch Num: [538/600] Discriminator Loss: 0.6062, Generator Loss: 2.0546 D(x): 0.7742, D(G(z)): 0.1623 Epoch: [13/20], Batch Num: [539/600] Discriminator Loss: 0.4589, Generator Loss: 2.2464 D(x): 0.8775, D(G(z)): 0.2033 Epoch: [13/20], Batch Num: [540/600] Discriminator Loss: 0.7288, Generator Loss: 2.1039 D(x): 0.7511, D(G(z)): 0.2201 Epoch: [13/20], Batch Num: [541/600] Discriminator Loss: 0.6672, Generator Loss: 2.0796 D(x): 0.7866, D(G(z)): 0.2050 Epoch: [13/20], Batch Num: [542/600] Discriminator Loss: 0.6854, Generator Loss: 2.1450 D(x): 0.7969, D(G(z)): 0.2089 Epoch: [13/20], Batch Num: [543/600] Discriminator Loss: 0.7371, Generator Loss: 1.9110 D(x): 0.7749, D(G(z)): 0.2196 Epoch: [13/20], Batch Num: [544/600] Discriminator Loss: 0.6703, Generator Loss: 2.0517 D(x): 0.8282, D(G(z)): 0.2467 Epoch: [13/20], Batch Num: [545/600] Discriminator Loss: 0.8549, Generator Loss: 2.1195 D(x): 0.7692, D(G(z)): 0.2591 Epoch: [13/20], Batch Num: [546/600] Discriminator Loss: 0.7321, Generator Loss: 2.2203 D(x): 0.8331, D(G(z)): 0.2211 Epoch: [13/20], Batch Num: [547/600] Discriminator Loss: 0.8420, Generator Loss: 2.4488 D(x): 0.6852, D(G(z)): 0.1868 Epoch: [13/20], Batch Num: [548/600] Discriminator Loss: 0.7539, Generator Loss: 2.1425 D(x): 0.7461, D(G(z)): 0.1957 Epoch: [13/20], Batch Num: [549/600] Discriminator Loss: 0.6591, Generator Loss: 1.8350 D(x): 0.7715, D(G(z)): 0.2078 Epoch: [13/20], Batch Num: [550/600] Discriminator Loss: 0.7198, Generator Loss: 1.7395 D(x): 0.7679, D(G(z)): 0.2500 Epoch: [13/20], Batch Num: [551/600] Discriminator Loss: 0.8360, Generator Loss: 1.9542 D(x): 0.7545, D(G(z)): 0.2902 Epoch: [13/20], Batch Num: [552/600] Discriminator Loss: 0.6254, Generator Loss: 2.0092 D(x): 0.7979, D(G(z)): 0.2337 Epoch: [13/20], Batch Num: [553/600] Discriminator Loss: 0.7333, Generator Loss: 2.2151 D(x): 0.7743, D(G(z)): 0.2377 Epoch: [13/20], Batch Num: [554/600] Discriminator Loss: 0.5667, Generator Loss: 2.1999 D(x): 0.8046, D(G(z)): 0.1924 Epoch: [13/20], Batch Num: [555/600] Discriminator Loss: 0.7142, Generator Loss: 2.4098 D(x): 0.7821, D(G(z)): 0.2319 Epoch: [13/20], Batch Num: [556/600] Discriminator Loss: 0.6726, Generator Loss: 2.5011 D(x): 0.7777, D(G(z)): 0.1870 Epoch: [13/20], Batch Num: [557/600] Discriminator Loss: 0.7555, Generator Loss: 2.3902 D(x): 0.7640, D(G(z)): 0.1878 Epoch: [13/20], Batch Num: [558/600] Discriminator Loss: 0.5206, Generator Loss: 2.1701 D(x): 0.8339, D(G(z)): 0.1946 Epoch: [13/20], Batch Num: [559/600] Discriminator Loss: 0.6644, Generator Loss: 2.0547 D(x): 0.8457, D(G(z)): 0.2224 Epoch: [13/20], Batch Num: [560/600] Discriminator Loss: 0.5570, Generator Loss: 2.1642 D(x): 0.8302, D(G(z)): 0.1951 Epoch: [13/20], Batch Num: [561/600] Discriminator Loss: 0.4956, Generator Loss: 2.1683 D(x): 0.8094, D(G(z)): 0.1557 Epoch: [13/20], Batch Num: [562/600] Discriminator Loss: 0.5385, Generator Loss: 2.2702 D(x): 0.8299, D(G(z)): 0.1828 Epoch: [13/20], Batch Num: [563/600] Discriminator Loss: 0.7115, Generator Loss: 2.3381 D(x): 0.7633, D(G(z)): 0.2180 Epoch: [13/20], Batch Num: [564/600] Discriminator Loss: 0.5481, Generator Loss: 2.0811 D(x): 0.8171, D(G(z)): 0.1794 Epoch: [13/20], Batch Num: [565/600] Discriminator Loss: 0.6646, Generator Loss: 2.0217 D(x): 0.7999, D(G(z)): 0.2323 Epoch: [13/20], Batch Num: [566/600] Discriminator Loss: 0.6126, Generator Loss: 2.3267 D(x): 0.8049, D(G(z)): 0.2117 Epoch: [13/20], Batch Num: [567/600] Discriminator Loss: 0.5297, Generator Loss: 2.1583 D(x): 0.8312, D(G(z)): 0.2036 Epoch: [13/20], Batch Num: [568/600] Discriminator Loss: 0.4896, Generator Loss: 2.1854 D(x): 0.8040, D(G(z)): 0.1501 Epoch: [13/20], Batch Num: [569/600] Discriminator Loss: 0.6984, Generator Loss: 2.4810 D(x): 0.8001, D(G(z)): 0.2050 Epoch: [13/20], Batch Num: [570/600] Discriminator Loss: 0.6470, Generator Loss: 2.4938 D(x): 0.7756, D(G(z)): 0.1668 Epoch: [13/20], Batch Num: [571/600] Discriminator Loss: 0.5253, Generator Loss: 2.4330 D(x): 0.8582, D(G(z)): 0.1971 Epoch: [13/20], Batch Num: [572/600] Discriminator Loss: 0.5719, Generator Loss: 2.5962 D(x): 0.8243, D(G(z)): 0.1829 Epoch: [13/20], Batch Num: [573/600] Discriminator Loss: 0.6786, Generator Loss: 2.5399 D(x): 0.7874, D(G(z)): 0.1885 Epoch: [13/20], Batch Num: [574/600] Discriminator Loss: 0.7000, Generator Loss: 2.1903 D(x): 0.7852, D(G(z)): 0.2021 Epoch: [13/20], Batch Num: [575/600] Discriminator Loss: 0.5891, Generator Loss: 2.2960 D(x): 0.8736, D(G(z)): 0.2503 Epoch: [13/20], Batch Num: [576/600] Discriminator Loss: 0.5031, Generator Loss: 2.6218 D(x): 0.8502, D(G(z)): 0.1661 Epoch: [13/20], Batch Num: [577/600] Discriminator Loss: 0.5350, Generator Loss: 2.8221 D(x): 0.7995, D(G(z)): 0.1422 Epoch: [13/20], Batch Num: [578/600] Discriminator Loss: 0.4481, Generator Loss: 2.7186 D(x): 0.8393, D(G(z)): 0.1362 Epoch: [13/20], Batch Num: [579/600] Discriminator Loss: 0.6457, Generator Loss: 2.2478 D(x): 0.7848, D(G(z)): 0.1440 Epoch: [13/20], Batch Num: [580/600] Discriminator Loss: 0.6052, Generator Loss: 2.2668 D(x): 0.8312, D(G(z)): 0.1950 Epoch: [13/20], Batch Num: [581/600] Discriminator Loss: 0.5508, Generator Loss: 2.0362 D(x): 0.8340, D(G(z)): 0.1871 Epoch: [13/20], Batch Num: [582/600] Discriminator Loss: 0.6220, Generator Loss: 2.1354 D(x): 0.8497, D(G(z)): 0.2112 Epoch: [13/20], Batch Num: [583/600] Discriminator Loss: 0.7102, Generator Loss: 2.6048 D(x): 0.8365, D(G(z)): 0.2350 Epoch: [13/20], Batch Num: [584/600] Discriminator Loss: 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Epoch: [14/20], Batch Num: [0/600] Discriminator Loss: 0.6927, Generator Loss: 2.0624 D(x): 0.7551, D(G(z)): 0.2137 Epoch: [14/20], Batch Num: [1/600] Discriminator Loss: 0.5777, Generator Loss: 2.2423 D(x): 0.7837, D(G(z)): 0.1585 Epoch: [14/20], Batch Num: [2/600] Discriminator Loss: 0.6839, Generator Loss: 2.0237 D(x): 0.7634, D(G(z)): 0.1732 Epoch: [14/20], Batch Num: [3/600] Discriminator Loss: 0.6819, Generator Loss: 2.0189 D(x): 0.7697, D(G(z)): 0.1825 Epoch: [14/20], Batch Num: [4/600] Discriminator Loss: 0.6362, Generator Loss: 1.9634 D(x): 0.8060, D(G(z)): 0.2199 Epoch: [14/20], Batch Num: [5/600] Discriminator Loss: 0.7274, Generator Loss: 1.8910 D(x): 0.7868, D(G(z)): 0.2298 Epoch: [14/20], Batch Num: [6/600] Discriminator Loss: 0.5991, Generator Loss: 1.9171 D(x): 0.8150, D(G(z)): 0.2224 Epoch: [14/20], Batch Num: [7/600] Discriminator Loss: 0.5778, Generator Loss: 1.7937 D(x): 0.8025, D(G(z)): 0.2117 Epoch: [14/20], Batch Num: [8/600] Discriminator Loss: 0.6345, Generator 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D(x): 0.8569, D(G(z)): 0.2527 Epoch: [14/20], Batch Num: [26/600] Discriminator Loss: 0.5742, Generator Loss: 2.4341 D(x): 0.8024, D(G(z)): 0.1853 Epoch: [14/20], Batch Num: [27/600] Discriminator Loss: 0.5773, Generator Loss: 2.1995 D(x): 0.7694, D(G(z)): 0.1620 Epoch: [14/20], Batch Num: [28/600] Discriminator Loss: 0.6750, Generator Loss: 2.0717 D(x): 0.7512, D(G(z)): 0.1646 Epoch: [14/20], Batch Num: [29/600] Discriminator Loss: 0.7476, Generator Loss: 1.8973 D(x): 0.7863, D(G(z)): 0.2445 Epoch: [14/20], Batch Num: [30/600] Discriminator Loss: 0.6639, Generator Loss: 1.9942 D(x): 0.8590, D(G(z)): 0.2776 Epoch: [14/20], Batch Num: [31/600] Discriminator Loss: 0.7419, Generator Loss: 2.4574 D(x): 0.8228, D(G(z)): 0.2595 Epoch: [14/20], Batch Num: [32/600] Discriminator Loss: 0.7117, Generator Loss: 2.7290 D(x): 0.7424, D(G(z)): 0.1768 Epoch: [14/20], Batch Num: [33/600] Discriminator Loss: 0.6993, Generator Loss: 2.5919 D(x): 0.7551, D(G(z)): 0.1503 Epoch: [14/20], Batch Num: 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D(x): 0.8466, D(G(z)): 0.2632 Epoch: [14/20], Batch Num: [43/600] Discriminator Loss: 0.6487, Generator Loss: 1.9682 D(x): 0.8253, D(G(z)): 0.2600 Epoch: [14/20], Batch Num: [44/600] Discriminator Loss: 0.7238, Generator Loss: 2.3711 D(x): 0.7870, D(G(z)): 0.2389 Epoch: [14/20], Batch Num: [45/600] Discriminator Loss: 0.6878, Generator Loss: 2.6475 D(x): 0.7906, D(G(z)): 0.2334 Epoch: [14/20], Batch Num: [46/600] Discriminator Loss: 0.6079, Generator Loss: 2.6980 D(x): 0.7788, D(G(z)): 0.1556 Epoch: [14/20], Batch Num: [47/600] Discriminator Loss: 0.7286, Generator Loss: 2.4254 D(x): 0.7648, D(G(z)): 0.1786 Epoch: [14/20], Batch Num: [48/600] Discriminator Loss: 0.6692, Generator Loss: 2.0719 D(x): 0.7385, D(G(z)): 0.1661 Epoch: [14/20], Batch Num: [49/600] Discriminator Loss: 0.6218, Generator Loss: 1.5189 D(x): 0.7906, D(G(z)): 0.2214 Epoch: [14/20], Batch Num: [50/600] Discriminator Loss: 0.5756, Generator Loss: 1.8097 D(x): 0.8852, D(G(z)): 0.3075 Epoch: [14/20], Batch Num: [51/600] Discriminator Loss: 0.6983, Generator Loss: 2.0215 D(x): 0.8098, D(G(z)): 0.2738 Epoch: [14/20], Batch Num: [52/600] Discriminator Loss: 0.6298, Generator Loss: 2.4238 D(x): 0.8180, D(G(z)): 0.2207 Epoch: [14/20], Batch Num: [53/600] Discriminator Loss: 0.5814, Generator Loss: 2.7950 D(x): 0.7801, D(G(z)): 0.1555 Epoch: [14/20], Batch Num: [54/600] Discriminator Loss: 0.6567, Generator Loss: 2.3750 D(x): 0.7360, D(G(z)): 0.1428 Epoch: [14/20], Batch Num: [55/600] Discriminator Loss: 0.5264, Generator Loss: 2.4388 D(x): 0.8395, D(G(z)): 0.1621 Epoch: [14/20], Batch Num: [56/600] Discriminator Loss: 0.6555, Generator Loss: 2.2266 D(x): 0.7842, D(G(z)): 0.1706 Epoch: [14/20], Batch Num: [57/600] Discriminator Loss: 0.7792, Generator Loss: 1.9825 D(x): 0.8051, D(G(z)): 0.2721 Epoch: [14/20], Batch Num: [58/600] Discriminator Loss: 0.6281, Generator Loss: 1.9562 D(x): 0.8272, D(G(z)): 0.2014 Epoch: [14/20], Batch Num: [59/600] Discriminator Loss: 0.7050, Generator Loss: 2.5625 D(x): 0.8505, D(G(z)): 0.2697 Epoch: [14/20], Batch Num: [60/600] Discriminator Loss: 0.4988, Generator Loss: 2.5578 D(x): 0.8193, D(G(z)): 0.1438 Epoch: [14/20], Batch Num: [61/600] Discriminator Loss: 0.6359, Generator Loss: 2.4581 D(x): 0.7639, D(G(z)): 0.1395 Epoch: [14/20], Batch Num: [62/600] Discriminator Loss: 0.7077, Generator Loss: 1.9944 D(x): 0.7290, D(G(z)): 0.1810 Epoch: [14/20], Batch Num: [63/600] Discriminator Loss: 0.6298, Generator Loss: 1.8858 D(x): 0.8272, D(G(z)): 0.2414 Epoch: [14/20], Batch Num: [64/600] Discriminator Loss: 0.6644, Generator Loss: 1.8056 D(x): 0.8159, D(G(z)): 0.2420 Epoch: [14/20], Batch Num: [65/600] Discriminator Loss: 0.6258, Generator Loss: 2.0606 D(x): 0.8144, D(G(z)): 0.2392 Epoch: [14/20], Batch Num: [66/600] Discriminator Loss: 0.6161, Generator Loss: 2.1619 D(x): 0.8216, D(G(z)): 0.2250 Epoch: [14/20], Batch Num: [67/600] Discriminator Loss: 0.6806, Generator Loss: 2.2965 D(x): 0.7616, D(G(z)): 0.2119 Epoch: [14/20], Batch Num: [68/600] Discriminator Loss: 0.6286, Generator Loss: 2.3799 D(x): 0.7490, D(G(z)): 0.1377 Epoch: [14/20], Batch Num: [69/600] Discriminator Loss: 0.6186, Generator Loss: 2.0493 D(x): 0.8087, D(G(z)): 0.1920 Epoch: [14/20], Batch Num: [70/600] Discriminator Loss: 0.7844, Generator Loss: 1.7621 D(x): 0.7624, D(G(z)): 0.2291 Epoch: [14/20], Batch Num: [71/600] Discriminator Loss: 0.6668, Generator Loss: 1.9592 D(x): 0.8534, D(G(z)): 0.2824 Epoch: [14/20], Batch Num: [72/600] Discriminator Loss: 0.5920, Generator Loss: 2.2322 D(x): 0.8202, D(G(z)): 0.2106 Epoch: [14/20], Batch Num: [73/600] Discriminator Loss: 0.8096, Generator Loss: 2.3573 D(x): 0.7356, D(G(z)): 0.2107 Epoch: [14/20], Batch Num: [74/600] Discriminator Loss: 0.6041, Generator Loss: 2.1507 D(x): 0.7621, D(G(z)): 0.1341 Epoch: [14/20], Batch Num: [75/600] Discriminator Loss: 0.6644, Generator Loss: 2.1113 D(x): 0.7948, D(G(z)): 0.1781 Epoch: [14/20], Batch Num: [76/600] Discriminator Loss: 0.6735, Generator Loss: 2.0529 D(x): 0.7904, D(G(z)): 0.2128 Epoch: [14/20], Batch Num: [77/600] Discriminator Loss: 0.7162, Generator Loss: 1.9206 D(x): 0.7930, D(G(z)): 0.2369 Epoch: [14/20], Batch Num: [78/600] Discriminator Loss: 0.7567, Generator Loss: 2.0774 D(x): 0.8456, D(G(z)): 0.2805 Epoch: [14/20], Batch Num: [79/600] Discriminator Loss: 0.5875, Generator Loss: 2.3222 D(x): 0.8031, D(G(z)): 0.1979 Epoch: [14/20], Batch Num: [80/600] Discriminator Loss: 0.5337, Generator Loss: 2.5222 D(x): 0.8132, D(G(z)): 0.1822 Epoch: [14/20], Batch Num: [81/600] Discriminator Loss: 0.5568, Generator Loss: 2.5188 D(x): 0.7686, D(G(z)): 0.1372 Epoch: [14/20], Batch Num: [82/600] Discriminator Loss: 0.5378, Generator Loss: 2.3534 D(x): 0.7875, D(G(z)): 0.1382 Epoch: [14/20], Batch Num: [83/600] Discriminator Loss: 0.6736, Generator Loss: 1.8079 D(x): 0.7631, D(G(z)): 0.1827 Epoch: [14/20], Batch Num: [84/600] Discriminator Loss: 0.6772, Generator Loss: 1.6389 D(x): 0.8692, D(G(z)): 0.2635 Epoch: [14/20], Batch Num: [85/600] Discriminator Loss: 0.7950, Generator Loss: 2.4046 D(x): 0.8601, D(G(z)): 0.3195 Epoch: [14/20], Batch Num: [86/600] Discriminator Loss: 0.6653, Generator Loss: 2.5397 D(x): 0.7666, D(G(z)): 0.1890 Epoch: [14/20], Batch Num: [87/600] Discriminator Loss: 0.6462, Generator Loss: 2.5144 D(x): 0.7610, D(G(z)): 0.1401 Epoch: [14/20], Batch Num: [88/600] Discriminator Loss: 0.7169, Generator Loss: 2.1982 D(x): 0.7512, D(G(z)): 0.1430 Epoch: [14/20], Batch Num: [89/600] Discriminator Loss: 0.5557, Generator Loss: 2.1553 D(x): 0.7787, D(G(z)): 0.1393 Epoch: [14/20], Batch Num: [90/600] Discriminator Loss: 0.6544, Generator Loss: 1.9167 D(x): 0.8132, D(G(z)): 0.2395 Epoch: [14/20], Batch Num: [91/600] Discriminator Loss: 0.6507, Generator Loss: 1.7100 D(x): 0.8252, D(G(z)): 0.2492 Epoch: [14/20], Batch Num: [92/600] Discriminator Loss: 0.6773, Generator Loss: 1.8852 D(x): 0.8356, D(G(z)): 0.2578 Epoch: [14/20], Batch Num: [93/600] Discriminator Loss: 0.5516, Generator Loss: 2.1182 D(x): 0.8288, D(G(z)): 0.1940 Epoch: [14/20], Batch Num: [94/600] Discriminator Loss: 0.7119, Generator Loss: 2.2709 D(x): 0.7440, D(G(z)): 0.1616 Epoch: [14/20], Batch Num: [95/600] Discriminator Loss: 0.6583, Generator Loss: 2.0007 D(x): 0.7717, D(G(z)): 0.1744 Epoch: [14/20], Batch Num: [96/600] Discriminator Loss: 0.7316, Generator Loss: 1.7994 D(x): 0.7452, D(G(z)): 0.1976 Epoch: [14/20], Batch Num: [97/600] Discriminator Loss: 0.6670, Generator Loss: 1.6955 D(x): 0.8195, D(G(z)): 0.2504 Epoch: [14/20], Batch Num: [98/600] Discriminator Loss: 0.8556, Generator Loss: 2.1366 D(x): 0.7503, D(G(z)): 0.2771 Epoch: [14/20], Batch Num: [99/600] Discriminator Loss: 0.5829, Generator Loss: 2.2855 D(x): 0.8415, D(G(z)): 0.2389 Epoch: 14, Batch Num: [100/600]
Epoch: [14/20], Batch Num: [100/600] Discriminator Loss: 0.6410, Generator Loss: 2.2160 D(x): 0.7483, D(G(z)): 0.1626 Epoch: [14/20], Batch Num: [101/600] Discriminator Loss: 0.7231, Generator Loss: 2.1686 D(x): 0.7213, D(G(z)): 0.1900 Epoch: [14/20], Batch Num: [102/600] Discriminator Loss: 0.7455, Generator Loss: 1.9637 D(x): 0.7697, D(G(z)): 0.2131 Epoch: [14/20], Batch Num: [103/600] Discriminator Loss: 0.6879, Generator Loss: 2.0389 D(x): 0.8101, D(G(z)): 0.2475 Epoch: [14/20], Batch Num: [104/600] Discriminator Loss: 0.8250, Generator Loss: 2.0223 D(x): 0.8076, D(G(z)): 0.3034 Epoch: [14/20], Batch Num: [105/600] Discriminator Loss: 0.6328, Generator Loss: 2.5264 D(x): 0.7873, D(G(z)): 0.1843 Epoch: [14/20], Batch Num: [106/600] Discriminator Loss: 0.7060, Generator Loss: 2.0499 D(x): 0.7330, D(G(z)): 0.1613 Epoch: [14/20], Batch Num: [107/600] Discriminator Loss: 0.7733, Generator Loss: 2.0889 D(x): 0.7765, D(G(z)): 0.2578 Epoch: [14/20], Batch Num: [108/600] Discriminator Loss: 0.6958, Generator Loss: 1.9876 D(x): 0.7826, D(G(z)): 0.2148 Epoch: [14/20], Batch Num: [109/600] Discriminator Loss: 0.4912, Generator Loss: 2.1945 D(x): 0.8607, D(G(z)): 0.1943 Epoch: [14/20], Batch Num: [110/600] Discriminator Loss: 0.6541, Generator Loss: 2.1763 D(x): 0.8144, D(G(z)): 0.2142 Epoch: [14/20], Batch Num: [111/600] Discriminator Loss: 0.6092, Generator Loss: 2.2505 D(x): 0.8134, D(G(z)): 0.1964 Epoch: [14/20], Batch Num: [112/600] Discriminator Loss: 0.5952, Generator Loss: 2.4158 D(x): 0.8321, D(G(z)): 0.1992 Epoch: [14/20], Batch Num: [113/600] Discriminator Loss: 0.6281, Generator Loss: 2.6196 D(x): 0.7747, D(G(z)): 0.1564 Epoch: [14/20], Batch Num: [114/600] Discriminator Loss: 0.8642, Generator Loss: 2.4820 D(x): 0.7003, D(G(z)): 0.1740 Epoch: [14/20], Batch Num: [115/600] Discriminator Loss: 0.6968, Generator Loss: 1.9291 D(x): 0.7660, D(G(z)): 0.1974 Epoch: [14/20], Batch Num: [116/600] Discriminator Loss: 0.5723, Generator Loss: 1.8609 D(x): 0.8485, D(G(z)): 0.2268 Epoch: [14/20], Batch Num: [117/600] Discriminator Loss: 0.5620, Generator Loss: 1.9276 D(x): 0.8487, D(G(z)): 0.2304 Epoch: [14/20], Batch Num: [118/600] Discriminator Loss: 0.7050, Generator Loss: 1.9713 D(x): 0.7907, D(G(z)): 0.2431 Epoch: [14/20], Batch Num: [119/600] Discriminator Loss: 0.6677, Generator Loss: 1.9997 D(x): 0.7621, D(G(z)): 0.1997 Epoch: [14/20], Batch Num: [120/600] Discriminator Loss: 0.7272, Generator Loss: 2.0826 D(x): 0.7604, D(G(z)): 0.2138 Epoch: [14/20], Batch Num: [121/600] Discriminator Loss: 0.5945, Generator Loss: 1.9555 D(x): 0.7953, D(G(z)): 0.1711 Epoch: [14/20], Batch Num: [122/600] Discriminator Loss: 0.6767, Generator Loss: 1.6399 D(x): 0.7778, D(G(z)): 0.2229 Epoch: [14/20], Batch Num: [123/600] Discriminator Loss: 0.7116, Generator Loss: 1.8450 D(x): 0.8406, D(G(z)): 0.2655 Epoch: [14/20], Batch Num: [124/600] Discriminator Loss: 0.7152, Generator Loss: 1.9391 D(x): 0.7987, D(G(z)): 0.2470 Epoch: [14/20], Batch Num: [125/600] Discriminator Loss: 0.6321, Generator Loss: 2.4428 D(x): 0.7755, D(G(z)): 0.1970 Epoch: [14/20], Batch Num: [126/600] Discriminator Loss: 0.7030, Generator Loss: 2.2193 D(x): 0.7502, D(G(z)): 0.1887 Epoch: [14/20], Batch Num: [127/600] Discriminator Loss: 0.6342, Generator Loss: 2.0186 D(x): 0.7534, D(G(z)): 0.1638 Epoch: [14/20], Batch Num: [128/600] Discriminator Loss: 0.7526, Generator Loss: 1.6791 D(x): 0.7330, D(G(z)): 0.2207 Epoch: [14/20], Batch Num: [129/600] Discriminator Loss: 0.8282, Generator Loss: 1.5898 D(x): 0.7777, D(G(z)): 0.2630 Epoch: [14/20], Batch Num: [130/600] Discriminator Loss: 0.6281, Generator Loss: 1.9870 D(x): 0.8441, D(G(z)): 0.2678 Epoch: [14/20], Batch Num: [131/600] Discriminator Loss: 0.7765, Generator Loss: 2.2806 D(x): 0.8020, D(G(z)): 0.2713 Epoch: [14/20], Batch Num: [132/600] Discriminator Loss: 0.7241, Generator Loss: 2.3877 D(x): 0.7584, D(G(z)): 0.1818 Epoch: [14/20], Batch Num: [133/600] Discriminator Loss: 0.5963, Generator Loss: 2.6228 D(x): 0.7695, D(G(z)): 0.1471 Epoch: [14/20], Batch Num: [134/600] Discriminator Loss: 0.6249, Generator Loss: 2.4084 D(x): 0.7801, D(G(z)): 0.1662 Epoch: [14/20], Batch Num: [135/600] Discriminator Loss: 0.7061, Generator Loss: 1.8129 D(x): 0.7424, D(G(z)): 0.1585 Epoch: [14/20], Batch Num: [136/600] Discriminator Loss: 0.6095, Generator Loss: 2.0817 D(x): 0.8699, D(G(z)): 0.2522 Epoch: [14/20], Batch Num: [137/600] Discriminator Loss: 0.7564, Generator Loss: 2.1148 D(x): 0.8338, D(G(z)): 0.2603 Epoch: [14/20], Batch Num: [138/600] Discriminator Loss: 0.6234, Generator Loss: 2.0855 D(x): 0.7962, D(G(z)): 0.2023 Epoch: [14/20], Batch Num: [139/600] Discriminator Loss: 0.6306, Generator Loss: 2.3040 D(x): 0.7968, D(G(z)): 0.1782 Epoch: [14/20], Batch Num: [140/600] Discriminator Loss: 0.7383, Generator Loss: 2.1935 D(x): 0.7958, D(G(z)): 0.2219 Epoch: [14/20], Batch Num: [141/600] Discriminator Loss: 0.7948, Generator Loss: 2.1696 D(x): 0.7305, D(G(z)): 0.1883 Epoch: [14/20], Batch Num: 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2.0254 D(x): 0.7750, D(G(z)): 0.2253 Epoch: [14/20], Batch Num: [151/600] Discriminator Loss: 0.6452, Generator Loss: 1.9534 D(x): 0.7914, D(G(z)): 0.1854 Epoch: [14/20], Batch Num: [152/600] Discriminator Loss: 0.7544, Generator Loss: 1.9781 D(x): 0.7747, D(G(z)): 0.2126 Epoch: [14/20], Batch Num: [153/600] Discriminator Loss: 0.6203, Generator Loss: 1.9001 D(x): 0.7781, D(G(z)): 0.1852 Epoch: [14/20], Batch Num: [154/600] Discriminator Loss: 0.6851, Generator Loss: 1.7942 D(x): 0.7348, D(G(z)): 0.1882 Epoch: [14/20], Batch Num: [155/600] Discriminator Loss: 0.6949, Generator Loss: 1.8851 D(x): 0.8353, D(G(z)): 0.2676 Epoch: [14/20], Batch Num: [156/600] Discriminator Loss: 0.5417, Generator Loss: 1.7568 D(x): 0.8414, D(G(z)): 0.2374 Epoch: [14/20], Batch Num: [157/600] Discriminator Loss: 0.6453, Generator Loss: 1.9835 D(x): 0.7779, D(G(z)): 0.2293 Epoch: [14/20], Batch Num: [158/600] Discriminator Loss: 0.6006, Generator Loss: 1.9560 D(x): 0.7920, D(G(z)): 0.2039 Epoch: [14/20], Batch Num: [159/600] Discriminator Loss: 0.5969, Generator Loss: 1.9610 D(x): 0.8033, D(G(z)): 0.2005 Epoch: [14/20], Batch Num: [160/600] Discriminator Loss: 0.7457, Generator Loss: 1.7185 D(x): 0.7390, D(G(z)): 0.2158 Epoch: [14/20], Batch Num: [161/600] Discriminator Loss: 0.6135, Generator Loss: 1.9528 D(x): 0.7974, D(G(z)): 0.2054 Epoch: [14/20], Batch Num: [162/600] Discriminator Loss: 0.6588, Generator Loss: 1.9770 D(x): 0.8026, D(G(z)): 0.2153 Epoch: [14/20], Batch Num: [163/600] Discriminator Loss: 0.6735, Generator Loss: 1.9333 D(x): 0.7982, D(G(z)): 0.1916 Epoch: [14/20], Batch Num: [164/600] Discriminator Loss: 0.6930, Generator Loss: 2.0256 D(x): 0.8425, D(G(z)): 0.2517 Epoch: [14/20], Batch Num: [165/600] Discriminator Loss: 0.7480, Generator Loss: 2.2395 D(x): 0.7597, D(G(z)): 0.1764 Epoch: [14/20], Batch Num: [166/600] Discriminator Loss: 0.7222, Generator Loss: 2.2507 D(x): 0.8005, D(G(z)): 0.2114 Epoch: [14/20], Batch Num: [167/600] Discriminator Loss: 0.7466, Generator Loss: 2.0620 D(x): 0.7590, D(G(z)): 0.1964 Epoch: [14/20], Batch Num: [168/600] Discriminator Loss: 0.7264, Generator Loss: 2.0557 D(x): 0.7414, D(G(z)): 0.1901 Epoch: [14/20], Batch Num: [169/600] Discriminator Loss: 0.6983, Generator Loss: 1.7574 D(x): 0.8021, D(G(z)): 0.2239 Epoch: [14/20], Batch Num: [170/600] Discriminator Loss: 0.6480, Generator Loss: 1.7145 D(x): 0.8054, D(G(z)): 0.2372 Epoch: [14/20], Batch Num: [171/600] Discriminator Loss: 0.6617, Generator Loss: 1.6560 D(x): 0.7965, D(G(z)): 0.2452 Epoch: [14/20], Batch Num: [172/600] Discriminator Loss: 0.6538, Generator Loss: 1.8863 D(x): 0.8293, D(G(z)): 0.2685 Epoch: [14/20], Batch Num: [173/600] Discriminator Loss: 0.7014, Generator Loss: 2.0688 D(x): 0.7733, D(G(z)): 0.2248 Epoch: [14/20], Batch Num: [174/600] Discriminator Loss: 0.7195, Generator Loss: 2.0578 D(x): 0.7449, D(G(z)): 0.1990 Epoch: [14/20], Batch Num: [175/600] Discriminator Loss: 0.6851, Generator Loss: 2.0263 D(x): 0.7534, D(G(z)): 0.1991 Epoch: [14/20], Batch Num: [176/600] Discriminator Loss: 0.6603, Generator Loss: 1.9208 D(x): 0.7996, D(G(z)): 0.2318 Epoch: [14/20], Batch Num: [177/600] Discriminator Loss: 0.6509, Generator Loss: 1.7167 D(x): 0.7910, D(G(z)): 0.2109 Epoch: [14/20], Batch Num: [178/600] Discriminator Loss: 0.6847, Generator Loss: 1.8504 D(x): 0.7692, D(G(z)): 0.2251 Epoch: [14/20], Batch Num: [179/600] Discriminator Loss: 0.4835, Generator Loss: 1.6544 D(x): 0.8472, D(G(z)): 0.1893 Epoch: [14/20], Batch Num: [180/600] Discriminator Loss: 0.7369, Generator Loss: 1.9563 D(x): 0.8206, D(G(z)): 0.2642 Epoch: [14/20], Batch Num: [181/600] Discriminator Loss: 0.4663, Generator Loss: 2.0078 D(x): 0.8495, D(G(z)): 0.2004 Epoch: [14/20], Batch Num: [182/600] Discriminator Loss: 0.6351, Generator Loss: 2.2150 D(x): 0.7915, D(G(z)): 0.2092 Epoch: [14/20], Batch Num: [183/600] Discriminator Loss: 0.7740, Generator Loss: 2.1120 D(x): 0.7403, D(G(z)): 0.1846 Epoch: [14/20], Batch Num: [184/600] Discriminator Loss: 0.7339, Generator Loss: 2.1630 D(x): 0.7715, D(G(z)): 0.2125 Epoch: [14/20], Batch Num: [185/600] Discriminator Loss: 0.6669, Generator Loss: 2.1302 D(x): 0.7881, D(G(z)): 0.1987 Epoch: [14/20], Batch Num: [186/600] Discriminator Loss: 0.6914, Generator Loss: 1.9940 D(x): 0.7827, D(G(z)): 0.2103 Epoch: [14/20], Batch Num: [187/600] Discriminator Loss: 0.5999, Generator Loss: 1.9390 D(x): 0.8006, D(G(z)): 0.2054 Epoch: [14/20], Batch Num: [188/600] Discriminator Loss: 0.6658, Generator Loss: 1.7830 D(x): 0.8153, D(G(z)): 0.2245 Epoch: [14/20], Batch Num: [189/600] Discriminator Loss: 0.6853, Generator Loss: 2.1927 D(x): 0.8146, D(G(z)): 0.2487 Epoch: [14/20], Batch Num: [190/600] Discriminator Loss: 0.5465, Generator Loss: 2.4370 D(x): 0.8285, D(G(z)): 0.1910 Epoch: [14/20], Batch Num: [191/600] Discriminator Loss: 0.5953, Generator Loss: 2.3325 D(x): 0.8076, D(G(z)): 0.1939 Epoch: [14/20], Batch Num: [192/600] Discriminator Loss: 0.6368, Generator Loss: 2.3550 D(x): 0.7844, D(G(z)): 0.1844 Epoch: [14/20], Batch Num: [193/600] Discriminator Loss: 0.6745, Generator Loss: 1.9644 D(x): 0.7662, D(G(z)): 0.1808 Epoch: [14/20], Batch Num: [194/600] Discriminator Loss: 0.7387, Generator Loss: 2.2076 D(x): 0.7755, D(G(z)): 0.2171 Epoch: [14/20], Batch Num: [195/600] Discriminator Loss: 0.6748, Generator Loss: 2.0852 D(x): 0.7788, D(G(z)): 0.2218 Epoch: [14/20], Batch Num: [196/600] Discriminator Loss: 0.7407, Generator Loss: 1.8748 D(x): 0.7857, D(G(z)): 0.2257 Epoch: [14/20], Batch Num: [197/600] Discriminator Loss: 0.6784, Generator Loss: 1.7773 D(x): 0.7830, D(G(z)): 0.2161 Epoch: [14/20], Batch Num: [198/600] Discriminator Loss: 0.6620, Generator Loss: 1.8321 D(x): 0.8167, D(G(z)): 0.2426 Epoch: [14/20], Batch Num: [199/600] Discriminator Loss: 0.5760, Generator Loss: 1.8745 D(x): 0.8645, D(G(z)): 0.2460 Epoch: 14, Batch Num: [200/600]
Epoch: [14/20], Batch Num: [200/600] Discriminator Loss: 0.7758, Generator Loss: 2.1792 D(x): 0.7645, D(G(z)): 0.2251 Epoch: [14/20], Batch Num: [201/600] Discriminator Loss: 0.6428, Generator Loss: 2.0254 D(x): 0.7450, D(G(z)): 0.1604 Epoch: [14/20], Batch Num: [202/600] Discriminator Loss: 0.7129, Generator Loss: 1.9597 D(x): 0.7715, D(G(z)): 0.2025 Epoch: [14/20], Batch Num: [203/600] Discriminator Loss: 0.7556, Generator Loss: 2.0801 D(x): 0.7448, D(G(z)): 0.2204 Epoch: [14/20], Batch Num: [204/600] Discriminator Loss: 0.6769, Generator Loss: 1.7907 D(x): 0.7702, D(G(z)): 0.2006 Epoch: [14/20], Batch Num: [205/600] Discriminator Loss: 0.8112, Generator Loss: 1.6654 D(x): 0.7559, D(G(z)): 0.2616 Epoch: [14/20], Batch Num: [206/600] Discriminator Loss: 0.9573, Generator Loss: 1.6229 D(x): 0.7551, D(G(z)): 0.3147 Epoch: [14/20], Batch Num: [207/600] Discriminator Loss: 0.6563, Generator Loss: 2.0763 D(x): 0.8441, D(G(z)): 0.2729 Epoch: [14/20], Batch Num: [208/600] Discriminator Loss: 0.6745, Generator Loss: 2.5358 D(x): 0.8057, D(G(z)): 0.2379 Epoch: [14/20], Batch Num: [209/600] Discriminator Loss: 0.7105, Generator Loss: 2.2216 D(x): 0.7080, D(G(z)): 0.1485 Epoch: [14/20], Batch Num: [210/600] Discriminator Loss: 0.7622, Generator Loss: 2.0086 D(x): 0.6990, D(G(z)): 0.1478 Epoch: [14/20], Batch Num: [211/600] Discriminator Loss: 0.6099, Generator Loss: 1.9100 D(x): 0.8041, D(G(z)): 0.1950 Epoch: [14/20], Batch Num: [212/600] Discriminator Loss: 0.6214, Generator Loss: 1.7136 D(x): 0.8215, D(G(z)): 0.2169 Epoch: [14/20], Batch Num: [213/600] Discriminator Loss: 0.7718, Generator Loss: 1.7822 D(x): 0.7894, D(G(z)): 0.2622 Epoch: [14/20], Batch Num: [214/600] Discriminator Loss: 0.7260, Generator Loss: 1.9296 D(x): 0.8103, D(G(z)): 0.2640 Epoch: [14/20], Batch Num: [215/600] Discriminator Loss: 0.8009, Generator Loss: 2.1129 D(x): 0.7845, D(G(z)): 0.2590 Epoch: [14/20], Batch Num: [216/600] Discriminator Loss: 0.7803, Generator Loss: 2.3167 D(x): 0.7335, D(G(z)): 0.2078 Epoch: [14/20], Batch Num: [217/600] Discriminator Loss: 0.7380, Generator Loss: 2.1124 D(x): 0.7174, D(G(z)): 0.1795 Epoch: [14/20], Batch Num: [218/600] Discriminator Loss: 0.6433, Generator Loss: 1.8799 D(x): 0.7537, D(G(z)): 0.1751 Epoch: [14/20], Batch Num: [219/600] Discriminator Loss: 0.7397, Generator Loss: 1.7630 D(x): 0.7515, D(G(z)): 0.2371 Epoch: [14/20], Batch Num: [220/600] Discriminator Loss: 0.6779, Generator Loss: 1.8308 D(x): 0.7988, D(G(z)): 0.2553 Epoch: [14/20], Batch Num: [221/600] Discriminator Loss: 0.6173, Generator Loss: 1.6890 D(x): 0.8398, D(G(z)): 0.2696 Epoch: [14/20], Batch Num: [222/600] Discriminator Loss: 0.6299, Generator Loss: 1.7945 D(x): 0.7995, D(G(z)): 0.2340 Epoch: [14/20], Batch Num: [223/600] Discriminator Loss: 0.7095, Generator Loss: 1.9208 D(x): 0.7594, D(G(z)): 0.2286 Epoch: [14/20], Batch Num: [224/600] Discriminator Loss: 0.8668, Generator Loss: 2.0162 D(x): 0.6949, D(G(z)): 0.2218 Epoch: [14/20], Batch Num: [225/600] Discriminator Loss: 0.5250, Generator Loss: 2.0027 D(x): 0.8277, D(G(z)): 0.1906 Epoch: [14/20], Batch Num: [226/600] Discriminator Loss: 0.7668, Generator Loss: 1.9900 D(x): 0.7394, D(G(z)): 0.2253 Epoch: [14/20], Batch Num: [227/600] Discriminator Loss: 0.6539, Generator Loss: 1.8492 D(x): 0.8075, D(G(z)): 0.2372 Epoch: [14/20], Batch Num: [228/600] Discriminator Loss: 0.6702, Generator Loss: 1.8901 D(x): 0.7794, D(G(z)): 0.2050 Epoch: [14/20], Batch Num: [229/600] Discriminator Loss: 0.6725, Generator Loss: 1.9380 D(x): 0.7834, D(G(z)): 0.2094 Epoch: [14/20], Batch Num: [230/600] Discriminator Loss: 0.6457, Generator Loss: 2.0166 D(x): 0.8095, D(G(z)): 0.2089 Epoch: [14/20], Batch Num: [231/600] Discriminator Loss: 0.6542, Generator Loss: 2.1754 D(x): 0.7868, D(G(z)): 0.2036 Epoch: [14/20], Batch Num: [232/600] Discriminator Loss: 0.6454, Generator Loss: 2.0836 D(x): 0.7777, D(G(z)): 0.1927 Epoch: [14/20], Batch Num: [233/600] Discriminator Loss: 0.6645, Generator Loss: 2.1505 D(x): 0.8112, D(G(z)): 0.1970 Epoch: [14/20], Batch Num: [234/600] Discriminator Loss: 0.7195, Generator Loss: 2.0811 D(x): 0.7599, D(G(z)): 0.1907 Epoch: [14/20], Batch Num: [235/600] Discriminator Loss: 0.7548, Generator Loss: 2.1978 D(x): 0.7938, D(G(z)): 0.2393 Epoch: [14/20], Batch Num: [236/600] Discriminator Loss: 0.5695, Generator Loss: 2.1633 D(x): 0.8155, D(G(z)): 0.1910 Epoch: [14/20], Batch Num: [237/600] Discriminator Loss: 0.5410, Generator Loss: 2.2190 D(x): 0.8075, D(G(z)): 0.1643 Epoch: [14/20], Batch Num: [238/600] Discriminator Loss: 0.6603, Generator Loss: 2.1377 D(x): 0.7785, D(G(z)): 0.1657 Epoch: [14/20], Batch Num: [239/600] Discriminator Loss: 0.5406, Generator Loss: 2.0894 D(x): 0.8452, D(G(z)): 0.2033 Epoch: [14/20], Batch Num: [240/600] Discriminator Loss: 0.6799, Generator Loss: 2.1317 D(x): 0.7827, D(G(z)): 0.1851 Epoch: [14/20], Batch Num: [241/600] Discriminator Loss: 0.6152, Generator Loss: 2.1224 D(x): 0.8101, D(G(z)): 0.1903 Epoch: [14/20], Batch Num: 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2.6983 D(x): 0.7560, D(G(z)): 0.1920 Epoch: [14/20], Batch Num: [251/600] Discriminator Loss: 0.7362, Generator Loss: 2.1502 D(x): 0.7307, D(G(z)): 0.1531 Epoch: [14/20], Batch Num: [252/600] Discriminator Loss: 0.7819, Generator Loss: 1.7521 D(x): 0.7211, D(G(z)): 0.1948 Epoch: [14/20], Batch Num: [253/600] Discriminator Loss: 0.8540, Generator Loss: 1.5912 D(x): 0.7604, D(G(z)): 0.2806 Epoch: [14/20], Batch Num: [254/600] Discriminator Loss: 0.6448, Generator Loss: 1.9388 D(x): 0.8634, D(G(z)): 0.2945 Epoch: [14/20], Batch Num: [255/600] Discriminator Loss: 0.6700, Generator Loss: 2.3115 D(x): 0.8215, D(G(z)): 0.2634 Epoch: [14/20], Batch Num: [256/600] Discriminator Loss: 0.5395, Generator Loss: 2.4545 D(x): 0.8212, D(G(z)): 0.1863 Epoch: [14/20], Batch Num: [257/600] Discriminator Loss: 0.6593, Generator Loss: 2.8070 D(x): 0.7332, D(G(z)): 0.1499 Epoch: [14/20], Batch Num: [258/600] Discriminator Loss: 0.7669, Generator Loss: 2.3155 D(x): 0.6816, D(G(z)): 0.1335 Epoch: [14/20], Batch Num: [259/600] Discriminator Loss: 0.6363, Generator Loss: 1.6204 D(x): 0.7526, D(G(z)): 0.1717 Epoch: [14/20], Batch Num: [260/600] Discriminator Loss: 0.7379, Generator Loss: 1.7230 D(x): 0.8789, D(G(z)): 0.3272 Epoch: [14/20], Batch Num: [261/600] Discriminator Loss: 0.7015, Generator Loss: 1.7707 D(x): 0.8075, D(G(z)): 0.2643 Epoch: [14/20], Batch Num: [262/600] Discriminator Loss: 0.6770, Generator Loss: 1.9644 D(x): 0.8181, D(G(z)): 0.2539 Epoch: [14/20], Batch Num: [263/600] Discriminator Loss: 0.6883, Generator Loss: 2.1705 D(x): 0.7293, D(G(z)): 0.1984 Epoch: [14/20], Batch Num: [264/600] Discriminator Loss: 0.5957, Generator Loss: 2.2521 D(x): 0.7828, D(G(z)): 0.1979 Epoch: [14/20], Batch Num: [265/600] Discriminator Loss: 0.5980, Generator Loss: 2.2248 D(x): 0.7943, D(G(z)): 0.1844 Epoch: [14/20], Batch Num: [266/600] Discriminator Loss: 0.6914, Generator Loss: 2.4326 D(x): 0.8034, D(G(z)): 0.2415 Epoch: [14/20], Batch Num: [267/600] Discriminator Loss: 0.6898, Generator Loss: 1.9442 D(x): 0.7217, D(G(z)): 0.1462 Epoch: [14/20], Batch Num: [268/600] Discriminator Loss: 0.6944, Generator Loss: 1.6829 D(x): 0.7519, D(G(z)): 0.2081 Epoch: [14/20], Batch Num: [269/600] Discriminator Loss: 0.7561, Generator Loss: 1.8479 D(x): 0.8412, D(G(z)): 0.3269 Epoch: [14/20], Batch Num: [270/600] Discriminator Loss: 0.6935, Generator Loss: 2.0210 D(x): 0.8089, D(G(z)): 0.2692 Epoch: [14/20], Batch Num: [271/600] Discriminator Loss: 0.6497, Generator Loss: 2.3309 D(x): 0.7756, D(G(z)): 0.1936 Epoch: [14/20], Batch Num: [272/600] Discriminator Loss: 0.6398, Generator Loss: 2.2193 D(x): 0.7925, D(G(z)): 0.1830 Epoch: [14/20], Batch Num: [273/600] Discriminator Loss: 0.8231, Generator Loss: 2.0950 D(x): 0.7180, D(G(z)): 0.1725 Epoch: [14/20], Batch Num: [274/600] Discriminator Loss: 0.7118, Generator Loss: 1.9007 D(x): 0.7684, D(G(z)): 0.2139 Epoch: [14/20], Batch Num: [275/600] Discriminator Loss: 0.7688, Generator Loss: 1.8207 D(x): 0.7520, D(G(z)): 0.2307 Epoch: [14/20], Batch Num: [276/600] Discriminator Loss: 0.7054, Generator Loss: 1.8527 D(x): 0.8291, D(G(z)): 0.2685 Epoch: [14/20], Batch Num: [277/600] Discriminator Loss: 0.8363, Generator Loss: 1.8947 D(x): 0.7660, D(G(z)): 0.2830 Epoch: [14/20], Batch Num: [278/600] Discriminator Loss: 0.6782, Generator Loss: 1.9251 D(x): 0.8277, D(G(z)): 0.2636 Epoch: [14/20], Batch Num: [279/600] Discriminator Loss: 0.5952, Generator Loss: 2.2906 D(x): 0.8022, D(G(z)): 0.1882 Epoch: [14/20], Batch Num: [280/600] Discriminator Loss: 0.6885, Generator Loss: 2.0073 D(x): 0.7113, D(G(z)): 0.1545 Epoch: [14/20], Batch Num: [281/600] Discriminator Loss: 0.7089, Generator Loss: 1.7471 D(x): 0.7401, D(G(z)): 0.2024 Epoch: [14/20], Batch Num: [282/600] Discriminator Loss: 0.7936, Generator Loss: 1.7103 D(x): 0.8073, D(G(z)): 0.2858 Epoch: [14/20], Batch Num: [283/600] Discriminator Loss: 0.7765, Generator Loss: 1.8796 D(x): 0.7761, D(G(z)): 0.2845 Epoch: [14/20], Batch Num: [284/600] Discriminator Loss: 0.7570, Generator Loss: 1.7783 D(x): 0.7533, D(G(z)): 0.2328 Epoch: [14/20], Batch Num: [285/600] Discriminator Loss: 0.7041, Generator Loss: 1.9861 D(x): 0.7459, D(G(z)): 0.2076 Epoch: [14/20], Batch Num: [286/600] Discriminator Loss: 0.7747, Generator Loss: 1.7604 D(x): 0.7267, D(G(z)): 0.2093 Epoch: [14/20], Batch Num: [287/600] Discriminator Loss: 0.7479, Generator Loss: 1.7174 D(x): 0.7635, D(G(z)): 0.2431 Epoch: [14/20], Batch Num: [288/600] Discriminator Loss: 0.6984, Generator Loss: 1.8913 D(x): 0.7779, D(G(z)): 0.2217 Epoch: [14/20], Batch Num: [289/600] Discriminator Loss: 0.8524, Generator Loss: 1.7063 D(x): 0.7333, D(G(z)): 0.2741 Epoch: [14/20], Batch Num: [290/600] Discriminator Loss: 0.8264, Generator Loss: 1.6666 D(x): 0.7669, D(G(z)): 0.2808 Epoch: [14/20], Batch Num: [291/600] Discriminator Loss: 0.7307, Generator Loss: 1.9545 D(x): 0.8029, D(G(z)): 0.2604 Epoch: [14/20], Batch Num: [292/600] Discriminator Loss: 0.6929, Generator Loss: 2.3909 D(x): 0.7913, D(G(z)): 0.2328 Epoch: [14/20], Batch Num: [293/600] Discriminator Loss: 0.6438, Generator Loss: 2.1675 D(x): 0.7520, D(G(z)): 0.1911 Epoch: [14/20], Batch Num: [294/600] Discriminator Loss: 0.8629, Generator Loss: 2.0833 D(x): 0.6949, D(G(z)): 0.1980 Epoch: [14/20], Batch Num: [295/600] Discriminator Loss: 0.6276, Generator Loss: 1.7309 D(x): 0.7659, D(G(z)): 0.1961 Epoch: [14/20], Batch Num: [296/600] Discriminator Loss: 0.6693, Generator Loss: 1.7235 D(x): 0.7699, D(G(z)): 0.2291 Epoch: [14/20], Batch Num: [297/600] Discriminator Loss: 0.6561, Generator Loss: 1.6674 D(x): 0.8242, D(G(z)): 0.2688 Epoch: [14/20], Batch Num: [298/600] Discriminator Loss: 0.6937, Generator Loss: 1.8118 D(x): 0.8100, D(G(z)): 0.2780 Epoch: [14/20], Batch Num: [299/600] Discriminator Loss: 0.7880, Generator Loss: 2.1782 D(x): 0.7885, D(G(z)): 0.2822 Epoch: 14, Batch Num: [300/600]
Epoch: [14/20], Batch Num: [300/600] Discriminator Loss: 0.8275, Generator Loss: 1.9760 D(x): 0.7225, D(G(z)): 0.2197 Epoch: [14/20], Batch Num: [301/600] Discriminator Loss: 0.8107, Generator Loss: 2.1150 D(x): 0.7075, D(G(z)): 0.2073 Epoch: [14/20], Batch Num: [302/600] Discriminator Loss: 0.8051, Generator Loss: 1.8613 D(x): 0.7032, D(G(z)): 0.1711 Epoch: [14/20], Batch Num: [303/600] Discriminator Loss: 0.6742, Generator Loss: 1.8135 D(x): 0.7582, D(G(z)): 0.2192 Epoch: [14/20], Batch Num: [304/600] Discriminator Loss: 0.8448, Generator Loss: 1.6608 D(x): 0.7577, D(G(z)): 0.2943 Epoch: [14/20], Batch Num: [305/600] Discriminator Loss: 0.7471, Generator Loss: 1.6253 D(x): 0.8071, D(G(z)): 0.3005 Epoch: [14/20], Batch Num: [306/600] Discriminator Loss: 0.6308, Generator Loss: 1.7553 D(x): 0.8360, D(G(z)): 0.2660 Epoch: [14/20], Batch Num: [307/600] Discriminator Loss: 0.6444, Generator Loss: 2.0445 D(x): 0.7958, D(G(z)): 0.2444 Epoch: [14/20], Batch Num: [308/600] Discriminator Loss: 0.7411, Generator Loss: 2.1695 D(x): 0.7187, D(G(z)): 0.1902 Epoch: [14/20], Batch Num: [309/600] Discriminator Loss: 0.7083, Generator Loss: 2.0255 D(x): 0.7532, D(G(z)): 0.2009 Epoch: [14/20], Batch Num: [310/600] Discriminator Loss: 0.6984, Generator Loss: 2.0677 D(x): 0.7473, D(G(z)): 0.1898 Epoch: [14/20], Batch Num: [311/600] Discriminator Loss: 0.6372, Generator Loss: 1.9349 D(x): 0.7515, D(G(z)): 0.1867 Epoch: [14/20], Batch Num: [312/600] Discriminator Loss: 0.5431, Generator Loss: 1.9775 D(x): 0.7993, D(G(z)): 0.1949 Epoch: [14/20], Batch Num: [313/600] Discriminator Loss: 0.6678, Generator Loss: 1.8123 D(x): 0.7980, D(G(z)): 0.2458 Epoch: [14/20], Batch Num: [314/600] Discriminator Loss: 0.6103, Generator Loss: 1.9107 D(x): 0.8268, D(G(z)): 0.2327 Epoch: [14/20], Batch Num: [315/600] Discriminator Loss: 0.6427, Generator Loss: 2.1526 D(x): 0.8036, D(G(z)): 0.2371 Epoch: [14/20], Batch Num: [316/600] Discriminator Loss: 0.8035, Generator Loss: 2.2097 D(x): 0.7521, D(G(z)): 0.2176 Epoch: [14/20], Batch Num: [317/600] Discriminator Loss: 0.6427, Generator Loss: 2.4242 D(x): 0.7989, D(G(z)): 0.1918 Epoch: [14/20], Batch Num: [318/600] Discriminator Loss: 0.6588, Generator Loss: 2.5485 D(x): 0.7745, D(G(z)): 0.1822 Epoch: [14/20], Batch Num: [319/600] Discriminator Loss: 0.6827, Generator Loss: 2.1295 D(x): 0.7278, D(G(z)): 0.1402 Epoch: [14/20], Batch Num: [320/600] Discriminator Loss: 0.7261, Generator Loss: 1.8457 D(x): 0.7604, D(G(z)): 0.1943 Epoch: [14/20], Batch Num: [321/600] Discriminator Loss: 0.6513, Generator Loss: 1.6283 D(x): 0.7987, D(G(z)): 0.2366 Epoch: [14/20], Batch Num: [322/600] Discriminator Loss: 0.5821, Generator Loss: 1.8071 D(x): 0.8429, D(G(z)): 0.2583 Epoch: [14/20], Batch Num: [323/600] Discriminator Loss: 0.6993, Generator Loss: 1.8506 D(x): 0.8121, D(G(z)): 0.2773 Epoch: [14/20], Batch Num: [324/600] Discriminator Loss: 0.8308, Generator Loss: 2.2819 D(x): 0.7956, D(G(z)): 0.2981 Epoch: [14/20], Batch Num: [325/600] Discriminator Loss: 0.8178, Generator Loss: 2.3444 D(x): 0.7175, D(G(z)): 0.1940 Epoch: [14/20], Batch Num: [326/600] Discriminator Loss: 0.9085, Generator Loss: 2.1235 D(x): 0.6786, D(G(z)): 0.1781 Epoch: [14/20], Batch Num: [327/600] Discriminator Loss: 0.9308, Generator Loss: 1.8882 D(x): 0.6758, D(G(z)): 0.1867 Epoch: [14/20], Batch Num: [328/600] Discriminator Loss: 0.6510, Generator Loss: 1.5161 D(x): 0.7545, D(G(z)): 0.1893 Epoch: [14/20], Batch Num: [329/600] Discriminator Loss: 0.7538, Generator Loss: 1.5358 D(x): 0.8113, D(G(z)): 0.3117 Epoch: [14/20], Batch Num: [330/600] Discriminator Loss: 0.7213, Generator Loss: 1.5279 D(x): 0.8091, D(G(z)): 0.3143 Epoch: [14/20], Batch Num: [331/600] Discriminator Loss: 0.7455, Generator Loss: 1.7966 D(x): 0.8010, D(G(z)): 0.3144 Epoch: [14/20], Batch Num: [332/600] Discriminator Loss: 0.7166, Generator Loss: 1.8883 D(x): 0.7727, D(G(z)): 0.2583 Epoch: [14/20], Batch Num: [333/600] Discriminator Loss: 0.7272, Generator Loss: 2.2599 D(x): 0.7376, D(G(z)): 0.2025 Epoch: [14/20], Batch Num: [334/600] Discriminator Loss: 0.7907, Generator Loss: 1.9408 D(x): 0.6754, D(G(z)): 0.1574 Epoch: [14/20], Batch Num: [335/600] Discriminator Loss: 0.6557, Generator Loss: 1.8696 D(x): 0.7496, D(G(z)): 0.1803 Epoch: [14/20], Batch Num: [336/600] Discriminator Loss: 0.7915, Generator Loss: 1.4794 D(x): 0.7595, D(G(z)): 0.2624 Epoch: [14/20], Batch Num: [337/600] Discriminator Loss: 0.6759, Generator Loss: 1.6844 D(x): 0.8404, D(G(z)): 0.2883 Epoch: [14/20], Batch Num: [338/600] Discriminator Loss: 0.7633, Generator Loss: 1.9887 D(x): 0.8447, D(G(z)): 0.2841 Epoch: [14/20], Batch Num: [339/600] Discriminator Loss: 0.6583, Generator Loss: 2.0378 D(x): 0.7861, D(G(z)): 0.2117 Epoch: [14/20], Batch Num: [340/600] Discriminator Loss: 0.5919, Generator Loss: 2.4895 D(x): 0.7899, D(G(z)): 0.1794 Epoch: [14/20], Batch Num: [341/600] Discriminator Loss: 0.6466, Generator Loss: 2.6945 D(x): 0.7513, D(G(z)): 0.1457 Epoch: [14/20], Batch Num: [342/600] Discriminator Loss: 0.5446, Generator Loss: 1.8735 D(x): 0.7700, D(G(z)): 0.1194 Epoch: [14/20], Batch Num: [343/600] Discriminator Loss: 0.6983, Generator Loss: 1.8106 D(x): 0.8055, D(G(z)): 0.2474 Epoch: [14/20], Batch Num: [344/600] Discriminator Loss: 0.7741, Generator Loss: 1.6591 D(x): 0.7772, D(G(z)): 0.2594 Epoch: [14/20], Batch Num: [345/600] Discriminator Loss: 0.6183, Generator Loss: 1.8677 D(x): 0.8732, D(G(z)): 0.2914 Epoch: [14/20], Batch Num: [346/600] Discriminator Loss: 0.6347, Generator Loss: 1.9614 D(x): 0.8012, D(G(z)): 0.2343 Epoch: [14/20], Batch Num: [347/600] Discriminator Loss: 0.5957, Generator Loss: 2.6503 D(x): 0.8331, D(G(z)): 0.2254 Epoch: [14/20], Batch Num: [348/600] Discriminator Loss: 0.8570, Generator Loss: 2.3433 D(x): 0.6718, D(G(z)): 0.1714 Epoch: [14/20], Batch Num: [349/600] Discriminator Loss: 0.7849, Generator Loss: 2.1205 D(x): 0.6663, D(G(z)): 0.1400 Epoch: [14/20], Batch Num: [350/600] Discriminator Loss: 0.7432, Generator Loss: 1.6843 D(x): 0.7340, D(G(z)): 0.2146 Epoch: [14/20], Batch Num: [351/600] Discriminator Loss: 0.6317, Generator Loss: 1.5449 D(x): 0.8057, D(G(z)): 0.2397 Epoch: [14/20], Batch Num: [352/600] Discriminator Loss: 0.8190, Generator Loss: 1.6463 D(x): 0.8467, D(G(z)): 0.3446 Epoch: [14/20], Batch Num: [353/600] Discriminator Loss: 0.8469, Generator Loss: 1.8594 D(x): 0.8344, D(G(z)): 0.3401 Epoch: [14/20], Batch Num: [354/600] Discriminator Loss: 0.6702, Generator Loss: 2.1461 D(x): 0.7712, D(G(z)): 0.2262 Epoch: [14/20], Batch Num: [355/600] Discriminator Loss: 0.7432, Generator Loss: 2.3239 D(x): 0.7445, D(G(z)): 0.1973 Epoch: [14/20], Batch Num: [356/600] Discriminator Loss: 0.8171, Generator Loss: 2.2745 D(x): 0.6380, D(G(z)): 0.1641 Epoch: [14/20], Batch Num: [357/600] Discriminator Loss: 0.6786, Generator Loss: 2.1073 D(x): 0.7489, D(G(z)): 0.1908 Epoch: [14/20], Batch Num: [358/600] Discriminator Loss: 0.5268, Generator Loss: 1.8027 D(x): 0.8285, D(G(z)): 0.2115 Epoch: [14/20], Batch Num: [359/600] Discriminator Loss: 0.8171, Generator Loss: 1.7151 D(x): 0.7809, D(G(z)): 0.2508 Epoch: [14/20], Batch Num: [360/600] Discriminator Loss: 0.5746, Generator Loss: 1.6398 D(x): 0.8503, D(G(z)): 0.2309 Epoch: [14/20], Batch Num: [361/600] Discriminator Loss: 0.6496, Generator Loss: 2.1088 D(x): 0.8024, D(G(z)): 0.2398 Epoch: [14/20], Batch Num: [362/600] Discriminator Loss: 0.5440, Generator Loss: 1.9755 D(x): 0.8243, D(G(z)): 0.2131 Epoch: [14/20], Batch Num: [363/600] Discriminator Loss: 0.6862, Generator Loss: 2.3811 D(x): 0.7898, D(G(z)): 0.2128 Epoch: [14/20], Batch Num: [364/600] Discriminator Loss: 0.4984, Generator Loss: 2.3866 D(x): 0.8188, D(G(z)): 0.1508 Epoch: [14/20], Batch Num: [365/600] Discriminator Loss: 0.5597, Generator Loss: 2.0030 D(x): 0.8081, D(G(z)): 0.1840 Epoch: [14/20], Batch Num: [366/600] Discriminator Loss: 0.6453, Generator Loss: 2.1722 D(x): 0.7842, D(G(z)): 0.2002 Epoch: [14/20], Batch Num: [367/600] Discriminator Loss: 0.7054, Generator Loss: 2.3360 D(x): 0.8218, D(G(z)): 0.2443 Epoch: [14/20], Batch Num: [368/600] Discriminator Loss: 0.7313, Generator Loss: 2.1587 D(x): 0.7356, D(G(z)): 0.1882 Epoch: [14/20], Batch Num: [369/600] Discriminator Loss: 0.6730, Generator Loss: 2.0685 D(x): 0.7462, D(G(z)): 0.1651 Epoch: [14/20], Batch Num: [370/600] Discriminator Loss: 0.7185, Generator Loss: 1.9894 D(x): 0.7574, D(G(z)): 0.1998 Epoch: [14/20], Batch Num: [371/600] Discriminator Loss: 0.6821, Generator Loss: 1.9033 D(x): 0.8310, D(G(z)): 0.2625 Epoch: [14/20], Batch Num: [372/600] Discriminator Loss: 0.6055, Generator Loss: 2.1913 D(x): 0.8595, D(G(z)): 0.2554 Epoch: [14/20], Batch Num: [373/600] Discriminator Loss: 0.5842, Generator Loss: 2.3439 D(x): 0.8116, D(G(z)): 0.2065 Epoch: [14/20], Batch Num: [374/600] Discriminator Loss: 0.6261, Generator Loss: 2.3083 D(x): 0.7585, D(G(z)): 0.1727 Epoch: [14/20], Batch Num: [375/600] Discriminator Loss: 0.6550, Generator Loss: 2.2393 D(x): 0.7609, D(G(z)): 0.1792 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Epoch: [14/20], Batch Num: [400/600] Discriminator Loss: 0.6083, Generator Loss: 1.9906 D(x): 0.8080, D(G(z)): 0.2128 Epoch: [14/20], Batch Num: [401/600] Discriminator Loss: 0.5746, Generator Loss: 1.8264 D(x): 0.7881, D(G(z)): 0.1733 Epoch: [14/20], Batch Num: [402/600] Discriminator Loss: 0.6458, Generator Loss: 1.8748 D(x): 0.8209, D(G(z)): 0.2476 Epoch: [14/20], Batch Num: [403/600] Discriminator Loss: 0.6371, Generator Loss: 2.0872 D(x): 0.8611, D(G(z)): 0.2752 Epoch: [14/20], Batch Num: [404/600] Discriminator Loss: 0.5672, Generator Loss: 2.7094 D(x): 0.8265, D(G(z)): 0.1954 Epoch: [14/20], Batch Num: [405/600] Discriminator Loss: 0.5523, Generator Loss: 2.9571 D(x): 0.8242, D(G(z)): 0.1768 Epoch: [14/20], Batch Num: [406/600] Discriminator Loss: 0.6662, Generator Loss: 2.8765 D(x): 0.7703, D(G(z)): 0.1577 Epoch: [14/20], Batch Num: [407/600] Discriminator Loss: 0.6913, Generator Loss: 2.6827 D(x): 0.7450, D(G(z)): 0.1291 Epoch: [14/20], Batch Num: [408/600] Discriminator Loss: 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Discriminator Loss: 0.6636, Generator Loss: 2.5093 D(x): 0.7454, D(G(z)): 0.1602 Epoch: [14/20], Batch Num: [426/600] Discriminator Loss: 0.9076, Generator Loss: 2.2877 D(x): 0.6370, D(G(z)): 0.1632 Epoch: [14/20], Batch Num: [427/600] Discriminator Loss: 0.9472, Generator Loss: 1.6363 D(x): 0.6952, D(G(z)): 0.2006 Epoch: [14/20], Batch Num: [428/600] Discriminator Loss: 0.6949, Generator Loss: 1.6044 D(x): 0.7997, D(G(z)): 0.2493 Epoch: [14/20], Batch Num: [429/600] Discriminator Loss: 0.8376, Generator Loss: 1.7903 D(x): 0.8684, D(G(z)): 0.3634 Epoch: [14/20], Batch Num: [430/600] Discriminator Loss: 0.6368, Generator Loss: 2.1048 D(x): 0.8415, D(G(z)): 0.2695 Epoch: [14/20], Batch Num: [431/600] Discriminator Loss: 0.7073, Generator Loss: 2.2252 D(x): 0.7239, D(G(z)): 0.1562 Epoch: [14/20], Batch Num: [432/600] Discriminator Loss: 0.5639, Generator Loss: 2.2864 D(x): 0.8043, D(G(z)): 0.1740 Epoch: [14/20], Batch Num: [433/600] Discriminator Loss: 0.6159, Generator Loss: 2.3970 D(x): 0.7508, D(G(z)): 0.1303 Epoch: [14/20], Batch Num: [434/600] Discriminator Loss: 0.7703, Generator Loss: 1.6530 D(x): 0.6723, D(G(z)): 0.1460 Epoch: [14/20], Batch Num: [435/600] Discriminator Loss: 0.6419, Generator Loss: 1.4018 D(x): 0.7965, D(G(z)): 0.2211 Epoch: [14/20], Batch Num: [436/600] Discriminator Loss: 0.6970, Generator Loss: 1.4358 D(x): 0.8512, D(G(z)): 0.2926 Epoch: [14/20], Batch Num: [437/600] Discriminator Loss: 0.6569, Generator Loss: 1.7375 D(x): 0.8544, D(G(z)): 0.2934 Epoch: [14/20], Batch Num: [438/600] Discriminator Loss: 0.6290, Generator Loss: 2.0326 D(x): 0.8086, D(G(z)): 0.2379 Epoch: [14/20], Batch Num: [439/600] Discriminator Loss: 0.5116, Generator Loss: 2.1303 D(x): 0.8296, D(G(z)): 0.1703 Epoch: [14/20], Batch Num: [440/600] Discriminator Loss: 0.6337, Generator Loss: 2.1851 D(x): 0.7524, D(G(z)): 0.1570 Epoch: [14/20], Batch Num: [441/600] Discriminator Loss: 0.7363, Generator Loss: 2.2514 D(x): 0.7280, D(G(z)): 0.1732 Epoch: [14/20], Batch Num: 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Generator Loss: 1.7419 D(x): 0.8210, D(G(z)): 0.2690 Epoch: [14/20], Batch Num: [468/600] Discriminator Loss: 0.6542, Generator Loss: 2.0588 D(x): 0.7922, D(G(z)): 0.2386 Epoch: [14/20], Batch Num: [469/600] Discriminator Loss: 0.7442, Generator Loss: 2.0898 D(x): 0.7392, D(G(z)): 0.2226 Epoch: [14/20], Batch Num: [470/600] Discriminator Loss: 0.7298, Generator Loss: 2.0425 D(x): 0.7393, D(G(z)): 0.1982 Epoch: [14/20], Batch Num: [471/600] Discriminator Loss: 0.6800, Generator Loss: 1.9557 D(x): 0.8157, D(G(z)): 0.2350 Epoch: [14/20], Batch Num: [472/600] Discriminator Loss: 0.7630, Generator Loss: 2.1208 D(x): 0.7837, D(G(z)): 0.2319 Epoch: [14/20], Batch Num: [473/600] Discriminator Loss: 0.5844, Generator Loss: 2.4109 D(x): 0.8129, D(G(z)): 0.1982 Epoch: [14/20], Batch Num: [474/600] Discriminator Loss: 0.5126, Generator Loss: 2.8366 D(x): 0.8375, D(G(z)): 0.1840 Epoch: [14/20], Batch Num: [475/600] Discriminator Loss: 0.6913, Generator Loss: 2.5319 D(x): 0.7362, D(G(z)): 0.1316 Epoch: [14/20], Batch Num: [476/600] Discriminator Loss: 0.7053, Generator Loss: 2.1142 D(x): 0.7132, D(G(z)): 0.1422 Epoch: [14/20], Batch Num: [477/600] Discriminator Loss: 0.6410, Generator Loss: 1.6982 D(x): 0.7947, D(G(z)): 0.2150 Epoch: [14/20], Batch Num: [478/600] Discriminator Loss: 0.7391, Generator Loss: 1.6711 D(x): 0.8814, D(G(z)): 0.3252 Epoch: [14/20], Batch Num: [479/600] Discriminator Loss: 0.5944, Generator Loss: 2.1130 D(x): 0.8691, D(G(z)): 0.2657 Epoch: [14/20], Batch Num: [480/600] Discriminator Loss: 0.6972, Generator Loss: 2.5886 D(x): 0.7810, D(G(z)): 0.2226 Epoch: [14/20], Batch Num: [481/600] Discriminator Loss: 0.6707, Generator Loss: 2.4445 D(x): 0.7292, D(G(z)): 0.1575 Epoch: [14/20], Batch Num: [482/600] Discriminator Loss: 0.7062, Generator Loss: 2.0352 D(x): 0.7186, D(G(z)): 0.1453 Epoch: [14/20], Batch Num: [483/600] Discriminator Loss: 0.6038, Generator Loss: 2.1734 D(x): 0.7857, D(G(z)): 0.1719 Epoch: [14/20], Batch Num: [484/600] Discriminator Loss: 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0.1909 Epoch: [14/20], Batch Num: [493/600] Discriminator Loss: 0.5876, Generator Loss: 1.6376 D(x): 0.8653, D(G(z)): 0.2621 Epoch: [14/20], Batch Num: [494/600] Discriminator Loss: 0.6748, Generator Loss: 1.8397 D(x): 0.8751, D(G(z)): 0.2741 Epoch: [14/20], Batch Num: [495/600] Discriminator Loss: 0.7397, Generator Loss: 2.2492 D(x): 0.7787, D(G(z)): 0.2373 Epoch: [14/20], Batch Num: [496/600] Discriminator Loss: 0.6474, Generator Loss: 2.1496 D(x): 0.7517, D(G(z)): 0.1667 Epoch: [14/20], Batch Num: [497/600] Discriminator Loss: 0.6726, Generator Loss: 2.0443 D(x): 0.7776, D(G(z)): 0.1905 Epoch: [14/20], Batch Num: [498/600] Discriminator Loss: 0.8453, Generator Loss: 2.0101 D(x): 0.6885, D(G(z)): 0.1726 Epoch: [14/20], Batch Num: [499/600] Discriminator Loss: 0.6678, Generator Loss: 1.6589 D(x): 0.7922, D(G(z)): 0.2271 Epoch: 14, Batch Num: [500/600]
Epoch: [14/20], Batch Num: [500/600] Discriminator Loss: 0.7349, Generator Loss: 1.7015 D(x): 0.8327, D(G(z)): 0.3154 Epoch: [14/20], Batch Num: [501/600] Discriminator Loss: 0.6690, Generator Loss: 2.0084 D(x): 0.8356, D(G(z)): 0.2610 Epoch: [14/20], Batch Num: [502/600] Discriminator Loss: 0.6283, Generator Loss: 2.2212 D(x): 0.8254, D(G(z)): 0.2369 Epoch: [14/20], Batch Num: [503/600] Discriminator Loss: 0.5790, Generator Loss: 2.2830 D(x): 0.7803, D(G(z)): 0.1565 Epoch: [14/20], Batch Num: [504/600] Discriminator Loss: 0.6572, Generator Loss: 2.2246 D(x): 0.7427, D(G(z)): 0.1340 Epoch: [14/20], Batch Num: [505/600] Discriminator Loss: 0.6838, Generator Loss: 2.0858 D(x): 0.7633, D(G(z)): 0.1774 Epoch: [14/20], Batch Num: [506/600] Discriminator Loss: 0.6926, Generator Loss: 1.8542 D(x): 0.7415, D(G(z)): 0.2004 Epoch: [14/20], Batch Num: [507/600] Discriminator Loss: 0.6388, Generator Loss: 1.6060 D(x): 0.8371, D(G(z)): 0.2612 Epoch: [14/20], Batch Num: [508/600] Discriminator Loss: 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Discriminator Loss: 0.7587, Generator Loss: 2.1107 D(x): 0.7377, D(G(z)): 0.2168 Epoch: [14/20], Batch Num: [526/600] Discriminator Loss: 0.6314, Generator Loss: 2.2121 D(x): 0.8077, D(G(z)): 0.2099 Epoch: [14/20], Batch Num: [527/600] Discriminator Loss: 0.9221, Generator Loss: 2.0615 D(x): 0.6556, D(G(z)): 0.2182 Epoch: [14/20], Batch Num: [528/600] Discriminator Loss: 0.7056, Generator Loss: 1.6758 D(x): 0.7383, D(G(z)): 0.2218 Epoch: [14/20], Batch Num: [529/600] Discriminator Loss: 0.7066, Generator Loss: 1.5786 D(x): 0.8120, D(G(z)): 0.2678 Epoch: [14/20], Batch Num: [530/600] Discriminator Loss: 0.6237, Generator Loss: 1.6119 D(x): 0.8268, D(G(z)): 0.2484 Epoch: [14/20], Batch Num: [531/600] Discriminator Loss: 0.6928, Generator Loss: 1.9349 D(x): 0.7740, D(G(z)): 0.2416 Epoch: [14/20], Batch Num: [532/600] Discriminator Loss: 0.5775, Generator Loss: 1.9757 D(x): 0.8176, D(G(z)): 0.2078 Epoch: [14/20], Batch Num: [533/600] Discriminator Loss: 0.6287, Generator Loss: 2.0020 D(x): 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2.1483 D(x): 0.7599, D(G(z)): 0.2188 Epoch: [14/20], Batch Num: [551/600] Discriminator Loss: 0.6997, Generator Loss: 1.8092 D(x): 0.7396, D(G(z)): 0.1981 Epoch: [14/20], Batch Num: [552/600] Discriminator Loss: 0.7980, Generator Loss: 1.8328 D(x): 0.8012, D(G(z)): 0.2693 Epoch: [14/20], Batch Num: [553/600] Discriminator Loss: 0.6511, Generator Loss: 1.8962 D(x): 0.8210, D(G(z)): 0.2441 Epoch: [14/20], Batch Num: [554/600] Discriminator Loss: 0.7109, Generator Loss: 2.2257 D(x): 0.7542, D(G(z)): 0.2129 Epoch: [14/20], Batch Num: [555/600] Discriminator Loss: 0.6856, Generator Loss: 2.2709 D(x): 0.7778, D(G(z)): 0.2266 Epoch: [14/20], Batch Num: [556/600] Discriminator Loss: 0.6969, Generator Loss: 2.1124 D(x): 0.7708, D(G(z)): 0.1953 Epoch: [14/20], Batch Num: [557/600] Discriminator Loss: 0.6353, Generator Loss: 2.0743 D(x): 0.7807, D(G(z)): 0.1912 Epoch: [14/20], Batch Num: [558/600] Discriminator Loss: 0.7348, Generator Loss: 1.8993 D(x): 0.7398, D(G(z)): 0.1855 Epoch: [14/20], 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Generator Loss: 1.6086 D(x): 0.7421, D(G(z)): 0.2006 Epoch: [14/20], Batch Num: [568/600] Discriminator Loss: 0.7132, Generator Loss: 1.6418 D(x): 0.7523, D(G(z)): 0.2186 Epoch: [14/20], Batch Num: [569/600] Discriminator Loss: 0.7364, Generator Loss: 1.8015 D(x): 0.8331, D(G(z)): 0.3121 Epoch: [14/20], Batch Num: [570/600] Discriminator Loss: 0.7341, Generator Loss: 2.0495 D(x): 0.7665, D(G(z)): 0.2570 Epoch: [14/20], Batch Num: [571/600] Discriminator Loss: 0.7316, Generator Loss: 2.2684 D(x): 0.7547, D(G(z)): 0.2282 Epoch: [14/20], Batch Num: [572/600] Discriminator Loss: 0.7953, Generator Loss: 2.0979 D(x): 0.7177, D(G(z)): 0.2175 Epoch: [14/20], Batch Num: [573/600] Discriminator Loss: 0.6362, Generator Loss: 1.9245 D(x): 0.7693, D(G(z)): 0.1746 Epoch: [14/20], Batch Num: [574/600] Discriminator Loss: 0.6630, Generator Loss: 1.8374 D(x): 0.8221, D(G(z)): 0.2594 Epoch: [14/20], Batch Num: [575/600] Discriminator Loss: 0.7416, Generator Loss: 1.8670 D(x): 0.8353, D(G(z)): 0.2933 Epoch: [14/20], Batch Num: [576/600] Discriminator Loss: 0.6226, Generator Loss: 1.8786 D(x): 0.7967, D(G(z)): 0.2094 Epoch: [14/20], Batch Num: [577/600] Discriminator Loss: 0.7025, Generator Loss: 2.0733 D(x): 0.7835, D(G(z)): 0.2225 Epoch: [14/20], Batch Num: [578/600] Discriminator Loss: 0.7797, Generator Loss: 2.2296 D(x): 0.7324, D(G(z)): 0.2096 Epoch: [14/20], Batch Num: [579/600] Discriminator Loss: 0.7571, Generator Loss: 1.8322 D(x): 0.7449, D(G(z)): 0.2289 Epoch: [14/20], Batch Num: [580/600] Discriminator Loss: 0.6961, Generator Loss: 1.6114 D(x): 0.7830, D(G(z)): 0.2415 Epoch: [14/20], Batch Num: [581/600] Discriminator Loss: 0.7118, Generator Loss: 2.1048 D(x): 0.8187, D(G(z)): 0.2651 Epoch: [14/20], Batch Num: [582/600] Discriminator Loss: 0.6639, Generator Loss: 1.8736 D(x): 0.8048, D(G(z)): 0.2421 Epoch: [14/20], Batch Num: [583/600] Discriminator Loss: 0.8524, Generator Loss: 2.1287 D(x): 0.7541, D(G(z)): 0.2234 Epoch: [14/20], Batch Num: [584/600] Discriminator Loss: 0.6575, Generator Loss: 2.3059 D(x): 0.7915, D(G(z)): 0.1957 Epoch: [14/20], Batch Num: [585/600] Discriminator Loss: 0.5542, Generator Loss: 2.2050 D(x): 0.7762, D(G(z)): 0.1674 Epoch: [14/20], Batch Num: [586/600] Discriminator Loss: 0.7884, Generator Loss: 1.8697 D(x): 0.7061, D(G(z)): 0.1926 Epoch: [14/20], Batch Num: [587/600] Discriminator Loss: 0.5822, Generator Loss: 1.7333 D(x): 0.8525, D(G(z)): 0.2389 Epoch: [14/20], Batch Num: [588/600] Discriminator Loss: 0.6678, Generator Loss: 1.9028 D(x): 0.8063, D(G(z)): 0.2344 Epoch: [14/20], Batch Num: [589/600] Discriminator Loss: 0.6634, Generator Loss: 1.9917 D(x): 0.8193, D(G(z)): 0.2532 Epoch: [14/20], Batch Num: [590/600] Discriminator Loss: 0.6515, Generator Loss: 2.2861 D(x): 0.8106, D(G(z)): 0.2287 Epoch: [14/20], Batch Num: [591/600] Discriminator Loss: 0.7612, Generator Loss: 2.4969 D(x): 0.7699, D(G(z)): 0.1988 Epoch: [14/20], Batch Num: [592/600] Discriminator Loss: 0.5379, Generator Loss: 2.4364 D(x): 0.7994, D(G(z)): 0.1422 Epoch: [14/20], Batch Num: [593/600] Discriminator Loss: 0.6636, Generator Loss: 2.3533 D(x): 0.7797, D(G(z)): 0.1809 Epoch: [14/20], Batch Num: [594/600] Discriminator Loss: 0.6204, Generator Loss: 2.0441 D(x): 0.7659, D(G(z)): 0.1785 Epoch: [14/20], Batch Num: [595/600] Discriminator Loss: 0.7346, Generator Loss: 1.7561 D(x): 0.7860, D(G(z)): 0.2324 Epoch: [14/20], Batch Num: [596/600] Discriminator Loss: 0.6578, Generator Loss: 1.6623 D(x): 0.8194, D(G(z)): 0.2414 Epoch: [14/20], Batch Num: [597/600] Discriminator Loss: 0.8600, Generator Loss: 2.1278 D(x): 0.8101, D(G(z)): 0.3232 Epoch: [14/20], Batch Num: [598/600] Discriminator Loss: 0.7555, Generator Loss: 2.5840 D(x): 0.7948, D(G(z)): 0.2601 Epoch: [14/20], Batch Num: [599/600] Discriminator Loss: 0.7916, Generator Loss: 2.4516 D(x): 0.7208, D(G(z)): 0.1590 Epoch: 15, Batch Num: [0/600]
Epoch: [15/20], Batch Num: [0/600] Discriminator Loss: 0.7187, Generator Loss: 2.3357 D(x): 0.6980, D(G(z)): 0.1226 Epoch: [15/20], Batch Num: [1/600] Discriminator Loss: 0.7042, Generator Loss: 1.8585 D(x): 0.7479, D(G(z)): 0.1698 Epoch: [15/20], Batch Num: [2/600] Discriminator Loss: 0.7387, Generator Loss: 1.6548 D(x): 0.7811, D(G(z)): 0.2523 Epoch: [15/20], Batch Num: [3/600] Discriminator Loss: 0.7197, Generator Loss: 1.7683 D(x): 0.8330, D(G(z)): 0.2889 Epoch: [15/20], Batch Num: [4/600] Discriminator Loss: 0.6761, Generator Loss: 1.9427 D(x): 0.8372, D(G(z)): 0.2895 Epoch: [15/20], Batch Num: [5/600] Discriminator Loss: 0.6162, Generator Loss: 2.2099 D(x): 0.7991, D(G(z)): 0.2258 Epoch: [15/20], Batch Num: [6/600] Discriminator Loss: 0.6699, Generator Loss: 2.1754 D(x): 0.7443, D(G(z)): 0.1752 Epoch: [15/20], Batch Num: [7/600] Discriminator Loss: 0.8164, Generator Loss: 2.0924 D(x): 0.7135, D(G(z)): 0.1886 Epoch: [15/20], Batch Num: [8/600] Discriminator Loss: 0.6282, Generator 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Num: [17/600] Discriminator Loss: 0.6357, Generator Loss: 1.9249 D(x): 0.8468, D(G(z)): 0.2687 Epoch: [15/20], Batch Num: [18/600] Discriminator Loss: 0.6546, Generator Loss: 1.9367 D(x): 0.7828, D(G(z)): 0.2263 Epoch: [15/20], Batch Num: [19/600] Discriminator Loss: 0.6289, Generator Loss: 2.0927 D(x): 0.8194, D(G(z)): 0.2556 Epoch: [15/20], Batch Num: [20/600] Discriminator Loss: 0.7774, Generator Loss: 2.1536 D(x): 0.7356, D(G(z)): 0.2131 Epoch: [15/20], Batch Num: [21/600] Discriminator Loss: 0.8531, Generator Loss: 1.8725 D(x): 0.7089, D(G(z)): 0.2253 Epoch: [15/20], Batch Num: [22/600] Discriminator Loss: 0.6963, Generator Loss: 1.6987 D(x): 0.7488, D(G(z)): 0.2140 Epoch: [15/20], Batch Num: [23/600] Discriminator Loss: 0.6062, Generator Loss: 1.5668 D(x): 0.8084, D(G(z)): 0.2385 Epoch: [15/20], Batch Num: [24/600] Discriminator Loss: 0.7251, Generator Loss: 1.7526 D(x): 0.8651, D(G(z)): 0.3195 Epoch: [15/20], Batch Num: [25/600] Discriminator Loss: 0.7019, Generator Loss: 2.2052 D(x): 0.8309, D(G(z)): 0.2839 Epoch: [15/20], Batch Num: [26/600] Discriminator Loss: 0.7288, Generator Loss: 2.7788 D(x): 0.7585, D(G(z)): 0.2062 Epoch: [15/20], Batch Num: [27/600] Discriminator Loss: 0.7263, Generator Loss: 2.6234 D(x): 0.6998, D(G(z)): 0.1228 Epoch: [15/20], Batch Num: [28/600] Discriminator Loss: 0.6110, Generator Loss: 2.2073 D(x): 0.7637, D(G(z)): 0.1476 Epoch: [15/20], Batch Num: [29/600] Discriminator Loss: 0.5241, Generator Loss: 1.9439 D(x): 0.8404, D(G(z)): 0.2017 Epoch: [15/20], Batch Num: [30/600] Discriminator Loss: 0.5837, Generator Loss: 1.7752 D(x): 0.8148, D(G(z)): 0.1929 Epoch: [15/20], Batch Num: [31/600] Discriminator Loss: 0.8019, Generator Loss: 1.9365 D(x): 0.8009, D(G(z)): 0.2455 Epoch: [15/20], Batch Num: [32/600] Discriminator Loss: 0.5383, Generator Loss: 2.3568 D(x): 0.8345, D(G(z)): 0.1898 Epoch: [15/20], Batch Num: [33/600] Discriminator Loss: 0.7834, Generator Loss: 2.4009 D(x): 0.7972, D(G(z)): 0.2642 Epoch: [15/20], Batch Num: 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D(x): 0.7744, D(G(z)): 0.2035 Epoch: [15/20], Batch Num: [43/600] Discriminator Loss: 0.6524, Generator Loss: 1.7223 D(x): 0.7742, D(G(z)): 0.1992 Epoch: [15/20], Batch Num: [44/600] Discriminator Loss: 0.6272, Generator Loss: 1.9141 D(x): 0.8202, D(G(z)): 0.2371 Epoch: [15/20], Batch Num: [45/600] Discriminator Loss: 0.6611, Generator Loss: 1.8833 D(x): 0.8116, D(G(z)): 0.2646 Epoch: [15/20], Batch Num: [46/600] Discriminator Loss: 0.7347, Generator Loss: 2.0637 D(x): 0.7680, D(G(z)): 0.2677 Epoch: [15/20], Batch Num: [47/600] Discriminator Loss: 0.7522, Generator Loss: 1.9807 D(x): 0.7514, D(G(z)): 0.2205 Epoch: [15/20], Batch Num: [48/600] Discriminator Loss: 0.5429, Generator Loss: 2.1748 D(x): 0.8710, D(G(z)): 0.2470 Epoch: [15/20], Batch Num: [49/600] Discriminator Loss: 0.7726, Generator Loss: 2.5173 D(x): 0.7714, D(G(z)): 0.2118 Epoch: [15/20], Batch Num: [50/600] Discriminator Loss: 0.7788, Generator Loss: 2.4183 D(x): 0.7149, D(G(z)): 0.1416 Epoch: [15/20], Batch Num: 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D(x): 0.7865, D(G(z)): 0.1963 Epoch: [15/20], Batch Num: [60/600] Discriminator Loss: 0.7931, Generator Loss: 2.0552 D(x): 0.8014, D(G(z)): 0.2713 Epoch: [15/20], Batch Num: [61/600] Discriminator Loss: 0.8097, Generator Loss: 1.9502 D(x): 0.7710, D(G(z)): 0.2416 Epoch: [15/20], Batch Num: [62/600] Discriminator Loss: 0.6980, Generator Loss: 1.9871 D(x): 0.7588, D(G(z)): 0.2103 Epoch: [15/20], Batch Num: [63/600] Discriminator Loss: 0.6023, Generator Loss: 2.0355 D(x): 0.8331, D(G(z)): 0.2006 Epoch: [15/20], Batch Num: [64/600] Discriminator Loss: 0.6720, Generator Loss: 2.0491 D(x): 0.7661, D(G(z)): 0.1799 Epoch: [15/20], Batch Num: [65/600] Discriminator Loss: 0.7692, Generator Loss: 1.9198 D(x): 0.7617, D(G(z)): 0.2294 Epoch: [15/20], Batch Num: [66/600] Discriminator Loss: 0.9055, Generator Loss: 1.8844 D(x): 0.6960, D(G(z)): 0.2012 Epoch: [15/20], Batch Num: [67/600] Discriminator Loss: 0.6532, Generator Loss: 1.8598 D(x): 0.8045, D(G(z)): 0.2278 Epoch: [15/20], Batch Num: 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D(x): 0.7287, D(G(z)): 0.2686 Epoch: [15/20], Batch Num: [77/600] Discriminator Loss: 0.7966, Generator Loss: 1.6817 D(x): 0.8093, D(G(z)): 0.2897 Epoch: [15/20], Batch Num: [78/600] Discriminator Loss: 0.7247, Generator Loss: 1.9138 D(x): 0.7887, D(G(z)): 0.2803 Epoch: [15/20], Batch Num: [79/600] Discriminator Loss: 0.6827, Generator Loss: 2.1496 D(x): 0.7791, D(G(z)): 0.2182 Epoch: [15/20], Batch Num: [80/600] Discriminator Loss: 0.7515, Generator Loss: 2.3834 D(x): 0.7612, D(G(z)): 0.2247 Epoch: [15/20], Batch Num: [81/600] Discriminator Loss: 0.6052, Generator Loss: 2.3075 D(x): 0.7589, D(G(z)): 0.1600 Epoch: [15/20], Batch Num: [82/600] Discriminator Loss: 0.6987, Generator Loss: 2.0764 D(x): 0.7558, D(G(z)): 0.2023 Epoch: [15/20], Batch Num: [83/600] Discriminator Loss: 0.7340, Generator Loss: 1.7468 D(x): 0.7213, D(G(z)): 0.1932 Epoch: [15/20], Batch Num: [84/600] Discriminator Loss: 0.7053, Generator Loss: 1.5244 D(x): 0.7795, D(G(z)): 0.2220 Epoch: [15/20], Batch Num: 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D(x): 0.7674, D(G(z)): 0.3705 Epoch: [15/20], Batch Num: [94/600] Discriminator Loss: 0.7857, Generator Loss: 1.5666 D(x): 0.7926, D(G(z)): 0.3050 Epoch: [15/20], Batch Num: [95/600] Discriminator Loss: 0.7642, Generator Loss: 1.7277 D(x): 0.7843, D(G(z)): 0.2908 Epoch: [15/20], Batch Num: [96/600] Discriminator Loss: 0.8239, Generator Loss: 1.7521 D(x): 0.7141, D(G(z)): 0.2546 Epoch: [15/20], Batch Num: [97/600] Discriminator Loss: 0.7619, Generator Loss: 1.9574 D(x): 0.7476, D(G(z)): 0.2440 Epoch: [15/20], Batch Num: [98/600] Discriminator Loss: 0.6851, Generator Loss: 1.9648 D(x): 0.7289, D(G(z)): 0.1894 Epoch: [15/20], Batch Num: [99/600] Discriminator Loss: 0.8319, Generator Loss: 1.8996 D(x): 0.7126, D(G(z)): 0.2235 Epoch: 15, Batch Num: [100/600]
Epoch: [15/20], Batch Num: [100/600] Discriminator Loss: 0.7410, Generator Loss: 1.8491 D(x): 0.7351, D(G(z)): 0.2158 Epoch: [15/20], Batch Num: [101/600] Discriminator Loss: 0.6688, Generator Loss: 1.6616 D(x): 0.8065, D(G(z)): 0.2516 Epoch: [15/20], Batch Num: [102/600] Discriminator Loss: 0.7340, Generator Loss: 1.7735 D(x): 0.7800, D(G(z)): 0.2571 Epoch: [15/20], Batch Num: [103/600] Discriminator Loss: 0.8665, Generator Loss: 1.7147 D(x): 0.6852, D(G(z)): 0.2401 Epoch: [15/20], Batch Num: [104/600] Discriminator Loss: 0.5943, Generator Loss: 1.6893 D(x): 0.8065, D(G(z)): 0.2430 Epoch: [15/20], Batch Num: [105/600] Discriminator Loss: 0.7001, Generator Loss: 1.6775 D(x): 0.7980, D(G(z)): 0.2482 Epoch: [15/20], Batch Num: [106/600] Discriminator Loss: 0.6163, Generator Loss: 1.8575 D(x): 0.8143, D(G(z)): 0.2369 Epoch: [15/20], Batch Num: [107/600] Discriminator Loss: 0.7875, Generator Loss: 1.9015 D(x): 0.7469, D(G(z)): 0.2641 Epoch: [15/20], Batch Num: [108/600] Discriminator Loss: 0.6853, Generator Loss: 1.9737 D(x): 0.7736, D(G(z)): 0.2337 Epoch: [15/20], Batch Num: [109/600] Discriminator Loss: 0.6489, Generator Loss: 1.8267 D(x): 0.7793, D(G(z)): 0.2321 Epoch: [15/20], Batch Num: [110/600] Discriminator Loss: 0.7989, Generator Loss: 1.9689 D(x): 0.7721, D(G(z)): 0.2592 Epoch: [15/20], Batch Num: [111/600] Discriminator Loss: 0.7353, Generator Loss: 2.0280 D(x): 0.7555, D(G(z)): 0.2195 Epoch: [15/20], Batch Num: [112/600] Discriminator Loss: 0.6336, Generator Loss: 1.9559 D(x): 0.7851, D(G(z)): 0.1881 Epoch: [15/20], Batch Num: [113/600] Discriminator Loss: 0.8512, Generator Loss: 1.9401 D(x): 0.7100, D(G(z)): 0.2284 Epoch: [15/20], Batch Num: [114/600] Discriminator Loss: 0.6851, Generator Loss: 1.6705 D(x): 0.7819, D(G(z)): 0.2164 Epoch: [15/20], Batch Num: [115/600] Discriminator Loss: 0.6854, Generator Loss: 1.6760 D(x): 0.7860, D(G(z)): 0.2449 Epoch: [15/20], Batch Num: [116/600] Discriminator Loss: 0.7748, Generator Loss: 1.8302 D(x): 0.7988, D(G(z)): 0.2962 Epoch: [15/20], Batch Num: [117/600] Discriminator Loss: 0.6338, Generator Loss: 1.9268 D(x): 0.7939, D(G(z)): 0.2312 Epoch: [15/20], Batch Num: [118/600] Discriminator Loss: 0.6124, Generator Loss: 2.0022 D(x): 0.7860, D(G(z)): 0.2019 Epoch: [15/20], Batch Num: [119/600] Discriminator Loss: 0.6415, Generator Loss: 2.1763 D(x): 0.7841, D(G(z)): 0.1944 Epoch: [15/20], Batch Num: [120/600] Discriminator Loss: 0.7424, Generator Loss: 2.1587 D(x): 0.7359, D(G(z)): 0.1900 Epoch: [15/20], Batch Num: [121/600] Discriminator Loss: 0.6668, Generator Loss: 1.9237 D(x): 0.7803, D(G(z)): 0.2098 Epoch: [15/20], Batch Num: [122/600] Discriminator Loss: 0.7912, Generator Loss: 1.8604 D(x): 0.8071, D(G(z)): 0.2719 Epoch: [15/20], Batch Num: [123/600] Discriminator Loss: 0.6344, Generator Loss: 2.0347 D(x): 0.7838, D(G(z)): 0.2163 Epoch: [15/20], Batch Num: [124/600] Discriminator Loss: 0.7235, Generator Loss: 1.9125 D(x): 0.7610, D(G(z)): 0.2409 Epoch: [15/20], Batch Num: [125/600] Discriminator Loss: 0.6305, Generator Loss: 1.9608 D(x): 0.7658, D(G(z)): 0.2123 Epoch: [15/20], Batch Num: [126/600] Discriminator Loss: 0.7659, Generator Loss: 1.8450 D(x): 0.7977, D(G(z)): 0.2469 Epoch: [15/20], Batch Num: [127/600] Discriminator Loss: 0.6084, Generator Loss: 1.8424 D(x): 0.7940, D(G(z)): 0.2056 Epoch: [15/20], Batch Num: [128/600] Discriminator Loss: 0.7745, Generator Loss: 1.6795 D(x): 0.7378, D(G(z)): 0.2347 Epoch: [15/20], Batch Num: [129/600] Discriminator Loss: 0.7507, Generator Loss: 1.9548 D(x): 0.8006, D(G(z)): 0.2664 Epoch: [15/20], Batch Num: [130/600] Discriminator Loss: 0.4859, Generator Loss: 1.8607 D(x): 0.8337, D(G(z)): 0.1827 Epoch: [15/20], Batch Num: [131/600] Discriminator Loss: 0.7363, Generator Loss: 2.2927 D(x): 0.7597, D(G(z)): 0.2198 Epoch: [15/20], Batch Num: [132/600] Discriminator Loss: 0.7288, Generator Loss: 2.1761 D(x): 0.7555, D(G(z)): 0.2002 Epoch: [15/20], Batch Num: [133/600] Discriminator Loss: 0.5722, Generator Loss: 2.2635 D(x): 0.8071, D(G(z)): 0.2053 Epoch: [15/20], Batch Num: [134/600] Discriminator Loss: 0.6716, Generator Loss: 2.2517 D(x): 0.7785, D(G(z)): 0.2088 Epoch: [15/20], Batch Num: [135/600] Discriminator Loss: 0.5974, Generator Loss: 2.1756 D(x): 0.7638, D(G(z)): 0.1612 Epoch: [15/20], Batch Num: [136/600] Discriminator Loss: 0.8251, Generator Loss: 1.9981 D(x): 0.7533, D(G(z)): 0.2668 Epoch: [15/20], Batch Num: [137/600] Discriminator Loss: 0.6844, Generator Loss: 1.7074 D(x): 0.8045, D(G(z)): 0.2542 Epoch: [15/20], Batch Num: [138/600] Discriminator Loss: 0.7065, Generator Loss: 2.3877 D(x): 0.8356, D(G(z)): 0.2696 Epoch: [15/20], Batch Num: [139/600] Discriminator Loss: 0.6298, Generator Loss: 2.2526 D(x): 0.7954, D(G(z)): 0.1951 Epoch: [15/20], Batch Num: [140/600] Discriminator Loss: 0.7072, Generator Loss: 2.5161 D(x): 0.7504, D(G(z)): 0.1846 Epoch: [15/20], Batch Num: [141/600] Discriminator Loss: 0.6994, Generator Loss: 2.3285 D(x): 0.7265, D(G(z)): 0.1507 Epoch: [15/20], Batch Num: [142/600] Discriminator Loss: 0.6325, Generator Loss: 1.8997 D(x): 0.7642, D(G(z)): 0.1832 Epoch: [15/20], Batch Num: [143/600] Discriminator Loss: 0.5999, Generator Loss: 1.8467 D(x): 0.8847, D(G(z)): 0.2778 Epoch: [15/20], Batch Num: [144/600] Discriminator Loss: 0.6627, Generator Loss: 2.2657 D(x): 0.8487, D(G(z)): 0.2650 Epoch: [15/20], Batch Num: [145/600] Discriminator Loss: 0.6343, Generator Loss: 2.4442 D(x): 0.8009, D(G(z)): 0.2224 Epoch: [15/20], Batch Num: [146/600] Discriminator Loss: 0.5656, Generator Loss: 2.5722 D(x): 0.8137, D(G(z)): 0.1630 Epoch: [15/20], Batch Num: [147/600] Discriminator Loss: 0.7423, Generator Loss: 2.4532 D(x): 0.7186, D(G(z)): 0.1454 Epoch: [15/20], Batch Num: [148/600] Discriminator Loss: 0.6591, Generator Loss: 1.9925 D(x): 0.7823, D(G(z)): 0.1833 Epoch: [15/20], Batch Num: [149/600] Discriminator Loss: 0.6376, Generator Loss: 2.1248 D(x): 0.7893, D(G(z)): 0.1758 Epoch: [15/20], Batch Num: [150/600] Discriminator Loss: 0.6529, Generator Loss: 1.6148 D(x): 0.8440, D(G(z)): 0.2364 Epoch: [15/20], Batch Num: [151/600] Discriminator Loss: 0.8620, Generator Loss: 1.9108 D(x): 0.8034, D(G(z)): 0.2805 Epoch: [15/20], Batch Num: [152/600] Discriminator Loss: 0.5686, Generator Loss: 2.2666 D(x): 0.8149, D(G(z)): 0.1879 Epoch: [15/20], Batch Num: [153/600] Discriminator Loss: 0.7414, Generator Loss: 2.0457 D(x): 0.7226, D(G(z)): 0.1656 Epoch: [15/20], Batch Num: [154/600] Discriminator Loss: 0.6426, Generator Loss: 2.0092 D(x): 0.7604, D(G(z)): 0.1746 Epoch: [15/20], Batch Num: [155/600] Discriminator Loss: 0.8862, Generator Loss: 2.0116 D(x): 0.7457, D(G(z)): 0.2720 Epoch: [15/20], Batch Num: [156/600] Discriminator Loss: 0.8514, Generator Loss: 1.7377 D(x): 0.7409, D(G(z)): 0.2550 Epoch: [15/20], Batch Num: [157/600] Discriminator Loss: 0.7332, Generator Loss: 1.4111 D(x): 0.7530, D(G(z)): 0.2285 Epoch: [15/20], Batch Num: [158/600] Discriminator Loss: 0.8694, Generator Loss: 1.8367 D(x): 0.7982, D(G(z)): 0.3295 Epoch: [15/20], Batch Num: [159/600] Discriminator Loss: 0.7563, Generator Loss: 2.0868 D(x): 0.8178, D(G(z)): 0.3123 Epoch: [15/20], Batch Num: [160/600] Discriminator Loss: 0.7695, Generator Loss: 2.3861 D(x): 0.7467, D(G(z)): 0.2147 Epoch: [15/20], Batch Num: [161/600] Discriminator Loss: 0.8549, Generator Loss: 2.2100 D(x): 0.7068, D(G(z)): 0.1933 Epoch: [15/20], Batch Num: [162/600] Discriminator Loss: 0.8182, Generator Loss: 2.0470 D(x): 0.7093, D(G(z)): 0.1842 Epoch: [15/20], Batch Num: [163/600] Discriminator Loss: 0.7404, Generator Loss: 1.7690 D(x): 0.7614, D(G(z)): 0.2544 Epoch: [15/20], Batch Num: [164/600] Discriminator Loss: 0.7772, Generator Loss: 1.8392 D(x): 0.7734, D(G(z)): 0.2714 Epoch: [15/20], Batch Num: [165/600] Discriminator Loss: 0.6844, Generator Loss: 1.8855 D(x): 0.7765, D(G(z)): 0.2418 Epoch: [15/20], Batch Num: [166/600] Discriminator Loss: 0.7044, Generator Loss: 1.8036 D(x): 0.7604, D(G(z)): 0.2370 Epoch: [15/20], Batch Num: [167/600] Discriminator Loss: 0.7426, Generator Loss: 1.9876 D(x): 0.7576, D(G(z)): 0.2305 Epoch: [15/20], Batch Num: [168/600] Discriminator Loss: 0.7408, Generator Loss: 1.7417 D(x): 0.7658, D(G(z)): 0.2417 Epoch: [15/20], Batch Num: [169/600] Discriminator Loss: 0.8004, Generator Loss: 1.8775 D(x): 0.7378, D(G(z)): 0.2418 Epoch: [15/20], Batch Num: [170/600] Discriminator Loss: 0.6579, Generator Loss: 1.8796 D(x): 0.7901, D(G(z)): 0.2370 Epoch: [15/20], Batch Num: [171/600] Discriminator Loss: 0.5834, Generator Loss: 1.7651 D(x): 0.7904, D(G(z)): 0.1948 Epoch: [15/20], Batch Num: [172/600] Discriminator Loss: 0.6464, Generator Loss: 1.6036 D(x): 0.7865, D(G(z)): 0.2135 Epoch: [15/20], Batch Num: [173/600] Discriminator Loss: 0.7755, Generator Loss: 1.8131 D(x): 0.7704, D(G(z)): 0.2674 Epoch: [15/20], Batch Num: [174/600] Discriminator Loss: 0.6664, Generator Loss: 1.8722 D(x): 0.8266, D(G(z)): 0.2622 Epoch: [15/20], Batch Num: [175/600] Discriminator Loss: 0.6924, Generator Loss: 1.7382 D(x): 0.7479, D(G(z)): 0.1984 Epoch: [15/20], Batch Num: [176/600] Discriminator Loss: 0.6305, Generator Loss: 1.9422 D(x): 0.8171, D(G(z)): 0.2414 Epoch: [15/20], Batch Num: [177/600] Discriminator Loss: 0.5724, Generator Loss: 2.1808 D(x): 0.8231, D(G(z)): 0.2094 Epoch: [15/20], Batch Num: [178/600] Discriminator Loss: 0.6136, Generator Loss: 2.1256 D(x): 0.7900, D(G(z)): 0.1897 Epoch: [15/20], Batch Num: [179/600] Discriminator Loss: 0.6993, Generator Loss: 2.3050 D(x): 0.7692, D(G(z)): 0.2121 Epoch: [15/20], Batch Num: [180/600] Discriminator Loss: 0.7273, Generator Loss: 2.3308 D(x): 0.7508, D(G(z)): 0.1732 Epoch: [15/20], Batch Num: [181/600] Discriminator Loss: 0.5695, Generator Loss: 2.0428 D(x): 0.7784, D(G(z)): 0.1477 Epoch: [15/20], Batch Num: [182/600] Discriminator Loss: 0.6256, Generator Loss: 1.9525 D(x): 0.8365, D(G(z)): 0.2349 Epoch: [15/20], Batch Num: [183/600] Discriminator Loss: 0.7369, Generator Loss: 2.0045 D(x): 0.7518, D(G(z)): 0.2156 Epoch: [15/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [15/20], Batch Num: [200/600] Discriminator Loss: 0.6744, Generator Loss: 1.8880 D(x): 0.8042, D(G(z)): 0.2400 Epoch: [15/20], Batch Num: [201/600] Discriminator Loss: 0.8593, Generator Loss: 2.0259 D(x): 0.7158, D(G(z)): 0.2586 Epoch: [15/20], Batch Num: [202/600] Discriminator Loss: 0.8794, Generator Loss: 1.9455 D(x): 0.6592, D(G(z)): 0.2163 Epoch: [15/20], Batch Num: [203/600] Discriminator Loss: 0.6204, Generator Loss: 1.7693 D(x): 0.8001, D(G(z)): 0.2200 Epoch: [15/20], Batch Num: [204/600] Discriminator Loss: 0.6477, Generator Loss: 1.8389 D(x): 0.7931, D(G(z)): 0.2254 Epoch: [15/20], Batch Num: [205/600] Discriminator Loss: 0.5505, Generator Loss: 1.5096 D(x): 0.8236, D(G(z)): 0.1968 Epoch: [15/20], Batch Num: [206/600] Discriminator Loss: 0.7744, Generator Loss: 1.9172 D(x): 0.8097, D(G(z)): 0.2951 Epoch: [15/20], Batch Num: [207/600] Discriminator Loss: 0.7032, Generator Loss: 1.6869 D(x): 0.7737, D(G(z)): 0.2232 Epoch: [15/20], Batch Num: [208/600] Discriminator Loss: 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0.2055 Epoch: [15/20], Batch Num: [217/600] Discriminator Loss: 0.5878, Generator Loss: 2.6191 D(x): 0.8125, D(G(z)): 0.1697 Epoch: [15/20], Batch Num: [218/600] Discriminator Loss: 0.6994, Generator Loss: 2.4857 D(x): 0.6943, D(G(z)): 0.0998 Epoch: [15/20], Batch Num: [219/600] Discriminator Loss: 0.6576, Generator Loss: 2.1451 D(x): 0.7163, D(G(z)): 0.1346 Epoch: [15/20], Batch Num: [220/600] Discriminator Loss: 0.7770, Generator Loss: 1.9236 D(x): 0.7280, D(G(z)): 0.1946 Epoch: [15/20], Batch Num: [221/600] Discriminator Loss: 0.6942, Generator Loss: 1.6181 D(x): 0.8356, D(G(z)): 0.2748 Epoch: [15/20], Batch Num: [222/600] Discriminator Loss: 0.6786, Generator Loss: 1.8351 D(x): 0.8538, D(G(z)): 0.2928 Epoch: [15/20], Batch Num: [223/600] Discriminator Loss: 0.6606, Generator Loss: 2.3108 D(x): 0.8552, D(G(z)): 0.2803 Epoch: [15/20], Batch Num: [224/600] Discriminator Loss: 0.5397, Generator Loss: 3.1170 D(x): 0.8314, D(G(z)): 0.1908 Epoch: [15/20], Batch Num: [225/600] Discriminator Loss: 0.5593, Generator Loss: 3.1987 D(x): 0.7733, D(G(z)): 0.1229 Epoch: [15/20], Batch Num: [226/600] Discriminator Loss: 0.6776, Generator Loss: 2.8824 D(x): 0.7127, D(G(z)): 0.1005 Epoch: [15/20], Batch Num: [227/600] Discriminator Loss: 0.6936, Generator Loss: 2.3650 D(x): 0.7398, D(G(z)): 0.1361 Epoch: [15/20], Batch Num: [228/600] Discriminator Loss: 0.7190, Generator Loss: 1.7477 D(x): 0.7269, D(G(z)): 0.1851 Epoch: [15/20], Batch Num: [229/600] Discriminator Loss: 0.5777, Generator Loss: 1.5671 D(x): 0.8968, D(G(z)): 0.3037 Epoch: [15/20], Batch Num: [230/600] Discriminator Loss: 0.7606, Generator Loss: 2.0919 D(x): 0.9106, D(G(z)): 0.3414 Epoch: [15/20], Batch Num: [231/600] Discriminator Loss: 0.7126, Generator Loss: 2.6858 D(x): 0.8136, D(G(z)): 0.2444 Epoch: [15/20], Batch Num: [232/600] Discriminator Loss: 0.6878, Generator Loss: 2.7338 D(x): 0.7387, D(G(z)): 0.1276 Epoch: [15/20], Batch Num: [233/600] Discriminator Loss: 0.8613, Generator Loss: 2.3692 D(x): 0.6895, D(G(z)): 0.1217 Epoch: [15/20], Batch Num: [234/600] Discriminator Loss: 0.7773, Generator Loss: 2.0541 D(x): 0.6940, D(G(z)): 0.1523 Epoch: [15/20], Batch Num: [235/600] Discriminator Loss: 0.8024, Generator Loss: 1.4357 D(x): 0.7796, D(G(z)): 0.2753 Epoch: [15/20], Batch Num: [236/600] Discriminator Loss: 0.8460, Generator Loss: 1.4676 D(x): 0.8324, D(G(z)): 0.3693 Epoch: [15/20], Batch Num: [237/600] Discriminator Loss: 0.7100, Generator Loss: 1.8730 D(x): 0.8456, D(G(z)): 0.3173 Epoch: [15/20], Batch Num: [238/600] Discriminator Loss: 0.7458, Generator Loss: 1.9358 D(x): 0.7533, D(G(z)): 0.2467 Epoch: [15/20], Batch Num: [239/600] Discriminator Loss: 0.7796, Generator Loss: 2.2008 D(x): 0.7304, D(G(z)): 0.2126 Epoch: [15/20], Batch Num: [240/600] Discriminator Loss: 0.9169, Generator Loss: 2.0931 D(x): 0.6842, D(G(z)): 0.2050 Epoch: [15/20], Batch Num: [241/600] Discriminator Loss: 0.7953, Generator Loss: 2.0039 D(x): 0.7016, D(G(z)): 0.1896 Epoch: [15/20], Batch Num: [242/600] Discriminator Loss: 0.7314, Generator Loss: 1.6291 D(x): 0.7127, D(G(z)): 0.1962 Epoch: [15/20], Batch Num: [243/600] Discriminator Loss: 0.7602, Generator Loss: 1.5988 D(x): 0.7837, D(G(z)): 0.2789 Epoch: [15/20], Batch Num: [244/600] Discriminator Loss: 0.7832, Generator Loss: 1.4469 D(x): 0.8222, D(G(z)): 0.3125 Epoch: [15/20], Batch Num: [245/600] Discriminator Loss: 0.7133, Generator Loss: 1.5176 D(x): 0.7760, D(G(z)): 0.2772 Epoch: [15/20], Batch Num: [246/600] Discriminator Loss: 0.8095, Generator Loss: 1.8522 D(x): 0.7486, D(G(z)): 0.2591 Epoch: [15/20], Batch Num: [247/600] Discriminator Loss: 0.6364, Generator Loss: 1.8185 D(x): 0.7867, D(G(z)): 0.2397 Epoch: [15/20], Batch Num: [248/600] Discriminator Loss: 0.5902, Generator Loss: 1.9244 D(x): 0.8061, D(G(z)): 0.1985 Epoch: [15/20], Batch Num: [249/600] Discriminator Loss: 0.7375, Generator Loss: 1.8779 D(x): 0.7007, D(G(z)): 0.1836 Epoch: [15/20], Batch Num: [250/600] Discriminator Loss: 0.6433, Generator Loss: 1.8150 D(x): 0.7570, D(G(z)): 0.1892 Epoch: [15/20], Batch Num: [251/600] Discriminator Loss: 0.6994, Generator Loss: 1.5742 D(x): 0.7574, D(G(z)): 0.2104 Epoch: [15/20], Batch Num: [252/600] Discriminator Loss: 0.8888, Generator Loss: 1.6030 D(x): 0.7659, D(G(z)): 0.3058 Epoch: [15/20], Batch Num: [253/600] Discriminator Loss: 0.6704, Generator Loss: 1.8218 D(x): 0.8291, D(G(z)): 0.2823 Epoch: [15/20], Batch Num: [254/600] Discriminator Loss: 0.6785, Generator Loss: 1.8686 D(x): 0.8030, D(G(z)): 0.2569 Epoch: [15/20], Batch Num: [255/600] Discriminator Loss: 0.8140, Generator Loss: 2.0536 D(x): 0.7479, D(G(z)): 0.2372 Epoch: [15/20], Batch Num: [256/600] Discriminator Loss: 0.6881, Generator Loss: 2.1437 D(x): 0.7294, D(G(z)): 0.1730 Epoch: [15/20], Batch Num: [257/600] Discriminator Loss: 0.6570, Generator Loss: 2.2783 D(x): 0.7600, D(G(z)): 0.1850 Epoch: [15/20], Batch Num: [258/600] Discriminator Loss: 0.5863, Generator Loss: 1.9559 D(x): 0.7745, D(G(z)): 0.1628 Epoch: [15/20], Batch Num: [259/600] Discriminator Loss: 0.5849, Generator Loss: 2.0480 D(x): 0.8286, D(G(z)): 0.2152 Epoch: [15/20], Batch Num: [260/600] Discriminator Loss: 0.8095, Generator Loss: 1.8015 D(x): 0.7885, D(G(z)): 0.2804 Epoch: [15/20], Batch Num: [261/600] Discriminator Loss: 0.5704, Generator Loss: 2.0666 D(x): 0.8022, D(G(z)): 0.1858 Epoch: [15/20], Batch Num: [262/600] Discriminator Loss: 0.7270, Generator Loss: 1.8567 D(x): 0.7344, D(G(z)): 0.1843 Epoch: [15/20], Batch Num: [263/600] Discriminator Loss: 0.6860, Generator Loss: 1.5867 D(x): 0.7825, D(G(z)): 0.2133 Epoch: [15/20], Batch Num: [264/600] Discriminator Loss: 0.7053, Generator Loss: 1.8682 D(x): 0.8114, D(G(z)): 0.2453 Epoch: [15/20], Batch Num: [265/600] Discriminator Loss: 0.5289, Generator Loss: 2.1348 D(x): 0.8452, D(G(z)): 0.2262 Epoch: [15/20], Batch Num: [266/600] Discriminator Loss: 0.7834, Generator Loss: 1.7986 D(x): 0.7419, D(G(z)): 0.2297 Epoch: [15/20], Batch Num: [267/600] Discriminator Loss: 0.6621, Generator Loss: 2.0533 D(x): 0.7671, D(G(z)): 0.1966 Epoch: [15/20], Batch Num: [268/600] Discriminator Loss: 0.7654, Generator Loss: 1.9109 D(x): 0.7441, D(G(z)): 0.2242 Epoch: [15/20], Batch Num: [269/600] Discriminator Loss: 0.6945, Generator Loss: 2.0390 D(x): 0.8176, D(G(z)): 0.2577 Epoch: [15/20], Batch Num: [270/600] Discriminator Loss: 0.7117, Generator Loss: 1.9186 D(x): 0.7744, D(G(z)): 0.2317 Epoch: [15/20], Batch Num: [271/600] Discriminator Loss: 0.7619, Generator Loss: 1.9615 D(x): 0.7705, D(G(z)): 0.2551 Epoch: [15/20], Batch Num: [272/600] Discriminator Loss: 0.6551, Generator Loss: 2.0650 D(x): 0.7407, D(G(z)): 0.1834 Epoch: [15/20], Batch Num: [273/600] Discriminator Loss: 0.5711, Generator Loss: 2.0112 D(x): 0.7998, D(G(z)): 0.1945 Epoch: [15/20], Batch Num: [274/600] Discriminator Loss: 0.7275, Generator Loss: 2.1538 D(x): 0.7575, D(G(z)): 0.2123 Epoch: [15/20], Batch Num: [275/600] Discriminator Loss: 0.7685, Generator Loss: 1.9937 D(x): 0.7541, D(G(z)): 0.2400 Epoch: [15/20], Batch Num: [276/600] Discriminator Loss: 0.7743, Generator Loss: 2.2309 D(x): 0.7764, D(G(z)): 0.2556 Epoch: [15/20], Batch Num: [277/600] Discriminator Loss: 0.6585, Generator Loss: 2.3537 D(x): 0.8309, D(G(z)): 0.2351 Epoch: [15/20], Batch Num: [278/600] Discriminator Loss: 0.6657, Generator Loss: 2.3850 D(x): 0.7607, D(G(z)): 0.1951 Epoch: [15/20], Batch Num: [279/600] Discriminator Loss: 0.6276, Generator Loss: 2.2461 D(x): 0.7749, D(G(z)): 0.1898 Epoch: [15/20], Batch Num: [280/600] Discriminator Loss: 0.7846, Generator Loss: 2.0480 D(x): 0.7241, D(G(z)): 0.1779 Epoch: [15/20], Batch Num: [281/600] Discriminator Loss: 0.7017, Generator Loss: 1.8333 D(x): 0.7475, D(G(z)): 0.1698 Epoch: [15/20], Batch Num: [282/600] Discriminator Loss: 0.7069, Generator Loss: 1.5980 D(x): 0.8003, D(G(z)): 0.2518 Epoch: [15/20], Batch Num: [283/600] Discriminator Loss: 0.8722, Generator Loss: 1.6880 D(x): 0.8153, D(G(z)): 0.3140 Epoch: [15/20], Batch Num: [284/600] Discriminator Loss: 0.6636, Generator Loss: 2.0657 D(x): 0.8429, D(G(z)): 0.2889 Epoch: [15/20], Batch Num: [285/600] Discriminator Loss: 0.7005, Generator Loss: 2.3294 D(x): 0.7930, D(G(z)): 0.2201 Epoch: [15/20], Batch Num: [286/600] Discriminator Loss: 0.7088, Generator Loss: 2.5146 D(x): 0.7520, D(G(z)): 0.1865 Epoch: [15/20], Batch Num: [287/600] Discriminator Loss: 0.7010, Generator Loss: 2.1729 D(x): 0.6824, D(G(z)): 0.1247 Epoch: [15/20], Batch Num: [288/600] Discriminator Loss: 0.6786, Generator Loss: 2.1113 D(x): 0.7312, D(G(z)): 0.1727 Epoch: [15/20], Batch Num: [289/600] Discriminator Loss: 0.7219, Generator Loss: 1.6338 D(x): 0.7588, D(G(z)): 0.2556 Epoch: [15/20], Batch Num: [290/600] Discriminator Loss: 0.6605, Generator Loss: 1.6507 D(x): 0.8325, D(G(z)): 0.2878 Epoch: [15/20], Batch Num: [291/600] Discriminator Loss: 0.5745, Generator Loss: 1.7470 D(x): 0.8901, D(G(z)): 0.2752 Epoch: [15/20], Batch Num: [292/600] Discriminator Loss: 0.7146, Generator Loss: 2.1482 D(x): 0.8042, D(G(z)): 0.2549 Epoch: [15/20], Batch Num: [293/600] Discriminator Loss: 0.6824, Generator Loss: 2.4894 D(x): 0.7687, D(G(z)): 0.1814 Epoch: [15/20], Batch Num: [294/600] Discriminator Loss: 0.9387, Generator Loss: 2.1259 D(x): 0.6788, D(G(z)): 0.1693 Epoch: [15/20], Batch Num: [295/600] Discriminator Loss: 0.7929, Generator Loss: 2.1722 D(x): 0.7031, D(G(z)): 0.1600 Epoch: [15/20], Batch Num: [296/600] Discriminator Loss: 0.7707, Generator Loss: 1.5976 D(x): 0.7274, D(G(z)): 0.2105 Epoch: [15/20], Batch Num: [297/600] Discriminator Loss: 0.6587, Generator Loss: 1.3885 D(x): 0.7950, D(G(z)): 0.2354 Epoch: [15/20], Batch Num: [298/600] Discriminator Loss: 0.8118, Generator Loss: 1.4990 D(x): 0.8642, D(G(z)): 0.3435 Epoch: [15/20], Batch Num: [299/600] Discriminator Loss: 0.7132, Generator Loss: 1.9490 D(x): 0.8488, D(G(z)): 0.3136 Epoch: 15, Batch Num: [300/600]
Epoch: [15/20], Batch Num: [300/600] Discriminator Loss: 0.7004, Generator Loss: 2.4902 D(x): 0.7777, D(G(z)): 0.2360 Epoch: [15/20], Batch Num: [301/600] Discriminator Loss: 0.7799, Generator Loss: 2.5374 D(x): 0.6888, D(G(z)): 0.1680 Epoch: [15/20], Batch Num: [302/600] Discriminator Loss: 0.8363, Generator Loss: 2.0954 D(x): 0.6349, D(G(z)): 0.1128 Epoch: [15/20], Batch Num: [303/600] Discriminator Loss: 0.7399, Generator Loss: 1.6259 D(x): 0.6786, D(G(z)): 0.1441 Epoch: [15/20], Batch Num: [304/600] Discriminator Loss: 0.6885, Generator Loss: 1.3204 D(x): 0.8087, D(G(z)): 0.2720 Epoch: [15/20], Batch Num: [305/600] Discriminator Loss: 0.8423, Generator Loss: 1.3119 D(x): 0.8295, D(G(z)): 0.3702 Epoch: [15/20], Batch Num: [306/600] Discriminator Loss: 0.7448, Generator Loss: 1.4077 D(x): 0.8413, D(G(z)): 0.3303 Epoch: [15/20], Batch Num: [307/600] Discriminator Loss: 0.7417, Generator Loss: 1.9613 D(x): 0.8473, D(G(z)): 0.3253 Epoch: [15/20], Batch Num: [308/600] Discriminator Loss: 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0.2773 Epoch: [15/20], Batch Num: [317/600] Discriminator Loss: 0.7008, Generator Loss: 2.0126 D(x): 0.7138, D(G(z)): 0.1971 Epoch: [15/20], Batch Num: [318/600] Discriminator Loss: 0.6103, Generator Loss: 2.0104 D(x): 0.7924, D(G(z)): 0.2115 Epoch: [15/20], Batch Num: [319/600] Discriminator Loss: 0.6098, Generator Loss: 2.1300 D(x): 0.7701, D(G(z)): 0.1902 Epoch: [15/20], Batch Num: [320/600] Discriminator Loss: 0.6592, Generator Loss: 1.8068 D(x): 0.7510, D(G(z)): 0.1940 Epoch: [15/20], Batch Num: [321/600] Discriminator Loss: 0.6865, Generator Loss: 1.8350 D(x): 0.7632, D(G(z)): 0.2139 Epoch: [15/20], Batch Num: [322/600] Discriminator Loss: 0.6358, Generator Loss: 1.8750 D(x): 0.8351, D(G(z)): 0.2382 Epoch: [15/20], Batch Num: [323/600] Discriminator Loss: 0.8928, Generator Loss: 1.9886 D(x): 0.7664, D(G(z)): 0.2855 Epoch: [15/20], Batch Num: [324/600] Discriminator Loss: 0.7403, Generator Loss: 1.6664 D(x): 0.7369, D(G(z)): 0.2256 Epoch: [15/20], Batch Num: [325/600] Discriminator Loss: 0.7091, Generator Loss: 1.9007 D(x): 0.7629, D(G(z)): 0.2272 Epoch: [15/20], Batch Num: [326/600] Discriminator Loss: 0.6354, Generator Loss: 1.9325 D(x): 0.7951, D(G(z)): 0.2159 Epoch: [15/20], Batch Num: [327/600] Discriminator Loss: 0.7079, Generator Loss: 1.8860 D(x): 0.8141, D(G(z)): 0.2609 Epoch: [15/20], Batch Num: [328/600] Discriminator Loss: 0.6365, Generator Loss: 2.3592 D(x): 0.8326, D(G(z)): 0.2481 Epoch: [15/20], Batch Num: [329/600] Discriminator Loss: 0.7236, Generator Loss: 2.3342 D(x): 0.7260, D(G(z)): 0.1607 Epoch: [15/20], Batch Num: [330/600] Discriminator Loss: 0.6931, Generator Loss: 2.2909 D(x): 0.7793, D(G(z)): 0.2018 Epoch: [15/20], Batch Num: [331/600] Discriminator Loss: 0.6840, Generator Loss: 1.9960 D(x): 0.7173, D(G(z)): 0.1467 Epoch: [15/20], Batch Num: [332/600] Discriminator Loss: 0.7079, Generator Loss: 1.8069 D(x): 0.7690, D(G(z)): 0.2027 Epoch: [15/20], Batch Num: [333/600] Discriminator Loss: 0.7629, Generator Loss: 1.6086 D(x): 0.7872, D(G(z)): 0.2667 Epoch: [15/20], Batch Num: [334/600] Discriminator Loss: 0.7533, Generator Loss: 1.7667 D(x): 0.8309, D(G(z)): 0.3140 Epoch: [15/20], Batch Num: [335/600] Discriminator Loss: 0.8242, Generator Loss: 1.8113 D(x): 0.7875, D(G(z)): 0.2630 Epoch: [15/20], Batch Num: [336/600] Discriminator Loss: 0.7838, Generator Loss: 2.1997 D(x): 0.7522, D(G(z)): 0.2339 Epoch: [15/20], Batch Num: [337/600] Discriminator Loss: 0.7320, Generator Loss: 2.0713 D(x): 0.7203, D(G(z)): 0.1675 Epoch: [15/20], Batch Num: [338/600] Discriminator Loss: 0.6731, Generator Loss: 1.9400 D(x): 0.7365, D(G(z)): 0.1749 Epoch: [15/20], Batch Num: [339/600] Discriminator Loss: 0.6880, Generator Loss: 1.7531 D(x): 0.7860, D(G(z)): 0.2306 Epoch: [15/20], Batch Num: [340/600] Discriminator Loss: 0.6576, Generator Loss: 1.7246 D(x): 0.7810, D(G(z)): 0.2189 Epoch: [15/20], Batch Num: [341/600] Discriminator Loss: 0.7307, Generator Loss: 1.6745 D(x): 0.7716, D(G(z)): 0.2538 Epoch: [15/20], Batch Num: 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2.0057 D(x): 0.7399, D(G(z)): 0.2269 Epoch: [15/20], Batch Num: [351/600] Discriminator Loss: 0.7251, Generator Loss: 2.0294 D(x): 0.7460, D(G(z)): 0.1903 Epoch: [15/20], Batch Num: [352/600] Discriminator Loss: 0.6114, Generator Loss: 1.8733 D(x): 0.8129, D(G(z)): 0.2327 Epoch: [15/20], Batch Num: [353/600] Discriminator Loss: 0.7717, Generator Loss: 2.1404 D(x): 0.7515, D(G(z)): 0.2434 Epoch: [15/20], Batch Num: [354/600] Discriminator Loss: 0.5893, Generator Loss: 2.0338 D(x): 0.8180, D(G(z)): 0.2161 Epoch: [15/20], Batch Num: [355/600] Discriminator Loss: 0.7049, Generator Loss: 2.0249 D(x): 0.7853, D(G(z)): 0.2139 Epoch: [15/20], Batch Num: [356/600] Discriminator Loss: 0.6491, Generator Loss: 2.1516 D(x): 0.8023, D(G(z)): 0.2245 Epoch: [15/20], Batch Num: [357/600] Discriminator Loss: 0.6889, Generator Loss: 2.0442 D(x): 0.7683, D(G(z)): 0.2098 Epoch: [15/20], Batch Num: [358/600] Discriminator Loss: 0.5962, Generator Loss: 2.1492 D(x): 0.8133, D(G(z)): 0.2035 Epoch: [15/20], 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Generator Loss: 2.1357 D(x): 0.7797, D(G(z)): 0.2378 Epoch: [15/20], Batch Num: [368/600] Discriminator Loss: 0.6608, Generator Loss: 2.0985 D(x): 0.7846, D(G(z)): 0.1916 Epoch: [15/20], Batch Num: [369/600] Discriminator Loss: 0.7810, Generator Loss: 1.8703 D(x): 0.7559, D(G(z)): 0.2282 Epoch: [15/20], Batch Num: [370/600] Discriminator Loss: 0.7028, Generator Loss: 1.8429 D(x): 0.7519, D(G(z)): 0.2036 Epoch: [15/20], Batch Num: [371/600] Discriminator Loss: 0.6037, Generator Loss: 1.8628 D(x): 0.8218, D(G(z)): 0.2393 Epoch: [15/20], Batch Num: [372/600] Discriminator Loss: 0.7158, Generator Loss: 1.9972 D(x): 0.7844, D(G(z)): 0.2548 Epoch: [15/20], Batch Num: [373/600] Discriminator Loss: 0.7128, Generator Loss: 1.9168 D(x): 0.8214, D(G(z)): 0.2453 Epoch: [15/20], Batch Num: [374/600] Discriminator Loss: 0.8049, Generator Loss: 2.3139 D(x): 0.7335, D(G(z)): 0.2358 Epoch: [15/20], Batch Num: [375/600] Discriminator Loss: 0.6647, Generator Loss: 2.1729 D(x): 0.7662, D(G(z)): 0.1812 Epoch: [15/20], Batch Num: [376/600] Discriminator Loss: 0.7674, Generator Loss: 2.1445 D(x): 0.7203, D(G(z)): 0.1715 Epoch: [15/20], Batch Num: [377/600] Discriminator Loss: 0.6824, Generator Loss: 1.8323 D(x): 0.8012, D(G(z)): 0.2281 Epoch: [15/20], Batch Num: [378/600] Discriminator Loss: 0.7646, Generator Loss: 2.0212 D(x): 0.7835, D(G(z)): 0.2499 Epoch: [15/20], Batch Num: [379/600] Discriminator Loss: 0.6975, Generator Loss: 1.9194 D(x): 0.8023, D(G(z)): 0.2500 Epoch: [15/20], Batch Num: [380/600] Discriminator Loss: 0.5872, Generator Loss: 1.8584 D(x): 0.8057, D(G(z)): 0.1880 Epoch: [15/20], Batch Num: [381/600] Discriminator Loss: 0.7497, Generator Loss: 1.9884 D(x): 0.7422, D(G(z)): 0.2171 Epoch: [15/20], Batch Num: [382/600] Discriminator Loss: 0.5904, Generator Loss: 1.9389 D(x): 0.8053, D(G(z)): 0.2072 Epoch: [15/20], Batch Num: [383/600] Discriminator Loss: 0.6903, Generator Loss: 2.2549 D(x): 0.7681, D(G(z)): 0.2160 Epoch: [15/20], Batch Num: [384/600] Discriminator Loss: 0.6345, Generator Loss: 2.0090 D(x): 0.7684, D(G(z)): 0.1815 Epoch: [15/20], Batch Num: [385/600] Discriminator Loss: 0.7927, Generator Loss: 1.7919 D(x): 0.7000, D(G(z)): 0.1910 Epoch: [15/20], Batch Num: [386/600] Discriminator Loss: 0.6651, Generator Loss: 1.6771 D(x): 0.7991, D(G(z)): 0.2327 Epoch: [15/20], Batch Num: [387/600] Discriminator Loss: 0.6369, Generator Loss: 1.7263 D(x): 0.8361, D(G(z)): 0.2436 Epoch: [15/20], Batch Num: [388/600] Discriminator Loss: 0.6482, Generator Loss: 1.8554 D(x): 0.8493, D(G(z)): 0.2756 Epoch: [15/20], Batch Num: [389/600] Discriminator Loss: 0.6408, Generator Loss: 2.5482 D(x): 0.8188, D(G(z)): 0.2418 Epoch: [15/20], Batch Num: [390/600] Discriminator Loss: 0.6236, Generator Loss: 2.7030 D(x): 0.7397, D(G(z)): 0.1452 Epoch: [15/20], Batch Num: [391/600] Discriminator Loss: 0.4915, Generator Loss: 2.3370 D(x): 0.8073, D(G(z)): 0.1383 Epoch: [15/20], Batch Num: [392/600] Discriminator Loss: 0.6682, Generator Loss: 2.3092 D(x): 0.7648, D(G(z)): 0.1646 Epoch: [15/20], Batch Num: [393/600] Discriminator Loss: 0.6902, Generator Loss: 1.8879 D(x): 0.7517, D(G(z)): 0.1729 Epoch: [15/20], Batch Num: [394/600] Discriminator Loss: 0.7716, Generator Loss: 1.5250 D(x): 0.7821, D(G(z)): 0.2210 Epoch: [15/20], Batch Num: [395/600] Discriminator Loss: 0.8140, Generator Loss: 1.6949 D(x): 0.8600, D(G(z)): 0.3088 Epoch: [15/20], Batch Num: [396/600] Discriminator Loss: 0.6231, Generator Loss: 2.0256 D(x): 0.8750, D(G(z)): 0.2866 Epoch: [15/20], Batch Num: [397/600] Discriminator Loss: 0.7587, Generator Loss: 2.5046 D(x): 0.7484, D(G(z)): 0.1997 Epoch: [15/20], Batch Num: [398/600] Discriminator Loss: 0.6753, Generator Loss: 2.5054 D(x): 0.7397, D(G(z)): 0.1419 Epoch: [15/20], Batch Num: [399/600] Discriminator Loss: 0.8364, Generator Loss: 2.2566 D(x): 0.7236, D(G(z)): 0.1614 Epoch: 15, Batch Num: [400/600]
Epoch: [15/20], Batch Num: [400/600] Discriminator Loss: 0.7011, Generator Loss: 2.0899 D(x): 0.7616, D(G(z)): 0.1833 Epoch: [15/20], Batch Num: [401/600] Discriminator Loss: 0.6263, Generator Loss: 1.5757 D(x): 0.7642, D(G(z)): 0.1815 Epoch: [15/20], Batch Num: [402/600] Discriminator Loss: 0.8570, Generator Loss: 1.6049 D(x): 0.7962, D(G(z)): 0.3297 Epoch: [15/20], Batch Num: [403/600] Discriminator Loss: 0.8390, Generator Loss: 1.6758 D(x): 0.8075, D(G(z)): 0.3463 Epoch: [15/20], Batch Num: [404/600] Discriminator Loss: 0.7731, Generator Loss: 2.2851 D(x): 0.8211, D(G(z)): 0.3117 Epoch: [15/20], Batch Num: [405/600] Discriminator Loss: 0.7400, Generator Loss: 2.4121 D(x): 0.7363, D(G(z)): 0.1671 Epoch: [15/20], Batch Num: [406/600] Discriminator Loss: 0.8518, Generator Loss: 2.1518 D(x): 0.6403, D(G(z)): 0.1267 Epoch: [15/20], Batch Num: [407/600] Discriminator Loss: 0.7728, Generator Loss: 1.7892 D(x): 0.6847, D(G(z)): 0.1395 Epoch: [15/20], Batch Num: [408/600] Discriminator Loss: 0.8912, Generator Loss: 1.6382 D(x): 0.7467, D(G(z)): 0.2993 Epoch: [15/20], Batch Num: [409/600] Discriminator Loss: 0.6752, Generator Loss: 1.3259 D(x): 0.8423, D(G(z)): 0.2846 Epoch: [15/20], Batch Num: [410/600] Discriminator Loss: 0.7076, Generator Loss: 1.5757 D(x): 0.8280, D(G(z)): 0.2926 Epoch: [15/20], Batch Num: [411/600] Discriminator Loss: 0.8680, Generator Loss: 1.7145 D(x): 0.7883, D(G(z)): 0.3301 Epoch: [15/20], Batch Num: [412/600] Discriminator Loss: 0.7302, Generator Loss: 1.9935 D(x): 0.8046, D(G(z)): 0.2671 Epoch: [15/20], Batch Num: [413/600] Discriminator Loss: 0.7561, Generator Loss: 2.1767 D(x): 0.7247, D(G(z)): 0.1895 Epoch: [15/20], Batch Num: [414/600] Discriminator Loss: 0.8158, Generator Loss: 2.2935 D(x): 0.7128, D(G(z)): 0.1834 Epoch: [15/20], Batch Num: [415/600] Discriminator Loss: 0.7177, Generator Loss: 2.0417 D(x): 0.7262, D(G(z)): 0.1648 Epoch: [15/20], Batch Num: [416/600] Discriminator Loss: 0.6569, Generator Loss: 1.8561 D(x): 0.7921, D(G(z)): 0.2025 Epoch: [15/20], Batch Num: [417/600] Discriminator Loss: 0.6651, Generator Loss: 1.6612 D(x): 0.7520, D(G(z)): 0.1956 Epoch: [15/20], Batch Num: [418/600] Discriminator Loss: 0.7252, Generator Loss: 1.4806 D(x): 0.7666, D(G(z)): 0.2515 Epoch: [15/20], Batch Num: [419/600] Discriminator Loss: 0.6474, Generator Loss: 1.6347 D(x): 0.8230, D(G(z)): 0.2839 Epoch: [15/20], Batch Num: [420/600] Discriminator Loss: 0.5891, Generator Loss: 1.7447 D(x): 0.8628, D(G(z)): 0.2796 Epoch: [15/20], Batch Num: [421/600] Discriminator Loss: 0.7365, Generator Loss: 1.9447 D(x): 0.7799, D(G(z)): 0.2655 Epoch: [15/20], Batch Num: [422/600] Discriminator Loss: 0.6179, Generator Loss: 2.0725 D(x): 0.7706, D(G(z)): 0.2038 Epoch: [15/20], Batch Num: [423/600] Discriminator Loss: 0.8381, Generator Loss: 2.1214 D(x): 0.6930, D(G(z)): 0.2035 Epoch: [15/20], Batch Num: [424/600] Discriminator Loss: 0.6998, Generator Loss: 2.2064 D(x): 0.7739, D(G(z)): 0.1878 Epoch: [15/20], Batch Num: [425/600] Discriminator Loss: 0.6942, Generator Loss: 2.1762 D(x): 0.7113, D(G(z)): 0.1539 Epoch: [15/20], Batch Num: [426/600] Discriminator Loss: 0.8315, Generator Loss: 1.7219 D(x): 0.6711, D(G(z)): 0.1763 Epoch: [15/20], Batch Num: [427/600] Discriminator Loss: 0.8046, Generator Loss: 1.4297 D(x): 0.7735, D(G(z)): 0.2826 Epoch: [15/20], Batch Num: [428/600] Discriminator Loss: 0.7191, Generator Loss: 1.4847 D(x): 0.8344, D(G(z)): 0.3157 Epoch: [15/20], Batch Num: [429/600] Discriminator Loss: 0.7682, Generator Loss: 1.4256 D(x): 0.7965, D(G(z)): 0.2781 Epoch: [15/20], Batch Num: [430/600] Discriminator Loss: 0.7329, Generator Loss: 1.9602 D(x): 0.8121, D(G(z)): 0.2739 Epoch: [15/20], Batch Num: [431/600] Discriminator Loss: 0.6740, Generator Loss: 2.3128 D(x): 0.7846, D(G(z)): 0.2272 Epoch: [15/20], Batch Num: [432/600] Discriminator Loss: 0.7736, Generator Loss: 2.1915 D(x): 0.7251, D(G(z)): 0.2023 Epoch: [15/20], Batch Num: [433/600] Discriminator Loss: 0.7537, Generator Loss: 2.2650 D(x): 0.7364, D(G(z)): 0.1871 Epoch: [15/20], Batch Num: [434/600] Discriminator Loss: 0.6192, Generator Loss: 2.1250 D(x): 0.7626, D(G(z)): 0.1519 Epoch: [15/20], Batch Num: [435/600] Discriminator Loss: 0.8121, Generator Loss: 1.6878 D(x): 0.7109, D(G(z)): 0.2044 Epoch: [15/20], Batch Num: [436/600] Discriminator Loss: 0.7542, Generator Loss: 1.5402 D(x): 0.7954, D(G(z)): 0.2562 Epoch: [15/20], Batch Num: [437/600] Discriminator Loss: 0.9180, Generator Loss: 1.5408 D(x): 0.7725, D(G(z)): 0.3451 Epoch: [15/20], Batch Num: [438/600] Discriminator Loss: 0.8105, Generator Loss: 1.7588 D(x): 0.7727, D(G(z)): 0.3004 Epoch: [15/20], Batch Num: [439/600] Discriminator Loss: 0.8615, Generator Loss: 2.0823 D(x): 0.7716, D(G(z)): 0.3110 Epoch: [15/20], Batch Num: [440/600] Discriminator Loss: 0.7191, Generator Loss: 1.9994 D(x): 0.7376, D(G(z)): 0.1944 Epoch: [15/20], Batch Num: [441/600] Discriminator Loss: 0.7368, Generator Loss: 2.0983 D(x): 0.7105, D(G(z)): 0.1786 Epoch: [15/20], Batch Num: 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1.8040 D(x): 0.7327, D(G(z)): 0.2081 Epoch: [15/20], Batch Num: [451/600] Discriminator Loss: 0.7597, Generator Loss: 1.8456 D(x): 0.7407, D(G(z)): 0.2284 Epoch: [15/20], Batch Num: [452/600] Discriminator Loss: 0.7939, Generator Loss: 1.7274 D(x): 0.7096, D(G(z)): 0.2358 Epoch: [15/20], Batch Num: [453/600] Discriminator Loss: 0.8551, Generator Loss: 1.6171 D(x): 0.6920, D(G(z)): 0.2524 Epoch: [15/20], Batch Num: [454/600] Discriminator Loss: 0.7832, Generator Loss: 1.4884 D(x): 0.7763, D(G(z)): 0.3122 Epoch: [15/20], Batch Num: [455/600] Discriminator Loss: 0.7858, Generator Loss: 1.6242 D(x): 0.7963, D(G(z)): 0.3156 Epoch: [15/20], Batch Num: [456/600] Discriminator Loss: 0.7376, Generator Loss: 1.7829 D(x): 0.7813, D(G(z)): 0.2885 Epoch: [15/20], Batch Num: [457/600] Discriminator Loss: 0.7343, Generator Loss: 1.8089 D(x): 0.7373, D(G(z)): 0.2275 Epoch: [15/20], Batch Num: [458/600] Discriminator Loss: 0.8210, Generator Loss: 1.7991 D(x): 0.7346, D(G(z)): 0.2469 Epoch: [15/20], 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Generator Loss: 2.0710 D(x): 0.7418, D(G(z)): 0.1790 Epoch: [15/20], Batch Num: [468/600] Discriminator Loss: 0.6812, Generator Loss: 1.8639 D(x): 0.7334, D(G(z)): 0.1902 Epoch: [15/20], Batch Num: [469/600] Discriminator Loss: 0.8360, Generator Loss: 1.6996 D(x): 0.7516, D(G(z)): 0.2780 Epoch: [15/20], Batch Num: [470/600] Discriminator Loss: 0.6921, Generator Loss: 1.9329 D(x): 0.7859, D(G(z)): 0.2358 Epoch: [15/20], Batch Num: [471/600] Discriminator Loss: 0.5658, Generator Loss: 1.7393 D(x): 0.8541, D(G(z)): 0.2524 Epoch: [15/20], Batch Num: [472/600] Discriminator Loss: 0.6765, Generator Loss: 1.7833 D(x): 0.7898, D(G(z)): 0.2240 Epoch: [15/20], Batch Num: [473/600] Discriminator Loss: 0.8048, Generator Loss: 2.0133 D(x): 0.7310, D(G(z)): 0.2441 Epoch: [15/20], Batch Num: [474/600] Discriminator Loss: 0.7670, Generator Loss: 1.9291 D(x): 0.7225, D(G(z)): 0.2176 Epoch: [15/20], Batch Num: [475/600] Discriminator Loss: 0.7623, Generator Loss: 1.8394 D(x): 0.7508, D(G(z)): 0.2462 Epoch: [15/20], Batch Num: [476/600] Discriminator Loss: 0.6737, Generator Loss: 1.8238 D(x): 0.7917, D(G(z)): 0.2386 Epoch: [15/20], Batch Num: [477/600] Discriminator Loss: 0.7698, Generator Loss: 1.8906 D(x): 0.7649, D(G(z)): 0.2585 Epoch: [15/20], Batch Num: [478/600] Discriminator Loss: 0.6559, Generator Loss: 1.8507 D(x): 0.8050, D(G(z)): 0.2578 Epoch: [15/20], Batch Num: [479/600] Discriminator Loss: 0.6768, Generator Loss: 1.9173 D(x): 0.7757, D(G(z)): 0.2122 Epoch: [15/20], Batch Num: [480/600] Discriminator Loss: 0.8161, Generator Loss: 1.9435 D(x): 0.7364, D(G(z)): 0.2315 Epoch: [15/20], Batch Num: [481/600] Discriminator Loss: 0.8102, Generator Loss: 1.8355 D(x): 0.7502, D(G(z)): 0.2410 Epoch: [15/20], Batch Num: [482/600] Discriminator Loss: 0.7269, Generator Loss: 1.9389 D(x): 0.7895, D(G(z)): 0.2406 Epoch: [15/20], Batch Num: [483/600] Discriminator Loss: 0.7538, Generator Loss: 1.7686 D(x): 0.7322, D(G(z)): 0.1990 Epoch: [15/20], Batch Num: [484/600] Discriminator Loss: 0.8129, Generator Loss: 1.8283 D(x): 0.7518, D(G(z)): 0.2559 Epoch: [15/20], Batch Num: [485/600] Discriminator Loss: 0.5973, Generator Loss: 1.7497 D(x): 0.7896, D(G(z)): 0.1958 Epoch: [15/20], Batch Num: [486/600] Discriminator Loss: 0.7080, Generator Loss: 1.6879 D(x): 0.8022, D(G(z)): 0.2427 Epoch: [15/20], Batch Num: [487/600] Discriminator Loss: 0.7172, Generator Loss: 2.0292 D(x): 0.8285, D(G(z)): 0.2763 Epoch: [15/20], Batch Num: [488/600] Discriminator Loss: 0.6733, Generator Loss: 1.9510 D(x): 0.7867, D(G(z)): 0.2222 Epoch: [15/20], Batch Num: [489/600] Discriminator Loss: 0.7646, Generator Loss: 2.1366 D(x): 0.7469, D(G(z)): 0.2186 Epoch: [15/20], Batch Num: [490/600] Discriminator Loss: 0.8692, Generator Loss: 2.0105 D(x): 0.6761, D(G(z)): 0.2035 Epoch: [15/20], Batch Num: [491/600] Discriminator Loss: 0.6763, Generator Loss: 1.8194 D(x): 0.7956, D(G(z)): 0.2200 Epoch: [15/20], Batch Num: [492/600] Discriminator Loss: 0.6977, Generator Loss: 1.6949 D(x): 0.7677, D(G(z)): 0.2113 Epoch: [15/20], Batch Num: [493/600] Discriminator Loss: 0.8677, Generator Loss: 1.6199 D(x): 0.7987, D(G(z)): 0.3327 Epoch: [15/20], Batch Num: [494/600] Discriminator Loss: 0.8743, Generator Loss: 1.9579 D(x): 0.8005, D(G(z)): 0.3059 Epoch: [15/20], Batch Num: [495/600] Discriminator Loss: 0.6364, Generator Loss: 2.2720 D(x): 0.8295, D(G(z)): 0.2493 Epoch: [15/20], Batch Num: [496/600] Discriminator Loss: 0.9429, Generator Loss: 2.2289 D(x): 0.6604, D(G(z)): 0.1772 Epoch: [15/20], Batch Num: [497/600] Discriminator Loss: 0.6466, Generator Loss: 2.2828 D(x): 0.7210, D(G(z)): 0.1169 Epoch: [15/20], Batch Num: [498/600] Discriminator Loss: 0.7792, Generator Loss: 2.0012 D(x): 0.7469, D(G(z)): 0.2318 Epoch: [15/20], Batch Num: [499/600] Discriminator Loss: 0.7642, Generator Loss: 1.6062 D(x): 0.7644, D(G(z)): 0.2255 Epoch: 15, Batch Num: [500/600]
Epoch: [15/20], Batch Num: [500/600] Discriminator Loss: 0.5763, Generator Loss: 1.6446 D(x): 0.8456, D(G(z)): 0.2441 Epoch: [15/20], Batch Num: [501/600] Discriminator Loss: 0.6298, Generator Loss: 1.7868 D(x): 0.8361, D(G(z)): 0.2671 Epoch: [15/20], Batch Num: [502/600] Discriminator Loss: 0.6349, Generator Loss: 1.8337 D(x): 0.7924, D(G(z)): 0.2180 Epoch: [15/20], Batch Num: [503/600] Discriminator Loss: 0.7189, Generator Loss: 1.8841 D(x): 0.7760, D(G(z)): 0.2382 Epoch: [15/20], Batch Num: [504/600] Discriminator Loss: 0.6873, Generator Loss: 1.7133 D(x): 0.7519, D(G(z)): 0.2054 Epoch: [15/20], Batch Num: [505/600] Discriminator Loss: 0.5528, Generator Loss: 1.8994 D(x): 0.7904, D(G(z)): 0.1741 Epoch: [15/20], Batch Num: [506/600] Discriminator Loss: 0.6015, Generator Loss: 1.9608 D(x): 0.7925, D(G(z)): 0.2072 Epoch: [15/20], Batch Num: [507/600] Discriminator Loss: 0.7676, Generator Loss: 1.7913 D(x): 0.7828, D(G(z)): 0.2609 Epoch: [15/20], Batch Num: [508/600] Discriminator Loss: 0.5888, Generator Loss: 2.0321 D(x): 0.8128, D(G(z)): 0.2342 Epoch: [15/20], Batch Num: [509/600] Discriminator Loss: 0.6732, Generator Loss: 2.3159 D(x): 0.8191, D(G(z)): 0.2425 Epoch: [15/20], Batch Num: [510/600] Discriminator Loss: 0.7089, Generator Loss: 2.1899 D(x): 0.7343, D(G(z)): 0.1723 Epoch: [15/20], Batch Num: [511/600] Discriminator Loss: 0.6725, Generator Loss: 2.2278 D(x): 0.7868, D(G(z)): 0.1783 Epoch: [15/20], Batch Num: [512/600] Discriminator Loss: 0.7075, Generator Loss: 1.9112 D(x): 0.7496, D(G(z)): 0.1829 Epoch: [15/20], Batch Num: [513/600] Discriminator Loss: 0.6262, Generator Loss: 1.8791 D(x): 0.8028, D(G(z)): 0.1907 Epoch: [15/20], Batch Num: [514/600] Discriminator Loss: 0.7484, Generator Loss: 1.7764 D(x): 0.8074, D(G(z)): 0.2455 Epoch: [15/20], Batch Num: [515/600] Discriminator Loss: 0.6153, Generator Loss: 1.9894 D(x): 0.8411, D(G(z)): 0.2572 Epoch: [15/20], Batch Num: [516/600] Discriminator Loss: 0.7605, Generator Loss: 2.3351 D(x): 0.7848, D(G(z)): 0.2307 Epoch: [15/20], Batch Num: [517/600] Discriminator Loss: 0.8706, Generator Loss: 2.2315 D(x): 0.6910, D(G(z)): 0.1911 Epoch: [15/20], Batch Num: [518/600] Discriminator Loss: 0.8037, Generator Loss: 1.8973 D(x): 0.6740, D(G(z)): 0.1400 Epoch: [15/20], Batch Num: [519/600] Discriminator Loss: 0.7520, Generator Loss: 1.4323 D(x): 0.7453, D(G(z)): 0.2051 Epoch: [15/20], Batch Num: [520/600] Discriminator Loss: 0.8691, Generator Loss: 1.3806 D(x): 0.7900, D(G(z)): 0.3181 Epoch: [15/20], Batch Num: [521/600] Discriminator Loss: 0.8102, Generator Loss: 1.5420 D(x): 0.8298, D(G(z)): 0.3460 Epoch: [15/20], Batch Num: [522/600] Discriminator Loss: 0.7246, Generator Loss: 1.8818 D(x): 0.8105, D(G(z)): 0.2878 Epoch: [15/20], Batch Num: [523/600] Discriminator Loss: 0.6702, Generator Loss: 2.0928 D(x): 0.7808, D(G(z)): 0.2346 Epoch: [15/20], Batch Num: [524/600] Discriminator Loss: 0.8217, Generator Loss: 2.3246 D(x): 0.6910, D(G(z)): 0.2017 Epoch: [15/20], Batch Num: [525/600] Discriminator Loss: 0.8016, Generator Loss: 2.0876 D(x): 0.6693, D(G(z)): 0.1643 Epoch: [15/20], Batch Num: [526/600] Discriminator Loss: 0.7576, Generator Loss: 1.8454 D(x): 0.7677, D(G(z)): 0.2206 Epoch: [15/20], Batch Num: [527/600] Discriminator Loss: 0.9369, Generator Loss: 1.6702 D(x): 0.6889, D(G(z)): 0.2528 Epoch: [15/20], Batch Num: [528/600] Discriminator Loss: 0.6364, Generator Loss: 1.7807 D(x): 0.8191, D(G(z)): 0.2577 Epoch: [15/20], Batch Num: [529/600] Discriminator Loss: 0.7925, Generator Loss: 1.4311 D(x): 0.7967, D(G(z)): 0.3047 Epoch: [15/20], Batch Num: [530/600] Discriminator Loss: 0.7821, Generator Loss: 1.8321 D(x): 0.8016, D(G(z)): 0.3094 Epoch: [15/20], Batch Num: [531/600] Discriminator Loss: 0.8978, Generator Loss: 1.8591 D(x): 0.7279, D(G(z)): 0.2810 Epoch: [15/20], Batch Num: [532/600] Discriminator Loss: 0.6670, Generator Loss: 1.9434 D(x): 0.7568, D(G(z)): 0.2078 Epoch: [15/20], Batch Num: [533/600] Discriminator Loss: 0.7212, Generator Loss: 2.0118 D(x): 0.7360, D(G(z)): 0.2043 Epoch: [15/20], Batch Num: [534/600] Discriminator Loss: 0.6813, Generator Loss: 1.7389 D(x): 0.7357, D(G(z)): 0.1907 Epoch: [15/20], Batch Num: [535/600] Discriminator Loss: 0.8100, Generator Loss: 1.5848 D(x): 0.7361, D(G(z)): 0.2695 Epoch: [15/20], Batch Num: [536/600] Discriminator Loss: 0.6431, Generator Loss: 1.6023 D(x): 0.7883, D(G(z)): 0.2351 Epoch: [15/20], Batch Num: [537/600] Discriminator Loss: 0.7274, Generator Loss: 1.5951 D(x): 0.7970, D(G(z)): 0.2816 Epoch: [15/20], Batch Num: [538/600] Discriminator Loss: 0.7083, Generator Loss: 1.7683 D(x): 0.7835, D(G(z)): 0.2780 Epoch: [15/20], Batch Num: [539/600] Discriminator Loss: 0.8125, Generator Loss: 1.7230 D(x): 0.7692, D(G(z)): 0.2925 Epoch: [15/20], Batch Num: [540/600] Discriminator Loss: 0.6886, Generator Loss: 2.0528 D(x): 0.7572, D(G(z)): 0.2072 Epoch: [15/20], Batch Num: [541/600] Discriminator Loss: 0.7455, Generator Loss: 2.0491 D(x): 0.7094, D(G(z)): 0.2000 Epoch: [15/20], Batch Num: [542/600] Discriminator Loss: 0.7779, Generator Loss: 1.9196 D(x): 0.7060, D(G(z)): 0.2205 Epoch: [15/20], Batch Num: [543/600] Discriminator Loss: 0.6684, Generator Loss: 1.8024 D(x): 0.7396, D(G(z)): 0.1877 Epoch: [15/20], Batch Num: [544/600] Discriminator Loss: 0.7144, Generator Loss: 1.8006 D(x): 0.8348, D(G(z)): 0.2949 Epoch: [15/20], Batch Num: [545/600] Discriminator Loss: 0.9162, Generator Loss: 1.9571 D(x): 0.7895, D(G(z)): 0.3024 Epoch: [15/20], Batch Num: [546/600] Discriminator Loss: 0.8015, Generator Loss: 2.2099 D(x): 0.7236, D(G(z)): 0.2062 Epoch: [15/20], Batch Num: [547/600] Discriminator Loss: 0.7104, Generator Loss: 2.0739 D(x): 0.7312, D(G(z)): 0.1843 Epoch: [15/20], Batch Num: [548/600] Discriminator Loss: 0.6868, Generator Loss: 1.8087 D(x): 0.7616, D(G(z)): 0.1949 Epoch: [15/20], Batch Num: [549/600] Discriminator Loss: 0.6947, Generator Loss: 1.7579 D(x): 0.7924, D(G(z)): 0.2660 Epoch: [15/20], Batch Num: [550/600] Discriminator Loss: 0.7627, Generator Loss: 1.8479 D(x): 0.7597, D(G(z)): 0.2171 Epoch: [15/20], Batch Num: [551/600] Discriminator Loss: 0.5532, Generator Loss: 1.7255 D(x): 0.8468, D(G(z)): 0.2144 Epoch: [15/20], Batch Num: [552/600] Discriminator Loss: 0.5618, Generator Loss: 2.0917 D(x): 0.8546, D(G(z)): 0.2299 Epoch: [15/20], Batch Num: [553/600] Discriminator Loss: 0.5018, Generator Loss: 2.4217 D(x): 0.8442, D(G(z)): 0.1848 Epoch: [15/20], Batch Num: [554/600] Discriminator Loss: 0.6964, Generator Loss: 2.4110 D(x): 0.7437, D(G(z)): 0.1652 Epoch: [15/20], Batch Num: [555/600] Discriminator Loss: 0.7000, Generator Loss: 2.0249 D(x): 0.7254, D(G(z)): 0.1839 Epoch: [15/20], Batch Num: [556/600] Discriminator Loss: 0.6259, Generator Loss: 1.7671 D(x): 0.7818, D(G(z)): 0.1981 Epoch: [15/20], Batch Num: [557/600] Discriminator Loss: 0.6977, Generator Loss: 1.9339 D(x): 0.8423, D(G(z)): 0.2640 Epoch: [15/20], Batch Num: [558/600] Discriminator Loss: 0.6285, Generator Loss: 2.1459 D(x): 0.8208, D(G(z)): 0.2437 Epoch: [15/20], Batch Num: [559/600] Discriminator Loss: 0.6414, Generator Loss: 2.4594 D(x): 0.8299, D(G(z)): 0.2309 Epoch: [15/20], Batch Num: [560/600] Discriminator Loss: 0.7962, Generator Loss: 2.4834 D(x): 0.7080, D(G(z)): 0.1403 Epoch: [15/20], Batch Num: [561/600] Discriminator Loss: 0.7174, Generator Loss: 2.1096 D(x): 0.7519, D(G(z)): 0.1750 Epoch: [15/20], Batch Num: [562/600] Discriminator Loss: 0.7687, Generator Loss: 1.9700 D(x): 0.7323, D(G(z)): 0.2246 Epoch: [15/20], Batch Num: [563/600] Discriminator Loss: 0.5437, Generator Loss: 1.8382 D(x): 0.8472, D(G(z)): 0.2239 Epoch: [15/20], Batch Num: [564/600] Discriminator Loss: 0.7095, Generator Loss: 1.9914 D(x): 0.8299, D(G(z)): 0.2657 Epoch: [15/20], Batch Num: [565/600] Discriminator Loss: 0.6772, Generator Loss: 1.9718 D(x): 0.7716, D(G(z)): 0.2176 Epoch: [15/20], Batch Num: [566/600] Discriminator Loss: 0.6648, Generator Loss: 1.9656 D(x): 0.7869, D(G(z)): 0.2023 Epoch: [15/20], Batch Num: [567/600] Discriminator Loss: 0.7165, Generator Loss: 2.1283 D(x): 0.7329, D(G(z)): 0.1930 Epoch: [15/20], Batch Num: [568/600] Discriminator Loss: 0.6440, Generator Loss: 1.9560 D(x): 0.7876, D(G(z)): 0.2011 Epoch: [15/20], Batch Num: [569/600] Discriminator Loss: 0.4887, Generator Loss: 2.2818 D(x): 0.8799, D(G(z)): 0.2152 Epoch: [15/20], Batch Num: [570/600] Discriminator Loss: 0.6797, Generator Loss: 2.1884 D(x): 0.7642, D(G(z)): 0.1879 Epoch: [15/20], Batch Num: [571/600] Discriminator Loss: 0.9224, Generator Loss: 2.0402 D(x): 0.7417, D(G(z)): 0.2530 Epoch: [15/20], Batch Num: [572/600] Discriminator Loss: 0.7485, Generator Loss: 2.0467 D(x): 0.7661, D(G(z)): 0.2425 Epoch: [15/20], Batch Num: [573/600] Discriminator Loss: 0.7130, Generator Loss: 2.0596 D(x): 0.8086, D(G(z)): 0.2685 Epoch: [15/20], Batch Num: [574/600] Discriminator Loss: 0.7953, Generator Loss: 2.0181 D(x): 0.7358, D(G(z)): 0.2233 Epoch: [15/20], Batch Num: [575/600] Discriminator Loss: 0.7356, Generator Loss: 2.1490 D(x): 0.7410, D(G(z)): 0.1837 Epoch: [15/20], Batch Num: [576/600] Discriminator Loss: 0.7689, Generator Loss: 1.9491 D(x): 0.7398, D(G(z)): 0.2262 Epoch: [15/20], Batch Num: [577/600] Discriminator Loss: 0.7407, Generator Loss: 1.7315 D(x): 0.8039, D(G(z)): 0.2838 Epoch: [15/20], Batch Num: [578/600] Discriminator Loss: 0.7420, Generator Loss: 2.1052 D(x): 0.8135, D(G(z)): 0.2791 Epoch: [15/20], Batch Num: [579/600] Discriminator Loss: 0.6928, Generator Loss: 2.1589 D(x): 0.7599, D(G(z)): 0.2189 Epoch: [15/20], Batch Num: [580/600] Discriminator Loss: 0.7080, Generator Loss: 2.1086 D(x): 0.7435, D(G(z)): 0.2024 Epoch: [15/20], Batch Num: [581/600] Discriminator Loss: 0.8495, Generator Loss: 2.1911 D(x): 0.7108, D(G(z)): 0.2074 Epoch: [15/20], Batch Num: [582/600] Discriminator Loss: 0.7282, Generator Loss: 1.9126 D(x): 0.7828, D(G(z)): 0.2188 Epoch: [15/20], Batch Num: [583/600] Discriminator Loss: 0.7719, Generator Loss: 2.0462 D(x): 0.7732, D(G(z)): 0.2453 Epoch: [15/20], Batch Num: [584/600] Discriminator Loss: 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Epoch: [16/20], Batch Num: [0/600] Discriminator Loss: 0.6774, Generator Loss: 1.7286 D(x): 0.7871, D(G(z)): 0.2548 Epoch: [16/20], Batch Num: [1/600] Discriminator Loss: 0.7168, Generator Loss: 1.9106 D(x): 0.7492, D(G(z)): 0.2300 Epoch: [16/20], Batch Num: [2/600] Discriminator Loss: 0.8187, Generator Loss: 2.0155 D(x): 0.7521, D(G(z)): 0.2438 Epoch: [16/20], Batch Num: [3/600] Discriminator Loss: 0.7429, Generator Loss: 1.8167 D(x): 0.7239, D(G(z)): 0.2295 Epoch: [16/20], Batch Num: [4/600] Discriminator Loss: 0.7307, Generator Loss: 1.7714 D(x): 0.7495, D(G(z)): 0.2288 Epoch: [16/20], Batch Num: [5/600] Discriminator Loss: 0.7570, Generator Loss: 1.8821 D(x): 0.7690, D(G(z)): 0.2585 Epoch: [16/20], Batch Num: [6/600] Discriminator Loss: 0.7036, Generator Loss: 1.9149 D(x): 0.7885, D(G(z)): 0.2548 Epoch: [16/20], Batch Num: [7/600] Discriminator Loss: 0.7809, Generator Loss: 2.0611 D(x): 0.7745, D(G(z)): 0.2377 Epoch: [16/20], Batch Num: [8/600] Discriminator Loss: 0.7011, Generator 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D(x): 0.7893, D(G(z)): 0.2589 Epoch: [16/20], Batch Num: [26/600] Discriminator Loss: 0.6712, Generator Loss: 2.2502 D(x): 0.8319, D(G(z)): 0.2535 Epoch: [16/20], Batch Num: [27/600] Discriminator Loss: 0.5503, Generator Loss: 2.5963 D(x): 0.8011, D(G(z)): 0.1880 Epoch: [16/20], Batch Num: [28/600] Discriminator Loss: 0.6659, Generator Loss: 2.3854 D(x): 0.7434, D(G(z)): 0.1867 Epoch: [16/20], Batch Num: [29/600] Discriminator Loss: 0.7519, Generator Loss: 2.3598 D(x): 0.7506, D(G(z)): 0.1844 Epoch: [16/20], Batch Num: [30/600] Discriminator Loss: 0.6304, Generator Loss: 1.9618 D(x): 0.7788, D(G(z)): 0.1872 Epoch: [16/20], Batch Num: [31/600] Discriminator Loss: 0.7338, Generator Loss: 1.9160 D(x): 0.8273, D(G(z)): 0.2720 Epoch: [16/20], Batch Num: [32/600] Discriminator Loss: 0.8027, Generator Loss: 1.6088 D(x): 0.7646, D(G(z)): 0.2551 Epoch: [16/20], Batch Num: [33/600] Discriminator Loss: 0.6530, Generator Loss: 1.9483 D(x): 0.8285, D(G(z)): 0.2658 Epoch: [16/20], Batch Num: 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D(x): 0.8044, D(G(z)): 0.3142 Epoch: [16/20], Batch Num: [43/600] Discriminator Loss: 0.8241, Generator Loss: 1.9395 D(x): 0.7127, D(G(z)): 0.2417 Epoch: [16/20], Batch Num: [44/600] Discriminator Loss: 0.6886, Generator Loss: 2.0991 D(x): 0.7859, D(G(z)): 0.2503 Epoch: [16/20], Batch Num: [45/600] Discriminator Loss: 0.9299, Generator Loss: 1.7608 D(x): 0.6281, D(G(z)): 0.1821 Epoch: [16/20], Batch Num: [46/600] Discriminator Loss: 0.9243, Generator Loss: 1.7669 D(x): 0.7081, D(G(z)): 0.2736 Epoch: [16/20], Batch Num: [47/600] Discriminator Loss: 0.7357, Generator Loss: 1.5236 D(x): 0.7549, D(G(z)): 0.2548 Epoch: [16/20], Batch Num: [48/600] Discriminator Loss: 0.7409, Generator Loss: 1.4899 D(x): 0.7887, D(G(z)): 0.2855 Epoch: [16/20], Batch Num: [49/600] Discriminator Loss: 0.7401, Generator Loss: 1.7695 D(x): 0.8042, D(G(z)): 0.2896 Epoch: [16/20], Batch Num: [50/600] Discriminator Loss: 0.6577, Generator Loss: 1.8783 D(x): 0.8113, D(G(z)): 0.2374 Epoch: [16/20], Batch Num: [51/600] Discriminator Loss: 0.8480, Generator Loss: 1.9580 D(x): 0.6778, D(G(z)): 0.2087 Epoch: [16/20], Batch Num: [52/600] Discriminator Loss: 0.6449, Generator Loss: 1.8293 D(x): 0.7284, D(G(z)): 0.1733 Epoch: [16/20], Batch Num: [53/600] Discriminator Loss: 0.8029, Generator Loss: 1.6644 D(x): 0.7427, D(G(z)): 0.2519 Epoch: [16/20], Batch Num: [54/600] Discriminator Loss: 0.8093, Generator Loss: 1.8288 D(x): 0.7850, D(G(z)): 0.2933 Epoch: [16/20], Batch Num: [55/600] Discriminator Loss: 0.8214, Generator Loss: 1.8502 D(x): 0.7350, D(G(z)): 0.2602 Epoch: [16/20], Batch Num: [56/600] Discriminator Loss: 0.6232, Generator Loss: 1.7344 D(x): 0.7818, D(G(z)): 0.2152 Epoch: [16/20], Batch Num: [57/600] Discriminator Loss: 0.6269, Generator Loss: 1.7011 D(x): 0.8008, D(G(z)): 0.2367 Epoch: [16/20], Batch Num: [58/600] Discriminator Loss: 0.7136, Generator Loss: 1.9711 D(x): 0.7699, D(G(z)): 0.2463 Epoch: [16/20], Batch Num: [59/600] Discriminator Loss: 0.8518, Generator Loss: 1.9035 D(x): 0.7187, D(G(z)): 0.2466 Epoch: [16/20], Batch Num: [60/600] Discriminator Loss: 0.6748, Generator Loss: 1.9072 D(x): 0.7690, D(G(z)): 0.2096 Epoch: [16/20], Batch Num: [61/600] Discriminator Loss: 0.6316, Generator Loss: 1.7166 D(x): 0.7953, D(G(z)): 0.2355 Epoch: [16/20], Batch Num: [62/600] Discriminator Loss: 0.7346, Generator Loss: 1.7360 D(x): 0.7998, D(G(z)): 0.2529 Epoch: [16/20], Batch Num: [63/600] Discriminator Loss: 0.8454, Generator Loss: 1.6259 D(x): 0.6973, D(G(z)): 0.2265 Epoch: [16/20], Batch Num: [64/600] Discriminator Loss: 0.8084, Generator Loss: 1.7706 D(x): 0.7372, D(G(z)): 0.2788 Epoch: [16/20], Batch Num: [65/600] Discriminator Loss: 0.8293, Generator Loss: 1.8247 D(x): 0.7858, D(G(z)): 0.2997 Epoch: [16/20], Batch Num: [66/600] Discriminator Loss: 0.7056, Generator Loss: 1.7162 D(x): 0.7546, D(G(z)): 0.2188 Epoch: [16/20], Batch Num: [67/600] Discriminator Loss: 0.6314, Generator Loss: 1.7253 D(x): 0.8065, D(G(z)): 0.2366 Epoch: [16/20], Batch Num: [68/600] Discriminator Loss: 0.6442, Generator Loss: 1.8672 D(x): 0.8186, D(G(z)): 0.2257 Epoch: [16/20], Batch Num: [69/600] Discriminator Loss: 0.7624, Generator Loss: 1.8868 D(x): 0.7284, D(G(z)): 0.2076 Epoch: [16/20], Batch Num: [70/600] Discriminator Loss: 0.7094, Generator Loss: 2.0727 D(x): 0.7667, D(G(z)): 0.2171 Epoch: [16/20], Batch Num: [71/600] Discriminator Loss: 0.7053, Generator Loss: 1.8311 D(x): 0.7439, D(G(z)): 0.1951 Epoch: [16/20], Batch Num: [72/600] Discriminator Loss: 0.8802, Generator Loss: 1.8650 D(x): 0.7355, D(G(z)): 0.2656 Epoch: [16/20], Batch Num: [73/600] Discriminator Loss: 0.8141, Generator Loss: 1.7452 D(x): 0.7958, D(G(z)): 0.3033 Epoch: [16/20], Batch Num: [74/600] Discriminator Loss: 0.7022, Generator Loss: 2.0907 D(x): 0.8325, D(G(z)): 0.2853 Epoch: [16/20], Batch Num: [75/600] Discriminator Loss: 0.7399, Generator Loss: 2.4271 D(x): 0.7373, D(G(z)): 0.1954 Epoch: [16/20], Batch Num: [76/600] Discriminator Loss: 0.8222, Generator Loss: 2.1898 D(x): 0.7034, D(G(z)): 0.1831 Epoch: [16/20], Batch Num: [77/600] Discriminator Loss: 0.7622, Generator Loss: 2.0607 D(x): 0.6903, D(G(z)): 0.1725 Epoch: [16/20], Batch Num: [78/600] Discriminator Loss: 0.9110, Generator Loss: 1.5108 D(x): 0.7063, D(G(z)): 0.2497 Epoch: [16/20], Batch Num: [79/600] Discriminator Loss: 0.6738, Generator Loss: 1.4162 D(x): 0.8121, D(G(z)): 0.2707 Epoch: [16/20], Batch Num: [80/600] Discriminator Loss: 0.6973, Generator Loss: 1.4967 D(x): 0.8514, D(G(z)): 0.3198 Epoch: [16/20], Batch Num: [81/600] Discriminator Loss: 0.8685, Generator Loss: 1.7630 D(x): 0.7774, D(G(z)): 0.3285 Epoch: [16/20], Batch Num: [82/600] Discriminator Loss: 0.6995, Generator Loss: 2.1509 D(x): 0.7962, D(G(z)): 0.2683 Epoch: [16/20], Batch Num: [83/600] Discriminator Loss: 0.7263, Generator Loss: 2.3084 D(x): 0.7447, D(G(z)): 0.1985 Epoch: [16/20], Batch Num: [84/600] Discriminator Loss: 0.8675, Generator Loss: 1.9660 D(x): 0.6507, D(G(z)): 0.1739 Epoch: [16/20], Batch Num: [85/600] Discriminator Loss: 0.8795, Generator Loss: 1.7160 D(x): 0.6951, D(G(z)): 0.2394 Epoch: [16/20], Batch Num: [86/600] Discriminator Loss: 0.9065, Generator Loss: 1.7796 D(x): 0.7643, D(G(z)): 0.3097 Epoch: [16/20], Batch Num: [87/600] Discriminator Loss: 0.9388, Generator Loss: 1.8109 D(x): 0.7823, D(G(z)): 0.3367 Epoch: [16/20], Batch Num: [88/600] Discriminator Loss: 0.9495, Generator Loss: 1.8484 D(x): 0.7504, D(G(z)): 0.2951 Epoch: [16/20], Batch Num: [89/600] Discriminator Loss: 0.8097, Generator Loss: 2.0900 D(x): 0.7468, D(G(z)): 0.2685 Epoch: [16/20], Batch Num: [90/600] Discriminator Loss: 0.7589, Generator Loss: 1.9969 D(x): 0.7246, D(G(z)): 0.2208 Epoch: [16/20], Batch Num: [91/600] Discriminator Loss: 0.7711, Generator Loss: 1.8617 D(x): 0.7447, D(G(z)): 0.2045 Epoch: [16/20], Batch Num: [92/600] Discriminator Loss: 0.8425, Generator Loss: 1.7917 D(x): 0.6878, D(G(z)): 0.2194 Epoch: [16/20], Batch Num: [93/600] Discriminator Loss: 0.8251, Generator Loss: 1.4682 D(x): 0.6997, D(G(z)): 0.2408 Epoch: [16/20], Batch Num: [94/600] Discriminator Loss: 0.7991, Generator Loss: 1.4212 D(x): 0.7713, D(G(z)): 0.3153 Epoch: [16/20], Batch Num: [95/600] Discriminator Loss: 0.7752, Generator Loss: 1.3187 D(x): 0.7979, D(G(z)): 0.3270 Epoch: [16/20], Batch Num: [96/600] Discriminator Loss: 0.7630, Generator Loss: 1.5851 D(x): 0.7965, D(G(z)): 0.3198 Epoch: [16/20], Batch Num: [97/600] Discriminator Loss: 0.7475, Generator Loss: 1.9817 D(x): 0.7710, D(G(z)): 0.2913 Epoch: [16/20], Batch Num: [98/600] Discriminator Loss: 0.8783, Generator Loss: 1.7853 D(x): 0.7177, D(G(z)): 0.2536 Epoch: [16/20], Batch Num: [99/600] Discriminator Loss: 0.8455, Generator Loss: 2.2572 D(x): 0.6870, D(G(z)): 0.1980 Epoch: 16, Batch Num: [100/600]
Epoch: [16/20], Batch Num: [100/600] Discriminator Loss: 0.8262, Generator Loss: 1.8288 D(x): 0.7098, D(G(z)): 0.2154 Epoch: [16/20], Batch Num: [101/600] Discriminator Loss: 0.7904, Generator Loss: 1.7699 D(x): 0.7646, D(G(z)): 0.2495 Epoch: [16/20], Batch Num: [102/600] Discriminator Loss: 0.8167, Generator Loss: 1.5600 D(x): 0.7452, D(G(z)): 0.2563 Epoch: [16/20], Batch Num: [103/600] Discriminator Loss: 0.7941, Generator Loss: 1.5192 D(x): 0.7551, D(G(z)): 0.2931 Epoch: [16/20], Batch Num: [104/600] Discriminator Loss: 0.7426, Generator Loss: 1.5934 D(x): 0.7747, D(G(z)): 0.2875 Epoch: [16/20], Batch Num: [105/600] Discriminator Loss: 0.7816, Generator Loss: 1.6145 D(x): 0.7505, D(G(z)): 0.2889 Epoch: [16/20], Batch Num: [106/600] Discriminator Loss: 0.8469, Generator Loss: 1.9157 D(x): 0.7803, D(G(z)): 0.3131 Epoch: [16/20], Batch Num: [107/600] Discriminator Loss: 0.8428, Generator Loss: 2.1578 D(x): 0.7504, D(G(z)): 0.2620 Epoch: [16/20], Batch Num: [108/600] Discriminator Loss: 0.9375, Generator Loss: 1.7880 D(x): 0.6290, D(G(z)): 0.1962 Epoch: [16/20], Batch Num: [109/600] Discriminator Loss: 0.7683, Generator Loss: 1.6409 D(x): 0.6799, D(G(z)): 0.1887 Epoch: [16/20], Batch Num: [110/600] Discriminator Loss: 0.7301, Generator Loss: 1.5487 D(x): 0.7787, D(G(z)): 0.2883 Epoch: [16/20], Batch Num: [111/600] Discriminator Loss: 0.7643, Generator Loss: 1.7735 D(x): 0.8014, D(G(z)): 0.3181 Epoch: [16/20], Batch Num: [112/600] Discriminator Loss: 0.8045, Generator Loss: 1.7320 D(x): 0.7618, D(G(z)): 0.2682 Epoch: [16/20], Batch Num: [113/600] Discriminator Loss: 0.6871, Generator Loss: 1.7677 D(x): 0.7773, D(G(z)): 0.2397 Epoch: [16/20], Batch Num: [114/600] Discriminator Loss: 0.7560, Generator Loss: 1.9065 D(x): 0.7635, D(G(z)): 0.2473 Epoch: [16/20], Batch Num: [115/600] Discriminator Loss: 0.7595, Generator Loss: 1.7864 D(x): 0.7100, D(G(z)): 0.2129 Epoch: [16/20], Batch Num: [116/600] Discriminator Loss: 0.6921, Generator Loss: 1.7243 D(x): 0.7443, D(G(z)): 0.1958 Epoch: [16/20], Batch Num: [117/600] Discriminator Loss: 0.7183, Generator Loss: 1.5353 D(x): 0.7670, D(G(z)): 0.2485 Epoch: [16/20], Batch Num: [118/600] Discriminator Loss: 0.8382, Generator Loss: 1.5123 D(x): 0.7618, D(G(z)): 0.2865 Epoch: [16/20], Batch Num: [119/600] Discriminator Loss: 0.8295, Generator Loss: 1.7669 D(x): 0.7989, D(G(z)): 0.2893 Epoch: [16/20], Batch Num: [120/600] Discriminator Loss: 0.6760, Generator Loss: 1.8883 D(x): 0.7771, D(G(z)): 0.2201 Epoch: [16/20], Batch Num: [121/600] Discriminator Loss: 0.7419, Generator Loss: 1.8817 D(x): 0.7251, D(G(z)): 0.1921 Epoch: [16/20], Batch Num: [122/600] Discriminator Loss: 0.7713, Generator Loss: 1.6283 D(x): 0.7095, D(G(z)): 0.1981 Epoch: [16/20], Batch Num: [123/600] Discriminator Loss: 0.6958, Generator Loss: 1.6961 D(x): 0.7944, D(G(z)): 0.2644 Epoch: [16/20], Batch Num: [124/600] Discriminator Loss: 0.8372, Generator Loss: 1.8985 D(x): 0.7896, D(G(z)): 0.3138 Epoch: [16/20], Batch Num: [125/600] Discriminator Loss: 0.8281, Generator Loss: 2.0577 D(x): 0.7379, D(G(z)): 0.2679 Epoch: [16/20], Batch Num: [126/600] Discriminator Loss: 0.8952, Generator Loss: 1.9666 D(x): 0.6658, D(G(z)): 0.2326 Epoch: [16/20], Batch Num: [127/600] Discriminator Loss: 0.7115, Generator Loss: 1.9905 D(x): 0.7769, D(G(z)): 0.2339 Epoch: [16/20], Batch Num: [128/600] Discriminator Loss: 0.7442, Generator Loss: 1.8302 D(x): 0.7132, D(G(z)): 0.2024 Epoch: [16/20], Batch Num: [129/600] Discriminator Loss: 0.6755, Generator Loss: 1.7897 D(x): 0.7859, D(G(z)): 0.2264 Epoch: [16/20], Batch Num: [130/600] Discriminator Loss: 0.6687, Generator Loss: 1.7438 D(x): 0.8083, D(G(z)): 0.2749 Epoch: [16/20], Batch Num: [131/600] Discriminator Loss: 0.6538, Generator Loss: 1.7679 D(x): 0.7953, D(G(z)): 0.2458 Epoch: [16/20], Batch Num: [132/600] Discriminator Loss: 0.7708, Generator Loss: 1.7887 D(x): 0.7172, D(G(z)): 0.2281 Epoch: [16/20], Batch Num: [133/600] Discriminator Loss: 0.7756, Generator Loss: 1.6715 D(x): 0.7715, D(G(z)): 0.2815 Epoch: [16/20], Batch Num: [134/600] Discriminator Loss: 0.6828, Generator Loss: 1.6946 D(x): 0.7785, D(G(z)): 0.2253 Epoch: [16/20], Batch Num: [135/600] Discriminator Loss: 0.7893, Generator Loss: 1.8533 D(x): 0.7563, D(G(z)): 0.2706 Epoch: [16/20], Batch Num: [136/600] Discriminator Loss: 0.8311, Generator Loss: 1.9076 D(x): 0.7570, D(G(z)): 0.2920 Epoch: [16/20], Batch Num: [137/600] Discriminator Loss: 0.7236, Generator Loss: 2.1318 D(x): 0.7887, D(G(z)): 0.2574 Epoch: [16/20], Batch Num: [138/600] Discriminator Loss: 0.7879, Generator Loss: 2.0545 D(x): 0.7239, D(G(z)): 0.1933 Epoch: [16/20], Batch Num: [139/600] Discriminator Loss: 0.9468, Generator Loss: 1.8981 D(x): 0.6386, D(G(z)): 0.2041 Epoch: [16/20], Batch Num: [140/600] Discriminator Loss: 0.7005, Generator Loss: 1.6092 D(x): 0.7561, D(G(z)): 0.2095 Epoch: [16/20], Batch Num: [141/600] Discriminator Loss: 0.8792, Generator Loss: 1.5500 D(x): 0.7595, D(G(z)): 0.2618 Epoch: [16/20], Batch Num: [142/600] Discriminator Loss: 0.6927, Generator Loss: 1.5030 D(x): 0.8636, D(G(z)): 0.3278 Epoch: [16/20], Batch Num: [143/600] Discriminator Loss: 0.6744, Generator Loss: 1.8718 D(x): 0.8480, D(G(z)): 0.2932 Epoch: [16/20], Batch Num: [144/600] Discriminator Loss: 0.6631, Generator Loss: 2.2064 D(x): 0.7777, D(G(z)): 0.2413 Epoch: [16/20], Batch Num: [145/600] Discriminator Loss: 0.7782, Generator Loss: 2.3056 D(x): 0.6889, D(G(z)): 0.1743 Epoch: [16/20], Batch Num: [146/600] Discriminator Loss: 0.7387, Generator Loss: 1.9927 D(x): 0.7010, D(G(z)): 0.1723 Epoch: [16/20], Batch Num: [147/600] Discriminator Loss: 0.6485, Generator Loss: 1.8911 D(x): 0.7651, D(G(z)): 0.2105 Epoch: [16/20], Batch Num: [148/600] Discriminator Loss: 0.7095, Generator Loss: 1.6839 D(x): 0.7997, D(G(z)): 0.2641 Epoch: [16/20], Batch Num: [149/600] Discriminator Loss: 0.7546, Generator Loss: 1.5427 D(x): 0.7573, D(G(z)): 0.2752 Epoch: [16/20], Batch Num: [150/600] Discriminator Loss: 0.6682, Generator Loss: 1.6146 D(x): 0.8216, D(G(z)): 0.2868 Epoch: [16/20], Batch Num: [151/600] Discriminator Loss: 0.6990, Generator Loss: 1.7250 D(x): 0.7811, D(G(z)): 0.2602 Epoch: [16/20], Batch Num: [152/600] Discriminator Loss: 0.7534, Generator Loss: 1.8509 D(x): 0.7648, D(G(z)): 0.2784 Epoch: [16/20], Batch Num: [153/600] Discriminator Loss: 0.6604, Generator Loss: 1.9452 D(x): 0.7733, D(G(z)): 0.2133 Epoch: [16/20], Batch Num: [154/600] Discriminator Loss: 0.7707, Generator Loss: 2.0604 D(x): 0.7299, D(G(z)): 0.2323 Epoch: [16/20], Batch Num: [155/600] Discriminator Loss: 0.8048, Generator Loss: 1.7923 D(x): 0.7082, D(G(z)): 0.1929 Epoch: [16/20], Batch Num: [156/600] Discriminator Loss: 0.7880, Generator Loss: 1.8644 D(x): 0.7609, D(G(z)): 0.2713 Epoch: [16/20], Batch Num: [157/600] Discriminator Loss: 0.8575, Generator Loss: 1.8259 D(x): 0.7557, D(G(z)): 0.2926 Epoch: [16/20], Batch Num: [158/600] Discriminator Loss: 0.8119, Generator Loss: 1.8827 D(x): 0.7556, D(G(z)): 0.2492 Epoch: [16/20], Batch Num: [159/600] Discriminator Loss: 0.7099, Generator Loss: 2.0169 D(x): 0.7680, D(G(z)): 0.2390 Epoch: [16/20], Batch Num: [160/600] Discriminator Loss: 0.7260, Generator Loss: 1.9494 D(x): 0.7480, D(G(z)): 0.2037 Epoch: [16/20], Batch Num: [161/600] Discriminator Loss: 0.6407, Generator Loss: 1.8982 D(x): 0.7787, D(G(z)): 0.2093 Epoch: [16/20], Batch Num: [162/600] Discriminator Loss: 0.6585, Generator Loss: 1.8401 D(x): 0.7617, D(G(z)): 0.2036 Epoch: [16/20], Batch Num: [163/600] Discriminator Loss: 0.6378, Generator Loss: 1.6364 D(x): 0.7852, D(G(z)): 0.2369 Epoch: [16/20], Batch Num: [164/600] Discriminator Loss: 0.6925, Generator Loss: 1.6812 D(x): 0.7931, D(G(z)): 0.2495 Epoch: [16/20], Batch Num: [165/600] Discriminator Loss: 0.6215, Generator Loss: 1.8751 D(x): 0.8172, D(G(z)): 0.2544 Epoch: [16/20], Batch Num: [166/600] Discriminator Loss: 0.7362, Generator Loss: 1.8203 D(x): 0.7960, D(G(z)): 0.2738 Epoch: [16/20], Batch Num: [167/600] Discriminator Loss: 0.6689, Generator Loss: 2.1923 D(x): 0.7992, D(G(z)): 0.2406 Epoch: [16/20], Batch Num: [168/600] Discriminator Loss: 0.8190, Generator Loss: 2.1254 D(x): 0.7237, D(G(z)): 0.1986 Epoch: [16/20], Batch Num: [169/600] Discriminator Loss: 0.8752, Generator Loss: 2.1813 D(x): 0.6938, D(G(z)): 0.1952 Epoch: [16/20], Batch Num: [170/600] Discriminator Loss: 0.7975, Generator Loss: 2.1157 D(x): 0.7538, D(G(z)): 0.2243 Epoch: [16/20], Batch Num: [171/600] Discriminator Loss: 0.7703, Generator Loss: 1.8288 D(x): 0.7863, D(G(z)): 0.2534 Epoch: [16/20], Batch Num: [172/600] Discriminator Loss: 0.6898, Generator Loss: 2.0219 D(x): 0.8308, D(G(z)): 0.2661 Epoch: [16/20], Batch Num: [173/600] Discriminator Loss: 0.7791, Generator Loss: 1.8632 D(x): 0.7679, D(G(z)): 0.2669 Epoch: [16/20], Batch Num: [174/600] Discriminator Loss: 0.7627, Generator Loss: 1.9309 D(x): 0.7378, D(G(z)): 0.2318 Epoch: [16/20], Batch Num: [175/600] Discriminator Loss: 0.7657, Generator Loss: 2.1449 D(x): 0.7637, D(G(z)): 0.2304 Epoch: [16/20], Batch Num: [176/600] Discriminator Loss: 0.8156, Generator Loss: 2.0490 D(x): 0.6988, D(G(z)): 0.1912 Epoch: [16/20], Batch Num: [177/600] Discriminator Loss: 0.8303, Generator Loss: 1.7408 D(x): 0.7126, D(G(z)): 0.2370 Epoch: [16/20], Batch Num: [178/600] Discriminator Loss: 0.7692, Generator Loss: 1.7859 D(x): 0.7713, D(G(z)): 0.2672 Epoch: [16/20], Batch Num: [179/600] Discriminator Loss: 0.9809, Generator Loss: 1.5457 D(x): 0.7219, D(G(z)): 0.2853 Epoch: [16/20], Batch Num: [180/600] Discriminator Loss: 0.7395, Generator Loss: 1.5104 D(x): 0.7654, D(G(z)): 0.2817 Epoch: [16/20], Batch Num: [181/600] Discriminator Loss: 0.7908, Generator Loss: 1.7895 D(x): 0.7203, D(G(z)): 0.2629 Epoch: [16/20], Batch Num: [182/600] Discriminator Loss: 0.7568, Generator Loss: 1.5198 D(x): 0.7155, D(G(z)): 0.2405 Epoch: [16/20], Batch Num: [183/600] Discriminator Loss: 0.7221, Generator Loss: 1.4974 D(x): 0.7377, D(G(z)): 0.2559 Epoch: [16/20], Batch Num: [184/600] Discriminator Loss: 0.7768, Generator Loss: 1.8763 D(x): 0.7948, D(G(z)): 0.2967 Epoch: [16/20], Batch Num: [185/600] Discriminator Loss: 0.7781, Generator Loss: 1.8579 D(x): 0.7561, D(G(z)): 0.2936 Epoch: [16/20], Batch Num: [186/600] Discriminator Loss: 0.6653, Generator Loss: 1.8937 D(x): 0.7656, D(G(z)): 0.2203 Epoch: [16/20], Batch Num: [187/600] Discriminator Loss: 0.7192, Generator Loss: 2.1768 D(x): 0.7170, D(G(z)): 0.2143 Epoch: [16/20], Batch Num: [188/600] Discriminator Loss: 0.8464, Generator Loss: 1.8088 D(x): 0.7145, D(G(z)): 0.1962 Epoch: [16/20], Batch Num: [189/600] Discriminator Loss: 0.7087, Generator Loss: 1.9428 D(x): 0.7829, D(G(z)): 0.2501 Epoch: [16/20], Batch Num: [190/600] Discriminator Loss: 0.6207, Generator Loss: 1.5600 D(x): 0.8139, D(G(z)): 0.2340 Epoch: [16/20], Batch Num: [191/600] Discriminator Loss: 0.6339, Generator Loss: 1.8573 D(x): 0.8191, D(G(z)): 0.2505 Epoch: [16/20], Batch Num: [192/600] Discriminator Loss: 0.8366, Generator Loss: 2.0386 D(x): 0.7697, D(G(z)): 0.2774 Epoch: [16/20], Batch Num: [193/600] Discriminator Loss: 0.7249, Generator Loss: 2.1236 D(x): 0.7210, D(G(z)): 0.1800 Epoch: [16/20], Batch Num: [194/600] Discriminator Loss: 0.6839, Generator Loss: 2.0767 D(x): 0.7740, D(G(z)): 0.2145 Epoch: [16/20], Batch Num: [195/600] Discriminator Loss: 0.7008, Generator Loss: 1.7763 D(x): 0.7493, D(G(z)): 0.1934 Epoch: [16/20], Batch Num: [196/600] Discriminator Loss: 0.6444, Generator Loss: 1.6341 D(x): 0.7614, D(G(z)): 0.1907 Epoch: [16/20], Batch Num: [197/600] Discriminator Loss: 0.6238, Generator Loss: 1.6101 D(x): 0.8050, D(G(z)): 0.2071 Epoch: [16/20], Batch Num: [198/600] Discriminator Loss: 0.7734, Generator Loss: 1.7347 D(x): 0.8450, D(G(z)): 0.3179 Epoch: [16/20], Batch Num: [199/600] Discriminator Loss: 0.7372, Generator Loss: 2.2124 D(x): 0.8577, D(G(z)): 0.2910 Epoch: 16, Batch Num: [200/600]
Epoch: [16/20], Batch Num: [200/600] Discriminator Loss: 0.6935, Generator Loss: 2.4760 D(x): 0.7508, D(G(z)): 0.1911 Epoch: [16/20], Batch Num: [201/600] Discriminator Loss: 0.9312, Generator Loss: 2.3713 D(x): 0.6415, D(G(z)): 0.1324 Epoch: [16/20], Batch Num: [202/600] Discriminator Loss: 0.7505, Generator Loss: 2.0513 D(x): 0.7249, D(G(z)): 0.1828 Epoch: [16/20], Batch Num: [203/600] Discriminator Loss: 0.8335, Generator Loss: 1.6516 D(x): 0.7029, D(G(z)): 0.1859 Epoch: [16/20], Batch Num: [204/600] Discriminator Loss: 0.7095, Generator Loss: 1.4210 D(x): 0.8311, D(G(z)): 0.2876 Epoch: [16/20], Batch Num: [205/600] Discriminator Loss: 0.7583, Generator Loss: 1.6694 D(x): 0.8221, D(G(z)): 0.3215 Epoch: [16/20], Batch Num: [206/600] Discriminator Loss: 0.6922, Generator Loss: 1.8481 D(x): 0.8060, D(G(z)): 0.2854 Epoch: [16/20], Batch Num: [207/600] Discriminator Loss: 0.7799, Generator Loss: 2.0306 D(x): 0.7729, D(G(z)): 0.2629 Epoch: [16/20], Batch Num: [208/600] Discriminator Loss: 0.7698, Generator Loss: 2.0875 D(x): 0.7266, D(G(z)): 0.2262 Epoch: [16/20], Batch Num: [209/600] Discriminator Loss: 0.6707, Generator Loss: 1.9292 D(x): 0.7803, D(G(z)): 0.2289 Epoch: [16/20], Batch Num: [210/600] Discriminator Loss: 0.8121, Generator Loss: 1.9682 D(x): 0.7110, D(G(z)): 0.2111 Epoch: [16/20], Batch Num: [211/600] Discriminator Loss: 0.6082, Generator Loss: 1.8508 D(x): 0.8007, D(G(z)): 0.2175 Epoch: [16/20], Batch Num: [212/600] Discriminator Loss: 0.8339, Generator Loss: 1.7053 D(x): 0.7276, D(G(z)): 0.2440 Epoch: [16/20], Batch Num: [213/600] Discriminator Loss: 0.6628, Generator Loss: 1.6689 D(x): 0.8010, D(G(z)): 0.2509 Epoch: [16/20], Batch Num: [214/600] Discriminator Loss: 0.7790, Generator Loss: 1.7117 D(x): 0.7861, D(G(z)): 0.2968 Epoch: [16/20], Batch Num: [215/600] Discriminator Loss: 0.7352, Generator Loss: 1.7874 D(x): 0.8036, D(G(z)): 0.2797 Epoch: [16/20], Batch Num: [216/600] Discriminator Loss: 0.8650, Generator Loss: 2.0173 D(x): 0.7482, D(G(z)): 0.2719 Epoch: [16/20], Batch Num: [217/600] Discriminator Loss: 0.7033, Generator Loss: 2.3145 D(x): 0.7463, D(G(z)): 0.2118 Epoch: [16/20], Batch Num: [218/600] Discriminator Loss: 0.7248, Generator Loss: 2.2563 D(x): 0.7244, D(G(z)): 0.1895 Epoch: [16/20], Batch Num: [219/600] Discriminator Loss: 0.6335, Generator Loss: 2.0371 D(x): 0.7808, D(G(z)): 0.2127 Epoch: [16/20], Batch Num: [220/600] Discriminator Loss: 0.7541, Generator Loss: 1.8287 D(x): 0.7312, D(G(z)): 0.2028 Epoch: [16/20], Batch Num: [221/600] Discriminator Loss: 0.7218, Generator Loss: 1.7968 D(x): 0.7581, D(G(z)): 0.2292 Epoch: [16/20], Batch Num: [222/600] Discriminator Loss: 0.7616, Generator Loss: 1.5937 D(x): 0.7487, D(G(z)): 0.2514 Epoch: [16/20], Batch Num: [223/600] Discriminator Loss: 0.7799, Generator Loss: 1.6231 D(x): 0.8100, D(G(z)): 0.2988 Epoch: [16/20], Batch Num: [224/600] Discriminator Loss: 0.7751, Generator Loss: 1.8361 D(x): 0.8456, D(G(z)): 0.3196 Epoch: [16/20], Batch Num: [225/600] Discriminator Loss: 0.7232, Generator Loss: 2.0815 D(x): 0.7468, D(G(z)): 0.2469 Epoch: [16/20], Batch Num: [226/600] Discriminator Loss: 0.8650, Generator Loss: 2.0971 D(x): 0.7089, D(G(z)): 0.2081 Epoch: [16/20], Batch Num: [227/600] Discriminator Loss: 0.7968, Generator Loss: 2.1358 D(x): 0.7167, D(G(z)): 0.2032 Epoch: [16/20], Batch Num: [228/600] Discriminator Loss: 0.7245, Generator Loss: 1.8903 D(x): 0.7375, D(G(z)): 0.1972 Epoch: [16/20], Batch Num: [229/600] Discriminator Loss: 0.9746, Generator Loss: 1.6743 D(x): 0.7271, D(G(z)): 0.3037 Epoch: [16/20], Batch Num: [230/600] Discriminator Loss: 0.7951, Generator Loss: 1.7148 D(x): 0.7545, D(G(z)): 0.2679 Epoch: [16/20], Batch Num: [231/600] Discriminator Loss: 0.8223, Generator Loss: 1.8836 D(x): 0.7721, D(G(z)): 0.2855 Epoch: [16/20], Batch Num: [232/600] Discriminator Loss: 0.7559, Generator Loss: 1.7860 D(x): 0.7255, D(G(z)): 0.1966 Epoch: [16/20], Batch Num: [233/600] Discriminator Loss: 0.7190, Generator Loss: 1.6230 D(x): 0.7625, D(G(z)): 0.2394 Epoch: [16/20], Batch Num: [234/600] Discriminator Loss: 0.7472, Generator Loss: 1.8367 D(x): 0.7888, D(G(z)): 0.2980 Epoch: [16/20], Batch Num: [235/600] Discriminator Loss: 0.7285, Generator Loss: 1.9744 D(x): 0.7581, D(G(z)): 0.2561 Epoch: [16/20], Batch Num: [236/600] Discriminator Loss: 0.8269, Generator Loss: 1.7923 D(x): 0.7367, D(G(z)): 0.2810 Epoch: [16/20], Batch Num: [237/600] Discriminator Loss: 0.7339, Generator Loss: 2.1290 D(x): 0.7796, D(G(z)): 0.2629 Epoch: [16/20], Batch Num: [238/600] Discriminator Loss: 0.7177, Generator Loss: 2.2599 D(x): 0.7311, D(G(z)): 0.2085 Epoch: [16/20], Batch Num: [239/600] Discriminator Loss: 0.7484, Generator Loss: 1.9271 D(x): 0.6938, D(G(z)): 0.1762 Epoch: [16/20], Batch Num: [240/600] Discriminator Loss: 0.7151, Generator Loss: 1.7269 D(x): 0.7771, D(G(z)): 0.2292 Epoch: [16/20], Batch Num: [241/600] Discriminator Loss: 0.8327, Generator Loss: 1.7147 D(x): 0.8052, D(G(z)): 0.2878 Epoch: [16/20], Batch Num: 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1.7836 D(x): 0.7830, D(G(z)): 0.3188 Epoch: [16/20], Batch Num: [251/600] Discriminator Loss: 0.8025, Generator Loss: 1.9403 D(x): 0.7529, D(G(z)): 0.2666 Epoch: [16/20], Batch Num: [252/600] Discriminator Loss: 0.7480, Generator Loss: 2.0954 D(x): 0.7373, D(G(z)): 0.2264 Epoch: [16/20], Batch Num: [253/600] Discriminator Loss: 0.8455, Generator Loss: 1.9021 D(x): 0.7124, D(G(z)): 0.2095 Epoch: [16/20], Batch Num: [254/600] Discriminator Loss: 0.6943, Generator Loss: 1.9685 D(x): 0.7512, D(G(z)): 0.2039 Epoch: [16/20], Batch Num: [255/600] Discriminator Loss: 0.6474, Generator Loss: 2.0905 D(x): 0.7837, D(G(z)): 0.2215 Epoch: [16/20], Batch Num: [256/600] Discriminator Loss: 0.5813, Generator Loss: 1.7454 D(x): 0.8018, D(G(z)): 0.2206 Epoch: [16/20], Batch Num: [257/600] Discriminator Loss: 0.7988, Generator Loss: 1.8659 D(x): 0.7588, D(G(z)): 0.2465 Epoch: [16/20], Batch Num: [258/600] Discriminator Loss: 0.8127, Generator Loss: 1.9762 D(x): 0.7502, D(G(z)): 0.2859 Epoch: [16/20], Batch Num: [259/600] Discriminator Loss: 0.7850, Generator Loss: 2.2208 D(x): 0.7202, D(G(z)): 0.2294 Epoch: [16/20], Batch Num: [260/600] Discriminator Loss: 0.7954, Generator Loss: 1.8255 D(x): 0.6912, D(G(z)): 0.1811 Epoch: [16/20], Batch Num: [261/600] Discriminator Loss: 0.7380, Generator Loss: 1.6022 D(x): 0.7993, D(G(z)): 0.2726 Epoch: [16/20], Batch Num: [262/600] Discriminator Loss: 0.7445, Generator Loss: 1.8510 D(x): 0.7723, D(G(z)): 0.2698 Epoch: [16/20], Batch Num: [263/600] Discriminator Loss: 0.7003, Generator Loss: 2.0745 D(x): 0.8126, D(G(z)): 0.2597 Epoch: [16/20], Batch Num: [264/600] Discriminator Loss: 0.6070, Generator Loss: 2.2002 D(x): 0.7797, D(G(z)): 0.1980 Epoch: [16/20], Batch Num: [265/600] Discriminator Loss: 0.6260, Generator Loss: 2.1607 D(x): 0.7973, D(G(z)): 0.2086 Epoch: [16/20], Batch Num: [266/600] Discriminator Loss: 0.7097, Generator Loss: 2.0897 D(x): 0.7400, D(G(z)): 0.2055 Epoch: [16/20], Batch Num: [267/600] Discriminator Loss: 0.7107, Generator Loss: 1.8848 D(x): 0.7638, D(G(z)): 0.2117 Epoch: [16/20], Batch Num: [268/600] Discriminator Loss: 0.6889, Generator Loss: 1.9833 D(x): 0.7631, D(G(z)): 0.2303 Epoch: [16/20], Batch Num: [269/600] Discriminator Loss: 0.7167, Generator Loss: 1.9548 D(x): 0.7694, D(G(z)): 0.2332 Epoch: [16/20], Batch Num: [270/600] Discriminator Loss: 0.8124, Generator Loss: 1.8854 D(x): 0.7468, D(G(z)): 0.2387 Epoch: [16/20], Batch Num: [271/600] Discriminator Loss: 0.7184, Generator Loss: 1.8672 D(x): 0.8166, D(G(z)): 0.2730 Epoch: [16/20], Batch Num: [272/600] Discriminator Loss: 0.6674, Generator Loss: 1.7764 D(x): 0.7747, D(G(z)): 0.2144 Epoch: [16/20], Batch Num: [273/600] Discriminator Loss: 0.8158, Generator Loss: 2.2345 D(x): 0.7817, D(G(z)): 0.2541 Epoch: [16/20], Batch Num: [274/600] Discriminator Loss: 0.7067, Generator Loss: 2.2102 D(x): 0.6993, D(G(z)): 0.1689 Epoch: [16/20], Batch Num: [275/600] Discriminator Loss: 0.5951, Generator Loss: 1.8637 D(x): 0.7789, D(G(z)): 0.1776 Epoch: [16/20], Batch Num: [276/600] Discriminator Loss: 0.6310, Generator Loss: 1.6200 D(x): 0.7798, D(G(z)): 0.2216 Epoch: [16/20], Batch Num: [277/600] Discriminator Loss: 0.7939, Generator Loss: 1.5472 D(x): 0.7452, D(G(z)): 0.2370 Epoch: [16/20], Batch Num: [278/600] Discriminator Loss: 0.8400, Generator Loss: 1.5820 D(x): 0.7993, D(G(z)): 0.3268 Epoch: [16/20], Batch Num: [279/600] Discriminator Loss: 0.7657, Generator Loss: 1.9478 D(x): 0.8047, D(G(z)): 0.2866 Epoch: [16/20], Batch Num: [280/600] Discriminator Loss: 0.8424, Generator Loss: 2.1582 D(x): 0.7787, D(G(z)): 0.2720 Epoch: [16/20], Batch Num: [281/600] Discriminator Loss: 0.7398, Generator Loss: 2.2939 D(x): 0.7321, D(G(z)): 0.2215 Epoch: [16/20], Batch Num: [282/600] Discriminator Loss: 0.9833, Generator Loss: 2.2504 D(x): 0.6515, D(G(z)): 0.2150 Epoch: [16/20], Batch Num: [283/600] Discriminator Loss: 0.8287, Generator Loss: 1.8510 D(x): 0.6734, D(G(z)): 0.1883 Epoch: [16/20], Batch Num: [284/600] Discriminator Loss: 0.7648, Generator Loss: 1.6587 D(x): 0.7708, D(G(z)): 0.2385 Epoch: [16/20], Batch Num: [285/600] Discriminator Loss: 0.7411, Generator Loss: 1.4188 D(x): 0.8305, D(G(z)): 0.3183 Epoch: [16/20], Batch Num: [286/600] Discriminator Loss: 0.7684, Generator Loss: 1.6768 D(x): 0.8307, D(G(z)): 0.3270 Epoch: [16/20], Batch Num: [287/600] Discriminator Loss: 0.6304, Generator Loss: 1.9193 D(x): 0.8212, D(G(z)): 0.2531 Epoch: [16/20], Batch Num: [288/600] Discriminator Loss: 0.6977, Generator Loss: 2.3474 D(x): 0.7306, D(G(z)): 0.1982 Epoch: [16/20], Batch Num: [289/600] Discriminator Loss: 0.7460, Generator Loss: 2.3241 D(x): 0.7335, D(G(z)): 0.1914 Epoch: [16/20], Batch Num: [290/600] Discriminator Loss: 0.6583, Generator Loss: 1.9941 D(x): 0.7483, D(G(z)): 0.1525 Epoch: [16/20], Batch Num: [291/600] Discriminator Loss: 0.6945, Generator Loss: 1.6859 D(x): 0.7419, D(G(z)): 0.1847 Epoch: [16/20], Batch Num: [292/600] Discriminator Loss: 0.6146, Generator Loss: 1.6545 D(x): 0.8347, D(G(z)): 0.2637 Epoch: [16/20], Batch Num: [293/600] Discriminator Loss: 0.6962, Generator Loss: 1.6372 D(x): 0.7938, D(G(z)): 0.2561 Epoch: [16/20], Batch Num: [294/600] Discriminator Loss: 0.7010, Generator Loss: 1.8206 D(x): 0.8291, D(G(z)): 0.3001 Epoch: [16/20], Batch Num: [295/600] Discriminator Loss: 0.8924, Generator Loss: 2.2165 D(x): 0.7409, D(G(z)): 0.2828 Epoch: [16/20], Batch Num: [296/600] Discriminator Loss: 0.7356, Generator Loss: 2.1863 D(x): 0.7564, D(G(z)): 0.2142 Epoch: [16/20], Batch Num: [297/600] Discriminator Loss: 0.7366, Generator Loss: 2.2565 D(x): 0.7226, D(G(z)): 0.1982 Epoch: [16/20], Batch Num: [298/600] Discriminator Loss: 0.7836, Generator Loss: 1.9031 D(x): 0.7304, D(G(z)): 0.2008 Epoch: [16/20], Batch Num: [299/600] Discriminator Loss: 0.7480, Generator Loss: 1.6405 D(x): 0.7278, D(G(z)): 0.1957 Epoch: 16, Batch Num: [300/600]
Epoch: [16/20], Batch Num: [300/600] Discriminator Loss: 0.6529, Generator Loss: 1.4319 D(x): 0.8383, D(G(z)): 0.2908 Epoch: [16/20], Batch Num: [301/600] Discriminator Loss: 0.6849, Generator Loss: 1.9348 D(x): 0.8432, D(G(z)): 0.3006 Epoch: [16/20], Batch Num: [302/600] Discriminator Loss: 0.6467, Generator Loss: 2.1183 D(x): 0.8168, D(G(z)): 0.2682 Epoch: [16/20], Batch Num: [303/600] Discriminator Loss: 0.8414, Generator Loss: 2.4008 D(x): 0.7279, D(G(z)): 0.2270 Epoch: [16/20], Batch Num: [304/600] Discriminator Loss: 0.8111, Generator Loss: 2.2994 D(x): 0.7114, D(G(z)): 0.1613 Epoch: [16/20], Batch Num: [305/600] Discriminator Loss: 0.6937, Generator Loss: 2.0457 D(x): 0.7288, D(G(z)): 0.1557 Epoch: [16/20], Batch Num: [306/600] Discriminator Loss: 0.7161, Generator Loss: 1.6001 D(x): 0.7476, D(G(z)): 0.2119 Epoch: [16/20], Batch Num: [307/600] Discriminator Loss: 0.8270, Generator Loss: 1.5469 D(x): 0.7931, D(G(z)): 0.3139 Epoch: [16/20], Batch Num: [308/600] Discriminator Loss: 0.7884, Generator Loss: 1.6085 D(x): 0.7956, D(G(z)): 0.3073 Epoch: [16/20], Batch Num: [309/600] Discriminator Loss: 0.7248, Generator Loss: 1.7186 D(x): 0.8050, D(G(z)): 0.2744 Epoch: [16/20], Batch Num: [310/600] Discriminator Loss: 0.6010, Generator Loss: 2.2346 D(x): 0.7974, D(G(z)): 0.2244 Epoch: [16/20], Batch Num: [311/600] Discriminator Loss: 0.7686, Generator Loss: 2.2676 D(x): 0.7277, D(G(z)): 0.2058 Epoch: [16/20], Batch Num: [312/600] Discriminator Loss: 0.7985, Generator Loss: 2.1266 D(x): 0.7085, D(G(z)): 0.2185 Epoch: [16/20], Batch Num: [313/600] Discriminator Loss: 0.8457, Generator Loss: 1.6512 D(x): 0.6566, D(G(z)): 0.1791 Epoch: [16/20], Batch Num: [314/600] Discriminator Loss: 0.8497, Generator Loss: 1.4133 D(x): 0.7183, D(G(z)): 0.2400 Epoch: [16/20], Batch Num: [315/600] Discriminator Loss: 0.9409, Generator Loss: 1.4119 D(x): 0.7888, D(G(z)): 0.3509 Epoch: [16/20], Batch Num: [316/600] Discriminator Loss: 0.8187, Generator Loss: 1.6436 D(x): 0.8378, D(G(z)): 0.3381 Epoch: [16/20], Batch Num: [317/600] Discriminator Loss: 0.7697, Generator Loss: 2.0395 D(x): 0.8028, D(G(z)): 0.3122 Epoch: [16/20], Batch Num: [318/600] Discriminator Loss: 0.8855, Generator Loss: 2.3452 D(x): 0.6874, D(G(z)): 0.1966 Epoch: [16/20], Batch Num: [319/600] Discriminator Loss: 0.8021, Generator Loss: 1.9460 D(x): 0.6850, D(G(z)): 0.1917 Epoch: [16/20], Batch Num: [320/600] Discriminator Loss: 0.8675, Generator Loss: 1.8661 D(x): 0.6989, D(G(z)): 0.2276 Epoch: [16/20], Batch Num: [321/600] Discriminator Loss: 0.8482, Generator Loss: 1.4701 D(x): 0.6886, D(G(z)): 0.1997 Epoch: [16/20], Batch Num: [322/600] Discriminator Loss: 0.8219, Generator Loss: 1.5418 D(x): 0.7965, D(G(z)): 0.3040 Epoch: [16/20], Batch Num: [323/600] Discriminator Loss: 0.8895, Generator Loss: 1.4763 D(x): 0.7830, D(G(z)): 0.3231 Epoch: [16/20], Batch Num: [324/600] Discriminator Loss: 0.8106, Generator Loss: 1.5501 D(x): 0.7994, D(G(z)): 0.3182 Epoch: [16/20], Batch Num: [325/600] Discriminator Loss: 0.7634, Generator Loss: 1.7767 D(x): 0.7971, D(G(z)): 0.2859 Epoch: [16/20], Batch Num: [326/600] Discriminator Loss: 0.9778, Generator Loss: 1.8194 D(x): 0.6605, D(G(z)): 0.2448 Epoch: [16/20], Batch Num: [327/600] Discriminator Loss: 0.8993, Generator Loss: 1.7583 D(x): 0.6749, D(G(z)): 0.2213 Epoch: [16/20], Batch Num: [328/600] Discriminator Loss: 0.9466, Generator Loss: 1.6186 D(x): 0.6323, D(G(z)): 0.2298 Epoch: [16/20], Batch Num: [329/600] Discriminator Loss: 0.8190, Generator Loss: 1.4094 D(x): 0.7421, D(G(z)): 0.2812 Epoch: [16/20], Batch Num: [330/600] Discriminator Loss: 0.8134, Generator Loss: 1.3147 D(x): 0.7542, D(G(z)): 0.3077 Epoch: [16/20], Batch Num: [331/600] Discriminator Loss: 0.7014, Generator Loss: 1.3047 D(x): 0.8052, D(G(z)): 0.2922 Epoch: [16/20], Batch Num: [332/600] Discriminator Loss: 0.7503, Generator Loss: 1.6596 D(x): 0.7987, D(G(z)): 0.3094 Epoch: [16/20], Batch Num: [333/600] Discriminator Loss: 0.8476, Generator Loss: 1.8059 D(x): 0.7799, D(G(z)): 0.3344 Epoch: [16/20], Batch Num: [334/600] Discriminator Loss: 0.7746, Generator Loss: 2.0585 D(x): 0.7191, D(G(z)): 0.2534 Epoch: [16/20], Batch Num: [335/600] Discriminator Loss: 0.7744, Generator Loss: 2.3137 D(x): 0.6861, D(G(z)): 0.1748 Epoch: [16/20], Batch Num: [336/600] Discriminator Loss: 0.6452, Generator Loss: 1.9605 D(x): 0.7421, D(G(z)): 0.1716 Epoch: [16/20], Batch Num: [337/600] Discriminator Loss: 0.9279, Generator Loss: 1.8845 D(x): 0.6653, D(G(z)): 0.2168 Epoch: [16/20], Batch Num: [338/600] Discriminator Loss: 0.7202, Generator Loss: 1.7101 D(x): 0.7390, D(G(z)): 0.2026 Epoch: [16/20], Batch Num: [339/600] Discriminator Loss: 0.9366, Generator Loss: 1.5964 D(x): 0.7063, D(G(z)): 0.2764 Epoch: [16/20], Batch Num: [340/600] Discriminator Loss: 0.7414, Generator Loss: 1.5432 D(x): 0.7883, D(G(z)): 0.2914 Epoch: [16/20], Batch Num: [341/600] Discriminator Loss: 0.8246, Generator Loss: 1.4663 D(x): 0.7867, D(G(z)): 0.3127 Epoch: [16/20], Batch Num: [342/600] Discriminator Loss: 0.7053, Generator Loss: 1.7153 D(x): 0.8008, D(G(z)): 0.2571 Epoch: [16/20], Batch Num: [343/600] Discriminator Loss: 0.7031, Generator Loss: 1.8134 D(x): 0.8243, D(G(z)): 0.2754 Epoch: [16/20], Batch Num: [344/600] Discriminator Loss: 0.8029, Generator Loss: 1.8259 D(x): 0.6977, D(G(z)): 0.2215 Epoch: [16/20], Batch Num: [345/600] Discriminator Loss: 0.9154, Generator Loss: 1.7884 D(x): 0.6763, D(G(z)): 0.2276 Epoch: [16/20], Batch Num: [346/600] Discriminator Loss: 0.6111, Generator Loss: 1.6002 D(x): 0.7987, D(G(z)): 0.2232 Epoch: [16/20], Batch Num: [347/600] Discriminator Loss: 0.8877, Generator Loss: 1.6564 D(x): 0.6913, D(G(z)): 0.2423 Epoch: [16/20], Batch Num: [348/600] Discriminator Loss: 0.6714, Generator Loss: 1.5532 D(x): 0.7833, D(G(z)): 0.2509 Epoch: [16/20], Batch Num: [349/600] Discriminator Loss: 0.7695, Generator Loss: 1.6789 D(x): 0.7587, D(G(z)): 0.2685 Epoch: [16/20], Batch Num: [350/600] Discriminator Loss: 0.8825, Generator Loss: 1.7365 D(x): 0.7683, D(G(z)): 0.3196 Epoch: [16/20], Batch Num: [351/600] Discriminator Loss: 0.9025, Generator Loss: 1.8554 D(x): 0.7565, D(G(z)): 0.3287 Epoch: [16/20], Batch Num: [352/600] Discriminator Loss: 0.8538, Generator Loss: 1.9267 D(x): 0.7071, D(G(z)): 0.2688 Epoch: [16/20], Batch Num: [353/600] Discriminator Loss: 0.7552, Generator Loss: 1.9469 D(x): 0.7064, D(G(z)): 0.2066 Epoch: [16/20], Batch Num: [354/600] Discriminator Loss: 0.6791, Generator Loss: 1.5299 D(x): 0.7281, D(G(z)): 0.1878 Epoch: [16/20], Batch Num: [355/600] Discriminator Loss: 0.7516, Generator Loss: 1.5420 D(x): 0.7483, D(G(z)): 0.2637 Epoch: [16/20], Batch Num: [356/600] Discriminator Loss: 0.8437, Generator Loss: 1.5187 D(x): 0.7638, D(G(z)): 0.3066 Epoch: [16/20], Batch Num: [357/600] Discriminator Loss: 0.9677, Generator Loss: 1.7155 D(x): 0.7861, D(G(z)): 0.3720 Epoch: [16/20], Batch Num: [358/600] Discriminator Loss: 0.9045, Generator Loss: 1.8531 D(x): 0.7275, D(G(z)): 0.3086 Epoch: [16/20], Batch Num: [359/600] Discriminator Loss: 0.8394, Generator Loss: 2.1773 D(x): 0.7204, D(G(z)): 0.2707 Epoch: [16/20], Batch Num: [360/600] Discriminator Loss: 0.8127, Generator Loss: 1.9942 D(x): 0.6893, D(G(z)): 0.2038 Epoch: [16/20], Batch Num: [361/600] Discriminator Loss: 0.9522, Generator Loss: 1.6586 D(x): 0.6331, D(G(z)): 0.2127 Epoch: [16/20], Batch Num: [362/600] Discriminator Loss: 0.8380, Generator Loss: 1.5099 D(x): 0.7246, D(G(z)): 0.2680 Epoch: [16/20], Batch Num: [363/600] Discriminator Loss: 0.7138, Generator Loss: 1.4314 D(x): 0.8062, D(G(z)): 0.3065 Epoch: [16/20], Batch Num: [364/600] Discriminator Loss: 0.9903, Generator Loss: 1.4770 D(x): 0.7631, D(G(z)): 0.3941 Epoch: [16/20], Batch Num: [365/600] Discriminator Loss: 0.7310, Generator Loss: 1.7386 D(x): 0.7636, D(G(z)): 0.2811 Epoch: [16/20], Batch Num: [366/600] Discriminator Loss: 0.7614, Generator Loss: 1.8933 D(x): 0.7339, D(G(z)): 0.2581 Epoch: [16/20], Batch Num: [367/600] Discriminator Loss: 0.7497, Generator Loss: 1.8688 D(x): 0.7210, D(G(z)): 0.2288 Epoch: [16/20], Batch Num: [368/600] Discriminator Loss: 0.8282, Generator Loss: 1.6467 D(x): 0.6803, D(G(z)): 0.2181 Epoch: [16/20], Batch Num: [369/600] Discriminator Loss: 0.6688, Generator Loss: 1.5854 D(x): 0.7689, D(G(z)): 0.2230 Epoch: [16/20], Batch Num: [370/600] Discriminator Loss: 0.8814, Generator Loss: 1.6174 D(x): 0.7468, D(G(z)): 0.3130 Epoch: [16/20], Batch Num: [371/600] Discriminator Loss: 0.7768, Generator Loss: 1.6058 D(x): 0.7399, D(G(z)): 0.2672 Epoch: [16/20], Batch Num: [372/600] Discriminator Loss: 0.7640, Generator Loss: 1.7738 D(x): 0.7827, D(G(z)): 0.2798 Epoch: [16/20], Batch Num: [373/600] Discriminator Loss: 0.7431, Generator Loss: 1.7921 D(x): 0.7387, D(G(z)): 0.2450 Epoch: [16/20], Batch Num: [374/600] Discriminator Loss: 0.7985, Generator Loss: 1.8413 D(x): 0.7466, D(G(z)): 0.2595 Epoch: [16/20], Batch Num: [375/600] Discriminator Loss: 0.9780, Generator Loss: 1.8047 D(x): 0.7353, D(G(z)): 0.3247 Epoch: [16/20], Batch Num: [376/600] Discriminator Loss: 0.6994, Generator Loss: 1.9719 D(x): 0.7657, D(G(z)): 0.2224 Epoch: [16/20], Batch Num: [377/600] Discriminator Loss: 0.7973, Generator Loss: 1.9823 D(x): 0.7123, D(G(z)): 0.2186 Epoch: [16/20], Batch Num: [378/600] Discriminator Loss: 0.7856, Generator Loss: 1.7846 D(x): 0.7062, D(G(z)): 0.2231 Epoch: [16/20], Batch Num: [379/600] Discriminator Loss: 0.7297, Generator Loss: 1.7431 D(x): 0.7426, D(G(z)): 0.2315 Epoch: [16/20], Batch Num: [380/600] Discriminator Loss: 0.8143, Generator Loss: 1.6750 D(x): 0.7848, D(G(z)): 0.3034 Epoch: [16/20], Batch Num: [381/600] Discriminator Loss: 0.7525, Generator Loss: 1.6685 D(x): 0.7578, D(G(z)): 0.2438 Epoch: [16/20], Batch Num: [382/600] Discriminator Loss: 0.7714, Generator Loss: 1.8729 D(x): 0.7414, D(G(z)): 0.2543 Epoch: [16/20], Batch Num: [383/600] Discriminator Loss: 0.8264, Generator Loss: 1.8100 D(x): 0.7432, D(G(z)): 0.2662 Epoch: [16/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [16/20], Batch Num: [400/600] Discriminator Loss: 0.9169, Generator Loss: 1.9162 D(x): 0.6646, D(G(z)): 0.2331 Epoch: [16/20], Batch Num: [401/600] Discriminator Loss: 0.6695, Generator Loss: 1.6295 D(x): 0.7469, D(G(z)): 0.2038 Epoch: [16/20], Batch Num: [402/600] Discriminator Loss: 0.8106, Generator Loss: 1.5015 D(x): 0.7218, D(G(z)): 0.2705 Epoch: [16/20], Batch Num: [403/600] Discriminator Loss: 0.7243, Generator Loss: 1.6836 D(x): 0.7814, D(G(z)): 0.2666 Epoch: [16/20], Batch Num: [404/600] Discriminator Loss: 0.8046, Generator Loss: 1.5650 D(x): 0.7357, D(G(z)): 0.2886 Epoch: [16/20], Batch Num: [405/600] Discriminator Loss: 0.7076, Generator Loss: 1.7168 D(x): 0.8140, D(G(z)): 0.2995 Epoch: [16/20], Batch Num: [406/600] Discriminator Loss: 0.8274, Generator Loss: 1.9010 D(x): 0.7662, D(G(z)): 0.3141 Epoch: [16/20], Batch Num: [407/600] Discriminator Loss: 0.8137, Generator Loss: 2.0905 D(x): 0.7132, D(G(z)): 0.2342 Epoch: [16/20], Batch Num: [408/600] Discriminator Loss: 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0.2008 Epoch: [16/20], Batch Num: [417/600] Discriminator Loss: 0.8204, Generator Loss: 1.6510 D(x): 0.7691, D(G(z)): 0.2716 Epoch: [16/20], Batch Num: [418/600] Discriminator Loss: 0.8405, Generator Loss: 1.7901 D(x): 0.7694, D(G(z)): 0.3182 Epoch: [16/20], Batch Num: [419/600] Discriminator Loss: 0.8588, Generator Loss: 1.8911 D(x): 0.7801, D(G(z)): 0.3007 Epoch: [16/20], Batch Num: [420/600] Discriminator Loss: 0.8149, Generator Loss: 2.0399 D(x): 0.7646, D(G(z)): 0.2858 Epoch: [16/20], Batch Num: [421/600] Discriminator Loss: 0.9668, Generator Loss: 2.0761 D(x): 0.6374, D(G(z)): 0.2393 Epoch: [16/20], Batch Num: [422/600] Discriminator Loss: 1.0283, Generator Loss: 1.8409 D(x): 0.6386, D(G(z)): 0.2306 Epoch: [16/20], Batch Num: [423/600] Discriminator Loss: 0.8189, Generator Loss: 1.6755 D(x): 0.6911, D(G(z)): 0.2200 Epoch: [16/20], Batch Num: [424/600] Discriminator Loss: 0.9217, Generator Loss: 1.4662 D(x): 0.6827, D(G(z)): 0.2635 Epoch: [16/20], Batch Num: [425/600] Discriminator Loss: 0.8277, Generator Loss: 1.2816 D(x): 0.7772, D(G(z)): 0.3279 Epoch: [16/20], Batch Num: [426/600] Discriminator Loss: 0.8107, Generator Loss: 1.2907 D(x): 0.8430, D(G(z)): 0.3593 Epoch: [16/20], Batch Num: [427/600] Discriminator Loss: 0.8425, Generator Loss: 1.4809 D(x): 0.7867, D(G(z)): 0.3492 Epoch: [16/20], Batch Num: [428/600] Discriminator Loss: 0.9212, Generator Loss: 2.0426 D(x): 0.6971, D(G(z)): 0.2956 Epoch: [16/20], Batch Num: [429/600] Discriminator Loss: 0.8009, Generator Loss: 1.8070 D(x): 0.6906, D(G(z)): 0.2334 Epoch: [16/20], Batch Num: [430/600] Discriminator Loss: 0.6544, Generator Loss: 1.9279 D(x): 0.7574, D(G(z)): 0.2199 Epoch: [16/20], Batch Num: [431/600] Discriminator Loss: 0.8122, Generator Loss: 1.9626 D(x): 0.6873, D(G(z)): 0.2068 Epoch: [16/20], Batch Num: [432/600] Discriminator Loss: 0.6721, Generator Loss: 1.7190 D(x): 0.7122, D(G(z)): 0.1805 Epoch: [16/20], Batch Num: [433/600] Discriminator Loss: 0.9202, Generator Loss: 1.5658 D(x): 0.7045, D(G(z)): 0.2674 Epoch: [16/20], Batch Num: [434/600] Discriminator Loss: 0.7149, Generator Loss: 1.5119 D(x): 0.8316, D(G(z)): 0.3169 Epoch: [16/20], Batch Num: [435/600] Discriminator Loss: 0.9175, Generator Loss: 1.9042 D(x): 0.7791, D(G(z)): 0.3590 Epoch: [16/20], Batch Num: [436/600] Discriminator Loss: 0.8276, Generator Loss: 2.1049 D(x): 0.7523, D(G(z)): 0.2597 Epoch: [16/20], Batch Num: [437/600] Discriminator Loss: 0.7364, Generator Loss: 2.3613 D(x): 0.7045, D(G(z)): 0.1843 Epoch: [16/20], Batch Num: [438/600] Discriminator Loss: 0.8229, Generator Loss: 1.8877 D(x): 0.6901, D(G(z)): 0.1908 Epoch: [16/20], Batch Num: [439/600] Discriminator Loss: 0.8045, Generator Loss: 1.7319 D(x): 0.7173, D(G(z)): 0.2300 Epoch: [16/20], Batch Num: [440/600] Discriminator Loss: 0.7994, Generator Loss: 1.5466 D(x): 0.7636, D(G(z)): 0.2853 Epoch: [16/20], Batch Num: [441/600] Discriminator Loss: 0.7616, Generator Loss: 1.6195 D(x): 0.7712, D(G(z)): 0.2741 Epoch: [16/20], Batch Num: [442/600] Discriminator Loss: 0.9203, Generator Loss: 1.6880 D(x): 0.7347, D(G(z)): 0.3180 Epoch: [16/20], Batch Num: [443/600] Discriminator Loss: 0.8331, Generator Loss: 1.8138 D(x): 0.7725, D(G(z)): 0.3053 Epoch: [16/20], Batch Num: [444/600] Discriminator Loss: 0.8766, Generator Loss: 1.9147 D(x): 0.6712, D(G(z)): 0.1873 Epoch: [16/20], Batch Num: [445/600] Discriminator Loss: 0.8325, Generator Loss: 1.5718 D(x): 0.6964, D(G(z)): 0.2309 Epoch: [16/20], Batch Num: [446/600] Discriminator Loss: 0.9094, Generator Loss: 1.4453 D(x): 0.7135, D(G(z)): 0.2774 Epoch: [16/20], Batch Num: [447/600] Discriminator Loss: 0.9295, Generator Loss: 1.4259 D(x): 0.6977, D(G(z)): 0.2982 Epoch: [16/20], Batch Num: [448/600] Discriminator Loss: 0.8322, Generator Loss: 1.2830 D(x): 0.7787, D(G(z)): 0.3446 Epoch: [16/20], Batch Num: [449/600] Discriminator Loss: 0.7904, Generator Loss: 1.5726 D(x): 0.7371, D(G(z)): 0.2920 Epoch: [16/20], Batch Num: [450/600] Discriminator Loss: 0.7561, Generator Loss: 1.4849 D(x): 0.7837, D(G(z)): 0.2974 Epoch: [16/20], Batch Num: [451/600] Discriminator Loss: 0.8143, Generator Loss: 1.8516 D(x): 0.7573, D(G(z)): 0.2957 Epoch: [16/20], Batch Num: [452/600] Discriminator Loss: 0.6551, Generator Loss: 1.6504 D(x): 0.7521, D(G(z)): 0.2363 Epoch: [16/20], Batch Num: [453/600] Discriminator Loss: 0.9230, Generator Loss: 1.9296 D(x): 0.6899, D(G(z)): 0.2754 Epoch: [16/20], Batch Num: [454/600] Discriminator Loss: 0.8472, Generator Loss: 1.8105 D(x): 0.7414, D(G(z)): 0.2993 Epoch: [16/20], Batch Num: [455/600] Discriminator Loss: 0.9006, Generator Loss: 1.7177 D(x): 0.6589, D(G(z)): 0.2505 Epoch: [16/20], Batch Num: [456/600] Discriminator Loss: 0.8348, Generator Loss: 1.5472 D(x): 0.7036, D(G(z)): 0.2552 Epoch: [16/20], Batch Num: [457/600] Discriminator Loss: 0.7243, Generator Loss: 1.6612 D(x): 0.7722, D(G(z)): 0.2714 Epoch: [16/20], Batch Num: [458/600] Discriminator Loss: 0.7484, Generator Loss: 1.5787 D(x): 0.7716, D(G(z)): 0.2887 Epoch: [16/20], Batch Num: [459/600] Discriminator Loss: 0.6488, Generator Loss: 1.6075 D(x): 0.7943, D(G(z)): 0.2585 Epoch: [16/20], Batch Num: [460/600] Discriminator Loss: 0.6946, Generator Loss: 1.6229 D(x): 0.7917, D(G(z)): 0.2829 Epoch: [16/20], Batch Num: [461/600] Discriminator Loss: 0.7534, Generator Loss: 1.7619 D(x): 0.7972, D(G(z)): 0.2921 Epoch: [16/20], Batch Num: [462/600] Discriminator Loss: 0.8225, Generator Loss: 1.9425 D(x): 0.7185, D(G(z)): 0.2341 Epoch: [16/20], Batch Num: [463/600] Discriminator Loss: 0.7532, Generator Loss: 1.8528 D(x): 0.7441, D(G(z)): 0.2335 Epoch: [16/20], Batch Num: [464/600] Discriminator Loss: 0.7353, Generator Loss: 1.5878 D(x): 0.7386, D(G(z)): 0.2151 Epoch: [16/20], Batch Num: [465/600] Discriminator Loss: 0.7986, Generator Loss: 1.8659 D(x): 0.7675, D(G(z)): 0.2653 Epoch: [16/20], Batch Num: [466/600] Discriminator Loss: 0.8323, Generator Loss: 1.7700 D(x): 0.7618, D(G(z)): 0.3000 Epoch: [16/20], Batch Num: [467/600] Discriminator Loss: 0.6854, Generator Loss: 1.9299 D(x): 0.8040, D(G(z)): 0.2617 Epoch: [16/20], Batch Num: [468/600] Discriminator Loss: 0.7006, Generator Loss: 2.0209 D(x): 0.7843, D(G(z)): 0.2550 Epoch: [16/20], Batch Num: [469/600] Discriminator Loss: 0.8190, Generator Loss: 2.4202 D(x): 0.7412, D(G(z)): 0.2296 Epoch: [16/20], Batch Num: [470/600] Discriminator Loss: 0.6737, Generator Loss: 2.1400 D(x): 0.7372, D(G(z)): 0.1693 Epoch: [16/20], Batch Num: [471/600] Discriminator Loss: 0.8292, Generator Loss: 1.9974 D(x): 0.6896, D(G(z)): 0.1967 Epoch: [16/20], Batch Num: [472/600] Discriminator Loss: 0.7951, Generator Loss: 1.8071 D(x): 0.7816, D(G(z)): 0.2784 Epoch: [16/20], Batch Num: [473/600] Discriminator Loss: 0.7315, Generator Loss: 1.7399 D(x): 0.7829, D(G(z)): 0.2517 Epoch: [16/20], Batch Num: [474/600] Discriminator Loss: 0.7358, Generator Loss: 1.3539 D(x): 0.7800, D(G(z)): 0.2633 Epoch: [16/20], Batch Num: [475/600] Discriminator Loss: 0.8775, Generator Loss: 1.7350 D(x): 0.7519, D(G(z)): 0.2655 Epoch: [16/20], Batch Num: [476/600] Discriminator Loss: 0.9825, Generator Loss: 1.9514 D(x): 0.7987, D(G(z)): 0.3023 Epoch: [16/20], Batch Num: [477/600] Discriminator Loss: 0.8535, Generator Loss: 2.0305 D(x): 0.7200, D(G(z)): 0.2560 Epoch: [16/20], Batch Num: [478/600] Discriminator Loss: 0.9144, Generator Loss: 2.0356 D(x): 0.6954, D(G(z)): 0.2406 Epoch: [16/20], Batch Num: [479/600] Discriminator Loss: 0.8577, Generator Loss: 1.8123 D(x): 0.6832, D(G(z)): 0.1931 Epoch: [16/20], Batch Num: [480/600] Discriminator Loss: 0.8692, Generator Loss: 1.7655 D(x): 0.7222, D(G(z)): 0.2551 Epoch: [16/20], Batch Num: [481/600] Discriminator Loss: 0.8907, Generator Loss: 1.7295 D(x): 0.7358, D(G(z)): 0.3025 Epoch: [16/20], Batch Num: [482/600] Discriminator Loss: 0.8552, Generator Loss: 1.5658 D(x): 0.7237, D(G(z)): 0.2859 Epoch: [16/20], Batch Num: [483/600] Discriminator Loss: 0.8110, Generator Loss: 1.6282 D(x): 0.7596, D(G(z)): 0.2866 Epoch: [16/20], Batch Num: [484/600] Discriminator Loss: 0.8855, Generator Loss: 1.6770 D(x): 0.7229, D(G(z)): 0.2938 Epoch: [16/20], Batch Num: [485/600] Discriminator Loss: 0.7631, Generator Loss: 1.7866 D(x): 0.7573, D(G(z)): 0.2572 Epoch: [16/20], Batch Num: [486/600] Discriminator Loss: 0.6628, Generator Loss: 1.8932 D(x): 0.7643, D(G(z)): 0.2181 Epoch: [16/20], Batch Num: [487/600] Discriminator Loss: 0.7373, Generator Loss: 1.9655 D(x): 0.7213, D(G(z)): 0.2092 Epoch: [16/20], Batch Num: [488/600] Discriminator Loss: 0.9304, Generator Loss: 1.6434 D(x): 0.6847, D(G(z)): 0.2448 Epoch: [16/20], Batch Num: [489/600] Discriminator Loss: 0.8472, Generator Loss: 1.4235 D(x): 0.7148, D(G(z)): 0.2655 Epoch: [16/20], Batch Num: [490/600] Discriminator Loss: 0.8126, Generator Loss: 1.3728 D(x): 0.7762, D(G(z)): 0.3199 Epoch: [16/20], Batch Num: [491/600] Discriminator Loss: 0.7865, Generator Loss: 1.6499 D(x): 0.8207, D(G(z)): 0.3231 Epoch: [16/20], Batch Num: [492/600] Discriminator Loss: 0.7161, Generator Loss: 1.8130 D(x): 0.8039, D(G(z)): 0.2908 Epoch: [16/20], Batch Num: [493/600] Discriminator Loss: 0.8518, Generator Loss: 1.9365 D(x): 0.7237, D(G(z)): 0.2418 Epoch: [16/20], Batch Num: [494/600] Discriminator Loss: 0.8597, Generator Loss: 1.9912 D(x): 0.6458, D(G(z)): 0.1778 Epoch: [16/20], Batch Num: [495/600] Discriminator Loss: 0.7563, Generator Loss: 1.7788 D(x): 0.7066, D(G(z)): 0.1876 Epoch: [16/20], Batch Num: [496/600] Discriminator Loss: 0.6967, Generator Loss: 1.7537 D(x): 0.7561, D(G(z)): 0.2454 Epoch: [16/20], Batch Num: [497/600] Discriminator Loss: 0.7314, Generator Loss: 1.6109 D(x): 0.7675, D(G(z)): 0.2566 Epoch: [16/20], Batch Num: [498/600] Discriminator Loss: 0.7481, Generator Loss: 1.4148 D(x): 0.7920, D(G(z)): 0.2985 Epoch: [16/20], Batch Num: [499/600] Discriminator Loss: 0.6470, Generator Loss: 1.6711 D(x): 0.8375, D(G(z)): 0.2705 Epoch: 16, Batch Num: [500/600]
Epoch: [16/20], Batch Num: [500/600] Discriminator Loss: 0.7398, Generator Loss: 1.6889 D(x): 0.7896, D(G(z)): 0.2717 Epoch: [16/20], Batch Num: [501/600] Discriminator Loss: 0.7826, Generator Loss: 1.9257 D(x): 0.7419, D(G(z)): 0.2493 Epoch: [16/20], Batch Num: [502/600] Discriminator Loss: 0.7473, Generator Loss: 2.0345 D(x): 0.7114, D(G(z)): 0.1898 Epoch: [16/20], Batch Num: [503/600] Discriminator Loss: 0.8524, Generator Loss: 1.7279 D(x): 0.6918, D(G(z)): 0.1979 Epoch: [16/20], Batch Num: [504/600] Discriminator Loss: 0.7127, Generator Loss: 1.6780 D(x): 0.7504, D(G(z)): 0.2164 Epoch: [16/20], Batch Num: [505/600] Discriminator Loss: 0.7217, Generator Loss: 1.6003 D(x): 0.7845, D(G(z)): 0.2582 Epoch: [16/20], Batch Num: [506/600] Discriminator Loss: 0.7138, Generator Loss: 1.6813 D(x): 0.7806, D(G(z)): 0.2530 Epoch: [16/20], Batch Num: [507/600] Discriminator Loss: 0.6002, Generator Loss: 1.7913 D(x): 0.8089, D(G(z)): 0.2173 Epoch: [16/20], Batch Num: [508/600] Discriminator Loss: 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0.2533 Epoch: [16/20], Batch Num: [517/600] Discriminator Loss: 0.7069, Generator Loss: 2.2568 D(x): 0.7768, D(G(z)): 0.2539 Epoch: [16/20], Batch Num: [518/600] Discriminator Loss: 0.6657, Generator Loss: 2.0281 D(x): 0.7591, D(G(z)): 0.1883 Epoch: [16/20], Batch Num: [519/600] Discriminator Loss: 0.6390, Generator Loss: 1.9677 D(x): 0.7749, D(G(z)): 0.1942 Epoch: [16/20], Batch Num: [520/600] Discriminator Loss: 0.6991, Generator Loss: 1.9627 D(x): 0.7554, D(G(z)): 0.2189 Epoch: [16/20], Batch Num: [521/600] Discriminator Loss: 0.7202, Generator Loss: 1.9333 D(x): 0.7918, D(G(z)): 0.2674 Epoch: [16/20], Batch Num: [522/600] Discriminator Loss: 0.7957, Generator Loss: 1.9722 D(x): 0.7512, D(G(z)): 0.2405 Epoch: [16/20], Batch Num: [523/600] Discriminator Loss: 0.7325, Generator Loss: 2.1210 D(x): 0.7628, D(G(z)): 0.2014 Epoch: [16/20], Batch Num: [524/600] Discriminator Loss: 0.7786, Generator Loss: 2.0728 D(x): 0.7415, D(G(z)): 0.2282 Epoch: [16/20], Batch Num: [525/600] Discriminator Loss: 0.8333, Generator Loss: 1.7469 D(x): 0.7142, D(G(z)): 0.1938 Epoch: [16/20], Batch Num: [526/600] Discriminator Loss: 0.7897, Generator Loss: 1.6643 D(x): 0.7361, D(G(z)): 0.2449 Epoch: [16/20], Batch Num: [527/600] Discriminator Loss: 0.7206, Generator Loss: 1.4909 D(x): 0.7717, D(G(z)): 0.2421 Epoch: [16/20], Batch Num: [528/600] Discriminator Loss: 0.8161, Generator Loss: 1.7006 D(x): 0.7788, D(G(z)): 0.3007 Epoch: [16/20], Batch Num: [529/600] Discriminator Loss: 1.0394, Generator Loss: 1.9643 D(x): 0.7316, D(G(z)): 0.3269 Epoch: [16/20], Batch Num: [530/600] Discriminator Loss: 1.1061, Generator Loss: 1.9646 D(x): 0.6584, D(G(z)): 0.2670 Epoch: [16/20], Batch Num: [531/600] Discriminator Loss: 1.0153, Generator Loss: 2.0005 D(x): 0.6372, D(G(z)): 0.2177 Epoch: [16/20], Batch Num: [532/600] Discriminator Loss: 0.7752, Generator Loss: 1.7164 D(x): 0.6982, D(G(z)): 0.1921 Epoch: [16/20], Batch Num: [533/600] Discriminator Loss: 0.8828, Generator Loss: 1.3293 D(x): 0.7383, D(G(z)): 0.2922 Epoch: [16/20], Batch Num: [534/600] Discriminator Loss: 1.0412, Generator Loss: 1.4303 D(x): 0.7671, D(G(z)): 0.3803 Epoch: [16/20], Batch Num: [535/600] Discriminator Loss: 0.9354, Generator Loss: 1.7517 D(x): 0.7300, D(G(z)): 0.3578 Epoch: [16/20], Batch Num: [536/600] Discriminator Loss: 0.8010, Generator Loss: 1.8649 D(x): 0.7312, D(G(z)): 0.2758 Epoch: [16/20], Batch Num: [537/600] Discriminator Loss: 0.8456, Generator Loss: 1.8839 D(x): 0.7007, D(G(z)): 0.2375 Epoch: [16/20], Batch Num: [538/600] Discriminator Loss: 1.1643, Generator Loss: 1.7352 D(x): 0.6350, D(G(z)): 0.3151 Epoch: [16/20], Batch Num: [539/600] Discriminator Loss: 0.9121, Generator Loss: 1.4966 D(x): 0.6519, D(G(z)): 0.2381 Epoch: [16/20], Batch Num: [540/600] Discriminator Loss: 0.8889, Generator Loss: 1.3131 D(x): 0.7046, D(G(z)): 0.2953 Epoch: [16/20], Batch Num: [541/600] Discriminator Loss: 1.0056, Generator Loss: 1.2098 D(x): 0.7263, D(G(z)): 0.3547 Epoch: [16/20], Batch Num: 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1.4221 D(x): 0.7493, D(G(z)): 0.3037 Epoch: [16/20], Batch Num: [551/600] Discriminator Loss: 0.7927, Generator Loss: 1.4311 D(x): 0.7649, D(G(z)): 0.3083 Epoch: [16/20], Batch Num: [552/600] Discriminator Loss: 0.8383, Generator Loss: 1.6187 D(x): 0.7532, D(G(z)): 0.3230 Epoch: [16/20], Batch Num: [553/600] Discriminator Loss: 1.0053, Generator Loss: 1.6847 D(x): 0.6716, D(G(z)): 0.3036 Epoch: [16/20], Batch Num: [554/600] Discriminator Loss: 0.8250, Generator Loss: 1.6797 D(x): 0.6803, D(G(z)): 0.2331 Epoch: [16/20], Batch Num: [555/600] Discriminator Loss: 0.7519, Generator Loss: 1.8088 D(x): 0.7364, D(G(z)): 0.2399 Epoch: [16/20], Batch Num: [556/600] Discriminator Loss: 0.8430, Generator Loss: 1.5575 D(x): 0.6778, D(G(z)): 0.2547 Epoch: [16/20], Batch Num: [557/600] Discriminator Loss: 0.7941, Generator Loss: 1.4805 D(x): 0.7503, D(G(z)): 0.2819 Epoch: [16/20], Batch Num: [558/600] Discriminator Loss: 0.8857, Generator Loss: 1.3603 D(x): 0.7292, D(G(z)): 0.3224 Epoch: [16/20], Batch Num: [559/600] Discriminator Loss: 0.8129, Generator Loss: 1.4796 D(x): 0.7818, D(G(z)): 0.3337 Epoch: [16/20], Batch Num: [560/600] Discriminator Loss: 0.7506, Generator Loss: 1.8357 D(x): 0.7643, D(G(z)): 0.2707 Epoch: [16/20], Batch Num: [561/600] Discriminator Loss: 0.8351, Generator Loss: 1.7223 D(x): 0.7079, D(G(z)): 0.2544 Epoch: [16/20], Batch Num: [562/600] Discriminator Loss: 0.8202, Generator Loss: 1.8787 D(x): 0.7456, D(G(z)): 0.2815 Epoch: [16/20], Batch Num: [563/600] Discriminator Loss: 0.9247, Generator Loss: 1.7918 D(x): 0.6932, D(G(z)): 0.2693 Epoch: [16/20], Batch Num: [564/600] Discriminator Loss: 0.7923, Generator Loss: 2.0044 D(x): 0.6877, D(G(z)): 0.2333 Epoch: [16/20], Batch Num: [565/600] Discriminator Loss: 0.7911, Generator Loss: 1.8952 D(x): 0.7907, D(G(z)): 0.3074 Epoch: [16/20], Batch Num: [566/600] Discriminator Loss: 0.7391, Generator Loss: 1.9405 D(x): 0.7456, D(G(z)): 0.2266 Epoch: [16/20], Batch Num: [567/600] Discriminator Loss: 0.7727, Generator Loss: 2.0188 D(x): 0.7369, D(G(z)): 0.2144 Epoch: [16/20], Batch Num: [568/600] Discriminator Loss: 0.8197, Generator Loss: 1.6699 D(x): 0.7492, D(G(z)): 0.2590 Epoch: [16/20], Batch Num: [569/600] Discriminator Loss: 0.8598, Generator Loss: 1.8559 D(x): 0.7489, D(G(z)): 0.3030 Epoch: [16/20], Batch Num: [570/600] Discriminator Loss: 0.7602, Generator Loss: 1.9945 D(x): 0.7763, D(G(z)): 0.2705 Epoch: [16/20], Batch Num: [571/600] Discriminator Loss: 0.7578, Generator Loss: 2.2328 D(x): 0.7732, D(G(z)): 0.2584 Epoch: [16/20], Batch Num: [572/600] Discriminator Loss: 0.7568, Generator Loss: 2.0619 D(x): 0.7186, D(G(z)): 0.2032 Epoch: [16/20], Batch Num: [573/600] Discriminator Loss: 0.8823, Generator Loss: 2.0503 D(x): 0.6671, D(G(z)): 0.2099 Epoch: [16/20], Batch Num: [574/600] Discriminator Loss: 0.9550, Generator Loss: 1.5811 D(x): 0.6968, D(G(z)): 0.2557 Epoch: [16/20], Batch Num: [575/600] Discriminator Loss: 0.8781, Generator Loss: 1.5417 D(x): 0.7315, D(G(z)): 0.2405 Epoch: [16/20], Batch Num: [576/600] Discriminator Loss: 0.8700, Generator Loss: 1.4180 D(x): 0.7772, D(G(z)): 0.3091 Epoch: [16/20], Batch Num: [577/600] Discriminator Loss: 0.9534, Generator Loss: 1.7128 D(x): 0.7318, D(G(z)): 0.3221 Epoch: [16/20], Batch Num: [578/600] Discriminator Loss: 1.0935, Generator Loss: 1.6554 D(x): 0.6702, D(G(z)): 0.3365 Epoch: [16/20], Batch Num: [579/600] Discriminator Loss: 0.9996, Generator Loss: 1.7251 D(x): 0.7307, D(G(z)): 0.3391 Epoch: [16/20], Batch Num: [580/600] Discriminator Loss: 0.8757, Generator Loss: 1.9911 D(x): 0.6518, D(G(z)): 0.2484 Epoch: [16/20], Batch Num: [581/600] Discriminator Loss: 0.8879, Generator Loss: 1.8601 D(x): 0.6841, D(G(z)): 0.2661 Epoch: [16/20], Batch Num: [582/600] Discriminator Loss: 0.9462, Generator Loss: 1.7268 D(x): 0.6586, D(G(z)): 0.2688 Epoch: [16/20], Batch Num: [583/600] Discriminator Loss: 0.8447, Generator Loss: 1.4377 D(x): 0.7222, D(G(z)): 0.2752 Epoch: [16/20], Batch Num: [584/600] Discriminator Loss: 0.8544, Generator Loss: 1.3200 D(x): 0.6997, D(G(z)): 0.2670 Epoch: [16/20], Batch Num: [585/600] Discriminator Loss: 0.8743, Generator Loss: 1.3211 D(x): 0.6762, D(G(z)): 0.2869 Epoch: [16/20], Batch Num: [586/600] Discriminator Loss: 0.8184, Generator Loss: 1.2323 D(x): 0.7591, D(G(z)): 0.3319 Epoch: [16/20], Batch Num: [587/600] Discriminator Loss: 1.0154, Generator Loss: 1.3768 D(x): 0.7058, D(G(z)): 0.3327 Epoch: [16/20], Batch Num: [588/600] Discriminator Loss: 1.0130, Generator Loss: 1.3804 D(x): 0.7181, D(G(z)): 0.3676 Epoch: [16/20], Batch Num: [589/600] Discriminator Loss: 0.8352, Generator Loss: 1.4786 D(x): 0.7310, D(G(z)): 0.3040 Epoch: [16/20], Batch Num: [590/600] Discriminator Loss: 0.7605, Generator Loss: 1.9124 D(x): 0.7632, D(G(z)): 0.2931 Epoch: [16/20], Batch Num: [591/600] Discriminator Loss: 0.8437, Generator Loss: 2.0345 D(x): 0.6714, D(G(z)): 0.2403 Epoch: [16/20], Batch Num: [592/600] Discriminator Loss: 1.0399, Generator Loss: 1.9684 D(x): 0.6007, D(G(z)): 0.2371 Epoch: [16/20], Batch Num: [593/600] Discriminator Loss: 0.9212, Generator Loss: 1.5910 D(x): 0.6194, D(G(z)): 0.2045 Epoch: [16/20], Batch Num: [594/600] Discriminator Loss: 0.8505, Generator Loss: 1.6244 D(x): 0.7810, D(G(z)): 0.3127 Epoch: [16/20], Batch Num: [595/600] Discriminator Loss: 0.8028, Generator Loss: 1.5084 D(x): 0.7689, D(G(z)): 0.3130 Epoch: [16/20], Batch Num: [596/600] Discriminator Loss: 0.8571, Generator Loss: 1.6887 D(x): 0.7416, D(G(z)): 0.3017 Epoch: [16/20], Batch Num: [597/600] Discriminator Loss: 0.8371, Generator Loss: 2.0321 D(x): 0.7517, D(G(z)): 0.2981 Epoch: [16/20], Batch Num: [598/600] Discriminator Loss: 0.8463, Generator Loss: 2.1507 D(x): 0.7334, D(G(z)): 0.2650 Epoch: [16/20], Batch Num: [599/600] Discriminator Loss: 0.7831, Generator Loss: 2.0435 D(x): 0.7111, D(G(z)): 0.1874 Epoch: 17, Batch Num: [0/600]
Epoch: [17/20], Batch Num: [0/600] Discriminator Loss: 0.7036, Generator Loss: 1.8399 D(x): 0.7116, D(G(z)): 0.1926 Epoch: [17/20], Batch Num: [1/600] Discriminator Loss: 0.8285, Generator Loss: 1.7117 D(x): 0.7191, D(G(z)): 0.2519 Epoch: [17/20], Batch Num: [2/600] Discriminator Loss: 0.8055, Generator Loss: 1.4464 D(x): 0.7440, D(G(z)): 0.2746 Epoch: [17/20], Batch Num: [3/600] Discriminator Loss: 0.8730, Generator Loss: 1.7080 D(x): 0.7804, D(G(z)): 0.3421 Epoch: [17/20], Batch Num: [4/600] Discriminator Loss: 0.8428, Generator Loss: 1.8920 D(x): 0.7708, D(G(z)): 0.3227 Epoch: [17/20], Batch Num: [5/600] Discriminator Loss: 0.6259, Generator Loss: 2.2427 D(x): 0.7865, D(G(z)): 0.2486 Epoch: [17/20], Batch Num: [6/600] Discriminator Loss: 0.8360, Generator Loss: 2.2845 D(x): 0.6538, D(G(z)): 0.1941 Epoch: [17/20], Batch Num: [7/600] Discriminator Loss: 0.7589, Generator Loss: 1.8700 D(x): 0.7093, D(G(z)): 0.1950 Epoch: [17/20], Batch Num: [8/600] Discriminator Loss: 0.9268, Generator Loss: 1.6717 D(x): 0.7087, D(G(z)): 0.2635 Epoch: [17/20], Batch Num: [9/600] Discriminator Loss: 0.7734, Generator Loss: 1.2754 D(x): 0.7761, D(G(z)): 0.3032 Epoch: [17/20], Batch Num: [10/600] Discriminator Loss: 0.8558, Generator Loss: 1.6863 D(x): 0.8173, D(G(z)): 0.3520 Epoch: [17/20], Batch Num: [11/600] Discriminator Loss: 0.9709, Generator Loss: 1.7781 D(x): 0.6991, D(G(z)): 0.3264 Epoch: [17/20], Batch Num: [12/600] Discriminator Loss: 0.8150, Generator Loss: 1.8066 D(x): 0.7245, D(G(z)): 0.2586 Epoch: [17/20], Batch Num: [13/600] Discriminator Loss: 0.8485, Generator Loss: 2.0042 D(x): 0.7149, D(G(z)): 0.2494 Epoch: [17/20], Batch Num: [14/600] Discriminator Loss: 0.8098, Generator Loss: 1.9741 D(x): 0.7067, D(G(z)): 0.2160 Epoch: [17/20], Batch Num: [15/600] Discriminator Loss: 0.8365, Generator Loss: 1.6835 D(x): 0.6914, D(G(z)): 0.2450 Epoch: [17/20], Batch Num: [16/600] Discriminator Loss: 0.9260, Generator Loss: 1.5411 D(x): 0.7100, D(G(z)): 0.3173 Epoch: [17/20], Batch Num: [17/600] Discriminator Loss: 0.7633, Generator Loss: 1.6997 D(x): 0.8018, D(G(z)): 0.3148 Epoch: [17/20], Batch Num: [18/600] Discriminator Loss: 0.7853, Generator Loss: 1.6385 D(x): 0.7510, D(G(z)): 0.2684 Epoch: [17/20], Batch Num: [19/600] Discriminator Loss: 0.6981, Generator Loss: 1.8155 D(x): 0.7656, D(G(z)): 0.2489 Epoch: [17/20], Batch Num: [20/600] Discriminator Loss: 0.8774, Generator Loss: 1.6257 D(x): 0.6922, D(G(z)): 0.2426 Epoch: [17/20], Batch Num: [21/600] Discriminator Loss: 0.8385, Generator Loss: 1.4817 D(x): 0.7103, D(G(z)): 0.2749 Epoch: [17/20], Batch Num: [22/600] Discriminator Loss: 0.7638, Generator Loss: 1.6353 D(x): 0.7769, D(G(z)): 0.2761 Epoch: [17/20], Batch Num: [23/600] Discriminator Loss: 0.8525, Generator Loss: 1.7556 D(x): 0.6886, D(G(z)): 0.2616 Epoch: [17/20], Batch Num: [24/600] Discriminator Loss: 0.8473, Generator Loss: 1.7909 D(x): 0.7256, D(G(z)): 0.2594 Epoch: [17/20], Batch Num: [25/600] Discriminator Loss: 0.7997, Generator Loss: 1.7647 D(x): 0.7978, D(G(z)): 0.3183 Epoch: [17/20], Batch Num: [26/600] Discriminator Loss: 0.7629, Generator Loss: 1.8793 D(x): 0.7534, D(G(z)): 0.2536 Epoch: [17/20], Batch Num: [27/600] Discriminator Loss: 0.6525, Generator Loss: 2.0912 D(x): 0.7829, D(G(z)): 0.2120 Epoch: [17/20], Batch Num: [28/600] Discriminator Loss: 0.7288, Generator Loss: 2.0499 D(x): 0.7541, D(G(z)): 0.2205 Epoch: [17/20], Batch Num: [29/600] Discriminator Loss: 0.7176, Generator Loss: 1.6340 D(x): 0.7248, D(G(z)): 0.1778 Epoch: [17/20], Batch Num: [30/600] Discriminator Loss: 0.8260, Generator Loss: 1.7413 D(x): 0.7503, D(G(z)): 0.2747 Epoch: [17/20], Batch Num: [31/600] Discriminator Loss: 0.7628, Generator Loss: 1.8098 D(x): 0.7893, D(G(z)): 0.3041 Epoch: [17/20], Batch Num: [32/600] Discriminator Loss: 0.6427, Generator Loss: 1.7393 D(x): 0.7855, D(G(z)): 0.2316 Epoch: [17/20], Batch Num: [33/600] Discriminator Loss: 0.6906, Generator Loss: 1.7813 D(x): 0.7587, D(G(z)): 0.2322 Epoch: [17/20], Batch Num: [34/600] Discriminator Loss: 0.7318, Generator Loss: 1.8683 D(x): 0.7645, D(G(z)): 0.2300 Epoch: [17/20], Batch Num: [35/600] Discriminator Loss: 0.8133, Generator Loss: 1.5867 D(x): 0.7567, D(G(z)): 0.2722 Epoch: [17/20], Batch Num: [36/600] Discriminator Loss: 0.7036, Generator Loss: 1.9355 D(x): 0.7400, D(G(z)): 0.2096 Epoch: [17/20], Batch Num: [37/600] Discriminator Loss: 0.7854, Generator Loss: 1.9048 D(x): 0.7576, D(G(z)): 0.2548 Epoch: [17/20], Batch Num: [38/600] Discriminator Loss: 0.8195, Generator Loss: 1.8871 D(x): 0.7272, D(G(z)): 0.2511 Epoch: [17/20], Batch Num: [39/600] Discriminator Loss: 0.7102, Generator Loss: 1.6593 D(x): 0.7737, D(G(z)): 0.2578 Epoch: [17/20], Batch Num: [40/600] Discriminator Loss: 0.7635, Generator Loss: 1.7458 D(x): 0.7929, D(G(z)): 0.2924 Epoch: [17/20], Batch Num: [41/600] Discriminator Loss: 0.7030, Generator Loss: 2.0850 D(x): 0.7531, D(G(z)): 0.2373 Epoch: [17/20], Batch Num: [42/600] Discriminator Loss: 0.8080, Generator Loss: 2.0524 D(x): 0.7204, D(G(z)): 0.2484 Epoch: [17/20], Batch Num: [43/600] Discriminator Loss: 0.9308, Generator Loss: 1.9428 D(x): 0.7525, D(G(z)): 0.2748 Epoch: [17/20], Batch Num: [44/600] Discriminator Loss: 0.8629, Generator Loss: 2.0705 D(x): 0.7238, D(G(z)): 0.2503 Epoch: [17/20], Batch Num: [45/600] Discriminator Loss: 0.7701, Generator Loss: 1.9448 D(x): 0.7133, D(G(z)): 0.2105 Epoch: [17/20], Batch Num: [46/600] Discriminator Loss: 0.6702, Generator Loss: 1.8514 D(x): 0.7980, D(G(z)): 0.2464 Epoch: [17/20], Batch Num: [47/600] Discriminator Loss: 0.7350, Generator Loss: 1.7987 D(x): 0.7302, D(G(z)): 0.2237 Epoch: [17/20], Batch Num: [48/600] Discriminator Loss: 0.7668, Generator Loss: 1.4113 D(x): 0.7588, D(G(z)): 0.2645 Epoch: [17/20], Batch Num: [49/600] Discriminator Loss: 0.8077, Generator Loss: 1.7751 D(x): 0.7655, D(G(z)): 0.2928 Epoch: [17/20], Batch Num: [50/600] Discriminator Loss: 0.9377, Generator Loss: 1.8527 D(x): 0.7231, D(G(z)): 0.3055 Epoch: [17/20], Batch Num: 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D(x): 0.6934, D(G(z)): 0.2301 Epoch: [17/20], Batch Num: [60/600] Discriminator Loss: 0.9186, Generator Loss: 1.8170 D(x): 0.6320, D(G(z)): 0.2008 Epoch: [17/20], Batch Num: [61/600] Discriminator Loss: 0.7840, Generator Loss: 1.5507 D(x): 0.6801, D(G(z)): 0.2036 Epoch: [17/20], Batch Num: [62/600] Discriminator Loss: 0.9154, Generator Loss: 1.5655 D(x): 0.7656, D(G(z)): 0.3403 Epoch: [17/20], Batch Num: [63/600] Discriminator Loss: 1.0510, Generator Loss: 1.7590 D(x): 0.7890, D(G(z)): 0.3975 Epoch: [17/20], Batch Num: [64/600] Discriminator Loss: 0.7932, Generator Loss: 1.9528 D(x): 0.7856, D(G(z)): 0.3069 Epoch: [17/20], Batch Num: [65/600] Discriminator Loss: 0.7906, Generator Loss: 2.0420 D(x): 0.7231, D(G(z)): 0.2193 Epoch: [17/20], Batch Num: [66/600] Discriminator Loss: 0.8331, Generator Loss: 1.9537 D(x): 0.6950, D(G(z)): 0.2340 Epoch: [17/20], Batch Num: [67/600] Discriminator Loss: 0.8595, Generator Loss: 1.8854 D(x): 0.6637, D(G(z)): 0.2067 Epoch: [17/20], Batch Num: 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D(x): 0.6953, D(G(z)): 0.2406 Epoch: [17/20], Batch Num: [77/600] Discriminator Loss: 0.8716, Generator Loss: 1.5611 D(x): 0.6945, D(G(z)): 0.2517 Epoch: [17/20], Batch Num: [78/600] Discriminator Loss: 0.7942, Generator Loss: 1.5185 D(x): 0.7668, D(G(z)): 0.2891 Epoch: [17/20], Batch Num: [79/600] Discriminator Loss: 0.8968, Generator Loss: 1.6376 D(x): 0.7781, D(G(z)): 0.3618 Epoch: [17/20], Batch Num: [80/600] Discriminator Loss: 0.7657, Generator Loss: 1.9299 D(x): 0.7685, D(G(z)): 0.2858 Epoch: [17/20], Batch Num: [81/600] Discriminator Loss: 0.8400, Generator Loss: 1.8571 D(x): 0.6865, D(G(z)): 0.2231 Epoch: [17/20], Batch Num: [82/600] Discriminator Loss: 0.7230, Generator Loss: 1.7723 D(x): 0.7413, D(G(z)): 0.2326 Epoch: [17/20], Batch Num: [83/600] Discriminator Loss: 0.8040, Generator Loss: 1.7280 D(x): 0.7135, D(G(z)): 0.2332 Epoch: [17/20], Batch Num: [84/600] Discriminator Loss: 0.8935, Generator Loss: 1.6692 D(x): 0.6914, D(G(z)): 0.2563 Epoch: [17/20], Batch Num: [85/600] Discriminator Loss: 0.9119, Generator Loss: 1.5180 D(x): 0.7560, D(G(z)): 0.3148 Epoch: [17/20], Batch Num: [86/600] Discriminator Loss: 0.9794, Generator Loss: 1.5890 D(x): 0.7370, D(G(z)): 0.3217 Epoch: [17/20], Batch Num: [87/600] Discriminator Loss: 0.8458, Generator Loss: 1.7670 D(x): 0.7322, D(G(z)): 0.2861 Epoch: [17/20], Batch Num: [88/600] Discriminator Loss: 0.9521, Generator Loss: 1.8665 D(x): 0.7207, D(G(z)): 0.3044 Epoch: [17/20], Batch Num: [89/600] Discriminator Loss: 0.8578, Generator Loss: 1.6685 D(x): 0.7005, D(G(z)): 0.2525 Epoch: [17/20], Batch Num: [90/600] Discriminator Loss: 0.9016, Generator Loss: 1.7757 D(x): 0.6924, D(G(z)): 0.2482 Epoch: [17/20], Batch Num: [91/600] Discriminator Loss: 0.9114, Generator Loss: 1.5518 D(x): 0.6440, D(G(z)): 0.2223 Epoch: [17/20], Batch Num: [92/600] Discriminator Loss: 0.8324, Generator Loss: 1.6047 D(x): 0.7282, D(G(z)): 0.2722 Epoch: [17/20], Batch Num: [93/600] Discriminator Loss: 0.8738, Generator Loss: 1.2749 D(x): 0.7059, D(G(z)): 0.2855 Epoch: [17/20], Batch Num: [94/600] Discriminator Loss: 0.7835, Generator Loss: 1.1774 D(x): 0.7966, D(G(z)): 0.3071 Epoch: [17/20], Batch Num: [95/600] Discriminator Loss: 0.8258, Generator Loss: 1.5752 D(x): 0.8434, D(G(z)): 0.3673 Epoch: [17/20], Batch Num: [96/600] Discriminator Loss: 0.8353, Generator Loss: 1.7188 D(x): 0.7622, D(G(z)): 0.3081 Epoch: [17/20], Batch Num: [97/600] Discriminator Loss: 0.9109, Generator Loss: 1.8257 D(x): 0.6633, D(G(z)): 0.2373 Epoch: [17/20], Batch Num: [98/600] Discriminator Loss: 0.8127, Generator Loss: 2.0379 D(x): 0.6918, D(G(z)): 0.2192 Epoch: [17/20], Batch Num: [99/600] Discriminator Loss: 0.7684, Generator Loss: 1.8683 D(x): 0.6896, D(G(z)): 0.1786 Epoch: 17, Batch Num: [100/600]
Epoch: [17/20], Batch Num: [100/600] Discriminator Loss: 0.7837, Generator Loss: 1.5241 D(x): 0.6952, D(G(z)): 0.2026 Epoch: [17/20], Batch Num: [101/600] Discriminator Loss: 0.7825, Generator Loss: 1.3743 D(x): 0.7622, D(G(z)): 0.2802 Epoch: [17/20], Batch Num: [102/600] Discriminator Loss: 0.8162, Generator Loss: 1.4109 D(x): 0.8102, D(G(z)): 0.3411 Epoch: [17/20], Batch Num: [103/600] Discriminator Loss: 0.9178, Generator Loss: 1.6197 D(x): 0.8074, D(G(z)): 0.3460 Epoch: [17/20], Batch Num: [104/600] Discriminator Loss: 0.7791, Generator Loss: 1.7746 D(x): 0.7758, D(G(z)): 0.3021 Epoch: [17/20], Batch Num: [105/600] Discriminator Loss: 0.8526, Generator Loss: 1.9023 D(x): 0.6681, D(G(z)): 0.2220 Epoch: [17/20], Batch Num: [106/600] Discriminator Loss: 0.6609, Generator Loss: 1.8176 D(x): 0.7598, D(G(z)): 0.2085 Epoch: [17/20], Batch Num: [107/600] Discriminator Loss: 0.6387, Generator Loss: 1.7010 D(x): 0.7358, D(G(z)): 0.1658 Epoch: [17/20], Batch Num: [108/600] Discriminator Loss: 0.7975, Generator Loss: 1.7768 D(x): 0.7074, D(G(z)): 0.2158 Epoch: [17/20], Batch Num: [109/600] Discriminator Loss: 0.8461, Generator Loss: 1.6233 D(x): 0.6943, D(G(z)): 0.2539 Epoch: [17/20], Batch Num: [110/600] Discriminator Loss: 0.7426, Generator Loss: 1.4311 D(x): 0.7821, D(G(z)): 0.2710 Epoch: [17/20], Batch Num: [111/600] Discriminator Loss: 0.7618, Generator Loss: 1.6056 D(x): 0.8138, D(G(z)): 0.3172 Epoch: [17/20], Batch Num: [112/600] Discriminator Loss: 0.8568, Generator Loss: 1.6144 D(x): 0.7605, D(G(z)): 0.2822 Epoch: [17/20], Batch Num: [113/600] Discriminator Loss: 0.8622, Generator Loss: 1.9201 D(x): 0.7083, D(G(z)): 0.2550 Epoch: [17/20], Batch Num: [114/600] Discriminator Loss: 0.7783, Generator Loss: 1.9051 D(x): 0.6949, D(G(z)): 0.1790 Epoch: [17/20], Batch Num: [115/600] Discriminator Loss: 0.8829, Generator Loss: 2.0876 D(x): 0.7479, D(G(z)): 0.2700 Epoch: [17/20], Batch Num: [116/600] Discriminator Loss: 0.7443, Generator Loss: 1.8510 D(x): 0.7854, D(G(z)): 0.2539 Epoch: [17/20], Batch Num: [117/600] Discriminator Loss: 0.8831, Generator Loss: 1.9495 D(x): 0.6878, D(G(z)): 0.2458 Epoch: [17/20], Batch Num: [118/600] Discriminator Loss: 0.8372, Generator Loss: 1.6192 D(x): 0.7031, D(G(z)): 0.2254 Epoch: [17/20], Batch Num: [119/600] Discriminator Loss: 0.8636, Generator Loss: 1.6955 D(x): 0.6944, D(G(z)): 0.2473 Epoch: [17/20], Batch Num: [120/600] Discriminator Loss: 0.9465, Generator Loss: 1.5857 D(x): 0.7181, D(G(z)): 0.3180 Epoch: [17/20], Batch Num: [121/600] Discriminator Loss: 0.7384, Generator Loss: 1.6685 D(x): 0.7961, D(G(z)): 0.2997 Epoch: [17/20], Batch Num: [122/600] Discriminator Loss: 0.8269, Generator Loss: 1.7705 D(x): 0.7795, D(G(z)): 0.3067 Epoch: [17/20], Batch Num: [123/600] Discriminator Loss: 0.8450, Generator Loss: 1.8659 D(x): 0.7057, D(G(z)): 0.2496 Epoch: [17/20], Batch Num: [124/600] Discriminator Loss: 0.9337, Generator Loss: 1.8296 D(x): 0.6660, D(G(z)): 0.2449 Epoch: [17/20], Batch Num: [125/600] Discriminator Loss: 0.8491, Generator Loss: 1.7297 D(x): 0.6782, D(G(z)): 0.2287 Epoch: [17/20], Batch Num: [126/600] Discriminator Loss: 0.8763, Generator Loss: 1.4196 D(x): 0.7099, D(G(z)): 0.2617 Epoch: [17/20], Batch Num: [127/600] Discriminator Loss: 1.0360, Generator Loss: 1.3113 D(x): 0.7024, D(G(z)): 0.3238 Epoch: [17/20], Batch Num: [128/600] Discriminator Loss: 0.9678, Generator Loss: 1.6647 D(x): 0.7753, D(G(z)): 0.3701 Epoch: [17/20], Batch Num: [129/600] Discriminator Loss: 0.9743, Generator Loss: 1.7413 D(x): 0.7513, D(G(z)): 0.3653 Epoch: [17/20], Batch Num: [130/600] Discriminator Loss: 0.9567, Generator Loss: 1.9271 D(x): 0.6683, D(G(z)): 0.2823 Epoch: [17/20], Batch Num: [131/600] Discriminator Loss: 0.9307, Generator Loss: 1.9776 D(x): 0.6238, D(G(z)): 0.2192 Epoch: [17/20], Batch Num: [132/600] Discriminator Loss: 0.7921, Generator Loss: 1.7238 D(x): 0.6892, D(G(z)): 0.2025 Epoch: [17/20], Batch Num: [133/600] Discriminator Loss: 0.9309, Generator Loss: 1.3926 D(x): 0.6670, D(G(z)): 0.2686 Epoch: [17/20], Batch Num: [134/600] Discriminator Loss: 0.8400, Generator Loss: 1.3937 D(x): 0.7165, D(G(z)): 0.3000 Epoch: [17/20], Batch Num: [135/600] Discriminator Loss: 0.7766, Generator Loss: 1.2627 D(x): 0.7897, D(G(z)): 0.3269 Epoch: [17/20], Batch Num: [136/600] Discriminator Loss: 0.9641, Generator Loss: 1.3234 D(x): 0.7641, D(G(z)): 0.3855 Epoch: [17/20], Batch Num: [137/600] Discriminator Loss: 0.8829, Generator Loss: 1.6518 D(x): 0.7416, D(G(z)): 0.3540 Epoch: [17/20], Batch Num: [138/600] Discriminator Loss: 0.9042, Generator Loss: 1.6264 D(x): 0.7146, D(G(z)): 0.3269 Epoch: [17/20], Batch Num: [139/600] Discriminator Loss: 0.8430, Generator Loss: 1.6931 D(x): 0.6968, D(G(z)): 0.2554 Epoch: [17/20], Batch Num: [140/600] Discriminator Loss: 0.9565, Generator Loss: 1.8234 D(x): 0.6201, D(G(z)): 0.2227 Epoch: [17/20], Batch Num: [141/600] Discriminator Loss: 0.8237, Generator Loss: 1.5876 D(x): 0.7247, D(G(z)): 0.2670 Epoch: [17/20], Batch Num: [142/600] Discriminator Loss: 0.8512, Generator Loss: 1.5743 D(x): 0.7004, D(G(z)): 0.2584 Epoch: [17/20], Batch Num: [143/600] Discriminator Loss: 0.8590, Generator Loss: 1.4766 D(x): 0.7153, D(G(z)): 0.2832 Epoch: [17/20], Batch Num: [144/600] Discriminator Loss: 0.8904, Generator Loss: 1.4436 D(x): 0.7652, D(G(z)): 0.3331 Epoch: [17/20], Batch Num: [145/600] Discriminator Loss: 0.9930, Generator Loss: 1.4607 D(x): 0.7108, D(G(z)): 0.3390 Epoch: [17/20], Batch Num: [146/600] Discriminator Loss: 0.7549, Generator Loss: 1.4677 D(x): 0.7825, D(G(z)): 0.2879 Epoch: [17/20], Batch Num: [147/600] Discriminator Loss: 0.9005, Generator Loss: 1.6230 D(x): 0.6585, D(G(z)): 0.2411 Epoch: [17/20], Batch Num: [148/600] Discriminator Loss: 0.9011, Generator Loss: 1.7979 D(x): 0.7212, D(G(z)): 0.2899 Epoch: [17/20], Batch Num: [149/600] Discriminator Loss: 0.8917, Generator Loss: 1.8809 D(x): 0.7382, D(G(z)): 0.2872 Epoch: [17/20], Batch Num: [150/600] Discriminator Loss: 0.8591, Generator Loss: 1.7359 D(x): 0.7100, D(G(z)): 0.2446 Epoch: [17/20], Batch Num: [151/600] Discriminator Loss: 0.8344, Generator Loss: 1.6429 D(x): 0.6940, D(G(z)): 0.2395 Epoch: [17/20], Batch Num: [152/600] Discriminator Loss: 0.6794, Generator Loss: 1.6919 D(x): 0.7571, D(G(z)): 0.2351 Epoch: [17/20], Batch Num: [153/600] Discriminator Loss: 0.8778, Generator Loss: 1.6088 D(x): 0.7081, D(G(z)): 0.2964 Epoch: [17/20], Batch Num: [154/600] Discriminator Loss: 0.9008, Generator Loss: 1.4533 D(x): 0.7350, D(G(z)): 0.3258 Epoch: [17/20], Batch Num: [155/600] Discriminator Loss: 0.7666, Generator Loss: 1.6747 D(x): 0.7544, D(G(z)): 0.2963 Epoch: [17/20], Batch Num: [156/600] Discriminator Loss: 0.8113, Generator Loss: 2.0275 D(x): 0.7356, D(G(z)): 0.2635 Epoch: [17/20], Batch Num: [157/600] Discriminator Loss: 0.7920, Generator Loss: 1.7238 D(x): 0.7019, D(G(z)): 0.2452 Epoch: [17/20], Batch Num: [158/600] Discriminator Loss: 0.7994, Generator Loss: 1.7368 D(x): 0.7129, D(G(z)): 0.2572 Epoch: [17/20], Batch Num: [159/600] Discriminator Loss: 0.9826, Generator Loss: 1.6518 D(x): 0.6700, D(G(z)): 0.2916 Epoch: [17/20], Batch Num: [160/600] Discriminator Loss: 0.9891, Generator Loss: 1.5333 D(x): 0.6536, D(G(z)): 0.2815 Epoch: [17/20], Batch Num: [161/600] Discriminator Loss: 0.6858, Generator Loss: 1.4653 D(x): 0.7695, D(G(z)): 0.2366 Epoch: [17/20], Batch Num: [162/600] Discriminator Loss: 0.8301, Generator Loss: 1.6125 D(x): 0.7521, D(G(z)): 0.3343 Epoch: [17/20], Batch Num: [163/600] Discriminator Loss: 0.8905, Generator Loss: 1.7517 D(x): 0.7253, D(G(z)): 0.2945 Epoch: [17/20], Batch Num: [164/600] Discriminator Loss: 0.7814, Generator Loss: 1.7762 D(x): 0.7146, D(G(z)): 0.2432 Epoch: [17/20], Batch Num: [165/600] Discriminator Loss: 0.9193, Generator Loss: 1.7976 D(x): 0.6626, D(G(z)): 0.2571 Epoch: [17/20], Batch Num: [166/600] Discriminator Loss: 0.8598, Generator Loss: 1.7396 D(x): 0.6744, D(G(z)): 0.2359 Epoch: [17/20], Batch Num: [167/600] Discriminator Loss: 0.7014, Generator Loss: 1.5213 D(x): 0.7511, D(G(z)): 0.2439 Epoch: [17/20], Batch Num: [168/600] Discriminator Loss: 0.7222, Generator Loss: 1.5454 D(x): 0.7793, D(G(z)): 0.2711 Epoch: [17/20], Batch Num: [169/600] Discriminator Loss: 0.8681, Generator Loss: 1.6108 D(x): 0.7440, D(G(z)): 0.3106 Epoch: [17/20], Batch Num: [170/600] Discriminator Loss: 0.7534, Generator Loss: 1.6231 D(x): 0.7443, D(G(z)): 0.2560 Epoch: [17/20], Batch Num: [171/600] Discriminator Loss: 0.8769, Generator Loss: 1.8396 D(x): 0.7353, D(G(z)): 0.2722 Epoch: [17/20], Batch Num: [172/600] Discriminator Loss: 0.7874, Generator Loss: 1.7361 D(x): 0.7370, D(G(z)): 0.2356 Epoch: [17/20], Batch Num: [173/600] Discriminator Loss: 0.6559, Generator Loss: 1.8287 D(x): 0.7965, D(G(z)): 0.2456 Epoch: [17/20], Batch Num: [174/600] Discriminator Loss: 0.9009, Generator Loss: 1.7889 D(x): 0.6834, D(G(z)): 0.2560 Epoch: [17/20], Batch Num: [175/600] Discriminator Loss: 0.9090, Generator Loss: 1.4786 D(x): 0.6986, D(G(z)): 0.2587 Epoch: [17/20], Batch Num: [176/600] Discriminator Loss: 0.6879, Generator Loss: 1.5645 D(x): 0.7916, D(G(z)): 0.2581 Epoch: [17/20], Batch Num: [177/600] Discriminator Loss: 0.7525, Generator Loss: 1.7387 D(x): 0.7684, D(G(z)): 0.2583 Epoch: [17/20], Batch Num: [178/600] Discriminator Loss: 0.7004, Generator Loss: 1.5744 D(x): 0.7953, D(G(z)): 0.2649 Epoch: [17/20], Batch Num: [179/600] Discriminator Loss: 0.7567, Generator Loss: 1.7485 D(x): 0.7556, D(G(z)): 0.2420 Epoch: [17/20], Batch Num: [180/600] Discriminator Loss: 0.7494, Generator Loss: 1.8924 D(x): 0.7914, D(G(z)): 0.2472 Epoch: [17/20], Batch Num: [181/600] Discriminator Loss: 0.6329, Generator Loss: 1.8026 D(x): 0.7517, D(G(z)): 0.1850 Epoch: [17/20], Batch Num: [182/600] Discriminator Loss: 0.7873, Generator Loss: 1.8352 D(x): 0.7540, D(G(z)): 0.2697 Epoch: [17/20], Batch Num: [183/600] Discriminator Loss: 0.8067, Generator Loss: 1.8736 D(x): 0.7135, D(G(z)): 0.2174 Epoch: [17/20], Batch Num: [184/600] Discriminator Loss: 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Epoch: [17/20], Batch Num: [200/600] Discriminator Loss: 0.8582, Generator Loss: 1.6116 D(x): 0.7119, D(G(z)): 0.2634 Epoch: [17/20], Batch Num: [201/600] Discriminator Loss: 0.7335, Generator Loss: 1.7685 D(x): 0.7613, D(G(z)): 0.2781 Epoch: [17/20], Batch Num: [202/600] Discriminator Loss: 0.8080, Generator Loss: 1.6245 D(x): 0.7419, D(G(z)): 0.2945 Epoch: [17/20], Batch Num: [203/600] Discriminator Loss: 0.8040, Generator Loss: 1.5816 D(x): 0.7090, D(G(z)): 0.2651 Epoch: [17/20], Batch Num: [204/600] Discriminator Loss: 0.9367, Generator Loss: 1.5737 D(x): 0.6656, D(G(z)): 0.2750 Epoch: [17/20], Batch Num: [205/600] Discriminator Loss: 0.7970, Generator Loss: 1.4187 D(x): 0.7731, D(G(z)): 0.3004 Epoch: [17/20], Batch Num: [206/600] Discriminator Loss: 0.8752, Generator Loss: 1.8160 D(x): 0.7511, D(G(z)): 0.3018 Epoch: [17/20], Batch Num: [207/600] Discriminator Loss: 0.8539, Generator Loss: 1.7496 D(x): 0.7341, D(G(z)): 0.3089 Epoch: [17/20], Batch Num: [208/600] Discriminator Loss: 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0.2010 Epoch: [17/20], Batch Num: [217/600] Discriminator Loss: 0.9136, Generator Loss: 1.6542 D(x): 0.6862, D(G(z)): 0.2634 Epoch: [17/20], Batch Num: [218/600] Discriminator Loss: 0.7940, Generator Loss: 1.6177 D(x): 0.7139, D(G(z)): 0.2401 Epoch: [17/20], Batch Num: [219/600] Discriminator Loss: 0.8104, Generator Loss: 1.3047 D(x): 0.7577, D(G(z)): 0.3065 Epoch: [17/20], Batch Num: [220/600] Discriminator Loss: 0.8038, Generator Loss: 1.5453 D(x): 0.7803, D(G(z)): 0.3254 Epoch: [17/20], Batch Num: [221/600] Discriminator Loss: 0.8471, Generator Loss: 1.5860 D(x): 0.7548, D(G(z)): 0.3345 Epoch: [17/20], Batch Num: [222/600] Discriminator Loss: 0.6991, Generator Loss: 1.7139 D(x): 0.7623, D(G(z)): 0.2568 Epoch: [17/20], Batch Num: [223/600] Discriminator Loss: 0.8639, Generator Loss: 1.6923 D(x): 0.7347, D(G(z)): 0.2750 Epoch: [17/20], Batch Num: [224/600] Discriminator Loss: 0.7216, Generator Loss: 1.8589 D(x): 0.7920, D(G(z)): 0.2743 Epoch: [17/20], Batch Num: [225/600] Discriminator Loss: 0.8166, Generator Loss: 2.0798 D(x): 0.6920, D(G(z)): 0.2175 Epoch: [17/20], Batch Num: [226/600] Discriminator Loss: 0.7974, Generator Loss: 1.9426 D(x): 0.6804, D(G(z)): 0.1787 Epoch: [17/20], Batch Num: [227/600] Discriminator Loss: 0.6793, Generator Loss: 1.5559 D(x): 0.7291, D(G(z)): 0.2124 Epoch: [17/20], Batch Num: [228/600] Discriminator Loss: 0.8681, Generator Loss: 1.6062 D(x): 0.7498, D(G(z)): 0.3075 Epoch: [17/20], Batch Num: [229/600] Discriminator Loss: 0.7522, Generator Loss: 1.5853 D(x): 0.8042, D(G(z)): 0.3087 Epoch: [17/20], Batch Num: [230/600] Discriminator Loss: 0.6856, Generator Loss: 1.5792 D(x): 0.7852, D(G(z)): 0.2549 Epoch: [17/20], Batch Num: [231/600] Discriminator Loss: 0.8688, Generator Loss: 2.0551 D(x): 0.7553, D(G(z)): 0.3049 Epoch: [17/20], Batch Num: [232/600] Discriminator Loss: 0.8158, Generator Loss: 2.0914 D(x): 0.7162, D(G(z)): 0.2198 Epoch: [17/20], Batch Num: [233/600] Discriminator Loss: 0.8717, Generator Loss: 2.0758 D(x): 0.6837, D(G(z)): 0.1896 Epoch: [17/20], Batch Num: [234/600] Discriminator Loss: 0.9597, Generator Loss: 2.0000 D(x): 0.6534, D(G(z)): 0.2276 Epoch: [17/20], Batch Num: [235/600] Discriminator Loss: 0.7611, Generator Loss: 1.5686 D(x): 0.7467, D(G(z)): 0.2365 Epoch: [17/20], Batch Num: [236/600] Discriminator Loss: 0.8116, Generator Loss: 1.4096 D(x): 0.7476, D(G(z)): 0.2910 Epoch: [17/20], Batch Num: [237/600] Discriminator Loss: 0.7042, Generator Loss: 1.3072 D(x): 0.8150, D(G(z)): 0.3098 Epoch: [17/20], Batch Num: [238/600] Discriminator Loss: 0.8054, Generator Loss: 1.5853 D(x): 0.7910, D(G(z)): 0.3328 Epoch: [17/20], Batch Num: [239/600] Discriminator Loss: 0.8374, Generator Loss: 1.7606 D(x): 0.7453, D(G(z)): 0.2996 Epoch: [17/20], Batch Num: [240/600] Discriminator Loss: 0.7469, Generator Loss: 2.1142 D(x): 0.7990, D(G(z)): 0.2706 Epoch: [17/20], Batch Num: [241/600] Discriminator Loss: 0.7849, Generator Loss: 2.1382 D(x): 0.7369, D(G(z)): 0.2314 Epoch: [17/20], Batch Num: 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2.2220 D(x): 0.6054, D(G(z)): 0.2045 Epoch: [17/20], Batch Num: [251/600] Discriminator Loss: 0.9059, Generator Loss: 1.9839 D(x): 0.6334, D(G(z)): 0.2034 Epoch: [17/20], Batch Num: [252/600] Discriminator Loss: 0.8129, Generator Loss: 1.6541 D(x): 0.7310, D(G(z)): 0.2273 Epoch: [17/20], Batch Num: [253/600] Discriminator Loss: 0.7416, Generator Loss: 1.3234 D(x): 0.7600, D(G(z)): 0.2653 Epoch: [17/20], Batch Num: [254/600] Discriminator Loss: 0.9417, Generator Loss: 1.3546 D(x): 0.7340, D(G(z)): 0.3431 Epoch: [17/20], Batch Num: [255/600] Discriminator Loss: 0.7872, Generator Loss: 1.4471 D(x): 0.7703, D(G(z)): 0.3032 Epoch: [17/20], Batch Num: [256/600] Discriminator Loss: 0.8978, Generator Loss: 1.3932 D(x): 0.7190, D(G(z)): 0.3206 Epoch: [17/20], Batch Num: [257/600] Discriminator Loss: 0.7776, Generator Loss: 1.6900 D(x): 0.7494, D(G(z)): 0.2693 Epoch: [17/20], Batch Num: [258/600] Discriminator Loss: 0.8037, Generator Loss: 1.8179 D(x): 0.7475, D(G(z)): 0.2835 Epoch: [17/20], Batch Num: [259/600] Discriminator Loss: 0.8103, Generator Loss: 1.8599 D(x): 0.7025, D(G(z)): 0.2621 Epoch: [17/20], Batch Num: [260/600] Discriminator Loss: 0.7989, Generator Loss: 1.7574 D(x): 0.6923, D(G(z)): 0.2243 Epoch: [17/20], Batch Num: [261/600] Discriminator Loss: 0.7414, Generator Loss: 1.6950 D(x): 0.7310, D(G(z)): 0.2330 Epoch: [17/20], Batch Num: [262/600] Discriminator Loss: 0.8686, Generator Loss: 1.6980 D(x): 0.7071, D(G(z)): 0.2410 Epoch: [17/20], Batch Num: [263/600] Discriminator Loss: 0.8175, Generator Loss: 1.6578 D(x): 0.7579, D(G(z)): 0.2852 Epoch: [17/20], Batch Num: [264/600] Discriminator Loss: 0.8627, Generator Loss: 1.6102 D(x): 0.7570, D(G(z)): 0.3130 Epoch: [17/20], Batch Num: [265/600] Discriminator Loss: 1.0846, Generator Loss: 1.7814 D(x): 0.7099, D(G(z)): 0.3605 Epoch: [17/20], Batch Num: [266/600] Discriminator Loss: 0.8588, Generator Loss: 1.5581 D(x): 0.7030, D(G(z)): 0.2753 Epoch: [17/20], Batch Num: [267/600] Discriminator Loss: 0.7957, Generator Loss: 1.6957 D(x): 0.7538, D(G(z)): 0.2562 Epoch: [17/20], Batch Num: [268/600] Discriminator Loss: 0.8708, Generator Loss: 1.6049 D(x): 0.7330, D(G(z)): 0.2915 Epoch: [17/20], Batch Num: [269/600] Discriminator Loss: 0.9259, Generator Loss: 1.7791 D(x): 0.6947, D(G(z)): 0.2771 Epoch: [17/20], Batch Num: [270/600] Discriminator Loss: 0.8388, Generator Loss: 1.6326 D(x): 0.7201, D(G(z)): 0.2676 Epoch: [17/20], Batch Num: [271/600] Discriminator Loss: 0.8584, Generator Loss: 1.7234 D(x): 0.7665, D(G(z)): 0.3156 Epoch: [17/20], Batch Num: [272/600] Discriminator Loss: 0.7003, Generator Loss: 1.8372 D(x): 0.7563, D(G(z)): 0.2455 Epoch: [17/20], Batch Num: [273/600] Discriminator Loss: 0.7647, Generator Loss: 1.7706 D(x): 0.7423, D(G(z)): 0.2468 Epoch: [17/20], Batch Num: [274/600] Discriminator Loss: 0.8850, Generator Loss: 1.7821 D(x): 0.7159, D(G(z)): 0.2933 Epoch: [17/20], Batch Num: [275/600] Discriminator Loss: 0.8300, Generator Loss: 1.7195 D(x): 0.7093, D(G(z)): 0.2613 Epoch: [17/20], Batch Num: [276/600] Discriminator Loss: 0.7827, Generator Loss: 1.7517 D(x): 0.7250, D(G(z)): 0.2224 Epoch: [17/20], Batch Num: [277/600] Discriminator Loss: 0.8782, Generator Loss: 1.4011 D(x): 0.6800, D(G(z)): 0.2519 Epoch: [17/20], Batch Num: [278/600] Discriminator Loss: 0.9116, Generator Loss: 1.5754 D(x): 0.7378, D(G(z)): 0.2960 Epoch: [17/20], Batch Num: [279/600] Discriminator Loss: 1.0117, Generator Loss: 1.3477 D(x): 0.6786, D(G(z)): 0.3100 Epoch: [17/20], Batch Num: [280/600] Discriminator Loss: 0.8728, Generator Loss: 1.4925 D(x): 0.7668, D(G(z)): 0.3443 Epoch: [17/20], Batch Num: [281/600] Discriminator Loss: 0.7880, Generator Loss: 1.5035 D(x): 0.7659, D(G(z)): 0.2989 Epoch: [17/20], Batch Num: [282/600] Discriminator Loss: 0.9363, Generator Loss: 1.6517 D(x): 0.7258, D(G(z)): 0.3078 Epoch: [17/20], Batch Num: [283/600] Discriminator Loss: 0.9509, Generator Loss: 1.7880 D(x): 0.6526, D(G(z)): 0.2581 Epoch: [17/20], Batch Num: [284/600] Discriminator Loss: 0.9324, Generator Loss: 1.5756 D(x): 0.6485, D(G(z)): 0.2563 Epoch: [17/20], Batch Num: [285/600] Discriminator Loss: 0.8112, Generator Loss: 1.5816 D(x): 0.6931, D(G(z)): 0.2416 Epoch: [17/20], Batch Num: [286/600] Discriminator Loss: 0.8315, Generator Loss: 1.4024 D(x): 0.7116, D(G(z)): 0.2712 Epoch: [17/20], Batch Num: [287/600] Discriminator Loss: 0.9571, Generator Loss: 1.3285 D(x): 0.6511, D(G(z)): 0.3051 Epoch: [17/20], Batch Num: [288/600] Discriminator Loss: 0.7789, Generator Loss: 1.3750 D(x): 0.7669, D(G(z)): 0.3082 Epoch: [17/20], Batch Num: [289/600] Discriminator Loss: 0.8111, Generator Loss: 1.6276 D(x): 0.7570, D(G(z)): 0.3245 Epoch: [17/20], Batch Num: [290/600] Discriminator Loss: 0.7782, Generator Loss: 1.5648 D(x): 0.7786, D(G(z)): 0.3249 Epoch: [17/20], Batch Num: [291/600] Discriminator Loss: 0.7605, Generator Loss: 1.6914 D(x): 0.7284, D(G(z)): 0.2519 Epoch: [17/20], Batch Num: [292/600] Discriminator Loss: 0.8249, Generator Loss: 1.6242 D(x): 0.6939, D(G(z)): 0.2596 Epoch: [17/20], Batch Num: [293/600] Discriminator Loss: 0.8325, Generator Loss: 1.5784 D(x): 0.7265, D(G(z)): 0.2813 Epoch: [17/20], Batch Num: [294/600] Discriminator Loss: 0.7664, Generator Loss: 1.6438 D(x): 0.7420, D(G(z)): 0.2584 Epoch: [17/20], Batch Num: [295/600] Discriminator Loss: 0.8529, Generator Loss: 1.5910 D(x): 0.6989, D(G(z)): 0.2535 Epoch: [17/20], Batch Num: [296/600] Discriminator Loss: 0.7631, Generator Loss: 1.6331 D(x): 0.7445, D(G(z)): 0.2622 Epoch: [17/20], Batch Num: [297/600] Discriminator Loss: 0.8295, Generator Loss: 1.5866 D(x): 0.7583, D(G(z)): 0.2981 Epoch: [17/20], Batch Num: [298/600] Discriminator Loss: 0.9328, Generator Loss: 1.7011 D(x): 0.7123, D(G(z)): 0.2989 Epoch: [17/20], Batch Num: [299/600] Discriminator Loss: 0.8819, Generator Loss: 1.7807 D(x): 0.7510, D(G(z)): 0.3026 Epoch: 17, Batch Num: [300/600]
Epoch: [17/20], Batch Num: [300/600] Discriminator Loss: 0.7801, Generator Loss: 1.8388 D(x): 0.7254, D(G(z)): 0.2419 Epoch: [17/20], Batch Num: [301/600] Discriminator Loss: 0.9453, Generator Loss: 2.0215 D(x): 0.7054, D(G(z)): 0.2788 Epoch: [17/20], Batch Num: [302/600] Discriminator Loss: 0.7322, Generator Loss: 1.8254 D(x): 0.7203, D(G(z)): 0.2202 Epoch: [17/20], Batch Num: [303/600] Discriminator Loss: 0.7095, Generator Loss: 1.8992 D(x): 0.7417, D(G(z)): 0.2119 Epoch: [17/20], Batch Num: [304/600] Discriminator Loss: 0.8961, Generator Loss: 1.4737 D(x): 0.6826, D(G(z)): 0.2635 Epoch: [17/20], Batch Num: [305/600] Discriminator Loss: 0.7466, Generator Loss: 1.5658 D(x): 0.7832, D(G(z)): 0.2952 Epoch: [17/20], Batch Num: [306/600] Discriminator Loss: 1.0062, Generator Loss: 1.6557 D(x): 0.7512, D(G(z)): 0.3198 Epoch: [17/20], Batch Num: [307/600] Discriminator Loss: 0.7695, Generator Loss: 1.7884 D(x): 0.7800, D(G(z)): 0.2778 Epoch: [17/20], Batch Num: [308/600] Discriminator Loss: 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0.2635 Epoch: [17/20], Batch Num: [317/600] Discriminator Loss: 0.7287, Generator Loss: 1.8434 D(x): 0.8194, D(G(z)): 0.3013 Epoch: [17/20], Batch Num: [318/600] Discriminator Loss: 0.7984, Generator Loss: 1.7517 D(x): 0.7276, D(G(z)): 0.2201 Epoch: [17/20], Batch Num: [319/600] Discriminator Loss: 0.9648, Generator Loss: 1.8405 D(x): 0.7178, D(G(z)): 0.3012 Epoch: [17/20], Batch Num: [320/600] Discriminator Loss: 0.8458, Generator Loss: 1.6497 D(x): 0.7336, D(G(z)): 0.2616 Epoch: [17/20], Batch Num: [321/600] Discriminator Loss: 0.8433, Generator Loss: 1.7855 D(x): 0.7150, D(G(z)): 0.2407 Epoch: [17/20], Batch Num: [322/600] Discriminator Loss: 0.8988, Generator Loss: 1.8011 D(x): 0.6911, D(G(z)): 0.2373 Epoch: [17/20], Batch Num: [323/600] Discriminator Loss: 0.8739, Generator Loss: 1.5500 D(x): 0.7333, D(G(z)): 0.2813 Epoch: [17/20], Batch Num: [324/600] Discriminator Loss: 0.7270, Generator Loss: 1.6274 D(x): 0.8165, D(G(z)): 0.2985 Epoch: [17/20], Batch Num: [325/600] Discriminator Loss: 0.8212, Generator Loss: 1.8712 D(x): 0.7768, D(G(z)): 0.2973 Epoch: [17/20], Batch Num: [326/600] Discriminator Loss: 0.8067, Generator Loss: 1.9552 D(x): 0.7338, D(G(z)): 0.2280 Epoch: [17/20], Batch Num: [327/600] Discriminator Loss: 0.7743, Generator Loss: 1.9247 D(x): 0.7025, D(G(z)): 0.2081 Epoch: [17/20], Batch Num: [328/600] Discriminator Loss: 0.7973, Generator Loss: 1.6582 D(x): 0.7002, D(G(z)): 0.2219 Epoch: [17/20], Batch Num: [329/600] Discriminator Loss: 0.8586, Generator Loss: 1.5860 D(x): 0.7129, D(G(z)): 0.2406 Epoch: [17/20], Batch Num: [330/600] Discriminator Loss: 0.8139, Generator Loss: 1.4643 D(x): 0.7459, D(G(z)): 0.3010 Epoch: [17/20], Batch Num: [331/600] Discriminator Loss: 0.9913, Generator Loss: 1.4919 D(x): 0.7869, D(G(z)): 0.3953 Epoch: [17/20], Batch Num: [332/600] Discriminator Loss: 0.8516, Generator Loss: 1.9771 D(x): 0.8058, D(G(z)): 0.3414 Epoch: [17/20], Batch Num: [333/600] Discriminator Loss: 0.8864, Generator Loss: 2.1388 D(x): 0.6872, D(G(z)): 0.2338 Epoch: [17/20], Batch Num: [334/600] Discriminator Loss: 0.8193, Generator Loss: 1.7950 D(x): 0.6936, D(G(z)): 0.2334 Epoch: [17/20], Batch Num: [335/600] Discriminator Loss: 0.8822, Generator Loss: 1.7127 D(x): 0.6676, D(G(z)): 0.2309 Epoch: [17/20], Batch Num: [336/600] Discriminator Loss: 0.9255, Generator Loss: 1.5159 D(x): 0.7023, D(G(z)): 0.2886 Epoch: [17/20], Batch Num: [337/600] Discriminator Loss: 0.7786, Generator Loss: 1.3238 D(x): 0.7462, D(G(z)): 0.2716 Epoch: [17/20], Batch Num: [338/600] Discriminator Loss: 0.7324, Generator Loss: 1.4254 D(x): 0.7827, D(G(z)): 0.2921 Epoch: [17/20], Batch Num: [339/600] Discriminator Loss: 0.8174, Generator Loss: 1.4877 D(x): 0.8024, D(G(z)): 0.3335 Epoch: [17/20], Batch Num: [340/600] Discriminator Loss: 0.8626, Generator Loss: 1.5864 D(x): 0.7549, D(G(z)): 0.3275 Epoch: [17/20], Batch Num: [341/600] Discriminator Loss: 0.7810, Generator Loss: 1.7835 D(x): 0.7225, D(G(z)): 0.2626 Epoch: [17/20], Batch Num: 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1.7165 D(x): 0.7390, D(G(z)): 0.2887 Epoch: [17/20], Batch Num: [351/600] Discriminator Loss: 0.9365, Generator Loss: 1.7123 D(x): 0.6726, D(G(z)): 0.2692 Epoch: [17/20], Batch Num: [352/600] Discriminator Loss: 0.9462, Generator Loss: 1.5707 D(x): 0.7017, D(G(z)): 0.2802 Epoch: [17/20], Batch Num: [353/600] Discriminator Loss: 0.8626, Generator Loss: 1.6367 D(x): 0.7178, D(G(z)): 0.2745 Epoch: [17/20], Batch Num: [354/600] Discriminator Loss: 0.8545, Generator Loss: 1.7697 D(x): 0.7284, D(G(z)): 0.2863 Epoch: [17/20], Batch Num: [355/600] Discriminator Loss: 0.7165, Generator Loss: 1.6008 D(x): 0.7858, D(G(z)): 0.2734 Epoch: [17/20], Batch Num: [356/600] Discriminator Loss: 0.7482, Generator Loss: 1.7488 D(x): 0.7377, D(G(z)): 0.2562 Epoch: [17/20], Batch Num: [357/600] Discriminator Loss: 0.9038, Generator Loss: 1.6410 D(x): 0.6905, D(G(z)): 0.2660 Epoch: [17/20], Batch Num: [358/600] Discriminator Loss: 0.8244, Generator Loss: 1.5530 D(x): 0.7394, D(G(z)): 0.2946 Epoch: [17/20], 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Generator Loss: 1.4080 D(x): 0.7130, D(G(z)): 0.2899 Epoch: [17/20], Batch Num: [368/600] Discriminator Loss: 1.1649, Generator Loss: 1.5462 D(x): 0.7372, D(G(z)): 0.3982 Epoch: [17/20], Batch Num: [369/600] Discriminator Loss: 0.8151, Generator Loss: 1.7149 D(x): 0.7707, D(G(z)): 0.3019 Epoch: [17/20], Batch Num: [370/600] Discriminator Loss: 0.6937, Generator Loss: 1.9310 D(x): 0.7539, D(G(z)): 0.2268 Epoch: [17/20], Batch Num: [371/600] Discriminator Loss: 0.8890, Generator Loss: 2.0887 D(x): 0.6501, D(G(z)): 0.2019 Epoch: [17/20], Batch Num: [372/600] Discriminator Loss: 0.8116, Generator Loss: 1.7978 D(x): 0.6920, D(G(z)): 0.2085 Epoch: [17/20], Batch Num: [373/600] Discriminator Loss: 0.7311, Generator Loss: 1.5884 D(x): 0.7399, D(G(z)): 0.2139 Epoch: [17/20], Batch Num: [374/600] Discriminator Loss: 0.7236, Generator Loss: 1.3629 D(x): 0.7748, D(G(z)): 0.2629 Epoch: [17/20], Batch Num: [375/600] Discriminator Loss: 0.8939, Generator Loss: 1.3247 D(x): 0.8063, D(G(z)): 0.3350 Epoch: [17/20], Batch Num: [376/600] Discriminator Loss: 0.8505, Generator Loss: 1.5761 D(x): 0.7962, D(G(z)): 0.3112 Epoch: [17/20], Batch Num: [377/600] Discriminator Loss: 0.9049, Generator Loss: 1.8336 D(x): 0.7834, D(G(z)): 0.3130 Epoch: [17/20], Batch Num: [378/600] Discriminator Loss: 0.8084, Generator Loss: 2.0189 D(x): 0.7209, D(G(z)): 0.2119 Epoch: [17/20], Batch Num: [379/600] Discriminator Loss: 0.6966, Generator Loss: 2.0897 D(x): 0.7200, D(G(z)): 0.1736 Epoch: [17/20], Batch Num: [380/600] Discriminator Loss: 0.9834, Generator Loss: 1.7624 D(x): 0.6464, D(G(z)): 0.2052 Epoch: [17/20], Batch Num: [381/600] Discriminator Loss: 0.8671, Generator Loss: 1.4527 D(x): 0.6824, D(G(z)): 0.2281 Epoch: [17/20], Batch Num: [382/600] Discriminator Loss: 0.7843, Generator Loss: 1.2027 D(x): 0.7340, D(G(z)): 0.2619 Epoch: [17/20], Batch Num: [383/600] Discriminator Loss: 0.9557, Generator Loss: 1.2727 D(x): 0.7643, D(G(z)): 0.3594 Epoch: [17/20], Batch Num: [384/600] Discriminator Loss: 0.8387, Generator Loss: 1.3781 D(x): 0.7829, D(G(z)): 0.3429 Epoch: [17/20], Batch Num: [385/600] Discriminator Loss: 0.8210, Generator Loss: 1.5736 D(x): 0.7874, D(G(z)): 0.3414 Epoch: [17/20], Batch Num: [386/600] Discriminator Loss: 0.6818, Generator Loss: 1.8619 D(x): 0.8050, D(G(z)): 0.2700 Epoch: [17/20], Batch Num: [387/600] Discriminator Loss: 0.8013, Generator Loss: 2.0537 D(x): 0.6927, D(G(z)): 0.2168 Epoch: [17/20], Batch Num: [388/600] Discriminator Loss: 0.9231, Generator Loss: 1.8942 D(x): 0.6199, D(G(z)): 0.2134 Epoch: [17/20], Batch Num: [389/600] Discriminator Loss: 0.8381, Generator Loss: 1.7051 D(x): 0.6538, D(G(z)): 0.2051 Epoch: [17/20], Batch Num: [390/600] Discriminator Loss: 0.8374, Generator Loss: 1.5013 D(x): 0.7076, D(G(z)): 0.2760 Epoch: [17/20], Batch Num: [391/600] Discriminator Loss: 0.7713, Generator Loss: 1.4338 D(x): 0.7898, D(G(z)): 0.3198 Epoch: [17/20], Batch Num: [392/600] Discriminator Loss: 0.7549, Generator Loss: 1.4249 D(x): 0.7800, D(G(z)): 0.2893 Epoch: [17/20], Batch Num: [393/600] Discriminator Loss: 0.8394, Generator Loss: 1.6019 D(x): 0.8024, D(G(z)): 0.3387 Epoch: [17/20], Batch Num: [394/600] Discriminator Loss: 0.7589, Generator Loss: 1.7069 D(x): 0.7251, D(G(z)): 0.2512 Epoch: [17/20], Batch Num: [395/600] Discriminator Loss: 0.8258, Generator Loss: 1.6981 D(x): 0.7025, D(G(z)): 0.2462 Epoch: [17/20], Batch Num: [396/600] Discriminator Loss: 0.8030, Generator Loss: 1.7101 D(x): 0.6958, D(G(z)): 0.2249 Epoch: [17/20], Batch Num: [397/600] Discriminator Loss: 0.8931, Generator Loss: 1.6660 D(x): 0.7314, D(G(z)): 0.2664 Epoch: [17/20], Batch Num: [398/600] Discriminator Loss: 0.9016, Generator Loss: 1.8078 D(x): 0.6834, D(G(z)): 0.2449 Epoch: [17/20], Batch Num: [399/600] Discriminator Loss: 0.7802, Generator Loss: 1.5743 D(x): 0.7683, D(G(z)): 0.2755 Epoch: 17, Batch Num: [400/600]
Epoch: [17/20], Batch Num: [400/600] Discriminator Loss: 0.8209, Generator Loss: 1.7791 D(x): 0.7493, D(G(z)): 0.2655 Epoch: [17/20], Batch Num: [401/600] Discriminator Loss: 0.9440, Generator Loss: 1.6245 D(x): 0.7262, D(G(z)): 0.2861 Epoch: [17/20], Batch Num: [402/600] Discriminator Loss: 0.7353, Generator Loss: 1.4755 D(x): 0.7746, D(G(z)): 0.2754 Epoch: [17/20], Batch Num: [403/600] Discriminator Loss: 0.8091, Generator Loss: 1.6778 D(x): 0.7537, D(G(z)): 0.2682 Epoch: [17/20], Batch Num: [404/600] Discriminator Loss: 0.7632, Generator Loss: 1.7347 D(x): 0.7356, D(G(z)): 0.2367 Epoch: [17/20], Batch Num: [405/600] Discriminator Loss: 0.8548, Generator Loss: 1.7987 D(x): 0.7560, D(G(z)): 0.2878 Epoch: [17/20], Batch Num: [406/600] Discriminator Loss: 0.7459, Generator Loss: 1.8938 D(x): 0.7302, D(G(z)): 0.2312 Epoch: [17/20], Batch Num: [407/600] Discriminator Loss: 0.7601, Generator Loss: 1.6995 D(x): 0.7219, D(G(z)): 0.2378 Epoch: [17/20], Batch Num: [408/600] Discriminator Loss: 0.7829, Generator Loss: 1.6512 D(x): 0.7390, D(G(z)): 0.2586 Epoch: [17/20], Batch Num: [409/600] Discriminator Loss: 0.7295, Generator Loss: 1.6539 D(x): 0.7546, D(G(z)): 0.2349 Epoch: [17/20], Batch Num: [410/600] Discriminator Loss: 0.7864, Generator Loss: 1.6239 D(x): 0.7572, D(G(z)): 0.2867 Epoch: [17/20], Batch Num: [411/600] Discriminator Loss: 0.8322, Generator Loss: 1.5655 D(x): 0.7295, D(G(z)): 0.2592 Epoch: [17/20], Batch Num: [412/600] Discriminator Loss: 0.9402, Generator Loss: 1.6926 D(x): 0.6971, D(G(z)): 0.2937 Epoch: [17/20], Batch Num: [413/600] Discriminator Loss: 0.9402, Generator Loss: 1.7809 D(x): 0.7388, D(G(z)): 0.3139 Epoch: [17/20], Batch Num: [414/600] Discriminator Loss: 1.0026, Generator Loss: 1.7316 D(x): 0.6693, D(G(z)): 0.2643 Epoch: [17/20], Batch Num: [415/600] Discriminator Loss: 0.6897, Generator Loss: 1.6160 D(x): 0.7727, D(G(z)): 0.2417 Epoch: [17/20], Batch Num: [416/600] Discriminator Loss: 0.9647, Generator Loss: 1.7043 D(x): 0.7365, D(G(z)): 0.2926 Epoch: [17/20], Batch Num: [417/600] Discriminator Loss: 0.9280, Generator Loss: 1.7381 D(x): 0.7013, D(G(z)): 0.2773 Epoch: [17/20], Batch Num: [418/600] Discriminator Loss: 0.6793, Generator Loss: 1.6518 D(x): 0.7537, D(G(z)): 0.2100 Epoch: [17/20], Batch Num: [419/600] Discriminator Loss: 0.7561, Generator Loss: 1.5269 D(x): 0.7496, D(G(z)): 0.2608 Epoch: [17/20], Batch Num: [420/600] Discriminator Loss: 0.6906, Generator Loss: 1.5140 D(x): 0.7767, D(G(z)): 0.2617 Epoch: [17/20], Batch Num: [421/600] Discriminator Loss: 0.9245, Generator Loss: 1.6947 D(x): 0.7102, D(G(z)): 0.3103 Epoch: [17/20], Batch Num: [422/600] Discriminator Loss: 0.8307, Generator Loss: 1.6746 D(x): 0.7045, D(G(z)): 0.2627 Epoch: [17/20], Batch Num: [423/600] Discriminator Loss: 0.9179, Generator Loss: 1.6557 D(x): 0.6745, D(G(z)): 0.2597 Epoch: [17/20], Batch Num: [424/600] Discriminator Loss: 0.7746, Generator Loss: 1.5359 D(x): 0.7414, D(G(z)): 0.2797 Epoch: [17/20], Batch Num: [425/600] Discriminator Loss: 0.7237, Generator Loss: 1.4113 D(x): 0.7792, D(G(z)): 0.2823 Epoch: [17/20], Batch Num: [426/600] Discriminator Loss: 0.8318, Generator Loss: 1.6922 D(x): 0.7505, D(G(z)): 0.3070 Epoch: [17/20], Batch Num: [427/600] Discriminator Loss: 0.7700, Generator Loss: 1.6358 D(x): 0.7620, D(G(z)): 0.2784 Epoch: [17/20], Batch Num: [428/600] Discriminator Loss: 0.7038, Generator Loss: 1.7808 D(x): 0.7745, D(G(z)): 0.2643 Epoch: [17/20], Batch Num: [429/600] Discriminator Loss: 0.9017, Generator Loss: 1.8821 D(x): 0.6792, D(G(z)): 0.2439 Epoch: [17/20], Batch Num: [430/600] Discriminator Loss: 0.7672, Generator Loss: 1.6640 D(x): 0.7411, D(G(z)): 0.2493 Epoch: [17/20], Batch Num: [431/600] Discriminator Loss: 0.6583, Generator Loss: 1.5823 D(x): 0.7615, D(G(z)): 0.2203 Epoch: [17/20], Batch Num: [432/600] Discriminator Loss: 0.8219, Generator Loss: 1.6643 D(x): 0.7250, D(G(z)): 0.2640 Epoch: [17/20], Batch Num: [433/600] Discriminator Loss: 0.7706, Generator Loss: 1.5907 D(x): 0.7669, D(G(z)): 0.2850 Epoch: [17/20], Batch Num: [434/600] Discriminator Loss: 0.8114, Generator Loss: 1.7326 D(x): 0.7489, D(G(z)): 0.2878 Epoch: [17/20], Batch Num: [435/600] Discriminator Loss: 0.9180, Generator Loss: 1.8891 D(x): 0.7381, D(G(z)): 0.3104 Epoch: [17/20], Batch Num: [436/600] Discriminator Loss: 0.9532, Generator Loss: 2.0061 D(x): 0.6752, D(G(z)): 0.2514 Epoch: [17/20], Batch Num: [437/600] Discriminator Loss: 0.8035, Generator Loss: 2.0653 D(x): 0.6842, D(G(z)): 0.1932 Epoch: [17/20], Batch Num: [438/600] Discriminator Loss: 0.7955, Generator Loss: 1.7731 D(x): 0.6897, D(G(z)): 0.1977 Epoch: [17/20], Batch Num: [439/600] Discriminator Loss: 0.7357, Generator Loss: 1.6055 D(x): 0.7388, D(G(z)): 0.2462 Epoch: [17/20], Batch Num: [440/600] Discriminator Loss: 0.7300, Generator Loss: 1.4179 D(x): 0.7659, D(G(z)): 0.2755 Epoch: [17/20], Batch Num: [441/600] Discriminator Loss: 0.7816, Generator Loss: 1.5217 D(x): 0.7887, D(G(z)): 0.3131 Epoch: [17/20], Batch Num: 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1.4546 D(x): 0.7706, D(G(z)): 0.2872 Epoch: [17/20], Batch Num: [451/600] Discriminator Loss: 0.8307, Generator Loss: 1.7752 D(x): 0.7939, D(G(z)): 0.3111 Epoch: [17/20], Batch Num: [452/600] Discriminator Loss: 0.7618, Generator Loss: 1.9832 D(x): 0.7307, D(G(z)): 0.2416 Epoch: [17/20], Batch Num: [453/600] Discriminator Loss: 0.7919, Generator Loss: 1.9392 D(x): 0.7058, D(G(z)): 0.2230 Epoch: [17/20], Batch Num: [454/600] Discriminator Loss: 1.0612, Generator Loss: 1.5495 D(x): 0.6406, D(G(z)): 0.2432 Epoch: [17/20], Batch Num: [455/600] Discriminator Loss: 0.7734, Generator Loss: 1.6469 D(x): 0.7679, D(G(z)): 0.2774 Epoch: [17/20], Batch Num: [456/600] Discriminator Loss: 0.9347, Generator Loss: 1.5780 D(x): 0.7283, D(G(z)): 0.2990 Epoch: [17/20], Batch Num: [457/600] Discriminator Loss: 0.8750, Generator Loss: 1.5295 D(x): 0.7853, D(G(z)): 0.3405 Epoch: [17/20], Batch Num: [458/600] Discriminator Loss: 0.9447, Generator Loss: 1.8758 D(x): 0.7525, D(G(z)): 0.3316 Epoch: [17/20], 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Generator Loss: 1.7120 D(x): 0.6857, D(G(z)): 0.2736 Epoch: [17/20], Batch Num: [468/600] Discriminator Loss: 0.6703, Generator Loss: 1.5715 D(x): 0.7600, D(G(z)): 0.2388 Epoch: [17/20], Batch Num: [469/600] Discriminator Loss: 0.8737, Generator Loss: 1.7431 D(x): 0.7082, D(G(z)): 0.2950 Epoch: [17/20], Batch Num: [470/600] Discriminator Loss: 0.8859, Generator Loss: 1.4206 D(x): 0.6712, D(G(z)): 0.2644 Epoch: [17/20], Batch Num: [471/600] Discriminator Loss: 0.8490, Generator Loss: 1.4233 D(x): 0.7829, D(G(z)): 0.3325 Epoch: [17/20], Batch Num: [472/600] Discriminator Loss: 0.7452, Generator Loss: 1.4469 D(x): 0.7795, D(G(z)): 0.3015 Epoch: [17/20], Batch Num: [473/600] Discriminator Loss: 0.7934, Generator Loss: 1.5950 D(x): 0.7055, D(G(z)): 0.2280 Epoch: [17/20], Batch Num: [474/600] Discriminator Loss: 0.7972, Generator Loss: 1.6721 D(x): 0.7195, D(G(z)): 0.2540 Epoch: [17/20], Batch Num: [475/600] Discriminator Loss: 0.7782, Generator Loss: 1.6244 D(x): 0.7716, D(G(z)): 0.2895 Epoch: [17/20], Batch Num: [476/600] Discriminator Loss: 0.8205, Generator Loss: 2.0099 D(x): 0.7443, D(G(z)): 0.2605 Epoch: [17/20], Batch Num: [477/600] Discriminator Loss: 0.9175, Generator Loss: 1.6014 D(x): 0.6940, D(G(z)): 0.2598 Epoch: [17/20], Batch Num: [478/600] Discriminator Loss: 0.7655, Generator Loss: 1.8129 D(x): 0.7503, D(G(z)): 0.2631 Epoch: [17/20], Batch Num: [479/600] Discriminator Loss: 1.0038, Generator Loss: 1.6645 D(x): 0.6871, D(G(z)): 0.2888 Epoch: [17/20], Batch Num: [480/600] Discriminator Loss: 0.8725, Generator Loss: 1.5844 D(x): 0.7033, D(G(z)): 0.2627 Epoch: [17/20], Batch Num: [481/600] Discriminator Loss: 0.7350, Generator Loss: 1.3005 D(x): 0.7628, D(G(z)): 0.2718 Epoch: [17/20], Batch Num: [482/600] Discriminator Loss: 0.7509, Generator Loss: 1.4179 D(x): 0.7982, D(G(z)): 0.2797 Epoch: [17/20], Batch Num: [483/600] Discriminator Loss: 0.9581, Generator Loss: 1.8672 D(x): 0.7634, D(G(z)): 0.3422 Epoch: [17/20], Batch Num: [484/600] Discriminator Loss: 0.7018, Generator Loss: 2.0453 D(x): 0.7570, D(G(z)): 0.2484 Epoch: [17/20], Batch Num: [485/600] Discriminator Loss: 0.9077, Generator Loss: 1.8056 D(x): 0.6881, D(G(z)): 0.2330 Epoch: [17/20], Batch Num: [486/600] Discriminator Loss: 0.7310, Generator Loss: 1.9358 D(x): 0.7337, D(G(z)): 0.2176 Epoch: [17/20], Batch Num: [487/600] Discriminator Loss: 0.9345, Generator Loss: 1.7197 D(x): 0.6782, D(G(z)): 0.2418 Epoch: [17/20], Batch Num: [488/600] Discriminator Loss: 0.8396, Generator Loss: 1.5020 D(x): 0.7730, D(G(z)): 0.2898 Epoch: [17/20], Batch Num: [489/600] Discriminator Loss: 0.8159, Generator Loss: 1.7479 D(x): 0.7601, D(G(z)): 0.2846 Epoch: [17/20], Batch Num: [490/600] Discriminator Loss: 0.7574, Generator Loss: 1.5665 D(x): 0.7837, D(G(z)): 0.2928 Epoch: [17/20], Batch Num: [491/600] Discriminator Loss: 0.9078, Generator Loss: 1.7154 D(x): 0.6934, D(G(z)): 0.2690 Epoch: [17/20], Batch Num: [492/600] Discriminator Loss: 0.9111, Generator Loss: 1.7461 D(x): 0.7134, D(G(z)): 0.2719 Epoch: [17/20], Batch Num: [493/600] Discriminator Loss: 0.7254, Generator Loss: 1.9650 D(x): 0.7414, D(G(z)): 0.2361 Epoch: [17/20], Batch Num: [494/600] Discriminator Loss: 0.8677, Generator Loss: 1.6188 D(x): 0.7144, D(G(z)): 0.2606 Epoch: [17/20], Batch Num: [495/600] Discriminator Loss: 0.8581, Generator Loss: 1.6831 D(x): 0.7117, D(G(z)): 0.2556 Epoch: [17/20], Batch Num: [496/600] Discriminator Loss: 0.8484, Generator Loss: 1.4983 D(x): 0.7030, D(G(z)): 0.2470 Epoch: [17/20], Batch Num: [497/600] Discriminator Loss: 0.8149, Generator Loss: 1.4866 D(x): 0.7811, D(G(z)): 0.3097 Epoch: [17/20], Batch Num: [498/600] Discriminator Loss: 0.8212, Generator Loss: 1.6121 D(x): 0.7432, D(G(z)): 0.2757 Epoch: [17/20], Batch Num: [499/600] Discriminator Loss: 0.9647, Generator Loss: 1.6708 D(x): 0.7415, D(G(z)): 0.3189 Epoch: 17, Batch Num: [500/600]
Epoch: [17/20], Batch Num: [500/600] Discriminator Loss: 0.8231, Generator Loss: 1.7366 D(x): 0.7043, D(G(z)): 0.2646 Epoch: [17/20], Batch Num: [501/600] Discriminator Loss: 0.8061, Generator Loss: 1.7525 D(x): 0.7453, D(G(z)): 0.2798 Epoch: [17/20], Batch Num: [502/600] Discriminator Loss: 1.1181, Generator Loss: 1.6979 D(x): 0.6273, D(G(z)): 0.3034 Epoch: [17/20], Batch Num: [503/600] Discriminator Loss: 0.8220, Generator Loss: 1.6859 D(x): 0.7393, D(G(z)): 0.2966 Epoch: [17/20], Batch Num: [504/600] Discriminator Loss: 0.8523, Generator Loss: 1.6124 D(x): 0.6666, D(G(z)): 0.2355 Epoch: [17/20], Batch Num: [505/600] Discriminator Loss: 0.8565, Generator Loss: 1.6064 D(x): 0.7460, D(G(z)): 0.3064 Epoch: [17/20], Batch Num: [506/600] Discriminator Loss: 0.6959, Generator Loss: 1.6429 D(x): 0.7427, D(G(z)): 0.2414 Epoch: [17/20], Batch Num: [507/600] Discriminator Loss: 0.8637, Generator Loss: 1.5436 D(x): 0.7185, D(G(z)): 0.3106 Epoch: [17/20], Batch Num: [508/600] Discriminator Loss: 0.7796, Generator Loss: 1.6057 D(x): 0.7409, D(G(z)): 0.2588 Epoch: [17/20], Batch Num: [509/600] Discriminator Loss: 0.8459, Generator Loss: 1.5677 D(x): 0.7444, D(G(z)): 0.2978 Epoch: [17/20], Batch Num: [510/600] Discriminator Loss: 0.8340, Generator Loss: 1.8729 D(x): 0.7473, D(G(z)): 0.2670 Epoch: [17/20], Batch Num: [511/600] Discriminator Loss: 0.8191, Generator Loss: 1.9031 D(x): 0.7343, D(G(z)): 0.2551 Epoch: [17/20], Batch Num: [512/600] Discriminator Loss: 0.9114, Generator Loss: 1.8319 D(x): 0.6825, D(G(z)): 0.2480 Epoch: [17/20], Batch Num: [513/600] Discriminator Loss: 0.7566, Generator Loss: 1.6650 D(x): 0.7750, D(G(z)): 0.2627 Epoch: [17/20], Batch Num: [514/600] Discriminator Loss: 0.9229, Generator Loss: 1.8073 D(x): 0.6889, D(G(z)): 0.2696 Epoch: [17/20], Batch Num: [515/600] Discriminator Loss: 0.7514, Generator Loss: 1.8527 D(x): 0.7552, D(G(z)): 0.2512 Epoch: [17/20], Batch Num: [516/600] Discriminator Loss: 0.7891, Generator Loss: 1.6326 D(x): 0.7245, D(G(z)): 0.2458 Epoch: [17/20], Batch Num: [517/600] Discriminator Loss: 0.8243, Generator Loss: 1.5094 D(x): 0.7342, D(G(z)): 0.2751 Epoch: [17/20], Batch Num: [518/600] Discriminator Loss: 0.8617, Generator Loss: 1.4587 D(x): 0.7516, D(G(z)): 0.3144 Epoch: [17/20], Batch Num: [519/600] Discriminator Loss: 0.7218, Generator Loss: 1.7722 D(x): 0.8382, D(G(z)): 0.3138 Epoch: [17/20], Batch Num: [520/600] Discriminator Loss: 0.7456, Generator Loss: 1.7908 D(x): 0.7351, D(G(z)): 0.2390 Epoch: [17/20], Batch Num: [521/600] Discriminator Loss: 0.7315, Generator Loss: 1.7690 D(x): 0.7116, D(G(z)): 0.2048 Epoch: [17/20], Batch Num: [522/600] Discriminator Loss: 0.9058, Generator Loss: 1.7313 D(x): 0.6632, D(G(z)): 0.2313 Epoch: [17/20], Batch Num: [523/600] Discriminator Loss: 0.7922, Generator Loss: 1.4850 D(x): 0.7258, D(G(z)): 0.2726 Epoch: [17/20], Batch Num: [524/600] Discriminator Loss: 0.8085, Generator Loss: 1.5707 D(x): 0.7650, D(G(z)): 0.2999 Epoch: [17/20], Batch Num: [525/600] Discriminator Loss: 0.8843, Generator Loss: 1.5403 D(x): 0.7639, D(G(z)): 0.3167 Epoch: [17/20], Batch Num: [526/600] Discriminator Loss: 0.8563, Generator Loss: 1.7386 D(x): 0.7505, D(G(z)): 0.3006 Epoch: [17/20], Batch Num: [527/600] Discriminator Loss: 0.7456, Generator Loss: 1.6964 D(x): 0.7389, D(G(z)): 0.2299 Epoch: [17/20], Batch Num: [528/600] Discriminator Loss: 0.7657, Generator Loss: 1.9610 D(x): 0.7259, D(G(z)): 0.2379 Epoch: [17/20], Batch Num: [529/600] Discriminator Loss: 0.8494, Generator Loss: 1.9767 D(x): 0.6999, D(G(z)): 0.2326 Epoch: [17/20], Batch Num: [530/600] Discriminator Loss: 0.8392, Generator Loss: 1.7688 D(x): 0.7142, D(G(z)): 0.2293 Epoch: [17/20], Batch Num: [531/600] Discriminator Loss: 0.8784, Generator Loss: 1.5255 D(x): 0.7144, D(G(z)): 0.2575 Epoch: [17/20], Batch Num: [532/600] Discriminator Loss: 0.9987, Generator Loss: 1.4911 D(x): 0.7380, D(G(z)): 0.3370 Epoch: [17/20], Batch Num: [533/600] Discriminator Loss: 0.8746, Generator Loss: 1.4516 D(x): 0.7572, D(G(z)): 0.3122 Epoch: [17/20], Batch Num: [534/600] Discriminator Loss: 0.6677, Generator Loss: 1.6710 D(x): 0.8250, D(G(z)): 0.2835 Epoch: [17/20], Batch Num: [535/600] Discriminator Loss: 0.7461, Generator Loss: 1.6068 D(x): 0.7632, D(G(z)): 0.2539 Epoch: [17/20], Batch Num: [536/600] Discriminator Loss: 0.7326, Generator Loss: 1.9834 D(x): 0.7489, D(G(z)): 0.2466 Epoch: [17/20], Batch Num: [537/600] Discriminator Loss: 0.9097, Generator Loss: 1.8057 D(x): 0.6507, D(G(z)): 0.2121 Epoch: [17/20], Batch Num: [538/600] Discriminator Loss: 0.8645, Generator Loss: 1.7665 D(x): 0.6904, D(G(z)): 0.2302 Epoch: [17/20], Batch Num: [539/600] Discriminator Loss: 0.9024, Generator Loss: 1.4053 D(x): 0.6631, D(G(z)): 0.2434 Epoch: [17/20], Batch Num: [540/600] Discriminator Loss: 0.9185, Generator Loss: 1.3735 D(x): 0.7319, D(G(z)): 0.3239 Epoch: [17/20], Batch Num: [541/600] Discriminator Loss: 1.0176, Generator Loss: 1.3065 D(x): 0.7446, D(G(z)): 0.3636 Epoch: [17/20], Batch Num: [542/600] Discriminator Loss: 0.9938, Generator Loss: 1.5226 D(x): 0.7233, D(G(z)): 0.3460 Epoch: [17/20], Batch Num: [543/600] Discriminator Loss: 0.8973, Generator Loss: 1.6967 D(x): 0.7323, D(G(z)): 0.3138 Epoch: [17/20], Batch Num: [544/600] Discriminator Loss: 0.8277, Generator Loss: 1.6397 D(x): 0.6922, D(G(z)): 0.2393 Epoch: [17/20], Batch Num: [545/600] Discriminator Loss: 0.9036, Generator Loss: 1.7648 D(x): 0.6922, D(G(z)): 0.2877 Epoch: [17/20], Batch Num: [546/600] Discriminator Loss: 0.9192, Generator Loss: 1.7603 D(x): 0.6924, D(G(z)): 0.2839 Epoch: [17/20], Batch Num: [547/600] Discriminator Loss: 0.8836, Generator Loss: 1.6472 D(x): 0.6869, D(G(z)): 0.2409 Epoch: [17/20], Batch Num: [548/600] Discriminator Loss: 0.8412, Generator Loss: 1.5287 D(x): 0.7394, D(G(z)): 0.2846 Epoch: [17/20], Batch Num: [549/600] Discriminator Loss: 0.9266, Generator Loss: 1.3856 D(x): 0.7223, D(G(z)): 0.3318 Epoch: [17/20], Batch Num: [550/600] Discriminator Loss: 1.0173, Generator Loss: 1.5090 D(x): 0.7521, D(G(z)): 0.3704 Epoch: [17/20], Batch Num: [551/600] Discriminator Loss: 0.8751, Generator Loss: 1.6319 D(x): 0.7642, D(G(z)): 0.3181 Epoch: [17/20], Batch Num: [552/600] Discriminator Loss: 0.8312, Generator Loss: 1.6482 D(x): 0.7176, D(G(z)): 0.2496 Epoch: [17/20], Batch Num: [553/600] Discriminator Loss: 0.7409, Generator Loss: 1.7878 D(x): 0.7276, D(G(z)): 0.2299 Epoch: [17/20], Batch Num: [554/600] Discriminator Loss: 0.9494, Generator Loss: 1.7348 D(x): 0.6642, D(G(z)): 0.2561 Epoch: [17/20], Batch Num: [555/600] Discriminator Loss: 0.9751, Generator Loss: 1.5444 D(x): 0.6481, D(G(z)): 0.2738 Epoch: [17/20], Batch Num: [556/600] Discriminator Loss: 0.8683, Generator Loss: 1.5183 D(x): 0.6952, D(G(z)): 0.2580 Epoch: [17/20], Batch Num: [557/600] Discriminator Loss: 0.9020, Generator Loss: 1.5066 D(x): 0.7275, D(G(z)): 0.3156 Epoch: [17/20], Batch Num: [558/600] Discriminator Loss: 0.7799, Generator Loss: 1.4130 D(x): 0.7676, D(G(z)): 0.3009 Epoch: [17/20], Batch Num: [559/600] Discriminator Loss: 0.9983, Generator Loss: 1.4714 D(x): 0.7151, D(G(z)): 0.3642 Epoch: [17/20], Batch Num: [560/600] Discriminator Loss: 0.8172, Generator Loss: 1.4579 D(x): 0.7712, D(G(z)): 0.3161 Epoch: [17/20], Batch Num: [561/600] Discriminator Loss: 0.9298, Generator Loss: 1.7589 D(x): 0.7050, D(G(z)): 0.3122 Epoch: [17/20], Batch Num: [562/600] Discriminator Loss: 0.8363, Generator Loss: 1.7849 D(x): 0.6858, D(G(z)): 0.2609 Epoch: [17/20], Batch Num: [563/600] Discriminator Loss: 0.8126, Generator Loss: 1.7397 D(x): 0.7164, D(G(z)): 0.2429 Epoch: [17/20], Batch Num: [564/600] Discriminator Loss: 0.8185, Generator Loss: 1.6043 D(x): 0.7139, D(G(z)): 0.2670 Epoch: [17/20], Batch Num: [565/600] Discriminator Loss: 0.9049, Generator Loss: 1.5134 D(x): 0.6797, D(G(z)): 0.2706 Epoch: [17/20], Batch Num: [566/600] Discriminator Loss: 0.8488, Generator Loss: 1.5736 D(x): 0.7291, D(G(z)): 0.2999 Epoch: [17/20], Batch Num: [567/600] Discriminator Loss: 0.8682, Generator Loss: 1.4995 D(x): 0.7240, D(G(z)): 0.2930 Epoch: [17/20], Batch Num: [568/600] Discriminator Loss: 0.7926, Generator Loss: 1.5619 D(x): 0.7581, D(G(z)): 0.2960 Epoch: [17/20], Batch Num: [569/600] Discriminator Loss: 0.9039, Generator Loss: 1.5336 D(x): 0.6634, D(G(z)): 0.2531 Epoch: [17/20], Batch Num: [570/600] Discriminator Loss: 0.8685, Generator Loss: 1.4818 D(x): 0.7211, D(G(z)): 0.2883 Epoch: [17/20], Batch Num: [571/600] Discriminator Loss: 0.8314, Generator Loss: 1.3915 D(x): 0.7131, D(G(z)): 0.2629 Epoch: [17/20], Batch Num: [572/600] Discriminator Loss: 0.8245, Generator Loss: 1.3660 D(x): 0.7436, D(G(z)): 0.3086 Epoch: [17/20], Batch Num: [573/600] Discriminator Loss: 0.8713, Generator Loss: 1.6359 D(x): 0.7533, D(G(z)): 0.3319 Epoch: [17/20], Batch Num: [574/600] Discriminator Loss: 0.8073, Generator Loss: 1.7567 D(x): 0.7538, D(G(z)): 0.2893 Epoch: [17/20], Batch Num: [575/600] Discriminator Loss: 0.7651, Generator Loss: 2.0545 D(x): 0.7569, D(G(z)): 0.2584 Epoch: [17/20], Batch Num: [576/600] Discriminator Loss: 0.9455, Generator Loss: 1.8774 D(x): 0.6658, D(G(z)): 0.2411 Epoch: [17/20], Batch Num: [577/600] Discriminator Loss: 0.8169, Generator Loss: 2.0093 D(x): 0.7221, D(G(z)): 0.2150 Epoch: [17/20], Batch Num: [578/600] Discriminator Loss: 1.0466, Generator Loss: 1.7492 D(x): 0.6324, D(G(z)): 0.2427 Epoch: [17/20], Batch Num: [579/600] Discriminator Loss: 0.8612, Generator Loss: 1.4530 D(x): 0.7188, D(G(z)): 0.2682 Epoch: [17/20], Batch Num: [580/600] Discriminator Loss: 0.8109, Generator Loss: 1.4097 D(x): 0.7945, D(G(z)): 0.3259 Epoch: [17/20], Batch Num: [581/600] Discriminator Loss: 0.7496, Generator Loss: 1.5501 D(x): 0.8236, D(G(z)): 0.3117 Epoch: [17/20], Batch Num: [582/600] Discriminator Loss: 0.7593, Generator Loss: 1.9174 D(x): 0.8091, D(G(z)): 0.2884 Epoch: [17/20], Batch Num: [583/600] Discriminator Loss: 0.8085, Generator Loss: 2.0986 D(x): 0.7329, D(G(z)): 0.2499 Epoch: [17/20], Batch Num: [584/600] Discriminator Loss: 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Epoch: [18/20], Batch Num: [0/600] Discriminator Loss: 0.8393, Generator Loss: 1.7921 D(x): 0.6920, D(G(z)): 0.2399 Epoch: [18/20], Batch Num: [1/600] Discriminator Loss: 0.7689, Generator Loss: 1.7211 D(x): 0.7053, D(G(z)): 0.2160 Epoch: [18/20], Batch Num: [2/600] Discriminator Loss: 0.8712, Generator Loss: 1.3693 D(x): 0.6765, D(G(z)): 0.2455 Epoch: [18/20], Batch Num: [3/600] Discriminator Loss: 0.9612, Generator Loss: 1.4841 D(x): 0.7074, D(G(z)): 0.3213 Epoch: [18/20], Batch Num: [4/600] Discriminator Loss: 0.9947, Generator Loss: 1.3596 D(x): 0.7309, D(G(z)): 0.3286 Epoch: [18/20], Batch Num: [5/600] Discriminator Loss: 0.8441, Generator Loss: 1.4950 D(x): 0.7546, D(G(z)): 0.3128 Epoch: [18/20], Batch Num: [6/600] Discriminator Loss: 1.0423, Generator Loss: 1.5350 D(x): 0.7344, D(G(z)): 0.3736 Epoch: [18/20], Batch Num: [7/600] Discriminator Loss: 0.8513, Generator Loss: 1.6337 D(x): 0.7219, D(G(z)): 0.2733 Epoch: [18/20], Batch Num: [8/600] Discriminator Loss: 0.8600, Generator 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Num: [17/600] Discriminator Loss: 0.8294, Generator Loss: 1.4836 D(x): 0.7126, D(G(z)): 0.2711 Epoch: [18/20], Batch Num: [18/600] Discriminator Loss: 0.8074, Generator Loss: 1.4968 D(x): 0.7123, D(G(z)): 0.2602 Epoch: [18/20], Batch Num: [19/600] Discriminator Loss: 0.8325, Generator Loss: 1.5728 D(x): 0.7373, D(G(z)): 0.2876 Epoch: [18/20], Batch Num: [20/600] Discriminator Loss: 0.8710, Generator Loss: 1.4200 D(x): 0.7087, D(G(z)): 0.2929 Epoch: [18/20], Batch Num: [21/600] Discriminator Loss: 0.8075, Generator Loss: 1.4816 D(x): 0.7878, D(G(z)): 0.3274 Epoch: [18/20], Batch Num: [22/600] Discriminator Loss: 0.8528, Generator Loss: 1.8289 D(x): 0.7284, D(G(z)): 0.3018 Epoch: [18/20], Batch Num: [23/600] Discriminator Loss: 0.7376, Generator Loss: 2.1161 D(x): 0.7476, D(G(z)): 0.2255 Epoch: [18/20], Batch Num: [24/600] Discriminator Loss: 0.9556, Generator Loss: 1.7886 D(x): 0.6362, D(G(z)): 0.2403 Epoch: [18/20], Batch Num: [25/600] Discriminator Loss: 0.8410, Generator Loss: 1.8723 D(x): 0.6934, D(G(z)): 0.2173 Epoch: [18/20], Batch Num: [26/600] Discriminator Loss: 0.9195, Generator Loss: 1.7040 D(x): 0.7393, D(G(z)): 0.3350 Epoch: [18/20], Batch Num: [27/600] Discriminator Loss: 0.8758, Generator Loss: 1.5935 D(x): 0.6960, D(G(z)): 0.2628 Epoch: [18/20], Batch Num: [28/600] Discriminator Loss: 0.9062, Generator Loss: 1.6257 D(x): 0.7212, D(G(z)): 0.2844 Epoch: [18/20], Batch Num: [29/600] Discriminator Loss: 0.8290, Generator Loss: 1.6385 D(x): 0.7187, D(G(z)): 0.2573 Epoch: [18/20], Batch Num: [30/600] Discriminator Loss: 0.8790, Generator Loss: 1.5340 D(x): 0.7422, D(G(z)): 0.3090 Epoch: [18/20], Batch Num: [31/600] Discriminator Loss: 0.8375, Generator Loss: 1.6300 D(x): 0.7855, D(G(z)): 0.3333 Epoch: [18/20], Batch Num: [32/600] Discriminator Loss: 0.6912, Generator Loss: 1.9204 D(x): 0.7452, D(G(z)): 0.2160 Epoch: [18/20], Batch Num: [33/600] Discriminator Loss: 0.9007, Generator Loss: 1.9858 D(x): 0.6990, D(G(z)): 0.2641 Epoch: [18/20], Batch Num: [34/600] Discriminator Loss: 1.0481, Generator Loss: 1.6589 D(x): 0.6829, D(G(z)): 0.3138 Epoch: [18/20], Batch Num: [35/600] Discriminator Loss: 0.9106, Generator Loss: 1.5789 D(x): 0.6515, D(G(z)): 0.2318 Epoch: [18/20], Batch Num: [36/600] Discriminator Loss: 1.0037, Generator Loss: 1.4478 D(x): 0.7314, D(G(z)): 0.3539 Epoch: [18/20], Batch Num: [37/600] Discriminator Loss: 0.9796, Generator Loss: 1.5418 D(x): 0.7306, D(G(z)): 0.3802 Epoch: [18/20], Batch Num: [38/600] Discriminator Loss: 0.8254, Generator Loss: 1.7617 D(x): 0.7253, D(G(z)): 0.2737 Epoch: [18/20], Batch Num: [39/600] Discriminator Loss: 0.8638, Generator Loss: 1.5495 D(x): 0.7268, D(G(z)): 0.2950 Epoch: [18/20], Batch Num: [40/600] Discriminator Loss: 0.8871, Generator Loss: 1.6710 D(x): 0.6754, D(G(z)): 0.2809 Epoch: [18/20], Batch Num: [41/600] Discriminator Loss: 0.8618, Generator Loss: 1.5064 D(x): 0.6548, D(G(z)): 0.2417 Epoch: [18/20], Batch Num: [42/600] Discriminator Loss: 0.7694, Generator Loss: 1.3710 D(x): 0.7304, D(G(z)): 0.2719 Epoch: [18/20], Batch Num: [43/600] Discriminator Loss: 0.8627, Generator Loss: 1.3924 D(x): 0.7208, D(G(z)): 0.3172 Epoch: [18/20], Batch Num: [44/600] Discriminator Loss: 1.0045, Generator Loss: 1.3125 D(x): 0.6730, D(G(z)): 0.3221 Epoch: [18/20], Batch Num: [45/600] Discriminator Loss: 0.8889, Generator Loss: 1.3836 D(x): 0.7605, D(G(z)): 0.3523 Epoch: [18/20], Batch Num: [46/600] Discriminator Loss: 0.8740, Generator Loss: 1.4998 D(x): 0.7238, D(G(z)): 0.3130 Epoch: [18/20], Batch Num: [47/600] Discriminator Loss: 0.8697, Generator Loss: 1.5620 D(x): 0.7127, D(G(z)): 0.2823 Epoch: [18/20], Batch Num: [48/600] Discriminator Loss: 0.8802, Generator Loss: 1.7457 D(x): 0.7196, D(G(z)): 0.3042 Epoch: [18/20], Batch Num: [49/600] Discriminator Loss: 0.9474, Generator Loss: 1.7949 D(x): 0.6461, D(G(z)): 0.2478 Epoch: [18/20], Batch Num: [50/600] Discriminator Loss: 0.8830, Generator Loss: 1.6214 D(x): 0.6751, D(G(z)): 0.2504 Epoch: [18/20], Batch Num: [51/600] Discriminator Loss: 0.9520, Generator Loss: 1.7320 D(x): 0.7143, D(G(z)): 0.3081 Epoch: [18/20], Batch Num: [52/600] Discriminator Loss: 1.0617, Generator Loss: 1.5289 D(x): 0.6373, D(G(z)): 0.2773 Epoch: [18/20], Batch Num: [53/600] Discriminator Loss: 0.7811, Generator Loss: 1.4650 D(x): 0.7502, D(G(z)): 0.2895 Epoch: [18/20], Batch Num: [54/600] Discriminator Loss: 0.9691, Generator Loss: 1.4338 D(x): 0.7352, D(G(z)): 0.3554 Epoch: [18/20], Batch Num: [55/600] Discriminator Loss: 0.8788, Generator Loss: 1.6672 D(x): 0.7278, D(G(z)): 0.2858 Epoch: [18/20], Batch Num: [56/600] Discriminator Loss: 0.9166, Generator Loss: 1.7685 D(x): 0.6786, D(G(z)): 0.2677 Epoch: [18/20], Batch Num: [57/600] Discriminator Loss: 0.9112, Generator Loss: 1.5606 D(x): 0.7372, D(G(z)): 0.3133 Epoch: [18/20], Batch Num: [58/600] Discriminator Loss: 0.7424, Generator Loss: 1.6561 D(x): 0.7353, D(G(z)): 0.2567 Epoch: [18/20], Batch Num: [59/600] Discriminator Loss: 0.9287, Generator Loss: 1.7264 D(x): 0.6953, D(G(z)): 0.3057 Epoch: [18/20], Batch Num: [60/600] Discriminator Loss: 0.8845, Generator Loss: 1.5479 D(x): 0.6787, D(G(z)): 0.2686 Epoch: [18/20], Batch Num: [61/600] Discriminator Loss: 1.0065, Generator Loss: 1.5842 D(x): 0.7025, D(G(z)): 0.3394 Epoch: [18/20], Batch Num: [62/600] Discriminator Loss: 0.8885, Generator Loss: 1.4656 D(x): 0.7364, D(G(z)): 0.3370 Epoch: [18/20], Batch Num: [63/600] Discriminator Loss: 0.9367, Generator Loss: 1.5268 D(x): 0.6787, D(G(z)): 0.2751 Epoch: [18/20], Batch Num: [64/600] Discriminator Loss: 0.9552, Generator Loss: 1.4195 D(x): 0.7263, D(G(z)): 0.3214 Epoch: [18/20], Batch Num: [65/600] Discriminator Loss: 0.8203, Generator Loss: 1.4455 D(x): 0.7642, D(G(z)): 0.2995 Epoch: [18/20], Batch Num: [66/600] Discriminator Loss: 0.8916, Generator Loss: 1.4002 D(x): 0.7060, D(G(z)): 0.2912 Epoch: [18/20], Batch Num: [67/600] Discriminator Loss: 0.9205, Generator Loss: 1.5267 D(x): 0.6806, D(G(z)): 0.2960 Epoch: [18/20], Batch Num: [68/600] Discriminator Loss: 1.0722, Generator Loss: 1.5155 D(x): 0.6511, D(G(z)): 0.3170 Epoch: [18/20], Batch Num: [69/600] Discriminator Loss: 1.0367, Generator Loss: 1.4149 D(x): 0.6813, D(G(z)): 0.3348 Epoch: [18/20], Batch Num: [70/600] Discriminator Loss: 0.8498, Generator Loss: 1.5144 D(x): 0.7227, D(G(z)): 0.2820 Epoch: [18/20], Batch Num: [71/600] Discriminator Loss: 1.0246, Generator Loss: 1.6343 D(x): 0.6697, D(G(z)): 0.3283 Epoch: [18/20], Batch Num: [72/600] Discriminator Loss: 0.9698, Generator Loss: 1.5604 D(x): 0.6742, D(G(z)): 0.3050 Epoch: [18/20], Batch Num: [73/600] Discriminator Loss: 0.9492, Generator Loss: 1.4618 D(x): 0.6909, D(G(z)): 0.2985 Epoch: [18/20], Batch Num: [74/600] Discriminator Loss: 1.0485, Generator Loss: 1.6091 D(x): 0.6760, D(G(z)): 0.3482 Epoch: [18/20], Batch Num: [75/600] Discriminator Loss: 0.9456, Generator Loss: 1.8174 D(x): 0.7053, D(G(z)): 0.3145 Epoch: [18/20], Batch Num: [76/600] Discriminator Loss: 1.0072, Generator Loss: 1.7408 D(x): 0.6834, D(G(z)): 0.3223 Epoch: [18/20], Batch Num: [77/600] Discriminator Loss: 1.0284, Generator Loss: 1.9279 D(x): 0.6591, D(G(z)): 0.3088 Epoch: [18/20], Batch Num: [78/600] Discriminator Loss: 1.0295, Generator Loss: 1.6195 D(x): 0.5927, D(G(z)): 0.2534 Epoch: [18/20], Batch Num: [79/600] Discriminator Loss: 0.9436, Generator Loss: 1.4383 D(x): 0.6430, D(G(z)): 0.2717 Epoch: [18/20], Batch Num: [80/600] Discriminator Loss: 0.9630, Generator Loss: 1.2197 D(x): 0.6587, D(G(z)): 0.2826 Epoch: [18/20], Batch Num: [81/600] Discriminator Loss: 1.0778, Generator Loss: 1.2376 D(x): 0.7540, D(G(z)): 0.4279 Epoch: [18/20], Batch Num: [82/600] Discriminator Loss: 1.0273, Generator Loss: 1.3632 D(x): 0.7462, D(G(z)): 0.3917 Epoch: [18/20], Batch Num: [83/600] Discriminator Loss: 0.9334, Generator Loss: 1.5386 D(x): 0.6778, D(G(z)): 0.3077 Epoch: [18/20], Batch Num: [84/600] Discriminator Loss: 1.1013, Generator Loss: 1.6114 D(x): 0.6200, D(G(z)): 0.3201 Epoch: [18/20], Batch Num: [85/600] Discriminator Loss: 0.8543, Generator Loss: 1.6682 D(x): 0.6697, D(G(z)): 0.2595 Epoch: [18/20], Batch Num: [86/600] Discriminator Loss: 0.9562, Generator Loss: 1.4997 D(x): 0.6109, D(G(z)): 0.2434 Epoch: [18/20], Batch Num: [87/600] Discriminator Loss: 1.0193, Generator Loss: 1.4915 D(x): 0.6133, D(G(z)): 0.2945 Epoch: [18/20], Batch Num: [88/600] Discriminator Loss: 0.9432, Generator Loss: 1.0926 D(x): 0.6668, D(G(z)): 0.3084 Epoch: [18/20], Batch Num: [89/600] Discriminator Loss: 0.8531, Generator Loss: 1.1594 D(x): 0.7675, D(G(z)): 0.3699 Epoch: [18/20], Batch Num: [90/600] Discriminator Loss: 0.9302, Generator Loss: 1.3035 D(x): 0.7842, D(G(z)): 0.4033 Epoch: [18/20], Batch Num: [91/600] Discriminator Loss: 0.9644, Generator Loss: 1.7770 D(x): 0.7032, D(G(z)): 0.3566 Epoch: [18/20], Batch Num: [92/600] Discriminator Loss: 0.8476, Generator Loss: 1.9128 D(x): 0.6738, D(G(z)): 0.2569 Epoch: [18/20], Batch Num: [93/600] Discriminator Loss: 0.9841, Generator Loss: 1.9498 D(x): 0.6139, D(G(z)): 0.2502 Epoch: [18/20], Batch Num: [94/600] Discriminator Loss: 0.8427, Generator Loss: 1.8732 D(x): 0.6379, D(G(z)): 0.1969 Epoch: [18/20], Batch Num: [95/600] Discriminator Loss: 0.8799, Generator Loss: 1.6827 D(x): 0.6727, D(G(z)): 0.2533 Epoch: [18/20], Batch Num: [96/600] Discriminator Loss: 0.9315, Generator Loss: 1.5080 D(x): 0.6807, D(G(z)): 0.3041 Epoch: [18/20], Batch Num: [97/600] Discriminator Loss: 0.7966, Generator Loss: 1.4381 D(x): 0.7565, D(G(z)): 0.3107 Epoch: [18/20], Batch Num: [98/600] Discriminator Loss: 0.8163, Generator Loss: 1.6101 D(x): 0.7558, D(G(z)): 0.3104 Epoch: [18/20], Batch Num: [99/600] Discriminator Loss: 0.8445, Generator Loss: 1.6274 D(x): 0.7587, D(G(z)): 0.3191 Epoch: 18, Batch Num: [100/600]
Epoch: [18/20], Batch Num: [100/600] Discriminator Loss: 0.8567, Generator Loss: 2.0586 D(x): 0.6848, D(G(z)): 0.2550 Epoch: [18/20], Batch Num: [101/600] Discriminator Loss: 0.9331, Generator Loss: 2.0193 D(x): 0.6558, D(G(z)): 0.2818 Epoch: [18/20], Batch Num: [102/600] Discriminator Loss: 1.1064, Generator Loss: 1.9576 D(x): 0.6043, D(G(z)): 0.2644 Epoch: [18/20], Batch Num: [103/600] Discriminator Loss: 0.9017, Generator Loss: 1.7441 D(x): 0.6494, D(G(z)): 0.2604 Epoch: [18/20], Batch Num: [104/600] Discriminator Loss: 0.9104, Generator Loss: 1.5347 D(x): 0.7269, D(G(z)): 0.3143 Epoch: [18/20], Batch Num: [105/600] Discriminator Loss: 0.9696, Generator Loss: 1.3943 D(x): 0.7291, D(G(z)): 0.3656 Epoch: [18/20], Batch Num: [106/600] Discriminator Loss: 0.8162, Generator Loss: 1.3182 D(x): 0.7675, D(G(z)): 0.3240 Epoch: [18/20], Batch Num: [107/600] Discriminator Loss: 0.9759, Generator Loss: 1.5554 D(x): 0.6579, D(G(z)): 0.3126 Epoch: [18/20], Batch Num: [108/600] Discriminator Loss: 0.8771, Generator Loss: 1.7667 D(x): 0.7307, D(G(z)): 0.3103 Epoch: [18/20], Batch Num: [109/600] Discriminator Loss: 0.9691, Generator Loss: 1.6141 D(x): 0.6724, D(G(z)): 0.3093 Epoch: [18/20], Batch Num: [110/600] Discriminator Loss: 0.9347, Generator Loss: 1.7905 D(x): 0.6459, D(G(z)): 0.2503 Epoch: [18/20], Batch Num: [111/600] Discriminator Loss: 0.9394, Generator Loss: 1.4400 D(x): 0.6321, D(G(z)): 0.2432 Epoch: [18/20], Batch Num: [112/600] Discriminator Loss: 0.9402, Generator Loss: 1.3225 D(x): 0.7053, D(G(z)): 0.3247 Epoch: [18/20], Batch Num: [113/600] Discriminator Loss: 1.0892, Generator Loss: 1.3355 D(x): 0.7035, D(G(z)): 0.3730 Epoch: [18/20], Batch Num: [114/600] Discriminator Loss: 0.9489, Generator Loss: 1.3859 D(x): 0.7667, D(G(z)): 0.3770 Epoch: [18/20], Batch Num: [115/600] Discriminator Loss: 0.8231, Generator Loss: 1.5554 D(x): 0.7294, D(G(z)): 0.2981 Epoch: [18/20], Batch Num: [116/600] Discriminator Loss: 0.8164, Generator Loss: 1.6078 D(x): 0.7329, D(G(z)): 0.2771 Epoch: [18/20], Batch Num: [117/600] Discriminator Loss: 0.8789, Generator Loss: 1.7361 D(x): 0.6566, D(G(z)): 0.2355 Epoch: [18/20], Batch Num: [118/600] Discriminator Loss: 0.9890, Generator Loss: 1.5927 D(x): 0.6381, D(G(z)): 0.2480 Epoch: [18/20], Batch Num: [119/600] Discriminator Loss: 0.8424, Generator Loss: 1.3639 D(x): 0.7112, D(G(z)): 0.2775 Epoch: [18/20], Batch Num: [120/600] Discriminator Loss: 1.0301, Generator Loss: 1.1748 D(x): 0.6783, D(G(z)): 0.3234 Epoch: [18/20], Batch Num: [121/600] Discriminator Loss: 1.0240, Generator Loss: 1.3622 D(x): 0.7109, D(G(z)): 0.3514 Epoch: [18/20], Batch Num: [122/600] Discriminator Loss: 0.8624, Generator Loss: 1.5736 D(x): 0.8066, D(G(z)): 0.3725 Epoch: [18/20], Batch Num: [123/600] Discriminator Loss: 0.9621, Generator Loss: 1.5919 D(x): 0.6673, D(G(z)): 0.2804 Epoch: [18/20], Batch Num: [124/600] Discriminator Loss: 0.9027, Generator Loss: 1.5790 D(x): 0.6812, D(G(z)): 0.2919 Epoch: [18/20], Batch Num: [125/600] Discriminator Loss: 0.8985, Generator Loss: 1.5024 D(x): 0.7018, D(G(z)): 0.2883 Epoch: [18/20], Batch Num: [126/600] Discriminator Loss: 0.8836, Generator Loss: 1.4681 D(x): 0.6669, D(G(z)): 0.2427 Epoch: [18/20], Batch Num: [127/600] Discriminator Loss: 0.9969, Generator Loss: 1.3965 D(x): 0.6538, D(G(z)): 0.2807 Epoch: [18/20], Batch Num: [128/600] Discriminator Loss: 0.8579, Generator Loss: 1.3360 D(x): 0.7512, D(G(z)): 0.3178 Epoch: [18/20], Batch Num: [129/600] Discriminator Loss: 0.9117, Generator Loss: 1.4160 D(x): 0.7432, D(G(z)): 0.3464 Epoch: [18/20], Batch Num: [130/600] Discriminator Loss: 0.7966, Generator Loss: 1.5642 D(x): 0.7819, D(G(z)): 0.3271 Epoch: [18/20], Batch Num: [131/600] Discriminator Loss: 0.7657, Generator Loss: 1.8628 D(x): 0.7315, D(G(z)): 0.2598 Epoch: [18/20], Batch Num: [132/600] Discriminator Loss: 0.9694, Generator Loss: 1.8697 D(x): 0.6244, D(G(z)): 0.2383 Epoch: [18/20], Batch Num: [133/600] Discriminator Loss: 0.8226, Generator Loss: 1.6852 D(x): 0.7023, D(G(z)): 0.2393 Epoch: [18/20], Batch Num: [134/600] Discriminator Loss: 0.7421, Generator Loss: 1.5318 D(x): 0.7281, D(G(z)): 0.2325 Epoch: [18/20], Batch Num: [135/600] Discriminator Loss: 0.8269, Generator Loss: 1.6713 D(x): 0.7449, D(G(z)): 0.3020 Epoch: [18/20], Batch Num: [136/600] Discriminator Loss: 0.7991, Generator Loss: 1.6398 D(x): 0.7349, D(G(z)): 0.2834 Epoch: [18/20], Batch Num: [137/600] Discriminator Loss: 0.7985, Generator Loss: 1.5590 D(x): 0.7634, D(G(z)): 0.3014 Epoch: [18/20], Batch Num: [138/600] Discriminator Loss: 0.8347, Generator Loss: 1.9358 D(x): 0.7370, D(G(z)): 0.2963 Epoch: [18/20], Batch Num: [139/600] Discriminator Loss: 0.7489, Generator Loss: 1.9560 D(x): 0.7450, D(G(z)): 0.2434 Epoch: [18/20], Batch Num: [140/600] Discriminator Loss: 0.8548, Generator Loss: 1.9837 D(x): 0.7018, D(G(z)): 0.2524 Epoch: [18/20], Batch Num: [141/600] Discriminator Loss: 0.8098, Generator Loss: 1.9065 D(x): 0.6996, D(G(z)): 0.2140 Epoch: [18/20], Batch Num: [142/600] Discriminator Loss: 0.8511, Generator Loss: 1.7595 D(x): 0.7441, D(G(z)): 0.2682 Epoch: [18/20], Batch Num: [143/600] Discriminator Loss: 0.8650, Generator Loss: 1.6752 D(x): 0.7126, D(G(z)): 0.2522 Epoch: [18/20], Batch Num: [144/600] Discriminator Loss: 0.8942, Generator Loss: 1.3169 D(x): 0.6847, D(G(z)): 0.2680 Epoch: [18/20], Batch Num: [145/600] Discriminator Loss: 0.8271, Generator Loss: 1.5772 D(x): 0.7892, D(G(z)): 0.3287 Epoch: [18/20], Batch Num: [146/600] Discriminator Loss: 0.8344, Generator Loss: 1.4674 D(x): 0.7706, D(G(z)): 0.3273 Epoch: [18/20], Batch Num: [147/600] Discriminator Loss: 0.9728, Generator Loss: 1.7543 D(x): 0.7440, D(G(z)): 0.3475 Epoch: [18/20], Batch Num: [148/600] Discriminator Loss: 0.8977, Generator Loss: 1.9815 D(x): 0.7317, D(G(z)): 0.3079 Epoch: [18/20], Batch Num: [149/600] Discriminator Loss: 0.9294, Generator Loss: 1.7196 D(x): 0.6395, D(G(z)): 0.2166 Epoch: [18/20], Batch Num: [150/600] Discriminator Loss: 0.9952, Generator Loss: 1.7116 D(x): 0.6556, D(G(z)): 0.2857 Epoch: [18/20], Batch Num: [151/600] Discriminator Loss: 0.8636, Generator Loss: 1.5932 D(x): 0.7065, D(G(z)): 0.2702 Epoch: [18/20], Batch Num: [152/600] Discriminator Loss: 0.9495, Generator Loss: 1.4911 D(x): 0.7222, D(G(z)): 0.3536 Epoch: [18/20], Batch Num: [153/600] Discriminator Loss: 0.9101, Generator Loss: 1.5221 D(x): 0.7661, D(G(z)): 0.3506 Epoch: [18/20], Batch Num: [154/600] Discriminator Loss: 0.9383, Generator Loss: 1.6494 D(x): 0.7544, D(G(z)): 0.3656 Epoch: [18/20], Batch Num: [155/600] Discriminator Loss: 0.9996, Generator Loss: 1.6197 D(x): 0.6824, D(G(z)): 0.3397 Epoch: [18/20], Batch Num: [156/600] Discriminator Loss: 0.8850, Generator Loss: 1.5446 D(x): 0.6882, D(G(z)): 0.2725 Epoch: [18/20], Batch Num: [157/600] Discriminator Loss: 1.0121, Generator Loss: 1.5090 D(x): 0.6331, D(G(z)): 0.2683 Epoch: [18/20], Batch Num: [158/600] Discriminator Loss: 1.0268, Generator Loss: 1.6138 D(x): 0.6733, D(G(z)): 0.3360 Epoch: [18/20], Batch Num: [159/600] Discriminator Loss: 1.0630, Generator Loss: 1.3501 D(x): 0.6226, D(G(z)): 0.3150 Epoch: [18/20], Batch Num: [160/600] Discriminator Loss: 0.8323, Generator Loss: 1.3750 D(x): 0.7433, D(G(z)): 0.3189 Epoch: [18/20], Batch Num: [161/600] Discriminator Loss: 0.8595, Generator Loss: 1.3859 D(x): 0.7161, D(G(z)): 0.3191 Epoch: [18/20], Batch Num: [162/600] Discriminator Loss: 0.8719, Generator Loss: 1.3544 D(x): 0.7167, D(G(z)): 0.3215 Epoch: [18/20], Batch Num: [163/600] Discriminator Loss: 1.0740, Generator Loss: 1.2705 D(x): 0.6873, D(G(z)): 0.3668 Epoch: [18/20], Batch Num: [164/600] Discriminator Loss: 0.9934, Generator Loss: 1.4524 D(x): 0.6781, D(G(z)): 0.3328 Epoch: [18/20], Batch Num: [165/600] Discriminator Loss: 0.9770, Generator Loss: 1.3972 D(x): 0.6766, D(G(z)): 0.3395 Epoch: [18/20], Batch Num: [166/600] Discriminator Loss: 0.8374, Generator Loss: 1.5844 D(x): 0.6935, D(G(z)): 0.2744 Epoch: [18/20], Batch Num: [167/600] Discriminator Loss: 0.9024, Generator Loss: 1.5833 D(x): 0.6590, D(G(z)): 0.2643 Epoch: [18/20], Batch Num: [168/600] Discriminator Loss: 0.9721, Generator Loss: 1.3958 D(x): 0.6667, D(G(z)): 0.2961 Epoch: [18/20], Batch Num: [169/600] Discriminator Loss: 0.9360, Generator Loss: 1.4322 D(x): 0.6652, D(G(z)): 0.2962 Epoch: [18/20], Batch Num: [170/600] Discriminator Loss: 0.9685, Generator Loss: 1.2364 D(x): 0.6666, D(G(z)): 0.3094 Epoch: [18/20], Batch Num: [171/600] Discriminator Loss: 0.9037, Generator Loss: 1.2835 D(x): 0.7269, D(G(z)): 0.3413 Epoch: [18/20], Batch Num: [172/600] Discriminator Loss: 0.8976, Generator Loss: 1.4148 D(x): 0.7166, D(G(z)): 0.3467 Epoch: [18/20], Batch Num: [173/600] Discriminator Loss: 0.8178, Generator Loss: 1.4530 D(x): 0.7580, D(G(z)): 0.3245 Epoch: [18/20], Batch Num: [174/600] Discriminator Loss: 0.8860, Generator Loss: 1.5590 D(x): 0.7148, D(G(z)): 0.3070 Epoch: [18/20], Batch Num: [175/600] Discriminator Loss: 0.8828, Generator Loss: 1.5544 D(x): 0.7160, D(G(z)): 0.2938 Epoch: [18/20], Batch Num: [176/600] Discriminator Loss: 0.9677, Generator Loss: 1.7011 D(x): 0.6575, D(G(z)): 0.2800 Epoch: [18/20], Batch Num: [177/600] Discriminator Loss: 1.0681, Generator Loss: 1.5363 D(x): 0.6454, D(G(z)): 0.2977 Epoch: [18/20], Batch Num: [178/600] Discriminator Loss: 0.9075, Generator Loss: 1.4461 D(x): 0.6938, D(G(z)): 0.2775 Epoch: [18/20], Batch Num: [179/600] Discriminator Loss: 0.8709, Generator Loss: 1.5373 D(x): 0.6792, D(G(z)): 0.2587 Epoch: [18/20], Batch Num: [180/600] Discriminator Loss: 0.9688, Generator Loss: 1.5045 D(x): 0.7386, D(G(z)): 0.3486 Epoch: [18/20], Batch Num: [181/600] Discriminator Loss: 0.9190, Generator Loss: 1.4892 D(x): 0.6754, D(G(z)): 0.2983 Epoch: [18/20], Batch Num: [182/600] Discriminator Loss: 1.0948, Generator Loss: 1.4606 D(x): 0.6637, D(G(z)): 0.3412 Epoch: [18/20], Batch Num: [183/600] Discriminator Loss: 0.8432, Generator Loss: 1.6090 D(x): 0.7043, D(G(z)): 0.2914 Epoch: [18/20], Batch Num: [184/600] Discriminator Loss: 0.9312, Generator Loss: 1.6042 D(x): 0.6585, D(G(z)): 0.2632 Epoch: [18/20], Batch Num: [185/600] Discriminator Loss: 0.7906, Generator Loss: 1.4932 D(x): 0.7108, D(G(z)): 0.2770 Epoch: [18/20], Batch Num: [186/600] Discriminator Loss: 0.8412, Generator Loss: 1.4938 D(x): 0.7128, D(G(z)): 0.2831 Epoch: [18/20], Batch Num: [187/600] Discriminator Loss: 0.8980, Generator Loss: 1.5703 D(x): 0.6953, D(G(z)): 0.2911 Epoch: [18/20], Batch Num: [188/600] Discriminator Loss: 0.7472, Generator Loss: 1.5562 D(x): 0.7928, D(G(z)): 0.3043 Epoch: [18/20], Batch Num: [189/600] Discriminator Loss: 0.8156, Generator Loss: 1.6362 D(x): 0.7328, D(G(z)): 0.2978 Epoch: [18/20], Batch Num: [190/600] Discriminator Loss: 0.8153, Generator Loss: 1.5272 D(x): 0.7237, D(G(z)): 0.2677 Epoch: [18/20], Batch Num: [191/600] Discriminator Loss: 0.7481, Generator Loss: 1.5271 D(x): 0.7214, D(G(z)): 0.2444 Epoch: [18/20], Batch Num: [192/600] Discriminator Loss: 0.9339, Generator Loss: 1.4448 D(x): 0.6873, D(G(z)): 0.3046 Epoch: [18/20], Batch Num: [193/600] Discriminator Loss: 0.8998, Generator Loss: 1.5358 D(x): 0.6914, D(G(z)): 0.2819 Epoch: [18/20], Batch Num: [194/600] Discriminator Loss: 0.8455, Generator Loss: 1.6430 D(x): 0.7191, D(G(z)): 0.2865 Epoch: [18/20], Batch Num: [195/600] Discriminator Loss: 0.9185, Generator Loss: 1.5123 D(x): 0.7078, D(G(z)): 0.3086 Epoch: [18/20], Batch Num: [196/600] Discriminator Loss: 1.0174, Generator Loss: 1.6766 D(x): 0.6909, D(G(z)): 0.3418 Epoch: [18/20], Batch Num: [197/600] Discriminator Loss: 1.0110, Generator Loss: 1.7470 D(x): 0.7091, D(G(z)): 0.3066 Epoch: [18/20], Batch Num: [198/600] Discriminator Loss: 0.9527, Generator Loss: 1.5457 D(x): 0.6760, D(G(z)): 0.2925 Epoch: [18/20], Batch Num: [199/600] Discriminator Loss: 0.8670, Generator Loss: 1.5110 D(x): 0.7152, D(G(z)): 0.2801 Epoch: 18, Batch Num: [200/600]
Epoch: [18/20], Batch Num: [200/600] Discriminator Loss: 0.8281, Generator Loss: 1.6863 D(x): 0.7451, D(G(z)): 0.3089 Epoch: [18/20], Batch Num: [201/600] Discriminator Loss: 0.9003, Generator Loss: 1.6920 D(x): 0.6982, D(G(z)): 0.2810 Epoch: [18/20], Batch Num: [202/600] Discriminator Loss: 0.8863, Generator Loss: 1.7686 D(x): 0.6984, D(G(z)): 0.2807 Epoch: [18/20], Batch Num: [203/600] Discriminator Loss: 1.0084, Generator Loss: 1.5805 D(x): 0.6550, D(G(z)): 0.2974 Epoch: [18/20], Batch Num: [204/600] Discriminator Loss: 0.9374, Generator Loss: 1.4976 D(x): 0.6536, D(G(z)): 0.2803 Epoch: [18/20], Batch Num: [205/600] Discriminator Loss: 1.0025, Generator Loss: 1.3092 D(x): 0.6967, D(G(z)): 0.3324 Epoch: [18/20], Batch Num: [206/600] Discriminator Loss: 1.0025, Generator Loss: 1.3333 D(x): 0.7296, D(G(z)): 0.3771 Epoch: [18/20], Batch Num: [207/600] Discriminator Loss: 1.0839, Generator Loss: 1.4497 D(x): 0.7397, D(G(z)): 0.4251 Epoch: [18/20], Batch Num: [208/600] Discriminator Loss: 0.9089, Generator Loss: 1.5636 D(x): 0.7287, D(G(z)): 0.3321 Epoch: [18/20], Batch Num: [209/600] Discriminator Loss: 0.9603, Generator Loss: 1.6704 D(x): 0.6643, D(G(z)): 0.2819 Epoch: [18/20], Batch Num: [210/600] Discriminator Loss: 0.8854, Generator Loss: 1.6422 D(x): 0.6735, D(G(z)): 0.2438 Epoch: [18/20], Batch Num: [211/600] Discriminator Loss: 0.9901, Generator Loss: 1.4268 D(x): 0.6382, D(G(z)): 0.2896 Epoch: [18/20], Batch Num: [212/600] Discriminator Loss: 0.9537, Generator Loss: 1.3221 D(x): 0.7347, D(G(z)): 0.3401 Epoch: [18/20], Batch Num: [213/600] Discriminator Loss: 0.9399, Generator Loss: 1.3974 D(x): 0.6856, D(G(z)): 0.3067 Epoch: [18/20], Batch Num: [214/600] Discriminator Loss: 0.8399, Generator Loss: 1.3260 D(x): 0.7306, D(G(z)): 0.3308 Epoch: [18/20], Batch Num: [215/600] Discriminator Loss: 1.0541, Generator Loss: 1.4761 D(x): 0.7113, D(G(z)): 0.3772 Epoch: [18/20], Batch Num: [216/600] Discriminator Loss: 0.9395, Generator Loss: 1.5552 D(x): 0.7092, D(G(z)): 0.3162 Epoch: [18/20], Batch Num: [217/600] Discriminator Loss: 0.9515, Generator Loss: 1.5578 D(x): 0.6380, D(G(z)): 0.2497 Epoch: [18/20], Batch Num: [218/600] Discriminator Loss: 1.0346, Generator Loss: 1.5535 D(x): 0.6234, D(G(z)): 0.2715 Epoch: [18/20], Batch Num: [219/600] Discriminator Loss: 0.9309, Generator Loss: 1.3767 D(x): 0.6843, D(G(z)): 0.3078 Epoch: [18/20], Batch Num: [220/600] Discriminator Loss: 0.8857, Generator Loss: 1.4054 D(x): 0.6574, D(G(z)): 0.2641 Epoch: [18/20], Batch Num: [221/600] Discriminator Loss: 0.9911, Generator Loss: 1.3629 D(x): 0.6703, D(G(z)): 0.3125 Epoch: [18/20], Batch Num: [222/600] Discriminator Loss: 1.1146, Generator Loss: 1.3190 D(x): 0.6946, D(G(z)): 0.3676 Epoch: [18/20], Batch Num: [223/600] Discriminator Loss: 1.0562, Generator Loss: 1.5289 D(x): 0.7228, D(G(z)): 0.3924 Epoch: [18/20], Batch Num: [224/600] Discriminator Loss: 0.8714, Generator Loss: 1.6554 D(x): 0.7257, D(G(z)): 0.3177 Epoch: [18/20], Batch Num: [225/600] Discriminator Loss: 0.8565, Generator Loss: 1.7970 D(x): 0.6899, D(G(z)): 0.2858 Epoch: [18/20], Batch Num: [226/600] Discriminator Loss: 0.8378, Generator Loss: 1.5698 D(x): 0.6348, D(G(z)): 0.2207 Epoch: [18/20], Batch Num: [227/600] Discriminator Loss: 0.9332, Generator Loss: 1.5552 D(x): 0.6563, D(G(z)): 0.2690 Epoch: [18/20], Batch Num: [228/600] Discriminator Loss: 0.9134, Generator Loss: 1.4751 D(x): 0.6542, D(G(z)): 0.2825 Epoch: [18/20], Batch Num: [229/600] Discriminator Loss: 0.8911, Generator Loss: 1.2060 D(x): 0.7104, D(G(z)): 0.3285 Epoch: [18/20], Batch Num: [230/600] Discriminator Loss: 0.9453, Generator Loss: 1.2169 D(x): 0.7197, D(G(z)): 0.3414 Epoch: [18/20], Batch Num: [231/600] Discriminator Loss: 0.7762, Generator Loss: 1.2634 D(x): 0.7454, D(G(z)): 0.3125 Epoch: [18/20], Batch Num: [232/600] Discriminator Loss: 0.9041, Generator Loss: 1.4834 D(x): 0.7609, D(G(z)): 0.3741 Epoch: [18/20], Batch Num: [233/600] Discriminator Loss: 0.7824, Generator Loss: 1.6503 D(x): 0.7445, D(G(z)): 0.2609 Epoch: [18/20], Batch Num: [234/600] Discriminator Loss: 0.8557, Generator Loss: 1.7556 D(x): 0.6732, D(G(z)): 0.2441 Epoch: [18/20], Batch Num: [235/600] Discriminator Loss: 0.8814, Generator Loss: 1.8752 D(x): 0.6896, D(G(z)): 0.2640 Epoch: [18/20], Batch Num: [236/600] Discriminator Loss: 0.9750, Generator Loss: 1.6966 D(x): 0.6613, D(G(z)): 0.2681 Epoch: [18/20], Batch Num: [237/600] Discriminator Loss: 0.8508, Generator Loss: 1.4685 D(x): 0.6913, D(G(z)): 0.2647 Epoch: [18/20], Batch Num: [238/600] Discriminator Loss: 0.7454, Generator Loss: 1.4301 D(x): 0.7404, D(G(z)): 0.2561 Epoch: [18/20], Batch Num: [239/600] Discriminator Loss: 0.8058, Generator Loss: 1.4191 D(x): 0.7696, D(G(z)): 0.3259 Epoch: [18/20], Batch Num: [240/600] Discriminator Loss: 0.9523, Generator Loss: 1.4848 D(x): 0.7523, D(G(z)): 0.3581 Epoch: [18/20], Batch Num: [241/600] Discriminator Loss: 0.9290, Generator Loss: 1.7490 D(x): 0.7095, D(G(z)): 0.2965 Epoch: [18/20], Batch Num: 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1.5925 D(x): 0.6994, D(G(z)): 0.2508 Epoch: [18/20], Batch Num: [251/600] Discriminator Loss: 0.9710, Generator Loss: 1.4002 D(x): 0.6448, D(G(z)): 0.2582 Epoch: [18/20], Batch Num: [252/600] Discriminator Loss: 1.0084, Generator Loss: 1.5348 D(x): 0.7249, D(G(z)): 0.3520 Epoch: [18/20], Batch Num: [253/600] Discriminator Loss: 0.8658, Generator Loss: 1.4241 D(x): 0.7537, D(G(z)): 0.3120 Epoch: [18/20], Batch Num: [254/600] Discriminator Loss: 0.8333, Generator Loss: 1.6813 D(x): 0.7274, D(G(z)): 0.3007 Epoch: [18/20], Batch Num: [255/600] Discriminator Loss: 0.7223, Generator Loss: 1.7354 D(x): 0.7651, D(G(z)): 0.2559 Epoch: [18/20], Batch Num: [256/600] Discriminator Loss: 0.8480, Generator Loss: 1.6400 D(x): 0.6544, D(G(z)): 0.2332 Epoch: [18/20], Batch Num: [257/600] Discriminator Loss: 0.8233, Generator Loss: 1.5153 D(x): 0.6801, D(G(z)): 0.2444 Epoch: [18/20], Batch Num: [258/600] Discriminator Loss: 0.8154, Generator Loss: 1.5580 D(x): 0.7631, D(G(z)): 0.3000 Epoch: [18/20], 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Generator Loss: 1.7644 D(x): 0.7187, D(G(z)): 0.2669 Epoch: [18/20], Batch Num: [268/600] Discriminator Loss: 0.8542, Generator Loss: 1.6790 D(x): 0.6993, D(G(z)): 0.2662 Epoch: [18/20], Batch Num: [269/600] Discriminator Loss: 0.9868, Generator Loss: 1.5669 D(x): 0.6736, D(G(z)): 0.2851 Epoch: [18/20], Batch Num: [270/600] Discriminator Loss: 0.8410, Generator Loss: 1.4747 D(x): 0.7417, D(G(z)): 0.3017 Epoch: [18/20], Batch Num: [271/600] Discriminator Loss: 0.9015, Generator Loss: 1.7170 D(x): 0.7230, D(G(z)): 0.3122 Epoch: [18/20], Batch Num: [272/600] Discriminator Loss: 1.0202, Generator Loss: 1.4892 D(x): 0.6568, D(G(z)): 0.3027 Epoch: [18/20], Batch Num: [273/600] Discriminator Loss: 0.9392, Generator Loss: 1.4860 D(x): 0.6902, D(G(z)): 0.2814 Epoch: [18/20], Batch Num: [274/600] Discriminator Loss: 0.9664, Generator Loss: 1.4634 D(x): 0.6864, D(G(z)): 0.2990 Epoch: [18/20], Batch Num: [275/600] Discriminator Loss: 0.7625, Generator Loss: 1.5422 D(x): 0.7772, D(G(z)): 0.2930 Epoch: [18/20], Batch Num: [276/600] Discriminator Loss: 1.1020, Generator Loss: 1.7498 D(x): 0.6571, D(G(z)): 0.3148 Epoch: [18/20], Batch Num: [277/600] Discriminator Loss: 0.9645, Generator Loss: 1.7892 D(x): 0.6928, D(G(z)): 0.3226 Epoch: [18/20], Batch Num: [278/600] Discriminator Loss: 0.9992, Generator Loss: 1.4790 D(x): 0.6469, D(G(z)): 0.2749 Epoch: [18/20], Batch Num: [279/600] Discriminator Loss: 0.9895, Generator Loss: 1.7092 D(x): 0.7003, D(G(z)): 0.3249 Epoch: [18/20], Batch Num: [280/600] Discriminator Loss: 0.9212, Generator Loss: 1.6036 D(x): 0.7153, D(G(z)): 0.3046 Epoch: [18/20], Batch Num: [281/600] Discriminator Loss: 0.7508, Generator Loss: 1.6161 D(x): 0.7393, D(G(z)): 0.2695 Epoch: [18/20], Batch Num: [282/600] Discriminator Loss: 0.9169, Generator Loss: 1.6330 D(x): 0.7183, D(G(z)): 0.2981 Epoch: [18/20], Batch Num: [283/600] Discriminator Loss: 0.9055, Generator Loss: 1.5632 D(x): 0.6877, D(G(z)): 0.2897 Epoch: [18/20], Batch Num: [284/600] Discriminator Loss: 0.9753, Generator Loss: 1.6214 D(x): 0.6731, D(G(z)): 0.3042 Epoch: [18/20], Batch Num: [285/600] Discriminator Loss: 0.8417, Generator Loss: 1.5399 D(x): 0.7253, D(G(z)): 0.2831 Epoch: [18/20], Batch Num: [286/600] Discriminator Loss: 0.7993, Generator Loss: 1.6208 D(x): 0.7244, D(G(z)): 0.2753 Epoch: [18/20], Batch Num: [287/600] Discriminator Loss: 0.8900, Generator Loss: 1.8499 D(x): 0.6999, D(G(z)): 0.2781 Epoch: [18/20], Batch Num: [288/600] Discriminator Loss: 0.7910, Generator Loss: 1.6867 D(x): 0.7552, D(G(z)): 0.2982 Epoch: [18/20], Batch Num: [289/600] Discriminator Loss: 0.8597, Generator Loss: 1.6357 D(x): 0.6819, D(G(z)): 0.2494 Epoch: [18/20], Batch Num: [290/600] Discriminator Loss: 0.9903, Generator Loss: 1.6274 D(x): 0.6732, D(G(z)): 0.2889 Epoch: [18/20], Batch Num: [291/600] Discriminator Loss: 1.0612, Generator Loss: 1.4027 D(x): 0.6803, D(G(z)): 0.3217 Epoch: [18/20], Batch Num: [292/600] Discriminator Loss: 0.9412, Generator Loss: 1.4250 D(x): 0.7471, D(G(z)): 0.3206 Epoch: [18/20], Batch Num: [293/600] Discriminator Loss: 1.0161, Generator Loss: 1.6656 D(x): 0.6978, D(G(z)): 0.3441 Epoch: [18/20], Batch Num: [294/600] Discriminator Loss: 0.8179, Generator Loss: 1.5463 D(x): 0.7364, D(G(z)): 0.2843 Epoch: [18/20], Batch Num: [295/600] Discriminator Loss: 0.7947, Generator Loss: 1.6944 D(x): 0.7244, D(G(z)): 0.2733 Epoch: [18/20], Batch Num: [296/600] Discriminator Loss: 0.8828, Generator Loss: 1.7011 D(x): 0.6931, D(G(z)): 0.2552 Epoch: [18/20], Batch Num: [297/600] Discriminator Loss: 0.8384, Generator Loss: 1.4097 D(x): 0.6809, D(G(z)): 0.2374 Epoch: [18/20], Batch Num: [298/600] Discriminator Loss: 1.0086, Generator Loss: 1.4217 D(x): 0.6775, D(G(z)): 0.3221 Epoch: [18/20], Batch Num: [299/600] Discriminator Loss: 0.9955, Generator Loss: 1.4940 D(x): 0.7011, D(G(z)): 0.3346 Epoch: 18, Batch Num: [300/600]
Epoch: [18/20], Batch Num: [300/600] Discriminator Loss: 0.9643, Generator Loss: 1.3914 D(x): 0.7102, D(G(z)): 0.3508 Epoch: [18/20], Batch Num: [301/600] Discriminator Loss: 1.0123, Generator Loss: 1.4129 D(x): 0.7268, D(G(z)): 0.3594 Epoch: [18/20], Batch Num: [302/600] Discriminator Loss: 0.9633, Generator Loss: 1.5067 D(x): 0.6555, D(G(z)): 0.2886 Epoch: [18/20], Batch Num: [303/600] Discriminator Loss: 1.0647, Generator Loss: 1.5148 D(x): 0.6671, D(G(z)): 0.3409 Epoch: [18/20], Batch Num: [304/600] Discriminator Loss: 0.9845, Generator Loss: 1.4344 D(x): 0.6397, D(G(z)): 0.2767 Epoch: [18/20], Batch Num: [305/600] Discriminator Loss: 0.9921, Generator Loss: 1.4807 D(x): 0.7226, D(G(z)): 0.3488 Epoch: [18/20], Batch Num: [306/600] Discriminator Loss: 0.9969, Generator Loss: 1.3175 D(x): 0.6694, D(G(z)): 0.3286 Epoch: [18/20], Batch Num: [307/600] Discriminator Loss: 1.0173, Generator Loss: 1.3486 D(x): 0.6868, D(G(z)): 0.3499 Epoch: [18/20], Batch Num: [308/600] Discriminator Loss: 1.0543, Generator Loss: 1.4592 D(x): 0.6484, D(G(z)): 0.3368 Epoch: [18/20], Batch Num: [309/600] Discriminator Loss: 1.1216, Generator Loss: 1.4559 D(x): 0.6918, D(G(z)): 0.3748 Epoch: [18/20], Batch Num: [310/600] Discriminator Loss: 0.9323, Generator Loss: 1.4216 D(x): 0.7001, D(G(z)): 0.3260 Epoch: [18/20], Batch Num: [311/600] Discriminator Loss: 0.9463, Generator Loss: 1.5292 D(x): 0.6899, D(G(z)): 0.3093 Epoch: [18/20], Batch Num: [312/600] Discriminator Loss: 0.9559, Generator Loss: 1.6077 D(x): 0.6400, D(G(z)): 0.2667 Epoch: [18/20], Batch Num: [313/600] Discriminator Loss: 1.0050, Generator Loss: 1.4401 D(x): 0.6456, D(G(z)): 0.3126 Epoch: [18/20], Batch Num: [314/600] Discriminator Loss: 1.0820, Generator Loss: 1.5165 D(x): 0.6641, D(G(z)): 0.3494 Epoch: [18/20], Batch Num: [315/600] Discriminator Loss: 0.9611, Generator Loss: 1.2524 D(x): 0.6722, D(G(z)): 0.3098 Epoch: [18/20], Batch Num: [316/600] Discriminator Loss: 0.8409, Generator Loss: 1.3411 D(x): 0.7212, D(G(z)): 0.3259 Epoch: [18/20], Batch Num: [317/600] Discriminator Loss: 1.0310, Generator Loss: 1.4343 D(x): 0.6882, D(G(z)): 0.3517 Epoch: [18/20], Batch Num: [318/600] Discriminator Loss: 0.9081, Generator Loss: 1.2159 D(x): 0.6851, D(G(z)): 0.3130 Epoch: [18/20], Batch Num: [319/600] Discriminator Loss: 0.9304, Generator Loss: 1.3777 D(x): 0.7256, D(G(z)): 0.3667 Epoch: [18/20], Batch Num: [320/600] Discriminator Loss: 0.9664, Generator Loss: 1.5270 D(x): 0.6952, D(G(z)): 0.3390 Epoch: [18/20], Batch Num: [321/600] Discriminator Loss: 0.9161, Generator Loss: 1.2725 D(x): 0.6660, D(G(z)): 0.2928 Epoch: [18/20], Batch Num: [322/600] Discriminator Loss: 0.8816, Generator Loss: 1.3533 D(x): 0.6613, D(G(z)): 0.2950 Epoch: [18/20], Batch Num: [323/600] Discriminator Loss: 0.9572, Generator Loss: 1.3100 D(x): 0.6841, D(G(z)): 0.3103 Epoch: [18/20], Batch Num: [324/600] Discriminator Loss: 0.9865, Generator Loss: 1.2588 D(x): 0.6888, D(G(z)): 0.3552 Epoch: [18/20], Batch Num: [325/600] Discriminator Loss: 0.9294, Generator Loss: 1.2284 D(x): 0.7117, D(G(z)): 0.3455 Epoch: [18/20], Batch Num: [326/600] Discriminator Loss: 1.0343, Generator Loss: 1.3615 D(x): 0.7057, D(G(z)): 0.3793 Epoch: [18/20], Batch Num: [327/600] Discriminator Loss: 0.9488, Generator Loss: 1.4640 D(x): 0.7222, D(G(z)): 0.3511 Epoch: [18/20], Batch Num: [328/600] Discriminator Loss: 1.0165, Generator Loss: 1.5148 D(x): 0.6199, D(G(z)): 0.2871 Epoch: [18/20], Batch Num: [329/600] Discriminator Loss: 0.8235, Generator Loss: 1.5247 D(x): 0.6985, D(G(z)): 0.2722 Epoch: [18/20], Batch Num: [330/600] Discriminator Loss: 0.9599, Generator Loss: 1.5919 D(x): 0.6442, D(G(z)): 0.2723 Epoch: [18/20], Batch Num: [331/600] Discriminator Loss: 0.8572, Generator Loss: 1.5268 D(x): 0.6812, D(G(z)): 0.2666 Epoch: [18/20], Batch Num: [332/600] Discriminator Loss: 0.9532, Generator Loss: 1.2549 D(x): 0.6563, D(G(z)): 0.3043 Epoch: [18/20], Batch Num: [333/600] Discriminator Loss: 1.0097, Generator Loss: 1.2614 D(x): 0.7193, D(G(z)): 0.3574 Epoch: [18/20], Batch Num: [334/600] Discriminator Loss: 0.9877, Generator Loss: 1.3463 D(x): 0.7116, D(G(z)): 0.3446 Epoch: [18/20], Batch Num: [335/600] Discriminator Loss: 0.9054, Generator Loss: 1.3162 D(x): 0.7295, D(G(z)): 0.3418 Epoch: [18/20], Batch Num: [336/600] Discriminator Loss: 0.9215, Generator Loss: 1.4750 D(x): 0.7125, D(G(z)): 0.3402 Epoch: [18/20], Batch Num: [337/600] Discriminator Loss: 1.0578, Generator Loss: 1.5856 D(x): 0.6225, D(G(z)): 0.2861 Epoch: [18/20], Batch Num: [338/600] Discriminator Loss: 0.9547, Generator Loss: 1.5454 D(x): 0.6154, D(G(z)): 0.2433 Epoch: [18/20], Batch Num: [339/600] Discriminator Loss: 0.9408, Generator Loss: 1.5396 D(x): 0.6535, D(G(z)): 0.2731 Epoch: [18/20], Batch Num: [340/600] Discriminator Loss: 0.8219, Generator Loss: 1.2387 D(x): 0.7329, D(G(z)): 0.3233 Epoch: [18/20], Batch Num: [341/600] Discriminator Loss: 0.8183, Generator Loss: 1.1485 D(x): 0.7422, D(G(z)): 0.3266 Epoch: [18/20], Batch Num: [342/600] Discriminator Loss: 0.8163, Generator Loss: 1.2365 D(x): 0.7576, D(G(z)): 0.3476 Epoch: [18/20], Batch Num: [343/600] Discriminator Loss: 0.8801, Generator Loss: 1.3746 D(x): 0.7129, D(G(z)): 0.3042 Epoch: [18/20], Batch Num: [344/600] Discriminator Loss: 0.9097, Generator Loss: 1.5454 D(x): 0.7213, D(G(z)): 0.3430 Epoch: [18/20], Batch Num: [345/600] Discriminator Loss: 0.8172, Generator Loss: 1.6591 D(x): 0.7424, D(G(z)): 0.3145 Epoch: [18/20], Batch Num: [346/600] Discriminator Loss: 0.9830, Generator Loss: 1.7305 D(x): 0.6374, D(G(z)): 0.2821 Epoch: [18/20], Batch Num: [347/600] Discriminator Loss: 0.9015, Generator Loss: 1.5179 D(x): 0.6422, D(G(z)): 0.2540 Epoch: [18/20], Batch Num: [348/600] Discriminator Loss: 0.8757, Generator Loss: 1.5753 D(x): 0.6690, D(G(z)): 0.2468 Epoch: [18/20], Batch Num: [349/600] Discriminator Loss: 0.9612, Generator Loss: 1.3292 D(x): 0.7080, D(G(z)): 0.3390 Epoch: [18/20], Batch Num: [350/600] Discriminator Loss: 0.9239, Generator Loss: 1.2216 D(x): 0.7584, D(G(z)): 0.3708 Epoch: [18/20], Batch Num: [351/600] Discriminator Loss: 0.9939, Generator Loss: 1.3663 D(x): 0.7183, D(G(z)): 0.3550 Epoch: [18/20], Batch Num: [352/600] Discriminator Loss: 0.9660, Generator Loss: 1.5145 D(x): 0.6968, D(G(z)): 0.3152 Epoch: [18/20], Batch Num: [353/600] Discriminator Loss: 0.8770, Generator Loss: 1.6623 D(x): 0.7078, D(G(z)): 0.2882 Epoch: [18/20], Batch Num: [354/600] Discriminator Loss: 0.9675, Generator Loss: 1.7604 D(x): 0.6511, D(G(z)): 0.2826 Epoch: [18/20], Batch Num: [355/600] Discriminator Loss: 0.8954, Generator Loss: 1.7140 D(x): 0.6803, D(G(z)): 0.2531 Epoch: [18/20], Batch Num: [356/600] Discriminator Loss: 0.9147, Generator Loss: 1.6821 D(x): 0.6843, D(G(z)): 0.2725 Epoch: [18/20], Batch Num: [357/600] Discriminator Loss: 0.9216, Generator Loss: 1.4779 D(x): 0.6890, D(G(z)): 0.2823 Epoch: [18/20], Batch Num: [358/600] Discriminator Loss: 0.8323, Generator Loss: 1.5099 D(x): 0.7329, D(G(z)): 0.2851 Epoch: [18/20], Batch Num: [359/600] Discriminator Loss: 0.8612, Generator Loss: 1.4551 D(x): 0.7135, D(G(z)): 0.2977 Epoch: [18/20], Batch Num: [360/600] Discriminator Loss: 0.9209, Generator Loss: 1.4163 D(x): 0.7230, D(G(z)): 0.3295 Epoch: [18/20], Batch Num: [361/600] Discriminator Loss: 0.8810, Generator Loss: 1.5074 D(x): 0.7320, D(G(z)): 0.3068 Epoch: [18/20], Batch Num: [362/600] Discriminator Loss: 0.9591, Generator Loss: 1.5565 D(x): 0.6514, D(G(z)): 0.2884 Epoch: [18/20], Batch Num: [363/600] Discriminator Loss: 1.0133, Generator Loss: 1.6088 D(x): 0.6610, D(G(z)): 0.2994 Epoch: [18/20], Batch Num: [364/600] Discriminator Loss: 0.9865, Generator Loss: 1.5236 D(x): 0.6682, D(G(z)): 0.3077 Epoch: [18/20], Batch Num: [365/600] Discriminator Loss: 0.9182, Generator Loss: 1.4142 D(x): 0.7030, D(G(z)): 0.3167 Epoch: [18/20], Batch Num: [366/600] Discriminator Loss: 0.8282, Generator Loss: 1.3671 D(x): 0.7742, D(G(z)): 0.3563 Epoch: [18/20], Batch Num: [367/600] Discriminator Loss: 0.7513, Generator Loss: 1.4552 D(x): 0.7483, D(G(z)): 0.2899 Epoch: [18/20], Batch Num: [368/600] Discriminator Loss: 0.9418, Generator Loss: 1.6395 D(x): 0.7042, D(G(z)): 0.3062 Epoch: [18/20], Batch Num: [369/600] Discriminator Loss: 0.8711, Generator Loss: 1.7885 D(x): 0.7241, D(G(z)): 0.3128 Epoch: [18/20], Batch Num: [370/600] Discriminator Loss: 0.8959, Generator Loss: 1.6750 D(x): 0.6525, D(G(z)): 0.2761 Epoch: [18/20], Batch Num: [371/600] Discriminator Loss: 0.9363, Generator Loss: 1.6103 D(x): 0.6787, D(G(z)): 0.2707 Epoch: [18/20], Batch Num: [372/600] Discriminator Loss: 1.0097, Generator Loss: 1.3675 D(x): 0.6850, D(G(z)): 0.3331 Epoch: [18/20], Batch Num: [373/600] Discriminator Loss: 1.0742, Generator Loss: 1.4444 D(x): 0.7482, D(G(z)): 0.4258 Epoch: [18/20], Batch Num: [374/600] Discriminator Loss: 0.9367, Generator Loss: 1.6647 D(x): 0.7240, D(G(z)): 0.3215 Epoch: [18/20], Batch Num: [375/600] Discriminator Loss: 0.9349, Generator Loss: 1.9212 D(x): 0.6919, D(G(z)): 0.2885 Epoch: [18/20], Batch Num: [376/600] Discriminator Loss: 0.8923, Generator Loss: 1.7315 D(x): 0.6194, D(G(z)): 0.1995 Epoch: [18/20], Batch Num: [377/600] Discriminator Loss: 0.9119, Generator Loss: 1.7134 D(x): 0.6725, D(G(z)): 0.2785 Epoch: [18/20], Batch Num: [378/600] Discriminator Loss: 0.9795, Generator Loss: 1.4794 D(x): 0.6938, D(G(z)): 0.3174 Epoch: [18/20], Batch Num: [379/600] Discriminator Loss: 0.8145, Generator Loss: 1.3541 D(x): 0.7646, D(G(z)): 0.3091 Epoch: [18/20], Batch Num: [380/600] Discriminator Loss: 0.9625, Generator Loss: 1.5161 D(x): 0.7180, D(G(z)): 0.3363 Epoch: [18/20], Batch Num: [381/600] Discriminator Loss: 0.8791, Generator Loss: 1.6513 D(x): 0.7451, D(G(z)): 0.3249 Epoch: [18/20], Batch Num: [382/600] Discriminator Loss: 0.8615, Generator Loss: 1.7048 D(x): 0.7232, D(G(z)): 0.2965 Epoch: [18/20], Batch Num: [383/600] Discriminator Loss: 1.1339, Generator Loss: 1.6324 D(x): 0.6038, D(G(z)): 0.2839 Epoch: [18/20], Batch Num: [384/600] Discriminator Loss: 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Epoch: [18/20], Batch Num: [400/600] Discriminator Loss: 1.0089, Generator Loss: 1.6094 D(x): 0.6367, D(G(z)): 0.2685 Epoch: [18/20], Batch Num: [401/600] Discriminator Loss: 1.0399, Generator Loss: 1.3522 D(x): 0.6197, D(G(z)): 0.2716 Epoch: [18/20], Batch Num: [402/600] Discriminator Loss: 1.0044, Generator Loss: 1.2227 D(x): 0.6800, D(G(z)): 0.3311 Epoch: [18/20], Batch Num: [403/600] Discriminator Loss: 0.9252, Generator Loss: 1.1864 D(x): 0.7477, D(G(z)): 0.3600 Epoch: [18/20], Batch Num: [404/600] Discriminator Loss: 0.8946, Generator Loss: 1.2055 D(x): 0.7476, D(G(z)): 0.3289 Epoch: [18/20], Batch Num: [405/600] Discriminator Loss: 0.9772, Generator Loss: 1.2351 D(x): 0.7303, D(G(z)): 0.3518 Epoch: [18/20], Batch Num: [406/600] Discriminator Loss: 0.8106, Generator Loss: 1.3557 D(x): 0.7251, D(G(z)): 0.2947 Epoch: [18/20], Batch Num: [407/600] Discriminator Loss: 0.8235, Generator Loss: 1.4035 D(x): 0.7250, D(G(z)): 0.2886 Epoch: [18/20], Batch Num: [408/600] Discriminator Loss: 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0.3068 Epoch: [18/20], Batch Num: [417/600] Discriminator Loss: 1.0453, Generator Loss: 1.6126 D(x): 0.6622, D(G(z)): 0.3046 Epoch: [18/20], Batch Num: [418/600] Discriminator Loss: 1.0520, Generator Loss: 1.3938 D(x): 0.5876, D(G(z)): 0.2613 Epoch: [18/20], Batch Num: [419/600] Discriminator Loss: 1.0301, Generator Loss: 1.3785 D(x): 0.5949, D(G(z)): 0.2832 Epoch: [18/20], Batch Num: [420/600] Discriminator Loss: 1.0891, Generator Loss: 1.2558 D(x): 0.6927, D(G(z)): 0.3799 Epoch: [18/20], Batch Num: [421/600] Discriminator Loss: 0.9127, Generator Loss: 1.2459 D(x): 0.7590, D(G(z)): 0.3592 Epoch: [18/20], Batch Num: [422/600] Discriminator Loss: 1.1020, Generator Loss: 1.3232 D(x): 0.6827, D(G(z)): 0.3861 Epoch: [18/20], Batch Num: [423/600] Discriminator Loss: 0.8798, Generator Loss: 1.6349 D(x): 0.6999, D(G(z)): 0.3093 Epoch: [18/20], Batch Num: [424/600] Discriminator Loss: 1.0585, Generator Loss: 1.6272 D(x): 0.6155, D(G(z)): 0.3158 Epoch: [18/20], Batch Num: [425/600] Discriminator Loss: 1.0018, Generator Loss: 1.5444 D(x): 0.6223, D(G(z)): 0.2913 Epoch: [18/20], Batch Num: [426/600] Discriminator Loss: 1.0282, Generator Loss: 1.4219 D(x): 0.6275, D(G(z)): 0.2964 Epoch: [18/20], Batch Num: [427/600] Discriminator Loss: 0.9771, Generator Loss: 1.2902 D(x): 0.6348, D(G(z)): 0.2834 Epoch: [18/20], Batch Num: [428/600] Discriminator Loss: 0.9044, Generator Loss: 1.2228 D(x): 0.7033, D(G(z)): 0.3367 Epoch: [18/20], Batch Num: [429/600] Discriminator Loss: 1.0119, Generator Loss: 1.1533 D(x): 0.7115, D(G(z)): 0.4066 Epoch: [18/20], Batch Num: [430/600] Discriminator Loss: 0.8982, Generator Loss: 1.2887 D(x): 0.7414, D(G(z)): 0.3712 Epoch: [18/20], Batch Num: [431/600] Discriminator Loss: 0.9418, Generator Loss: 1.2493 D(x): 0.7423, D(G(z)): 0.3753 Epoch: [18/20], Batch Num: [432/600] Discriminator Loss: 0.8616, Generator Loss: 1.3688 D(x): 0.7188, D(G(z)): 0.3269 Epoch: [18/20], Batch Num: [433/600] Discriminator Loss: 0.8723, Generator Loss: 1.4765 D(x): 0.6758, D(G(z)): 0.2782 Epoch: [18/20], Batch Num: [434/600] Discriminator Loss: 1.0653, Generator Loss: 1.5315 D(x): 0.6033, D(G(z)): 0.3060 Epoch: [18/20], Batch Num: [435/600] Discriminator Loss: 0.9509, Generator Loss: 1.3442 D(x): 0.6465, D(G(z)): 0.3024 Epoch: [18/20], Batch Num: [436/600] Discriminator Loss: 1.0565, Generator Loss: 1.4596 D(x): 0.6156, D(G(z)): 0.3121 Epoch: [18/20], Batch Num: [437/600] Discriminator Loss: 1.0048, Generator Loss: 1.2809 D(x): 0.6759, D(G(z)): 0.3440 Epoch: [18/20], Batch Num: [438/600] Discriminator Loss: 0.9644, Generator Loss: 1.1840 D(x): 0.6823, D(G(z)): 0.3334 Epoch: [18/20], Batch Num: [439/600] Discriminator Loss: 0.9974, Generator Loss: 1.2913 D(x): 0.6737, D(G(z)): 0.3207 Epoch: [18/20], Batch Num: [440/600] Discriminator Loss: 0.9438, Generator Loss: 1.2693 D(x): 0.7167, D(G(z)): 0.3475 Epoch: [18/20], Batch Num: [441/600] Discriminator Loss: 0.9205, Generator Loss: 1.3266 D(x): 0.6929, D(G(z)): 0.3219 Epoch: [18/20], Batch Num: [442/600] Discriminator Loss: 0.8460, Generator Loss: 1.3733 D(x): 0.7098, D(G(z)): 0.3132 Epoch: [18/20], Batch Num: [443/600] Discriminator Loss: 0.9669, Generator Loss: 1.4404 D(x): 0.6651, D(G(z)): 0.3108 Epoch: [18/20], Batch Num: [444/600] Discriminator Loss: 0.8857, Generator Loss: 1.3728 D(x): 0.7161, D(G(z)): 0.3006 Epoch: [18/20], Batch Num: [445/600] Discriminator Loss: 1.0213, Generator Loss: 1.3331 D(x): 0.6735, D(G(z)): 0.3439 Epoch: [18/20], Batch Num: [446/600] Discriminator Loss: 1.0939, Generator Loss: 1.3919 D(x): 0.6511, D(G(z)): 0.3458 Epoch: [18/20], Batch Num: [447/600] Discriminator Loss: 1.0417, Generator Loss: 1.4833 D(x): 0.6434, D(G(z)): 0.3356 Epoch: [18/20], Batch Num: [448/600] Discriminator Loss: 0.9506, Generator Loss: 1.2442 D(x): 0.6320, D(G(z)): 0.2807 Epoch: [18/20], Batch Num: [449/600] Discriminator Loss: 0.8843, Generator Loss: 1.2591 D(x): 0.7080, D(G(z)): 0.3084 Epoch: [18/20], Batch Num: [450/600] Discriminator Loss: 0.9437, Generator Loss: 1.2184 D(x): 0.6756, D(G(z)): 0.3142 Epoch: [18/20], Batch Num: [451/600] Discriminator Loss: 1.0146, Generator Loss: 1.1522 D(x): 0.6970, D(G(z)): 0.3709 Epoch: [18/20], Batch Num: [452/600] Discriminator Loss: 0.9790, Generator Loss: 1.2362 D(x): 0.7335, D(G(z)): 0.3794 Epoch: [18/20], Batch Num: [453/600] Discriminator Loss: 1.0466, Generator Loss: 1.3973 D(x): 0.6908, D(G(z)): 0.3839 Epoch: [18/20], Batch Num: [454/600] Discriminator Loss: 0.9587, Generator Loss: 1.5179 D(x): 0.6939, D(G(z)): 0.3487 Epoch: [18/20], Batch Num: [455/600] Discriminator Loss: 0.9568, Generator Loss: 1.5647 D(x): 0.6640, D(G(z)): 0.2957 Epoch: [18/20], Batch Num: [456/600] Discriminator Loss: 0.9711, Generator Loss: 1.5729 D(x): 0.6190, D(G(z)): 0.2635 Epoch: [18/20], Batch Num: [457/600] Discriminator Loss: 0.9485, Generator Loss: 1.4504 D(x): 0.6737, D(G(z)): 0.3108 Epoch: [18/20], Batch Num: [458/600] Discriminator Loss: 1.0153, Generator Loss: 1.5177 D(x): 0.6598, D(G(z)): 0.3343 Epoch: [18/20], Batch Num: [459/600] Discriminator Loss: 0.9536, Generator Loss: 1.3071 D(x): 0.6938, D(G(z)): 0.3278 Epoch: [18/20], Batch Num: [460/600] Discriminator Loss: 0.8869, Generator Loss: 1.4594 D(x): 0.7073, D(G(z)): 0.3086 Epoch: [18/20], Batch Num: [461/600] Discriminator Loss: 0.8148, Generator Loss: 1.3311 D(x): 0.7383, D(G(z)): 0.3103 Epoch: [18/20], Batch Num: [462/600] Discriminator Loss: 0.9166, Generator Loss: 1.4959 D(x): 0.7466, D(G(z)): 0.3446 Epoch: [18/20], Batch Num: [463/600] Discriminator Loss: 0.8829, Generator Loss: 1.7589 D(x): 0.7261, D(G(z)): 0.3250 Epoch: [18/20], Batch Num: [464/600] Discriminator Loss: 0.9486, Generator Loss: 1.8279 D(x): 0.6601, D(G(z)): 0.2681 Epoch: [18/20], Batch Num: [465/600] Discriminator Loss: 0.9352, Generator Loss: 1.7896 D(x): 0.6337, D(G(z)): 0.2654 Epoch: [18/20], Batch Num: [466/600] Discriminator Loss: 0.9497, Generator Loss: 1.5348 D(x): 0.6381, D(G(z)): 0.2611 Epoch: [18/20], Batch Num: [467/600] Discriminator Loss: 0.9249, Generator Loss: 1.3624 D(x): 0.6841, D(G(z)): 0.2912 Epoch: [18/20], Batch Num: [468/600] Discriminator Loss: 0.9882, Generator Loss: 1.3280 D(x): 0.7020, D(G(z)): 0.3511 Epoch: [18/20], Batch Num: [469/600] Discriminator Loss: 0.9604, Generator Loss: 1.4563 D(x): 0.7414, D(G(z)): 0.3566 Epoch: [18/20], Batch Num: [470/600] Discriminator Loss: 0.8795, Generator Loss: 1.5913 D(x): 0.7530, D(G(z)): 0.3224 Epoch: [18/20], Batch Num: [471/600] Discriminator Loss: 0.8189, Generator Loss: 1.6250 D(x): 0.7474, D(G(z)): 0.2995 Epoch: [18/20], Batch Num: [472/600] Discriminator Loss: 0.9260, Generator Loss: 1.8236 D(x): 0.6643, D(G(z)): 0.2891 Epoch: [18/20], Batch Num: [473/600] Discriminator Loss: 0.9219, Generator Loss: 1.5567 D(x): 0.6303, D(G(z)): 0.2247 Epoch: [18/20], Batch Num: [474/600] Discriminator Loss: 0.9353, Generator Loss: 1.5128 D(x): 0.6902, D(G(z)): 0.2935 Epoch: [18/20], Batch Num: [475/600] Discriminator Loss: 0.9554, Generator Loss: 1.4031 D(x): 0.7042, D(G(z)): 0.3024 Epoch: [18/20], Batch Num: [476/600] Discriminator Loss: 0.7772, Generator Loss: 1.3871 D(x): 0.7480, D(G(z)): 0.2972 Epoch: [18/20], Batch Num: [477/600] Discriminator Loss: 0.8862, Generator Loss: 1.4360 D(x): 0.7205, D(G(z)): 0.3435 Epoch: [18/20], Batch Num: [478/600] Discriminator Loss: 0.8938, Generator Loss: 1.6044 D(x): 0.7161, D(G(z)): 0.3231 Epoch: [18/20], Batch Num: [479/600] Discriminator Loss: 0.9392, Generator Loss: 1.6353 D(x): 0.6499, D(G(z)): 0.2938 Epoch: [18/20], Batch Num: [480/600] Discriminator Loss: 0.9075, Generator Loss: 1.4882 D(x): 0.6749, D(G(z)): 0.2882 Epoch: [18/20], Batch Num: [481/600] Discriminator Loss: 0.8143, Generator Loss: 1.4488 D(x): 0.7221, D(G(z)): 0.2669 Epoch: [18/20], Batch Num: [482/600] Discriminator Loss: 0.8549, Generator Loss: 1.3547 D(x): 0.7502, D(G(z)): 0.3008 Epoch: [18/20], Batch Num: [483/600] Discriminator Loss: 0.7628, Generator Loss: 1.6383 D(x): 0.7503, D(G(z)): 0.2775 Epoch: [18/20], Batch Num: [484/600] Discriminator Loss: 0.8195, Generator Loss: 1.8324 D(x): 0.7296, D(G(z)): 0.2798 Epoch: [18/20], Batch Num: [485/600] Discriminator Loss: 0.7275, Generator Loss: 1.9275 D(x): 0.7325, D(G(z)): 0.2319 Epoch: [18/20], Batch Num: [486/600] Discriminator Loss: 0.9070, Generator Loss: 1.5910 D(x): 0.7339, D(G(z)): 0.2844 Epoch: [18/20], Batch Num: [487/600] Discriminator Loss: 0.8640, Generator Loss: 1.8525 D(x): 0.7280, D(G(z)): 0.2753 Epoch: [18/20], Batch Num: [488/600] Discriminator Loss: 0.7802, Generator Loss: 1.6542 D(x): 0.7520, D(G(z)): 0.2560 Epoch: [18/20], Batch Num: [489/600] Discriminator Loss: 0.7845, Generator Loss: 1.7037 D(x): 0.7440, D(G(z)): 0.2452 Epoch: [18/20], Batch Num: [490/600] Discriminator Loss: 0.8618, Generator Loss: 1.6605 D(x): 0.7307, D(G(z)): 0.2799 Epoch: [18/20], Batch Num: [491/600] Discriminator Loss: 0.8953, Generator Loss: 1.5295 D(x): 0.7332, D(G(z)): 0.2984 Epoch: [18/20], Batch Num: [492/600] Discriminator Loss: 0.8666, Generator Loss: 1.6333 D(x): 0.7425, D(G(z)): 0.2950 Epoch: [18/20], Batch Num: [493/600] Discriminator Loss: 0.9226, Generator Loss: 1.7833 D(x): 0.7193, D(G(z)): 0.2699 Epoch: [18/20], Batch Num: [494/600] Discriminator Loss: 0.7661, Generator Loss: 1.8883 D(x): 0.7545, D(G(z)): 0.2565 Epoch: [18/20], Batch Num: [495/600] Discriminator Loss: 0.9028, Generator Loss: 2.0401 D(x): 0.7229, D(G(z)): 0.2540 Epoch: [18/20], Batch Num: [496/600] Discriminator Loss: 0.8579, Generator Loss: 1.7933 D(x): 0.6879, D(G(z)): 0.2270 Epoch: [18/20], Batch Num: [497/600] Discriminator Loss: 1.0341, Generator Loss: 1.5102 D(x): 0.6453, D(G(z)): 0.2774 Epoch: [18/20], Batch Num: [498/600] Discriminator Loss: 1.0005, Generator Loss: 1.3525 D(x): 0.7399, D(G(z)): 0.3392 Epoch: [18/20], Batch Num: [499/600] Discriminator Loss: 1.1712, Generator Loss: 1.5782 D(x): 0.7491, D(G(z)): 0.4317 Epoch: 18, Batch Num: [500/600]
Epoch: [18/20], Batch Num: [500/600] Discriminator Loss: 0.8109, Generator Loss: 2.0280 D(x): 0.7643, D(G(z)): 0.2965 Epoch: [18/20], Batch Num: [501/600] Discriminator Loss: 0.8353, Generator Loss: 1.9085 D(x): 0.6909, D(G(z)): 0.2411 Epoch: [18/20], Batch Num: [502/600] Discriminator Loss: 0.9029, Generator Loss: 1.9607 D(x): 0.6765, D(G(z)): 0.2379 Epoch: [18/20], Batch Num: [503/600] Discriminator Loss: 0.8398, Generator Loss: 1.8110 D(x): 0.7291, D(G(z)): 0.2626 Epoch: [18/20], Batch Num: [504/600] Discriminator Loss: 0.9049, Generator Loss: 1.7112 D(x): 0.7026, D(G(z)): 0.2503 Epoch: [18/20], Batch Num: [505/600] Discriminator Loss: 0.9285, Generator Loss: 1.6919 D(x): 0.6744, D(G(z)): 0.2285 Epoch: [18/20], Batch Num: [506/600] Discriminator Loss: 0.9837, Generator Loss: 1.5293 D(x): 0.6880, D(G(z)): 0.3033 Epoch: [18/20], Batch Num: [507/600] Discriminator Loss: 0.8717, Generator Loss: 1.3878 D(x): 0.7110, D(G(z)): 0.2808 Epoch: [18/20], Batch Num: [508/600] Discriminator Loss: 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0.3120 Epoch: [18/20], Batch Num: [517/600] Discriminator Loss: 0.8696, Generator Loss: 1.1754 D(x): 0.7553, D(G(z)): 0.3444 Epoch: [18/20], Batch Num: [518/600] Discriminator Loss: 0.8944, Generator Loss: 1.2386 D(x): 0.7306, D(G(z)): 0.3459 Epoch: [18/20], Batch Num: [519/600] Discriminator Loss: 1.0260, Generator Loss: 1.3337 D(x): 0.6842, D(G(z)): 0.3506 Epoch: [18/20], Batch Num: [520/600] Discriminator Loss: 1.0902, Generator Loss: 1.4480 D(x): 0.6873, D(G(z)): 0.3806 Epoch: [18/20], Batch Num: [521/600] Discriminator Loss: 1.0860, Generator Loss: 1.5740 D(x): 0.6031, D(G(z)): 0.2795 Epoch: [18/20], Batch Num: [522/600] Discriminator Loss: 0.9813, Generator Loss: 1.5786 D(x): 0.6484, D(G(z)): 0.2938 Epoch: [18/20], Batch Num: [523/600] Discriminator Loss: 0.8789, Generator Loss: 1.4376 D(x): 0.6879, D(G(z)): 0.2875 Epoch: [18/20], Batch Num: [524/600] Discriminator Loss: 1.0347, Generator Loss: 1.4438 D(x): 0.6654, D(G(z)): 0.3360 Epoch: [18/20], Batch Num: [525/600] Discriminator Loss: 0.9727, Generator Loss: 1.3446 D(x): 0.6846, D(G(z)): 0.3333 Epoch: [18/20], Batch Num: [526/600] Discriminator Loss: 0.9636, Generator Loss: 1.4220 D(x): 0.7450, D(G(z)): 0.3828 Epoch: [18/20], Batch Num: [527/600] Discriminator Loss: 0.8541, Generator Loss: 1.5775 D(x): 0.6810, D(G(z)): 0.2836 Epoch: [18/20], Batch Num: [528/600] Discriminator Loss: 0.9304, Generator Loss: 1.5912 D(x): 0.6668, D(G(z)): 0.3093 Epoch: [18/20], Batch Num: [529/600] Discriminator Loss: 0.9830, Generator Loss: 1.5661 D(x): 0.6075, D(G(z)): 0.2594 Epoch: [18/20], Batch Num: [530/600] Discriminator Loss: 0.8653, Generator Loss: 1.7557 D(x): 0.6873, D(G(z)): 0.2940 Epoch: [18/20], Batch Num: [531/600] Discriminator Loss: 0.8998, Generator Loss: 1.2994 D(x): 0.6960, D(G(z)): 0.3152 Epoch: [18/20], Batch Num: [532/600] Discriminator Loss: 0.8476, Generator Loss: 1.3687 D(x): 0.7702, D(G(z)): 0.3324 Epoch: [18/20], Batch Num: [533/600] Discriminator Loss: 0.8100, Generator Loss: 1.3389 D(x): 0.7462, D(G(z)): 0.3002 Epoch: [18/20], Batch Num: [534/600] Discriminator Loss: 0.9810, Generator Loss: 1.6272 D(x): 0.7099, D(G(z)): 0.3575 Epoch: [18/20], Batch Num: [535/600] Discriminator Loss: 0.8001, Generator Loss: 1.6534 D(x): 0.7389, D(G(z)): 0.2755 Epoch: [18/20], Batch Num: [536/600] Discriminator Loss: 0.7387, Generator Loss: 1.7901 D(x): 0.7047, D(G(z)): 0.2289 Epoch: [18/20], Batch Num: [537/600] Discriminator Loss: 0.7604, Generator Loss: 1.6440 D(x): 0.7073, D(G(z)): 0.2268 Epoch: [18/20], Batch Num: [538/600] Discriminator Loss: 0.8168, Generator Loss: 1.6035 D(x): 0.7177, D(G(z)): 0.2772 Epoch: [18/20], Batch Num: [539/600] Discriminator Loss: 0.9122, Generator Loss: 1.5835 D(x): 0.7013, D(G(z)): 0.2913 Epoch: [18/20], Batch Num: [540/600] Discriminator Loss: 0.8451, Generator Loss: 1.6275 D(x): 0.7369, D(G(z)): 0.2815 Epoch: [18/20], Batch Num: [541/600] Discriminator Loss: 0.8545, Generator Loss: 1.7690 D(x): 0.7559, D(G(z)): 0.2929 Epoch: [18/20], Batch Num: 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1.4959 D(x): 0.7146, D(G(z)): 0.2596 Epoch: [18/20], Batch Num: [551/600] Discriminator Loss: 0.8940, Generator Loss: 1.5553 D(x): 0.7498, D(G(z)): 0.3087 Epoch: [18/20], Batch Num: [552/600] Discriminator Loss: 0.8062, Generator Loss: 1.6763 D(x): 0.7916, D(G(z)): 0.3183 Epoch: [18/20], Batch Num: [553/600] Discriminator Loss: 0.8662, Generator Loss: 1.8020 D(x): 0.7135, D(G(z)): 0.2999 Epoch: [18/20], Batch Num: [554/600] Discriminator Loss: 1.0501, Generator Loss: 1.8002 D(x): 0.6235, D(G(z)): 0.2869 Epoch: [18/20], Batch Num: [555/600] Discriminator Loss: 0.8173, Generator Loss: 1.8268 D(x): 0.6899, D(G(z)): 0.2170 Epoch: [18/20], Batch Num: [556/600] Discriminator Loss: 1.0068, Generator Loss: 1.7087 D(x): 0.6673, D(G(z)): 0.3012 Epoch: [18/20], Batch Num: [557/600] Discriminator Loss: 0.9033, Generator Loss: 1.6284 D(x): 0.6874, D(G(z)): 0.2750 Epoch: [18/20], Batch Num: [558/600] Discriminator Loss: 0.9592, Generator Loss: 1.5100 D(x): 0.7147, D(G(z)): 0.3136 Epoch: [18/20], Batch Num: [559/600] Discriminator Loss: 0.8460, Generator Loss: 1.6123 D(x): 0.7025, D(G(z)): 0.2639 Epoch: [18/20], Batch Num: [560/600] Discriminator Loss: 0.9513, Generator Loss: 1.6735 D(x): 0.6944, D(G(z)): 0.3124 Epoch: [18/20], Batch Num: [561/600] Discriminator Loss: 0.9296, Generator Loss: 1.6335 D(x): 0.6906, D(G(z)): 0.2963 Epoch: [18/20], Batch Num: [562/600] Discriminator Loss: 0.7978, Generator Loss: 1.5017 D(x): 0.7204, D(G(z)): 0.2884 Epoch: [18/20], Batch Num: [563/600] Discriminator Loss: 1.0183, Generator Loss: 1.2339 D(x): 0.6569, D(G(z)): 0.2909 Epoch: [18/20], Batch Num: [564/600] Discriminator Loss: 1.0052, Generator Loss: 1.1952 D(x): 0.7064, D(G(z)): 0.3721 Epoch: [18/20], Batch Num: [565/600] Discriminator Loss: 0.9727, Generator Loss: 1.3269 D(x): 0.7102, D(G(z)): 0.3670 Epoch: [18/20], Batch Num: [566/600] Discriminator Loss: 1.0569, Generator Loss: 1.2967 D(x): 0.6574, D(G(z)): 0.3580 Epoch: [18/20], Batch Num: [567/600] Discriminator Loss: 0.8930, Generator Loss: 1.7088 D(x): 0.7216, D(G(z)): 0.3443 Epoch: [18/20], Batch Num: [568/600] Discriminator Loss: 0.9266, Generator Loss: 1.5533 D(x): 0.6526, D(G(z)): 0.2797 Epoch: [18/20], Batch Num: [569/600] Discriminator Loss: 0.8699, Generator Loss: 1.5443 D(x): 0.6631, D(G(z)): 0.2473 Epoch: [18/20], Batch Num: [570/600] Discriminator Loss: 0.8520, Generator Loss: 1.3018 D(x): 0.7033, D(G(z)): 0.2712 Epoch: [18/20], Batch Num: [571/600] Discriminator Loss: 0.8565, Generator Loss: 1.3089 D(x): 0.7133, D(G(z)): 0.3075 Epoch: [18/20], Batch Num: [572/600] Discriminator Loss: 0.9635, Generator Loss: 1.4648 D(x): 0.7463, D(G(z)): 0.3426 Epoch: [18/20], Batch Num: [573/600] Discriminator Loss: 0.8345, Generator Loss: 1.5302 D(x): 0.7266, D(G(z)): 0.3034 Epoch: [18/20], Batch Num: [574/600] Discriminator Loss: 0.8280, Generator Loss: 1.5987 D(x): 0.7100, D(G(z)): 0.2803 Epoch: [18/20], Batch Num: [575/600] Discriminator Loss: 0.9435, Generator Loss: 1.5420 D(x): 0.6735, D(G(z)): 0.2900 Epoch: [18/20], Batch Num: [576/600] Discriminator Loss: 0.8955, Generator Loss: 1.5412 D(x): 0.7221, D(G(z)): 0.2930 Epoch: [18/20], Batch Num: [577/600] Discriminator Loss: 0.8481, Generator Loss: 1.6462 D(x): 0.6764, D(G(z)): 0.2287 Epoch: [18/20], Batch Num: [578/600] Discriminator Loss: 0.7580, Generator Loss: 1.5386 D(x): 0.7362, D(G(z)): 0.2637 Epoch: [18/20], Batch Num: [579/600] Discriminator Loss: 0.8573, Generator Loss: 1.5159 D(x): 0.7334, D(G(z)): 0.2974 Epoch: [18/20], Batch Num: [580/600] Discriminator Loss: 0.8116, Generator Loss: 1.7516 D(x): 0.7797, D(G(z)): 0.3169 Epoch: [18/20], Batch Num: [581/600] Discriminator Loss: 0.9127, Generator Loss: 1.7763 D(x): 0.7200, D(G(z)): 0.2725 Epoch: [18/20], Batch Num: [582/600] Discriminator Loss: 0.9867, Generator Loss: 1.6591 D(x): 0.7092, D(G(z)): 0.3032 Epoch: [18/20], Batch Num: [583/600] Discriminator Loss: 0.8219, Generator Loss: 1.8605 D(x): 0.7097, D(G(z)): 0.2661 Epoch: [18/20], Batch Num: [584/600] Discriminator Loss: 0.9250, Generator Loss: 1.7837 D(x): 0.6913, D(G(z)): 0.2682 Epoch: [18/20], Batch Num: [585/600] Discriminator Loss: 0.9310, Generator Loss: 1.6461 D(x): 0.6752, D(G(z)): 0.2715 Epoch: [18/20], Batch Num: [586/600] Discriminator Loss: 0.8548, Generator Loss: 1.4815 D(x): 0.7302, D(G(z)): 0.2956 Epoch: [18/20], Batch Num: [587/600] Discriminator Loss: 0.9182, Generator Loss: 1.5171 D(x): 0.7095, D(G(z)): 0.3134 Epoch: [18/20], Batch Num: [588/600] Discriminator Loss: 0.8247, Generator Loss: 1.5083 D(x): 0.7719, D(G(z)): 0.3202 Epoch: [18/20], Batch Num: [589/600] Discriminator Loss: 0.9041, Generator Loss: 1.6967 D(x): 0.7692, D(G(z)): 0.3348 Epoch: [18/20], Batch Num: [590/600] Discriminator Loss: 1.0198, Generator Loss: 2.1648 D(x): 0.6187, D(G(z)): 0.2353 Epoch: [18/20], Batch Num: [591/600] Discriminator Loss: 0.9324, Generator Loss: 1.7613 D(x): 0.6165, D(G(z)): 0.2201 Epoch: [18/20], Batch Num: [592/600] Discriminator Loss: 0.7837, Generator Loss: 1.6957 D(x): 0.7354, D(G(z)): 0.2842 Epoch: [18/20], Batch Num: [593/600] Discriminator Loss: 0.8243, Generator Loss: 1.3757 D(x): 0.7063, D(G(z)): 0.2594 Epoch: [18/20], Batch Num: [594/600] Discriminator Loss: 0.8401, Generator Loss: 1.4106 D(x): 0.7340, D(G(z)): 0.3132 Epoch: [18/20], Batch Num: [595/600] Discriminator Loss: 0.7786, Generator Loss: 1.4500 D(x): 0.8049, D(G(z)): 0.3501 Epoch: [18/20], Batch Num: [596/600] Discriminator Loss: 1.0172, Generator Loss: 1.6212 D(x): 0.7187, D(G(z)): 0.3644 Epoch: [18/20], Batch Num: [597/600] Discriminator Loss: 0.9712, Generator Loss: 1.8407 D(x): 0.6493, D(G(z)): 0.2696 Epoch: [18/20], Batch Num: [598/600] Discriminator Loss: 0.8587, Generator Loss: 1.7014 D(x): 0.6578, D(G(z)): 0.2172 Epoch: [18/20], Batch Num: [599/600] Discriminator Loss: 0.9792, Generator Loss: 1.3030 D(x): 0.6294, D(G(z)): 0.2349 Epoch: 19, Batch Num: [0/600]
Epoch: [19/20], Batch Num: [0/600] Discriminator Loss: 0.9647, Generator Loss: 1.1736 D(x): 0.7117, D(G(z)): 0.3385 Epoch: [19/20], Batch Num: [1/600] Discriminator Loss: 1.1578, Generator Loss: 1.2891 D(x): 0.7645, D(G(z)): 0.4390 Epoch: [19/20], Batch Num: [2/600] Discriminator Loss: 1.0803, Generator Loss: 1.5122 D(x): 0.7727, D(G(z)): 0.4304 Epoch: [19/20], Batch Num: [3/600] Discriminator Loss: 1.0946, Generator Loss: 1.9243 D(x): 0.6210, D(G(z)): 0.3105 Epoch: [19/20], Batch Num: [4/600] Discriminator Loss: 1.0329, Generator Loss: 1.9470 D(x): 0.5850, D(G(z)): 0.2219 Epoch: [19/20], Batch Num: [5/600] Discriminator Loss: 0.9081, Generator Loss: 1.5375 D(x): 0.6138, D(G(z)): 0.2264 Epoch: [19/20], Batch Num: [6/600] Discriminator Loss: 0.9388, Generator Loss: 1.1529 D(x): 0.6489, D(G(z)): 0.2549 Epoch: [19/20], Batch Num: [7/600] Discriminator Loss: 1.0073, Generator Loss: 1.2467 D(x): 0.6936, D(G(z)): 0.3560 Epoch: [19/20], Batch Num: [8/600] Discriminator Loss: 1.0109, Generator Loss: 1.2319 D(x): 0.7449, D(G(z)): 0.3857 Epoch: [19/20], Batch Num: [9/600] Discriminator Loss: 0.8614, Generator Loss: 1.3304 D(x): 0.7535, D(G(z)): 0.3369 Epoch: [19/20], Batch Num: [10/600] Discriminator Loss: 0.8457, Generator Loss: 1.4378 D(x): 0.7605, D(G(z)): 0.3577 Epoch: [19/20], Batch Num: [11/600] Discriminator Loss: 0.9142, Generator Loss: 1.4370 D(x): 0.7243, D(G(z)): 0.3340 Epoch: [19/20], Batch Num: [12/600] Discriminator Loss: 1.0500, Generator Loss: 1.5234 D(x): 0.5975, D(G(z)): 0.2801 Epoch: [19/20], Batch Num: [13/600] Discriminator Loss: 0.9379, Generator Loss: 1.5199 D(x): 0.6466, D(G(z)): 0.2798 Epoch: [19/20], Batch Num: [14/600] Discriminator Loss: 1.0046, Generator Loss: 1.4268 D(x): 0.6248, D(G(z)): 0.2752 Epoch: [19/20], Batch Num: [15/600] Discriminator Loss: 0.8570, Generator Loss: 1.2704 D(x): 0.6935, D(G(z)): 0.2864 Epoch: [19/20], Batch Num: [16/600] Discriminator Loss: 0.9549, Generator Loss: 1.1791 D(x): 0.7176, D(G(z)): 0.3694 Epoch: [19/20], Batch Num: [17/600] Discriminator Loss: 0.9595, Generator Loss: 1.2964 D(x): 0.7050, D(G(z)): 0.3446 Epoch: [19/20], Batch Num: [18/600] Discriminator Loss: 0.8268, Generator Loss: 1.4241 D(x): 0.7744, D(G(z)): 0.3536 Epoch: [19/20], Batch Num: [19/600] Discriminator Loss: 1.0291, Generator Loss: 1.3946 D(x): 0.6761, D(G(z)): 0.3373 Epoch: [19/20], Batch Num: [20/600] Discriminator Loss: 1.0983, Generator Loss: 1.5894 D(x): 0.6007, D(G(z)): 0.2942 Epoch: [19/20], Batch Num: [21/600] Discriminator Loss: 1.0430, Generator Loss: 1.6502 D(x): 0.6166, D(G(z)): 0.2765 Epoch: [19/20], Batch Num: [22/600] Discriminator Loss: 0.8860, Generator Loss: 1.5970 D(x): 0.6993, D(G(z)): 0.2994 Epoch: [19/20], Batch Num: [23/600] Discriminator Loss: 0.8677, Generator Loss: 1.4150 D(x): 0.6902, D(G(z)): 0.2814 Epoch: [19/20], Batch Num: [24/600] Discriminator Loss: 0.8595, Generator Loss: 1.4075 D(x): 0.6848, D(G(z)): 0.2621 Epoch: [19/20], Batch Num: [25/600] Discriminator Loss: 0.9334, Generator Loss: 1.2311 D(x): 0.7248, D(G(z)): 0.3262 Epoch: [19/20], Batch Num: [26/600] Discriminator Loss: 0.9454, Generator Loss: 1.2939 D(x): 0.6945, D(G(z)): 0.3309 Epoch: [19/20], Batch Num: [27/600] Discriminator Loss: 1.0236, Generator Loss: 1.3906 D(x): 0.7110, D(G(z)): 0.3788 Epoch: [19/20], Batch Num: [28/600] Discriminator Loss: 1.0131, Generator Loss: 1.3700 D(x): 0.6736, D(G(z)): 0.3450 Epoch: [19/20], Batch Num: [29/600] Discriminator Loss: 1.0863, Generator Loss: 1.5921 D(x): 0.6574, D(G(z)): 0.3602 Epoch: [19/20], Batch Num: [30/600] Discriminator Loss: 0.9684, Generator Loss: 1.4803 D(x): 0.6710, D(G(z)): 0.3070 Epoch: [19/20], Batch Num: [31/600] Discriminator Loss: 0.9487, Generator Loss: 1.5085 D(x): 0.6690, D(G(z)): 0.2956 Epoch: [19/20], Batch Num: [32/600] Discriminator Loss: 0.9754, Generator Loss: 1.7323 D(x): 0.6502, D(G(z)): 0.2718 Epoch: [19/20], Batch Num: [33/600] Discriminator Loss: 0.9331, Generator Loss: 1.5220 D(x): 0.6307, D(G(z)): 0.2316 Epoch: [19/20], Batch Num: [34/600] Discriminator Loss: 0.9309, Generator Loss: 1.3063 D(x): 0.6836, D(G(z)): 0.2920 Epoch: [19/20], Batch Num: [35/600] Discriminator Loss: 0.7962, Generator Loss: 1.2501 D(x): 0.7082, D(G(z)): 0.2703 Epoch: [19/20], Batch Num: [36/600] Discriminator Loss: 0.9103, Generator Loss: 1.1850 D(x): 0.7327, D(G(z)): 0.3431 Epoch: [19/20], Batch Num: [37/600] Discriminator Loss: 0.8156, Generator Loss: 1.2957 D(x): 0.7411, D(G(z)): 0.3179 Epoch: [19/20], Batch Num: [38/600] Discriminator Loss: 0.9158, Generator Loss: 1.3065 D(x): 0.7545, D(G(z)): 0.3759 Epoch: [19/20], Batch Num: [39/600] Discriminator Loss: 0.8233, Generator Loss: 1.5455 D(x): 0.7411, D(G(z)): 0.3120 Epoch: [19/20], Batch Num: [40/600] Discriminator Loss: 0.9274, Generator Loss: 1.5831 D(x): 0.6991, D(G(z)): 0.3306 Epoch: [19/20], Batch Num: [41/600] Discriminator Loss: 0.8312, Generator Loss: 1.8387 D(x): 0.6752, D(G(z)): 0.2500 Epoch: [19/20], Batch Num: [42/600] Discriminator Loss: 0.9789, Generator Loss: 1.6394 D(x): 0.6137, D(G(z)): 0.2530 Epoch: [19/20], Batch Num: [43/600] Discriminator Loss: 0.8984, Generator Loss: 1.6469 D(x): 0.6549, D(G(z)): 0.2519 Epoch: [19/20], Batch Num: [44/600] Discriminator Loss: 0.8743, Generator Loss: 1.5287 D(x): 0.6775, D(G(z)): 0.2758 Epoch: [19/20], Batch Num: [45/600] Discriminator Loss: 0.8480, Generator Loss: 1.4629 D(x): 0.7491, D(G(z)): 0.3245 Epoch: [19/20], Batch Num: [46/600] Discriminator Loss: 0.9130, Generator Loss: 1.4376 D(x): 0.7115, D(G(z)): 0.3410 Epoch: [19/20], Batch Num: [47/600] Discriminator Loss: 0.8443, Generator Loss: 1.5106 D(x): 0.7561, D(G(z)): 0.3282 Epoch: [19/20], Batch Num: [48/600] Discriminator Loss: 0.9474, Generator Loss: 1.6743 D(x): 0.6817, D(G(z)): 0.3237 Epoch: [19/20], Batch Num: [49/600] Discriminator Loss: 0.8658, Generator Loss: 1.8076 D(x): 0.7281, D(G(z)): 0.3047 Epoch: [19/20], Batch Num: [50/600] Discriminator Loss: 0.8271, Generator Loss: 1.8707 D(x): 0.6952, D(G(z)): 0.2528 Epoch: [19/20], Batch Num: 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D(x): 0.6865, D(G(z)): 0.2481 Epoch: [19/20], Batch Num: [60/600] Discriminator Loss: 1.0127, Generator Loss: 1.5354 D(x): 0.7480, D(G(z)): 0.3828 Epoch: [19/20], Batch Num: [61/600] Discriminator Loss: 1.0100, Generator Loss: 1.5285 D(x): 0.6866, D(G(z)): 0.3000 Epoch: [19/20], Batch Num: [62/600] Discriminator Loss: 1.1514, Generator Loss: 1.6729 D(x): 0.6615, D(G(z)): 0.3259 Epoch: [19/20], Batch Num: [63/600] Discriminator Loss: 0.8959, Generator Loss: 1.4455 D(x): 0.6775, D(G(z)): 0.2794 Epoch: [19/20], Batch Num: [64/600] Discriminator Loss: 0.8488, Generator Loss: 1.5421 D(x): 0.6745, D(G(z)): 0.2488 Epoch: [19/20], Batch Num: [65/600] Discriminator Loss: 0.8361, Generator Loss: 1.5212 D(x): 0.7335, D(G(z)): 0.3148 Epoch: [19/20], Batch Num: [66/600] Discriminator Loss: 0.9673, Generator Loss: 1.4918 D(x): 0.6886, D(G(z)): 0.3345 Epoch: [19/20], Batch Num: [67/600] Discriminator Loss: 1.0413, Generator Loss: 1.4306 D(x): 0.6491, D(G(z)): 0.3002 Epoch: [19/20], Batch Num: 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D(x): 0.6983, D(G(z)): 0.2719 Epoch: [19/20], Batch Num: [77/600] Discriminator Loss: 0.9602, Generator Loss: 1.2999 D(x): 0.7464, D(G(z)): 0.3865 Epoch: [19/20], Batch Num: [78/600] Discriminator Loss: 0.9818, Generator Loss: 1.3129 D(x): 0.7161, D(G(z)): 0.3769 Epoch: [19/20], Batch Num: [79/600] Discriminator Loss: 0.9019, Generator Loss: 1.6120 D(x): 0.7011, D(G(z)): 0.3135 Epoch: [19/20], Batch Num: [80/600] Discriminator Loss: 0.8647, Generator Loss: 1.5825 D(x): 0.6940, D(G(z)): 0.2577 Epoch: [19/20], Batch Num: [81/600] Discriminator Loss: 1.0243, Generator Loss: 1.7100 D(x): 0.6199, D(G(z)): 0.2614 Epoch: [19/20], Batch Num: [82/600] Discriminator Loss: 0.8078, Generator Loss: 1.6879 D(x): 0.6945, D(G(z)): 0.2685 Epoch: [19/20], Batch Num: [83/600] Discriminator Loss: 0.8748, Generator Loss: 1.3910 D(x): 0.7087, D(G(z)): 0.2882 Epoch: [19/20], Batch Num: [84/600] Discriminator Loss: 0.7890, Generator Loss: 1.3537 D(x): 0.7150, D(G(z)): 0.2517 Epoch: [19/20], Batch Num: [85/600] Discriminator Loss: 0.8501, Generator Loss: 1.3735 D(x): 0.7344, D(G(z)): 0.3022 Epoch: [19/20], Batch Num: [86/600] Discriminator Loss: 1.0233, Generator Loss: 1.3154 D(x): 0.7628, D(G(z)): 0.4079 Epoch: [19/20], Batch Num: [87/600] Discriminator Loss: 0.8532, Generator Loss: 1.5894 D(x): 0.6952, D(G(z)): 0.2771 Epoch: [19/20], Batch Num: [88/600] Discriminator Loss: 0.8664, Generator Loss: 1.7698 D(x): 0.7254, D(G(z)): 0.2962 Epoch: [19/20], Batch Num: [89/600] Discriminator Loss: 0.7291, Generator Loss: 1.7858 D(x): 0.7414, D(G(z)): 0.2362 Epoch: [19/20], Batch Num: [90/600] Discriminator Loss: 0.9207, Generator Loss: 1.7898 D(x): 0.7003, D(G(z)): 0.2804 Epoch: [19/20], Batch Num: [91/600] Discriminator Loss: 0.8781, Generator Loss: 1.9221 D(x): 0.6583, D(G(z)): 0.2193 Epoch: [19/20], Batch Num: [92/600] Discriminator Loss: 0.6982, Generator Loss: 1.6918 D(x): 0.7342, D(G(z)): 0.2162 Epoch: [19/20], Batch Num: [93/600] Discriminator Loss: 0.9473, Generator Loss: 1.5118 D(x): 0.7167, D(G(z)): 0.2857 Epoch: [19/20], Batch Num: [94/600] Discriminator Loss: 0.9198, Generator Loss: 1.4684 D(x): 0.7393, D(G(z)): 0.3111 Epoch: [19/20], Batch Num: [95/600] Discriminator Loss: 0.8709, Generator Loss: 1.4724 D(x): 0.7200, D(G(z)): 0.2700 Epoch: [19/20], Batch Num: [96/600] Discriminator Loss: 0.8763, Generator Loss: 1.4846 D(x): 0.7501, D(G(z)): 0.3107 Epoch: [19/20], Batch Num: [97/600] Discriminator Loss: 0.8609, Generator Loss: 1.5561 D(x): 0.7326, D(G(z)): 0.2783 Epoch: [19/20], Batch Num: [98/600] Discriminator Loss: 0.9739, Generator Loss: 1.4598 D(x): 0.6514, D(G(z)): 0.2545 Epoch: [19/20], Batch Num: [99/600] Discriminator Loss: 0.9460, Generator Loss: 1.5308 D(x): 0.6792, D(G(z)): 0.2704 Epoch: 19, Batch Num: [100/600]
Epoch: [19/20], Batch Num: [100/600] Discriminator Loss: 0.9800, Generator Loss: 1.3924 D(x): 0.6869, D(G(z)): 0.2948 Epoch: [19/20], Batch Num: [101/600] Discriminator Loss: 0.8135, Generator Loss: 1.2850 D(x): 0.7564, D(G(z)): 0.3058 Epoch: [19/20], Batch Num: [102/600] Discriminator Loss: 0.8296, Generator Loss: 1.4918 D(x): 0.7818, D(G(z)): 0.3595 Epoch: [19/20], Batch Num: [103/600] Discriminator Loss: 0.8700, Generator Loss: 1.3564 D(x): 0.6999, D(G(z)): 0.2968 Epoch: [19/20], Batch Num: [104/600] Discriminator Loss: 0.8178, Generator Loss: 1.5716 D(x): 0.7494, D(G(z)): 0.3037 Epoch: [19/20], Batch Num: [105/600] Discriminator Loss: 0.8099, Generator Loss: 1.5713 D(x): 0.7063, D(G(z)): 0.2897 Epoch: [19/20], Batch Num: [106/600] Discriminator Loss: 1.0170, Generator Loss: 1.5526 D(x): 0.6623, D(G(z)): 0.2899 Epoch: [19/20], Batch Num: [107/600] Discriminator Loss: 0.9456, Generator Loss: 1.5736 D(x): 0.6943, D(G(z)): 0.2962 Epoch: [19/20], Batch Num: [108/600] Discriminator Loss: 0.9406, Generator Loss: 1.6081 D(x): 0.7247, D(G(z)): 0.3155 Epoch: [19/20], Batch Num: [109/600] Discriminator Loss: 0.9007, Generator Loss: 1.5260 D(x): 0.6815, D(G(z)): 0.2760 Epoch: [19/20], Batch Num: [110/600] Discriminator Loss: 0.9308, Generator Loss: 1.4743 D(x): 0.7073, D(G(z)): 0.3111 Epoch: [19/20], Batch Num: [111/600] Discriminator Loss: 1.0010, Generator Loss: 1.5058 D(x): 0.6619, D(G(z)): 0.2886 Epoch: [19/20], Batch Num: [112/600] Discriminator Loss: 0.9788, Generator Loss: 1.4783 D(x): 0.6776, D(G(z)): 0.2890 Epoch: [19/20], Batch Num: [113/600] Discriminator Loss: 1.0441, Generator Loss: 1.4208 D(x): 0.7285, D(G(z)): 0.3569 Epoch: [19/20], Batch Num: [114/600] Discriminator Loss: 0.8379, Generator Loss: 1.5831 D(x): 0.7441, D(G(z)): 0.3079 Epoch: [19/20], Batch Num: [115/600] Discriminator Loss: 0.8778, Generator Loss: 1.5301 D(x): 0.6657, D(G(z)): 0.2507 Epoch: [19/20], Batch Num: [116/600] Discriminator Loss: 0.8892, Generator Loss: 1.5607 D(x): 0.7041, D(G(z)): 0.2787 Epoch: [19/20], Batch Num: [117/600] Discriminator Loss: 0.8412, Generator Loss: 1.4837 D(x): 0.7012, D(G(z)): 0.2683 Epoch: [19/20], Batch Num: [118/600] Discriminator Loss: 0.8598, Generator Loss: 1.4087 D(x): 0.7051, D(G(z)): 0.2832 Epoch: [19/20], Batch Num: [119/600] Discriminator Loss: 0.9085, Generator Loss: 1.4486 D(x): 0.6818, D(G(z)): 0.2875 Epoch: [19/20], Batch Num: [120/600] Discriminator Loss: 0.9461, Generator Loss: 1.3536 D(x): 0.7053, D(G(z)): 0.3492 Epoch: [19/20], Batch Num: [121/600] Discriminator Loss: 0.8200, Generator Loss: 1.5265 D(x): 0.7347, D(G(z)): 0.3024 Epoch: [19/20], Batch Num: [122/600] Discriminator Loss: 0.7937, Generator Loss: 1.5479 D(x): 0.7307, D(G(z)): 0.2996 Epoch: [19/20], Batch Num: [123/600] Discriminator Loss: 0.9045, Generator Loss: 1.5819 D(x): 0.7378, D(G(z)): 0.3312 Epoch: [19/20], Batch Num: [124/600] Discriminator Loss: 0.9521, Generator Loss: 1.5686 D(x): 0.6907, D(G(z)): 0.3265 Epoch: [19/20], Batch Num: [125/600] Discriminator Loss: 0.8000, Generator Loss: 1.6239 D(x): 0.7371, D(G(z)): 0.2761 Epoch: [19/20], Batch Num: [126/600] Discriminator Loss: 0.9228, Generator Loss: 1.6442 D(x): 0.6678, D(G(z)): 0.2742 Epoch: [19/20], Batch Num: [127/600] Discriminator Loss: 0.8724, Generator Loss: 1.5165 D(x): 0.6961, D(G(z)): 0.2869 Epoch: [19/20], Batch Num: [128/600] Discriminator Loss: 0.9007, Generator Loss: 1.5654 D(x): 0.6762, D(G(z)): 0.2699 Epoch: [19/20], Batch Num: [129/600] Discriminator Loss: 1.0092, Generator Loss: 1.4416 D(x): 0.6554, D(G(z)): 0.2860 Epoch: [19/20], Batch Num: [130/600] Discriminator Loss: 1.0256, Generator Loss: 1.3341 D(x): 0.7085, D(G(z)): 0.3417 Epoch: [19/20], Batch Num: [131/600] Discriminator Loss: 0.8608, Generator Loss: 1.4272 D(x): 0.7241, D(G(z)): 0.2864 Epoch: [19/20], Batch Num: [132/600] Discriminator Loss: 0.7901, Generator Loss: 1.4543 D(x): 0.7287, D(G(z)): 0.2689 Epoch: [19/20], Batch Num: [133/600] Discriminator Loss: 0.9212, Generator Loss: 1.5508 D(x): 0.7324, D(G(z)): 0.3104 Epoch: [19/20], Batch Num: [134/600] Discriminator Loss: 0.8998, Generator Loss: 1.6315 D(x): 0.7028, D(G(z)): 0.2988 Epoch: [19/20], Batch Num: [135/600] Discriminator Loss: 0.8127, Generator Loss: 1.9348 D(x): 0.7610, D(G(z)): 0.3059 Epoch: [19/20], Batch Num: [136/600] Discriminator Loss: 0.8542, Generator Loss: 1.9761 D(x): 0.6989, D(G(z)): 0.2461 Epoch: [19/20], Batch Num: [137/600] Discriminator Loss: 0.7740, Generator Loss: 1.8050 D(x): 0.7057, D(G(z)): 0.2106 Epoch: [19/20], Batch Num: [138/600] Discriminator Loss: 0.8065, Generator Loss: 1.6738 D(x): 0.7254, D(G(z)): 0.2567 Epoch: [19/20], Batch Num: [139/600] Discriminator Loss: 0.8939, Generator Loss: 1.5044 D(x): 0.6848, D(G(z)): 0.2469 Epoch: [19/20], Batch Num: [140/600] Discriminator Loss: 0.8964, Generator Loss: 1.3931 D(x): 0.7050, D(G(z)): 0.2866 Epoch: [19/20], Batch Num: [141/600] Discriminator Loss: 0.8691, Generator Loss: 1.2930 D(x): 0.7493, D(G(z)): 0.3105 Epoch: [19/20], Batch Num: [142/600] Discriminator Loss: 0.9627, Generator Loss: 1.5249 D(x): 0.7611, D(G(z)): 0.3678 Epoch: [19/20], Batch Num: [143/600] Discriminator Loss: 0.7602, Generator Loss: 1.7292 D(x): 0.7749, D(G(z)): 0.2884 Epoch: [19/20], Batch Num: [144/600] Discriminator Loss: 0.8676, Generator Loss: 1.7369 D(x): 0.6659, D(G(z)): 0.2386 Epoch: [19/20], Batch Num: [145/600] Discriminator Loss: 0.8393, Generator Loss: 1.8670 D(x): 0.6838, D(G(z)): 0.2192 Epoch: [19/20], Batch Num: [146/600] Discriminator Loss: 0.9527, Generator Loss: 1.3126 D(x): 0.6541, D(G(z)): 0.2702 Epoch: [19/20], Batch Num: [147/600] Discriminator Loss: 0.9518, Generator Loss: 1.4770 D(x): 0.7122, D(G(z)): 0.3026 Epoch: [19/20], Batch Num: [148/600] Discriminator Loss: 0.9233, Generator Loss: 1.4536 D(x): 0.7100, D(G(z)): 0.3075 Epoch: [19/20], Batch Num: [149/600] Discriminator Loss: 0.9344, Generator Loss: 1.2996 D(x): 0.7233, D(G(z)): 0.3110 Epoch: [19/20], Batch Num: [150/600] Discriminator Loss: 0.7477, Generator Loss: 1.4318 D(x): 0.7878, D(G(z)): 0.2961 Epoch: [19/20], Batch Num: [151/600] Discriminator Loss: 0.8983, Generator Loss: 1.8421 D(x): 0.7073, D(G(z)): 0.3144 Epoch: [19/20], Batch Num: [152/600] Discriminator Loss: 0.8219, Generator Loss: 1.7723 D(x): 0.7116, D(G(z)): 0.2391 Epoch: [19/20], Batch Num: [153/600] Discriminator Loss: 0.6683, Generator Loss: 1.7845 D(x): 0.7564, D(G(z)): 0.2471 Epoch: [19/20], Batch Num: [154/600] Discriminator Loss: 0.8786, Generator Loss: 1.6971 D(x): 0.6819, D(G(z)): 0.2504 Epoch: [19/20], Batch Num: [155/600] Discriminator Loss: 0.8119, Generator Loss: 1.5742 D(x): 0.7118, D(G(z)): 0.2573 Epoch: [19/20], Batch Num: [156/600] Discriminator Loss: 0.8526, Generator Loss: 1.3661 D(x): 0.7344, D(G(z)): 0.3150 Epoch: [19/20], Batch Num: [157/600] Discriminator Loss: 0.8321, Generator Loss: 1.4271 D(x): 0.7558, D(G(z)): 0.3220 Epoch: [19/20], Batch Num: [158/600] Discriminator Loss: 0.7834, Generator Loss: 1.6900 D(x): 0.7912, D(G(z)): 0.3346 Epoch: [19/20], Batch Num: [159/600] Discriminator Loss: 0.9098, Generator Loss: 1.8792 D(x): 0.7224, D(G(z)): 0.3142 Epoch: [19/20], Batch Num: [160/600] Discriminator Loss: 0.9203, Generator Loss: 1.9701 D(x): 0.6830, D(G(z)): 0.2685 Epoch: [19/20], Batch Num: [161/600] Discriminator Loss: 0.8184, Generator Loss: 1.9762 D(x): 0.6987, D(G(z)): 0.2195 Epoch: [19/20], Batch Num: [162/600] Discriminator Loss: 0.9863, Generator Loss: 1.8183 D(x): 0.6417, D(G(z)): 0.2521 Epoch: [19/20], Batch Num: [163/600] Discriminator Loss: 0.8724, Generator Loss: 1.5967 D(x): 0.7115, D(G(z)): 0.2741 Epoch: [19/20], Batch Num: [164/600] Discriminator Loss: 1.0237, Generator Loss: 1.4460 D(x): 0.7130, D(G(z)): 0.3154 Epoch: [19/20], Batch Num: [165/600] Discriminator Loss: 1.0421, Generator Loss: 1.4746 D(x): 0.7278, D(G(z)): 0.3549 Epoch: [19/20], Batch Num: [166/600] Discriminator Loss: 0.8458, Generator Loss: 1.4966 D(x): 0.7705, D(G(z)): 0.3283 Epoch: [19/20], Batch Num: [167/600] Discriminator Loss: 0.9731, Generator Loss: 1.5806 D(x): 0.7267, D(G(z)): 0.3427 Epoch: [19/20], Batch Num: [168/600] Discriminator Loss: 0.8817, Generator Loss: 1.7561 D(x): 0.7096, D(G(z)): 0.2791 Epoch: [19/20], Batch Num: [169/600] Discriminator Loss: 1.2152, Generator Loss: 1.6482 D(x): 0.5561, D(G(z)): 0.2691 Epoch: [19/20], Batch Num: [170/600] Discriminator Loss: 0.9262, Generator Loss: 1.6385 D(x): 0.7099, D(G(z)): 0.2751 Epoch: [19/20], Batch Num: [171/600] Discriminator Loss: 0.8967, Generator Loss: 1.2528 D(x): 0.6513, D(G(z)): 0.2515 Epoch: [19/20], Batch Num: [172/600] Discriminator Loss: 0.9345, Generator Loss: 1.3599 D(x): 0.7576, D(G(z)): 0.3602 Epoch: [19/20], Batch Num: [173/600] Discriminator Loss: 1.0534, Generator Loss: 1.2649 D(x): 0.6876, D(G(z)): 0.3743 Epoch: [19/20], Batch Num: [174/600] Discriminator Loss: 0.9250, Generator Loss: 1.3847 D(x): 0.7568, D(G(z)): 0.3757 Epoch: [19/20], Batch Num: [175/600] Discriminator Loss: 0.9750, Generator Loss: 1.2864 D(x): 0.6667, D(G(z)): 0.3105 Epoch: [19/20], Batch Num: [176/600] Discriminator Loss: 0.9333, Generator Loss: 1.4051 D(x): 0.7172, D(G(z)): 0.3172 Epoch: [19/20], Batch Num: [177/600] Discriminator Loss: 0.9964, Generator Loss: 1.6387 D(x): 0.6453, D(G(z)): 0.3027 Epoch: [19/20], Batch Num: [178/600] Discriminator Loss: 0.9414, Generator Loss: 1.4945 D(x): 0.6843, D(G(z)): 0.3140 Epoch: [19/20], Batch Num: [179/600] Discriminator Loss: 0.9167, Generator Loss: 1.4165 D(x): 0.6484, D(G(z)): 0.2632 Epoch: [19/20], Batch Num: [180/600] Discriminator Loss: 0.8993, Generator Loss: 1.2965 D(x): 0.6906, D(G(z)): 0.2864 Epoch: [19/20], Batch Num: [181/600] Discriminator Loss: 0.9566, Generator Loss: 1.3028 D(x): 0.6895, D(G(z)): 0.3205 Epoch: [19/20], Batch Num: [182/600] Discriminator Loss: 0.9623, Generator Loss: 1.3521 D(x): 0.6810, D(G(z)): 0.3203 Epoch: [19/20], Batch Num: [183/600] Discriminator Loss: 0.8813, Generator Loss: 1.3869 D(x): 0.7080, D(G(z)): 0.3097 Epoch: [19/20], Batch Num: [184/600] Discriminator Loss: 1.0015, Generator Loss: 1.2512 D(x): 0.7092, D(G(z)): 0.3484 Epoch: [19/20], Batch Num: [185/600] Discriminator Loss: 0.8670, Generator Loss: 1.5007 D(x): 0.7661, D(G(z)): 0.3540 Epoch: [19/20], Batch Num: [186/600] Discriminator Loss: 0.8825, Generator Loss: 1.4775 D(x): 0.7181, D(G(z)): 0.3062 Epoch: [19/20], Batch Num: [187/600] Discriminator Loss: 0.9726, Generator Loss: 1.6888 D(x): 0.6367, D(G(z)): 0.2807 Epoch: [19/20], Batch Num: [188/600] Discriminator Loss: 0.9012, Generator Loss: 1.7244 D(x): 0.6740, D(G(z)): 0.2868 Epoch: [19/20], Batch Num: [189/600] Discriminator Loss: 0.8395, Generator Loss: 1.6500 D(x): 0.7127, D(G(z)): 0.2586 Epoch: [19/20], Batch Num: [190/600] Discriminator Loss: 0.8845, Generator Loss: 1.3507 D(x): 0.6930, D(G(z)): 0.2697 Epoch: [19/20], Batch Num: [191/600] Discriminator Loss: 0.9552, Generator Loss: 1.3555 D(x): 0.6835, D(G(z)): 0.3126 Epoch: [19/20], Batch Num: [192/600] Discriminator Loss: 0.8283, Generator Loss: 1.1808 D(x): 0.7288, D(G(z)): 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Epoch: [19/20], Batch Num: [200/600] Discriminator Loss: 0.9058, Generator Loss: 1.4391 D(x): 0.6909, D(G(z)): 0.2592 Epoch: [19/20], Batch Num: [201/600] Discriminator Loss: 0.7562, Generator Loss: 1.3552 D(x): 0.7720, D(G(z)): 0.2836 Epoch: [19/20], Batch Num: [202/600] Discriminator Loss: 0.8986, Generator Loss: 1.4110 D(x): 0.7315, D(G(z)): 0.3231 Epoch: [19/20], Batch Num: [203/600] Discriminator Loss: 0.9569, Generator Loss: 1.5063 D(x): 0.7213, D(G(z)): 0.3262 Epoch: [19/20], Batch Num: [204/600] Discriminator Loss: 0.8611, Generator Loss: 1.6077 D(x): 0.7576, D(G(z)): 0.3254 Epoch: [19/20], Batch Num: [205/600] Discriminator Loss: 1.0166, Generator Loss: 1.7772 D(x): 0.6527, D(G(z)): 0.3008 Epoch: [19/20], Batch Num: [206/600] Discriminator Loss: 1.0794, Generator Loss: 1.5981 D(x): 0.5907, D(G(z)): 0.2518 Epoch: [19/20], Batch Num: [207/600] Discriminator Loss: 0.8582, Generator Loss: 1.4476 D(x): 0.7119, D(G(z)): 0.2854 Epoch: [19/20], Batch Num: [208/600] Discriminator Loss: 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0.2972 Epoch: [19/20], Batch Num: [217/600] Discriminator Loss: 0.8646, Generator Loss: 1.3208 D(x): 0.7388, D(G(z)): 0.3189 Epoch: [19/20], Batch Num: [218/600] Discriminator Loss: 0.9361, Generator Loss: 1.4339 D(x): 0.7710, D(G(z)): 0.3727 Epoch: [19/20], Batch Num: [219/600] Discriminator Loss: 0.8735, Generator Loss: 1.5858 D(x): 0.7602, D(G(z)): 0.3411 Epoch: [19/20], Batch Num: [220/600] Discriminator Loss: 0.8452, Generator Loss: 1.9485 D(x): 0.6914, D(G(z)): 0.2653 Epoch: [19/20], Batch Num: [221/600] Discriminator Loss: 0.9088, Generator Loss: 1.7353 D(x): 0.6417, D(G(z)): 0.2423 Epoch: [19/20], Batch Num: [222/600] Discriminator Loss: 0.9094, Generator Loss: 1.7105 D(x): 0.6455, D(G(z)): 0.2529 Epoch: [19/20], Batch Num: [223/600] Discriminator Loss: 0.8996, Generator Loss: 1.5444 D(x): 0.6727, D(G(z)): 0.2603 Epoch: [19/20], Batch Num: [224/600] Discriminator Loss: 0.9510, Generator Loss: 1.3343 D(x): 0.6815, D(G(z)): 0.2859 Epoch: [19/20], Batch Num: [225/600] Discriminator Loss: 0.7854, Generator Loss: 1.3128 D(x): 0.7446, D(G(z)): 0.2913 Epoch: [19/20], Batch Num: [226/600] Discriminator Loss: 0.8203, Generator Loss: 1.3611 D(x): 0.7931, D(G(z)): 0.3459 Epoch: [19/20], Batch Num: [227/600] Discriminator Loss: 1.0959, Generator Loss: 1.6287 D(x): 0.7597, D(G(z)): 0.4262 Epoch: [19/20], Batch Num: [228/600] Discriminator Loss: 0.8661, Generator Loss: 2.0260 D(x): 0.6965, D(G(z)): 0.2641 Epoch: [19/20], Batch Num: [229/600] Discriminator Loss: 0.9421, Generator Loss: 1.9051 D(x): 0.6235, D(G(z)): 0.2018 Epoch: [19/20], Batch Num: [230/600] Discriminator Loss: 1.0308, Generator Loss: 1.8152 D(x): 0.6135, D(G(z)): 0.2340 Epoch: [19/20], Batch Num: [231/600] Discriminator Loss: 0.8568, Generator Loss: 1.5261 D(x): 0.7224, D(G(z)): 0.2715 Epoch: [19/20], Batch Num: [232/600] Discriminator Loss: 0.9281, Generator Loss: 1.3603 D(x): 0.7248, D(G(z)): 0.2964 Epoch: [19/20], Batch Num: [233/600] Discriminator Loss: 1.0042, Generator Loss: 1.3400 D(x): 0.7332, D(G(z)): 0.3546 Epoch: [19/20], Batch Num: [234/600] Discriminator Loss: 0.9803, Generator Loss: 1.2618 D(x): 0.7222, D(G(z)): 0.3489 Epoch: [19/20], Batch Num: [235/600] Discriminator Loss: 0.7940, Generator Loss: 1.4796 D(x): 0.7533, D(G(z)): 0.2999 Epoch: [19/20], Batch Num: [236/600] Discriminator Loss: 0.9067, Generator Loss: 1.4509 D(x): 0.6962, D(G(z)): 0.3186 Epoch: [19/20], Batch Num: [237/600] Discriminator Loss: 0.8537, Generator Loss: 1.5112 D(x): 0.7433, D(G(z)): 0.3334 Epoch: [19/20], Batch Num: [238/600] Discriminator Loss: 0.9766, Generator Loss: 1.7196 D(x): 0.6579, D(G(z)): 0.2918 Epoch: [19/20], Batch Num: [239/600] Discriminator Loss: 1.0358, Generator Loss: 1.6338 D(x): 0.6664, D(G(z)): 0.3106 Epoch: [19/20], Batch Num: [240/600] Discriminator Loss: 0.9550, Generator Loss: 1.4426 D(x): 0.6357, D(G(z)): 0.2629 Epoch: [19/20], Batch Num: [241/600] Discriminator Loss: 0.8864, Generator Loss: 1.5020 D(x): 0.6933, D(G(z)): 0.3038 Epoch: [19/20], Batch Num: [242/600] Discriminator Loss: 0.8218, Generator Loss: 1.2980 D(x): 0.7036, D(G(z)): 0.2767 Epoch: [19/20], Batch Num: [243/600] Discriminator Loss: 0.9916, Generator Loss: 1.2596 D(x): 0.7089, D(G(z)): 0.3340 Epoch: [19/20], Batch Num: [244/600] Discriminator Loss: 0.7627, Generator Loss: 1.3511 D(x): 0.7688, D(G(z)): 0.3187 Epoch: [19/20], Batch Num: [245/600] Discriminator Loss: 1.0480, Generator Loss: 1.2694 D(x): 0.6930, D(G(z)): 0.3657 Epoch: [19/20], Batch Num: [246/600] Discriminator Loss: 0.8873, Generator Loss: 1.4302 D(x): 0.7264, D(G(z)): 0.3156 Epoch: [19/20], Batch Num: [247/600] Discriminator Loss: 1.0604, Generator Loss: 1.5110 D(x): 0.6775, D(G(z)): 0.3252 Epoch: [19/20], Batch Num: [248/600] Discriminator Loss: 0.8886, Generator Loss: 1.4386 D(x): 0.6909, D(G(z)): 0.2896 Epoch: [19/20], Batch Num: [249/600] Discriminator Loss: 0.9621, Generator Loss: 1.4295 D(x): 0.6510, D(G(z)): 0.2802 Epoch: [19/20], Batch Num: [250/600] Discriminator Loss: 0.8720, Generator Loss: 1.3530 D(x): 0.6866, D(G(z)): 0.2887 Epoch: [19/20], Batch Num: [251/600] Discriminator Loss: 0.8824, Generator Loss: 1.4072 D(x): 0.7104, D(G(z)): 0.3133 Epoch: [19/20], Batch Num: [252/600] Discriminator Loss: 0.9498, Generator Loss: 1.4265 D(x): 0.7167, D(G(z)): 0.3574 Epoch: [19/20], Batch Num: [253/600] Discriminator Loss: 0.9872, Generator Loss: 1.5225 D(x): 0.6752, D(G(z)): 0.3240 Epoch: [19/20], Batch Num: [254/600] Discriminator Loss: 0.9332, Generator Loss: 1.3343 D(x): 0.7019, D(G(z)): 0.3137 Epoch: [19/20], Batch Num: [255/600] Discriminator Loss: 0.8151, Generator Loss: 1.4275 D(x): 0.7557, D(G(z)): 0.3153 Epoch: [19/20], Batch Num: [256/600] Discriminator Loss: 0.8675, Generator Loss: 1.6311 D(x): 0.6921, D(G(z)): 0.2700 Epoch: [19/20], Batch Num: [257/600] Discriminator Loss: 0.8683, Generator Loss: 1.7418 D(x): 0.6974, D(G(z)): 0.2912 Epoch: [19/20], Batch Num: [258/600] Discriminator Loss: 0.9808, Generator Loss: 1.6744 D(x): 0.6455, D(G(z)): 0.2695 Epoch: [19/20], Batch Num: [259/600] Discriminator Loss: 0.8421, Generator Loss: 1.5100 D(x): 0.7202, D(G(z)): 0.2823 Epoch: [19/20], Batch Num: [260/600] Discriminator Loss: 0.9586, Generator Loss: 1.4635 D(x): 0.6645, D(G(z)): 0.2742 Epoch: [19/20], Batch Num: [261/600] Discriminator Loss: 0.9628, Generator Loss: 1.3879 D(x): 0.7126, D(G(z)): 0.3143 Epoch: [19/20], Batch Num: [262/600] Discriminator Loss: 0.7958, Generator Loss: 1.4765 D(x): 0.7684, D(G(z)): 0.3219 Epoch: [19/20], Batch Num: [263/600] Discriminator Loss: 0.9403, Generator Loss: 1.5386 D(x): 0.7027, D(G(z)): 0.3112 Epoch: [19/20], Batch Num: [264/600] Discriminator Loss: 0.8561, Generator Loss: 1.7755 D(x): 0.7026, D(G(z)): 0.2785 Epoch: [19/20], Batch Num: [265/600] Discriminator Loss: 0.9069, Generator Loss: 1.7213 D(x): 0.6922, D(G(z)): 0.2773 Epoch: [19/20], Batch Num: [266/600] Discriminator Loss: 0.8027, Generator Loss: 1.7318 D(x): 0.7016, D(G(z)): 0.2311 Epoch: [19/20], Batch Num: [267/600] Discriminator Loss: 0.9825, Generator Loss: 1.6228 D(x): 0.6809, D(G(z)): 0.2927 Epoch: [19/20], Batch Num: [268/600] Discriminator Loss: 0.9209, Generator Loss: 1.3923 D(x): 0.7214, D(G(z)): 0.3212 Epoch: [19/20], Batch Num: [269/600] Discriminator Loss: 1.0645, Generator Loss: 1.6311 D(x): 0.7684, D(G(z)): 0.4129 Epoch: [19/20], Batch Num: [270/600] Discriminator Loss: 1.1350, Generator Loss: 1.6772 D(x): 0.6992, D(G(z)): 0.3533 Epoch: [19/20], Batch Num: [271/600] Discriminator Loss: 0.9493, Generator Loss: 1.7333 D(x): 0.6627, D(G(z)): 0.2618 Epoch: [19/20], Batch Num: [272/600] Discriminator Loss: 0.9468, Generator Loss: 1.8948 D(x): 0.7014, D(G(z)): 0.2779 Epoch: [19/20], Batch Num: [273/600] Discriminator Loss: 0.9255, Generator Loss: 1.5818 D(x): 0.6495, D(G(z)): 0.2813 Epoch: [19/20], Batch Num: [274/600] Discriminator Loss: 0.8728, Generator Loss: 1.1281 D(x): 0.6575, D(G(z)): 0.2430 Epoch: [19/20], Batch Num: [275/600] Discriminator Loss: 0.9399, Generator Loss: 1.0661 D(x): 0.7129, D(G(z)): 0.3395 Epoch: [19/20], Batch Num: [276/600] Discriminator Loss: 0.9653, Generator Loss: 1.1899 D(x): 0.7675, D(G(z)): 0.4014 Epoch: [19/20], Batch Num: [277/600] Discriminator Loss: 0.9454, Generator Loss: 1.2360 D(x): 0.7879, D(G(z)): 0.4045 Epoch: [19/20], Batch Num: [278/600] Discriminator Loss: 0.8497, Generator Loss: 1.5130 D(x): 0.7711, D(G(z)): 0.3460 Epoch: [19/20], Batch Num: [279/600] Discriminator Loss: 0.8865, Generator Loss: 1.8037 D(x): 0.6555, D(G(z)): 0.2462 Epoch: [19/20], Batch Num: [280/600] Discriminator Loss: 1.1027, Generator Loss: 1.6474 D(x): 0.5572, D(G(z)): 0.2296 Epoch: [19/20], Batch Num: [281/600] Discriminator Loss: 0.9462, Generator Loss: 1.6611 D(x): 0.6685, D(G(z)): 0.2900 Epoch: [19/20], Batch Num: [282/600] Discriminator Loss: 0.8333, Generator Loss: 1.2848 D(x): 0.6899, D(G(z)): 0.2801 Epoch: [19/20], Batch Num: [283/600] Discriminator Loss: 0.8337, Generator Loss: 1.2431 D(x): 0.7398, D(G(z)): 0.2943 Epoch: [19/20], Batch Num: [284/600] Discriminator Loss: 0.9691, Generator Loss: 1.2425 D(x): 0.7526, D(G(z)): 0.3740 Epoch: [19/20], Batch Num: [285/600] Discriminator Loss: 0.9326, Generator Loss: 1.5399 D(x): 0.7831, D(G(z)): 0.3477 Epoch: [19/20], Batch Num: [286/600] Discriminator Loss: 0.8852, Generator Loss: 1.6006 D(x): 0.7189, D(G(z)): 0.3000 Epoch: [19/20], Batch Num: [287/600] Discriminator Loss: 0.8811, Generator Loss: 1.5916 D(x): 0.6674, D(G(z)): 0.2397 Epoch: [19/20], Batch Num: [288/600] Discriminator Loss: 0.7760, Generator Loss: 1.7070 D(x): 0.6987, D(G(z)): 0.2357 Epoch: [19/20], Batch Num: [289/600] Discriminator Loss: 0.8923, Generator Loss: 1.4348 D(x): 0.6813, D(G(z)): 0.2710 Epoch: [19/20], Batch Num: [290/600] Discriminator Loss: 1.1127, Generator Loss: 1.3814 D(x): 0.6306, D(G(z)): 0.3411 Epoch: [19/20], Batch Num: [291/600] Discriminator Loss: 0.9143, Generator Loss: 1.3523 D(x): 0.7003, D(G(z)): 0.3248 Epoch: [19/20], Batch Num: [292/600] Discriminator Loss: 0.8379, Generator Loss: 1.3045 D(x): 0.7471, D(G(z)): 0.3210 Epoch: [19/20], Batch Num: [293/600] Discriminator Loss: 0.9777, Generator Loss: 1.4087 D(x): 0.7360, D(G(z)): 0.3657 Epoch: [19/20], Batch Num: [294/600] Discriminator Loss: 0.9211, Generator Loss: 1.5203 D(x): 0.6766, D(G(z)): 0.2881 Epoch: [19/20], Batch Num: [295/600] Discriminator Loss: 0.9349, Generator Loss: 1.4592 D(x): 0.6523, D(G(z)): 0.2612 Epoch: [19/20], Batch Num: [296/600] Discriminator Loss: 0.9068, Generator Loss: 1.4206 D(x): 0.6848, D(G(z)): 0.2916 Epoch: [19/20], Batch Num: [297/600] Discriminator Loss: 0.8525, Generator Loss: 1.1751 D(x): 0.7147, D(G(z)): 0.2922 Epoch: [19/20], Batch Num: [298/600] Discriminator Loss: 0.9600, Generator Loss: 1.2369 D(x): 0.6949, D(G(z)): 0.3280 Epoch: [19/20], Batch Num: [299/600] Discriminator Loss: 1.0510, Generator Loss: 1.2876 D(x): 0.6995, D(G(z)): 0.3845 Epoch: 19, Batch Num: [300/600]
Epoch: [19/20], Batch Num: [300/600] Discriminator Loss: 0.8833, Generator Loss: 1.2749 D(x): 0.7254, D(G(z)): 0.3354 Epoch: [19/20], Batch Num: [301/600] Discriminator Loss: 1.0194, Generator Loss: 1.4937 D(x): 0.6987, D(G(z)): 0.3665 Epoch: [19/20], Batch Num: [302/600] Discriminator Loss: 0.9390, Generator Loss: 1.5337 D(x): 0.6434, D(G(z)): 0.2609 Epoch: [19/20], Batch Num: [303/600] Discriminator Loss: 0.8608, Generator Loss: 1.5724 D(x): 0.6772, D(G(z)): 0.2524 Epoch: [19/20], Batch Num: [304/600] Discriminator Loss: 0.8539, Generator Loss: 1.4819 D(x): 0.6990, D(G(z)): 0.2613 Epoch: [19/20], Batch Num: [305/600] Discriminator Loss: 0.9004, Generator Loss: 1.4921 D(x): 0.6947, D(G(z)): 0.2939 Epoch: [19/20], Batch Num: [306/600] Discriminator Loss: 0.8893, Generator Loss: 1.4048 D(x): 0.6917, D(G(z)): 0.3054 Epoch: [19/20], Batch Num: [307/600] Discriminator Loss: 0.9978, Generator Loss: 1.2864 D(x): 0.7124, D(G(z)): 0.3285 Epoch: [19/20], Batch Num: [308/600] Discriminator Loss: 0.9456, Generator Loss: 1.3427 D(x): 0.7393, D(G(z)): 0.3607 Epoch: [19/20], Batch Num: [309/600] Discriminator Loss: 0.8684, Generator Loss: 1.4248 D(x): 0.7224, D(G(z)): 0.3278 Epoch: [19/20], Batch Num: [310/600] Discriminator Loss: 0.9908, Generator Loss: 1.6642 D(x): 0.6719, D(G(z)): 0.3017 Epoch: [19/20], Batch Num: [311/600] Discriminator Loss: 1.0134, Generator Loss: 1.6247 D(x): 0.6440, D(G(z)): 0.2773 Epoch: [19/20], Batch Num: [312/600] Discriminator Loss: 1.0139, Generator Loss: 1.6435 D(x): 0.6221, D(G(z)): 0.2773 Epoch: [19/20], Batch Num: [313/600] Discriminator Loss: 0.9194, Generator Loss: 1.4973 D(x): 0.6740, D(G(z)): 0.2852 Epoch: [19/20], Batch Num: [314/600] Discriminator Loss: 0.9892, Generator Loss: 1.3741 D(x): 0.6793, D(G(z)): 0.3165 Epoch: [19/20], Batch Num: [315/600] Discriminator Loss: 1.1073, Generator Loss: 1.4017 D(x): 0.6024, D(G(z)): 0.3114 Epoch: [19/20], Batch Num: [316/600] Discriminator Loss: 0.8855, Generator Loss: 1.3107 D(x): 0.7091, D(G(z)): 0.2953 Epoch: [19/20], Batch Num: [317/600] Discriminator Loss: 0.8900, Generator Loss: 1.1912 D(x): 0.7228, D(G(z)): 0.3262 Epoch: [19/20], Batch Num: [318/600] Discriminator Loss: 0.8631, Generator Loss: 1.3377 D(x): 0.7205, D(G(z)): 0.3390 Epoch: [19/20], Batch Num: [319/600] Discriminator Loss: 0.9616, Generator Loss: 1.2934 D(x): 0.7086, D(G(z)): 0.3238 Epoch: [19/20], Batch Num: [320/600] Discriminator Loss: 0.9466, Generator Loss: 1.3359 D(x): 0.6801, D(G(z)): 0.3272 Epoch: [19/20], Batch Num: [321/600] Discriminator Loss: 0.8255, Generator Loss: 1.3108 D(x): 0.7117, D(G(z)): 0.2898 Epoch: [19/20], Batch Num: [322/600] Discriminator Loss: 0.9162, Generator Loss: 1.4812 D(x): 0.6623, D(G(z)): 0.2893 Epoch: [19/20], Batch Num: [323/600] Discriminator Loss: 0.9008, Generator Loss: 1.4525 D(x): 0.7280, D(G(z)): 0.3166 Epoch: [19/20], Batch Num: [324/600] Discriminator Loss: 0.9023, Generator Loss: 1.4071 D(x): 0.7244, D(G(z)): 0.3519 Epoch: [19/20], Batch Num: [325/600] Discriminator Loss: 0.9275, Generator Loss: 1.5161 D(x): 0.6400, D(G(z)): 0.2805 Epoch: [19/20], Batch Num: [326/600] Discriminator Loss: 0.8361, Generator Loss: 1.2989 D(x): 0.7224, D(G(z)): 0.2901 Epoch: [19/20], Batch Num: [327/600] Discriminator Loss: 0.9740, Generator Loss: 1.3328 D(x): 0.6863, D(G(z)): 0.3203 Epoch: [19/20], Batch Num: [328/600] Discriminator Loss: 0.9079, Generator Loss: 1.3956 D(x): 0.6971, D(G(z)): 0.3071 Epoch: [19/20], Batch Num: [329/600] Discriminator Loss: 0.9038, Generator Loss: 1.4425 D(x): 0.7112, D(G(z)): 0.3438 Epoch: [19/20], Batch Num: [330/600] Discriminator Loss: 0.9828, Generator Loss: 1.4709 D(x): 0.6536, D(G(z)): 0.2988 Epoch: [19/20], Batch Num: [331/600] Discriminator Loss: 0.7834, Generator Loss: 1.3883 D(x): 0.7290, D(G(z)): 0.2711 Epoch: [19/20], Batch Num: [332/600] Discriminator Loss: 0.7475, Generator Loss: 1.5433 D(x): 0.7572, D(G(z)): 0.2869 Epoch: [19/20], Batch Num: [333/600] Discriminator Loss: 0.8541, Generator Loss: 1.5727 D(x): 0.6800, D(G(z)): 0.2696 Epoch: [19/20], Batch Num: [334/600] Discriminator Loss: 0.8476, Generator Loss: 1.2439 D(x): 0.6877, D(G(z)): 0.2705 Epoch: [19/20], Batch Num: [335/600] Discriminator Loss: 0.9286, Generator Loss: 1.3491 D(x): 0.7111, D(G(z)): 0.3271 Epoch: [19/20], Batch Num: [336/600] Discriminator Loss: 0.8494, Generator Loss: 1.4931 D(x): 0.7443, D(G(z)): 0.3352 Epoch: [19/20], Batch Num: [337/600] Discriminator Loss: 0.9225, Generator Loss: 1.5341 D(x): 0.7653, D(G(z)): 0.3620 Epoch: [19/20], Batch Num: [338/600] Discriminator Loss: 0.8997, Generator Loss: 1.6939 D(x): 0.7028, D(G(z)): 0.2910 Epoch: [19/20], Batch Num: [339/600] Discriminator Loss: 0.9460, Generator Loss: 1.5425 D(x): 0.6606, D(G(z)): 0.2551 Epoch: [19/20], Batch Num: [340/600] Discriminator Loss: 0.8150, Generator Loss: 1.8371 D(x): 0.7138, D(G(z)): 0.2469 Epoch: [19/20], Batch Num: [341/600] Discriminator Loss: 0.9357, Generator Loss: 1.5773 D(x): 0.6963, D(G(z)): 0.2719 Epoch: [19/20], Batch Num: 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1.5367 D(x): 0.7123, D(G(z)): 0.2850 Epoch: [19/20], Batch Num: [351/600] Discriminator Loss: 0.9545, Generator Loss: 1.4802 D(x): 0.6892, D(G(z)): 0.3117 Epoch: [19/20], Batch Num: [352/600] Discriminator Loss: 0.8243, Generator Loss: 1.4572 D(x): 0.7511, D(G(z)): 0.3112 Epoch: [19/20], Batch Num: [353/600] Discriminator Loss: 0.9521, Generator Loss: 1.5797 D(x): 0.6936, D(G(z)): 0.2980 Epoch: [19/20], Batch Num: [354/600] Discriminator Loss: 1.1070, Generator Loss: 1.4859 D(x): 0.6553, D(G(z)): 0.3484 Epoch: [19/20], Batch Num: [355/600] Discriminator Loss: 1.0311, Generator Loss: 1.4480 D(x): 0.6526, D(G(z)): 0.3010 Epoch: [19/20], Batch Num: [356/600] Discriminator Loss: 0.8835, Generator Loss: 1.4551 D(x): 0.7141, D(G(z)): 0.3137 Epoch: [19/20], Batch Num: [357/600] Discriminator Loss: 1.0116, Generator Loss: 1.4314 D(x): 0.7002, D(G(z)): 0.3361 Epoch: [19/20], Batch Num: [358/600] Discriminator Loss: 0.9048, Generator Loss: 1.5120 D(x): 0.6999, D(G(z)): 0.2903 Epoch: [19/20], Batch Num: [359/600] Discriminator Loss: 1.0061, Generator Loss: 1.5113 D(x): 0.6487, D(G(z)): 0.2987 Epoch: [19/20], Batch Num: [360/600] Discriminator Loss: 0.8866, Generator Loss: 1.5852 D(x): 0.6901, D(G(z)): 0.2806 Epoch: [19/20], Batch Num: [361/600] Discriminator Loss: 0.9714, Generator Loss: 1.3541 D(x): 0.6905, D(G(z)): 0.3124 Epoch: [19/20], Batch Num: [362/600] Discriminator Loss: 1.0146, Generator Loss: 1.4068 D(x): 0.6748, D(G(z)): 0.3097 Epoch: [19/20], Batch Num: [363/600] Discriminator Loss: 0.9817, Generator Loss: 1.4195 D(x): 0.7031, D(G(z)): 0.3542 Epoch: [19/20], Batch Num: [364/600] Discriminator Loss: 0.9122, Generator Loss: 1.3868 D(x): 0.6654, D(G(z)): 0.2832 Epoch: [19/20], Batch Num: [365/600] Discriminator Loss: 0.8474, Generator Loss: 1.3846 D(x): 0.7175, D(G(z)): 0.3086 Epoch: [19/20], Batch Num: [366/600] Discriminator Loss: 0.7968, Generator Loss: 1.3878 D(x): 0.7220, D(G(z)): 0.2718 Epoch: [19/20], Batch Num: [367/600] Discriminator Loss: 0.9146, Generator Loss: 1.3708 D(x): 0.6946, D(G(z)): 0.3100 Epoch: [19/20], Batch Num: [368/600] Discriminator Loss: 1.0151, Generator Loss: 1.4364 D(x): 0.6875, D(G(z)): 0.3323 Epoch: [19/20], Batch Num: [369/600] Discriminator Loss: 0.8794, Generator Loss: 1.3058 D(x): 0.7451, D(G(z)): 0.3113 Epoch: [19/20], Batch Num: [370/600] Discriminator Loss: 0.9148, Generator Loss: 1.4881 D(x): 0.6901, D(G(z)): 0.3015 Epoch: [19/20], Batch Num: [371/600] Discriminator Loss: 0.8250, Generator Loss: 1.4066 D(x): 0.7319, D(G(z)): 0.2938 Epoch: [19/20], Batch Num: [372/600] Discriminator Loss: 0.9939, Generator Loss: 1.2309 D(x): 0.6338, D(G(z)): 0.2703 Epoch: [19/20], Batch Num: [373/600] Discriminator Loss: 1.0114, Generator Loss: 1.2351 D(x): 0.7398, D(G(z)): 0.3925 Epoch: [19/20], Batch Num: [374/600] Discriminator Loss: 1.1513, Generator Loss: 1.5246 D(x): 0.7117, D(G(z)): 0.3967 Epoch: [19/20], Batch Num: [375/600] Discriminator Loss: 0.9238, Generator Loss: 1.4688 D(x): 0.6889, D(G(z)): 0.3031 Epoch: [19/20], Batch Num: [376/600] Discriminator Loss: 0.9939, Generator Loss: 1.6345 D(x): 0.6323, D(G(z)): 0.2577 Epoch: [19/20], Batch Num: [377/600] Discriminator Loss: 1.0764, Generator Loss: 1.3668 D(x): 0.6182, D(G(z)): 0.2934 Epoch: [19/20], Batch Num: [378/600] Discriminator Loss: 1.0092, Generator Loss: 1.4371 D(x): 0.6628, D(G(z)): 0.3202 Epoch: [19/20], Batch Num: [379/600] Discriminator Loss: 0.9794, Generator Loss: 1.3384 D(x): 0.7092, D(G(z)): 0.3384 Epoch: [19/20], Batch Num: [380/600] Discriminator Loss: 1.0451, Generator Loss: 1.3399 D(x): 0.6743, D(G(z)): 0.3503 Epoch: [19/20], Batch Num: [381/600] Discriminator Loss: 0.9681, Generator Loss: 1.4320 D(x): 0.7149, D(G(z)): 0.3561 Epoch: [19/20], Batch Num: [382/600] Discriminator Loss: 0.8363, Generator Loss: 1.3730 D(x): 0.7275, D(G(z)): 0.3037 Epoch: [19/20], Batch Num: [383/600] Discriminator Loss: 0.8880, Generator Loss: 1.5108 D(x): 0.6902, D(G(z)): 0.2941 Epoch: [19/20], Batch Num: [384/600] Discriminator Loss: 0.9824, Generator Loss: 1.6140 D(x): 0.6445, D(G(z)): 0.2817 Epoch: [19/20], Batch Num: [385/600] Discriminator Loss: 0.9437, Generator Loss: 1.5307 D(x): 0.6963, D(G(z)): 0.3132 Epoch: [19/20], Batch Num: [386/600] Discriminator Loss: 0.9256, Generator Loss: 1.5000 D(x): 0.6367, D(G(z)): 0.2642 Epoch: [19/20], Batch Num: [387/600] Discriminator Loss: 0.9838, Generator Loss: 1.3930 D(x): 0.6693, D(G(z)): 0.2978 Epoch: [19/20], Batch Num: [388/600] Discriminator Loss: 1.0418, Generator Loss: 1.4735 D(x): 0.6857, D(G(z)): 0.3410 Epoch: [19/20], Batch Num: [389/600] Discriminator Loss: 0.9370, Generator Loss: 1.4860 D(x): 0.7071, D(G(z)): 0.3306 Epoch: [19/20], Batch Num: [390/600] Discriminator Loss: 0.9957, Generator Loss: 1.5358 D(x): 0.7044, D(G(z)): 0.3512 Epoch: [19/20], Batch Num: [391/600] Discriminator Loss: 0.9920, Generator Loss: 1.5996 D(x): 0.6595, D(G(z)): 0.2749 Epoch: [19/20], Batch Num: [392/600] Discriminator Loss: 0.8537, Generator Loss: 1.5082 D(x): 0.6850, D(G(z)): 0.2751 Epoch: [19/20], Batch Num: [393/600] Discriminator Loss: 0.8794, Generator Loss: 1.4833 D(x): 0.7044, D(G(z)): 0.2941 Epoch: [19/20], Batch Num: [394/600] Discriminator Loss: 0.8343, Generator Loss: 1.4576 D(x): 0.6936, D(G(z)): 0.2658 Epoch: [19/20], Batch Num: [395/600] Discriminator Loss: 1.0266, Generator Loss: 1.4131 D(x): 0.6513, D(G(z)): 0.3154 Epoch: [19/20], Batch Num: [396/600] Discriminator Loss: 0.9034, Generator Loss: 1.4118 D(x): 0.7007, D(G(z)): 0.3112 Epoch: [19/20], Batch Num: [397/600] Discriminator Loss: 0.9648, Generator Loss: 1.4194 D(x): 0.7059, D(G(z)): 0.3431 Epoch: [19/20], Batch Num: [398/600] Discriminator Loss: 0.9458, Generator Loss: 1.3774 D(x): 0.6974, D(G(z)): 0.3306 Epoch: [19/20], Batch Num: [399/600] Discriminator Loss: 0.9680, Generator Loss: 1.4491 D(x): 0.6811, D(G(z)): 0.3273 Epoch: 19, Batch Num: [400/600]
Epoch: [19/20], Batch Num: [400/600] Discriminator Loss: 0.9445, Generator Loss: 1.4475 D(x): 0.6982, D(G(z)): 0.3188 Epoch: [19/20], Batch Num: [401/600] Discriminator Loss: 0.9236, Generator Loss: 1.5144 D(x): 0.6871, D(G(z)): 0.2946 Epoch: [19/20], Batch Num: [402/600] Discriminator Loss: 1.0021, Generator Loss: 1.6166 D(x): 0.6736, D(G(z)): 0.3140 Epoch: [19/20], Batch Num: [403/600] Discriminator Loss: 0.8174, Generator Loss: 1.5969 D(x): 0.6949, D(G(z)): 0.2440 Epoch: [19/20], Batch Num: [404/600] Discriminator Loss: 0.9388, Generator Loss: 1.6817 D(x): 0.6594, D(G(z)): 0.2692 Epoch: [19/20], Batch Num: [405/600] Discriminator Loss: 0.8976, Generator Loss: 1.5372 D(x): 0.6853, D(G(z)): 0.2875 Epoch: [19/20], Batch Num: [406/600] Discriminator Loss: 0.9453, Generator Loss: 1.5401 D(x): 0.7359, D(G(z)): 0.3454 Epoch: [19/20], Batch Num: [407/600] Discriminator Loss: 0.9886, Generator Loss: 1.6095 D(x): 0.6902, D(G(z)): 0.3296 Epoch: [19/20], Batch Num: [408/600] Discriminator Loss: 0.9097, Generator Loss: 1.9551 D(x): 0.7224, D(G(z)): 0.3031 Epoch: [19/20], Batch Num: [409/600] Discriminator Loss: 0.9731, Generator Loss: 1.7063 D(x): 0.6431, D(G(z)): 0.2563 Epoch: [19/20], Batch Num: [410/600] Discriminator Loss: 0.7903, Generator Loss: 1.6554 D(x): 0.6794, D(G(z)): 0.2104 Epoch: [19/20], Batch Num: [411/600] Discriminator Loss: 0.9132, Generator Loss: 1.4775 D(x): 0.6562, D(G(z)): 0.2657 Epoch: [19/20], Batch Num: [412/600] Discriminator Loss: 0.8057, Generator Loss: 1.4907 D(x): 0.7602, D(G(z)): 0.3167 Epoch: [19/20], Batch Num: [413/600] Discriminator Loss: 0.8974, Generator Loss: 1.3366 D(x): 0.7472, D(G(z)): 0.3324 Epoch: [19/20], Batch Num: [414/600] Discriminator Loss: 0.8147, Generator Loss: 1.5528 D(x): 0.7758, D(G(z)): 0.3262 Epoch: [19/20], Batch Num: [415/600] Discriminator Loss: 0.8375, Generator Loss: 1.7157 D(x): 0.7260, D(G(z)): 0.2951 Epoch: [19/20], Batch Num: [416/600] Discriminator Loss: 0.8292, Generator Loss: 1.6264 D(x): 0.7230, D(G(z)): 0.2880 Epoch: [19/20], Batch Num: [417/600] Discriminator Loss: 1.0730, Generator Loss: 1.5214 D(x): 0.5954, D(G(z)): 0.2465 Epoch: [19/20], Batch Num: [418/600] Discriminator Loss: 0.9441, Generator Loss: 1.5870 D(x): 0.6658, D(G(z)): 0.2832 Epoch: [19/20], Batch Num: [419/600] Discriminator Loss: 0.8721, Generator Loss: 1.4586 D(x): 0.6964, D(G(z)): 0.2810 Epoch: [19/20], Batch Num: [420/600] Discriminator Loss: 0.9022, Generator Loss: 1.4221 D(x): 0.7062, D(G(z)): 0.3209 Epoch: [19/20], Batch Num: [421/600] Discriminator Loss: 0.8749, Generator Loss: 1.5262 D(x): 0.7644, D(G(z)): 0.3454 Epoch: [19/20], Batch Num: [422/600] Discriminator Loss: 0.8638, Generator Loss: 1.5599 D(x): 0.7633, D(G(z)): 0.3563 Epoch: [19/20], Batch Num: [423/600] Discriminator Loss: 0.8486, Generator Loss: 1.6739 D(x): 0.7025, D(G(z)): 0.2897 Epoch: [19/20], Batch Num: [424/600] Discriminator Loss: 0.8924, Generator Loss: 1.6268 D(x): 0.6706, D(G(z)): 0.2721 Epoch: [19/20], Batch Num: [425/600] Discriminator Loss: 0.8383, Generator Loss: 1.5241 D(x): 0.6954, D(G(z)): 0.2632 Epoch: [19/20], Batch Num: [426/600] Discriminator Loss: 0.8212, Generator Loss: 1.4800 D(x): 0.6912, D(G(z)): 0.2657 Epoch: [19/20], Batch Num: [427/600] Discriminator Loss: 0.8193, Generator Loss: 1.4413 D(x): 0.7471, D(G(z)): 0.3255 Epoch: [19/20], Batch Num: [428/600] Discriminator Loss: 0.9020, Generator Loss: 1.3751 D(x): 0.7331, D(G(z)): 0.3311 Epoch: [19/20], Batch Num: [429/600] Discriminator Loss: 0.8914, Generator Loss: 1.4327 D(x): 0.7087, D(G(z)): 0.2908 Epoch: [19/20], Batch Num: [430/600] Discriminator Loss: 1.1458, Generator Loss: 1.5701 D(x): 0.6606, D(G(z)): 0.3401 Epoch: [19/20], Batch Num: [431/600] Discriminator Loss: 0.7721, Generator Loss: 1.4885 D(x): 0.7288, D(G(z)): 0.2548 Epoch: [19/20], Batch Num: [432/600] Discriminator Loss: 0.9487, Generator Loss: 1.4926 D(x): 0.6950, D(G(z)): 0.3178 Epoch: [19/20], Batch Num: [433/600] Discriminator Loss: 0.9165, Generator Loss: 1.4947 D(x): 0.6593, D(G(z)): 0.2864 Epoch: [19/20], Batch Num: [434/600] Discriminator Loss: 0.8149, Generator Loss: 1.3498 D(x): 0.7611, D(G(z)): 0.3131 Epoch: [19/20], Batch Num: [435/600] Discriminator Loss: 1.0545, Generator Loss: 1.4175 D(x): 0.6929, D(G(z)): 0.3323 Epoch: [19/20], Batch Num: [436/600] Discriminator Loss: 0.8732, Generator Loss: 1.4974 D(x): 0.7345, D(G(z)): 0.3146 Epoch: [19/20], Batch Num: [437/600] Discriminator Loss: 0.7867, Generator Loss: 1.4971 D(x): 0.7294, D(G(z)): 0.2774 Epoch: [19/20], Batch Num: [438/600] Discriminator Loss: 1.0285, Generator Loss: 1.5239 D(x): 0.6455, D(G(z)): 0.2789 Epoch: [19/20], Batch Num: [439/600] Discriminator Loss: 0.9346, Generator Loss: 1.5103 D(x): 0.7311, D(G(z)): 0.3134 Epoch: [19/20], Batch Num: [440/600] Discriminator Loss: 0.9390, Generator Loss: 1.5444 D(x): 0.6491, D(G(z)): 0.2585 Epoch: [19/20], Batch Num: [441/600] Discriminator Loss: 0.9199, Generator Loss: 1.3715 D(x): 0.6879, D(G(z)): 0.2770 Epoch: [19/20], Batch Num: 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1.6672 D(x): 0.6939, D(G(z)): 0.2581 Epoch: [19/20], Batch Num: [451/600] Discriminator Loss: 0.9027, Generator Loss: 1.5752 D(x): 0.7194, D(G(z)): 0.3078 Epoch: [19/20], Batch Num: [452/600] Discriminator Loss: 0.8191, Generator Loss: 1.6614 D(x): 0.7689, D(G(z)): 0.2958 Epoch: [19/20], Batch Num: [453/600] Discriminator Loss: 0.8321, Generator Loss: 1.6689 D(x): 0.7285, D(G(z)): 0.2879 Epoch: [19/20], Batch Num: [454/600] Discriminator Loss: 0.9558, Generator Loss: 1.7131 D(x): 0.6876, D(G(z)): 0.2875 Epoch: [19/20], Batch Num: [455/600] Discriminator Loss: 1.2208, Generator Loss: 1.7662 D(x): 0.6265, D(G(z)): 0.3076 Epoch: [19/20], Batch Num: [456/600] Discriminator Loss: 0.8809, Generator Loss: 1.6456 D(x): 0.7195, D(G(z)): 0.2848 Epoch: [19/20], Batch Num: [457/600] Discriminator Loss: 0.8215, Generator Loss: 1.5484 D(x): 0.6876, D(G(z)): 0.2423 Epoch: [19/20], Batch Num: [458/600] Discriminator Loss: 0.8750, Generator Loss: 1.4695 D(x): 0.7002, D(G(z)): 0.2899 Epoch: [19/20], Batch Num: [459/600] Discriminator Loss: 0.9447, Generator Loss: 1.3995 D(x): 0.7384, D(G(z)): 0.3468 Epoch: [19/20], Batch Num: [460/600] Discriminator Loss: 0.9696, Generator Loss: 1.4326 D(x): 0.6650, D(G(z)): 0.2863 Epoch: [19/20], Batch Num: [461/600] Discriminator Loss: 0.9950, Generator Loss: 1.4966 D(x): 0.6788, D(G(z)): 0.3164 Epoch: [19/20], Batch Num: [462/600] Discriminator Loss: 1.0770, Generator Loss: 1.4837 D(x): 0.6942, D(G(z)): 0.3563 Epoch: [19/20], Batch Num: [463/600] Discriminator Loss: 0.9212, Generator Loss: 1.4422 D(x): 0.6919, D(G(z)): 0.3068 Epoch: [19/20], Batch Num: [464/600] Discriminator Loss: 1.0003, Generator Loss: 1.3862 D(x): 0.6581, D(G(z)): 0.2906 Epoch: [19/20], Batch Num: [465/600] Discriminator Loss: 0.9384, Generator Loss: 1.3826 D(x): 0.7071, D(G(z)): 0.3103 Epoch: [19/20], Batch Num: [466/600] Discriminator Loss: 1.0085, Generator Loss: 1.2515 D(x): 0.6638, D(G(z)): 0.3199 Epoch: [19/20], Batch Num: [467/600] Discriminator Loss: 0.9745, Generator Loss: 1.2703 D(x): 0.6620, D(G(z)): 0.3263 Epoch: [19/20], Batch Num: [468/600] Discriminator Loss: 1.0436, Generator Loss: 1.3097 D(x): 0.6772, D(G(z)): 0.3611 Epoch: [19/20], Batch Num: [469/600] Discriminator Loss: 1.0158, Generator Loss: 1.2378 D(x): 0.6515, D(G(z)): 0.3340 Epoch: [19/20], Batch Num: [470/600] Discriminator Loss: 0.8403, Generator Loss: 1.3967 D(x): 0.7535, D(G(z)): 0.3458 Epoch: [19/20], Batch Num: [471/600] Discriminator Loss: 0.8689, Generator Loss: 1.4054 D(x): 0.7385, D(G(z)): 0.3235 Epoch: [19/20], Batch Num: [472/600] Discriminator Loss: 1.0265, Generator Loss: 1.3897 D(x): 0.6118, D(G(z)): 0.2802 Epoch: [19/20], Batch Num: [473/600] Discriminator Loss: 0.9127, Generator Loss: 1.5483 D(x): 0.6884, D(G(z)): 0.3071 Epoch: [19/20], Batch Num: [474/600] Discriminator Loss: 0.9611, Generator Loss: 1.4552 D(x): 0.6470, D(G(z)): 0.2950 Epoch: [19/20], Batch Num: [475/600] Discriminator Loss: 0.9891, Generator Loss: 1.3450 D(x): 0.6986, D(G(z)): 0.3386 Epoch: [19/20], Batch Num: [476/600] Discriminator Loss: 1.0920, Generator Loss: 1.4305 D(x): 0.6611, D(G(z)): 0.3692 Epoch: [19/20], Batch Num: [477/600] Discriminator Loss: 1.0474, Generator Loss: 1.4586 D(x): 0.6875, D(G(z)): 0.3502 Epoch: [19/20], Batch Num: [478/600] Discriminator Loss: 0.9716, Generator Loss: 1.3450 D(x): 0.6750, D(G(z)): 0.3080 Epoch: [19/20], Batch Num: [479/600] Discriminator Loss: 0.9635, Generator Loss: 1.4373 D(x): 0.6474, D(G(z)): 0.2970 Epoch: [19/20], Batch Num: [480/600] Discriminator Loss: 0.9610, Generator Loss: 1.5230 D(x): 0.7093, D(G(z)): 0.3485 Epoch: [19/20], Batch Num: [481/600] Discriminator Loss: 0.9359, Generator Loss: 1.5456 D(x): 0.7175, D(G(z)): 0.3295 Epoch: [19/20], Batch Num: [482/600] Discriminator Loss: 0.9921, Generator Loss: 1.6597 D(x): 0.6793, D(G(z)): 0.3286 Epoch: [19/20], Batch Num: [483/600] Discriminator Loss: 1.0011, Generator Loss: 1.4284 D(x): 0.6654, D(G(z)): 0.2926 Epoch: [19/20], Batch Num: [484/600] Discriminator Loss: 0.8915, Generator Loss: 1.3961 D(x): 0.7038, D(G(z)): 0.3018 Epoch: [19/20], Batch Num: [485/600] Discriminator Loss: 0.9530, Generator Loss: 1.5229 D(x): 0.6781, D(G(z)): 0.2953 Epoch: [19/20], Batch Num: [486/600] Discriminator Loss: 0.8909, Generator Loss: 1.4403 D(x): 0.6950, D(G(z)): 0.2985 Epoch: [19/20], Batch Num: [487/600] Discriminator Loss: 0.9282, Generator Loss: 1.1718 D(x): 0.6807, D(G(z)): 0.2885 Epoch: [19/20], Batch Num: [488/600] Discriminator Loss: 0.8702, Generator Loss: 1.3827 D(x): 0.7213, D(G(z)): 0.3087 Epoch: [19/20], Batch Num: [489/600] Discriminator Loss: 0.9179, Generator Loss: 1.3139 D(x): 0.7044, D(G(z)): 0.3467 Epoch: [19/20], Batch Num: [490/600] Discriminator Loss: 0.9102, Generator Loss: 1.4655 D(x): 0.7499, D(G(z)): 0.3482 Epoch: [19/20], Batch Num: [491/600] Discriminator Loss: 1.0410, Generator Loss: 1.5969 D(x): 0.6228, D(G(z)): 0.2930 Epoch: [19/20], Batch Num: [492/600] Discriminator Loss: 0.9534, Generator Loss: 1.7191 D(x): 0.6868, D(G(z)): 0.3210 Epoch: [19/20], Batch Num: [493/600] Discriminator Loss: 0.9653, Generator Loss: 1.6214 D(x): 0.6731, D(G(z)): 0.2919 Epoch: [19/20], Batch Num: [494/600] Discriminator Loss: 0.9662, Generator Loss: 1.6669 D(x): 0.6355, D(G(z)): 0.2580 Epoch: [19/20], Batch Num: [495/600] Discriminator Loss: 0.8889, Generator Loss: 1.3986 D(x): 0.6781, D(G(z)): 0.2768 Epoch: [19/20], Batch Num: [496/600] Discriminator Loss: 1.0252, Generator Loss: 1.2843 D(x): 0.6959, D(G(z)): 0.3366 Epoch: [19/20], Batch Num: [497/600] Discriminator Loss: 0.9360, Generator Loss: 1.3549 D(x): 0.7359, D(G(z)): 0.3601 Epoch: [19/20], Batch Num: [498/600] Discriminator Loss: 0.9228, Generator Loss: 1.2662 D(x): 0.7063, D(G(z)): 0.3364 Epoch: [19/20], Batch Num: [499/600] Discriminator Loss: 1.0092, Generator Loss: 1.5632 D(x): 0.7098, D(G(z)): 0.3614 Epoch: 19, Batch Num: [500/600]
Epoch: [19/20], Batch Num: [500/600] Discriminator Loss: 0.9347, Generator Loss: 1.5522 D(x): 0.6698, D(G(z)): 0.2857 Epoch: [19/20], Batch Num: [501/600] Discriminator Loss: 0.9140, Generator Loss: 1.3782 D(x): 0.6783, D(G(z)): 0.2860 Epoch: [19/20], Batch Num: [502/600] Discriminator Loss: 0.9800, Generator Loss: 1.3941 D(x): 0.6688, D(G(z)): 0.3045 Epoch: [19/20], Batch Num: [503/600] Discriminator Loss: 1.1533, Generator Loss: 1.2678 D(x): 0.6366, D(G(z)): 0.3525 Epoch: [19/20], Batch Num: [504/600] Discriminator Loss: 0.8968, Generator Loss: 1.3221 D(x): 0.7052, D(G(z)): 0.3221 Epoch: [19/20], Batch Num: [505/600] Discriminator Loss: 0.9858, Generator Loss: 1.2397 D(x): 0.7289, D(G(z)): 0.3627 Epoch: [19/20], Batch Num: [506/600] Discriminator Loss: 0.9394, Generator Loss: 1.4261 D(x): 0.7477, D(G(z)): 0.3578 Epoch: [19/20], Batch Num: [507/600] Discriminator Loss: 0.9437, Generator Loss: 1.4752 D(x): 0.6628, D(G(z)): 0.2915 Epoch: [19/20], Batch Num: [508/600] Discriminator Loss: 0.9428, Generator Loss: 1.3687 D(x): 0.6906, D(G(z)): 0.3179 Epoch: [19/20], Batch Num: [509/600] Discriminator Loss: 0.9870, Generator Loss: 1.3017 D(x): 0.6456, D(G(z)): 0.2825 Epoch: [19/20], Batch Num: [510/600] Discriminator Loss: 0.9454, Generator Loss: 1.4479 D(x): 0.6890, D(G(z)): 0.3262 Epoch: [19/20], Batch Num: [511/600] Discriminator Loss: 0.9357, Generator Loss: 1.2655 D(x): 0.7093, D(G(z)): 0.3425 Epoch: [19/20], Batch Num: [512/600] Discriminator Loss: 1.0222, Generator Loss: 1.3623 D(x): 0.6802, D(G(z)): 0.3399 Epoch: [19/20], Batch Num: [513/600] Discriminator Loss: 1.0532, Generator Loss: 1.3549 D(x): 0.7037, D(G(z)): 0.3723 Epoch: [19/20], Batch Num: [514/600] Discriminator Loss: 0.9095, Generator Loss: 1.3985 D(x): 0.6892, D(G(z)): 0.3047 Epoch: [19/20], Batch Num: [515/600] Discriminator Loss: 0.8705, Generator Loss: 1.5239 D(x): 0.7085, D(G(z)): 0.2981 Epoch: [19/20], Batch Num: [516/600] Discriminator Loss: 0.8772, Generator Loss: 1.4575 D(x): 0.6887, D(G(z)): 0.2910 Epoch: [19/20], Batch Num: [517/600] Discriminator Loss: 1.0083, Generator Loss: 1.5113 D(x): 0.6620, D(G(z)): 0.3049 Epoch: [19/20], Batch Num: [518/600] Discriminator Loss: 0.8605, Generator Loss: 1.3335 D(x): 0.7111, D(G(z)): 0.3119 Epoch: [19/20], Batch Num: [519/600] Discriminator Loss: 1.0151, Generator Loss: 1.4636 D(x): 0.6515, D(G(z)): 0.3082 Epoch: [19/20], Batch Num: [520/600] Discriminator Loss: 0.9379, Generator Loss: 1.4544 D(x): 0.7033, D(G(z)): 0.3113 Epoch: [19/20], Batch Num: [521/600] Discriminator Loss: 0.9727, Generator Loss: 1.3760 D(x): 0.6801, D(G(z)): 0.3157 Epoch: [19/20], Batch Num: [522/600] Discriminator Loss: 1.0550, Generator Loss: 1.6233 D(x): 0.6675, D(G(z)): 0.3506 Epoch: [19/20], Batch Num: [523/600] Discriminator Loss: 0.8579, Generator Loss: 1.3389 D(x): 0.6711, D(G(z)): 0.2527 Epoch: [19/20], Batch Num: [524/600] Discriminator Loss: 1.0397, Generator Loss: 1.3060 D(x): 0.6105, D(G(z)): 0.3115 Epoch: [19/20], Batch Num: [525/600] Discriminator Loss: 1.0479, Generator Loss: 1.2726 D(x): 0.6515, D(G(z)): 0.3289 Epoch: [19/20], Batch Num: [526/600] Discriminator Loss: 0.8815, Generator Loss: 1.1930 D(x): 0.7174, D(G(z)): 0.3288 Epoch: [19/20], Batch Num: [527/600] Discriminator Loss: 0.9806, Generator Loss: 1.2494 D(x): 0.6839, D(G(z)): 0.3452 Epoch: [19/20], Batch Num: [528/600] Discriminator Loss: 0.9456, Generator Loss: 1.4499 D(x): 0.7168, D(G(z)): 0.3646 Epoch: [19/20], Batch Num: [529/600] Discriminator Loss: 0.9738, Generator Loss: 1.4408 D(x): 0.6693, D(G(z)): 0.3220 Epoch: [19/20], Batch Num: [530/600] Discriminator Loss: 0.9051, Generator Loss: 1.5337 D(x): 0.6695, D(G(z)): 0.2955 Epoch: [19/20], Batch Num: [531/600] Discriminator Loss: 0.9490, Generator Loss: 1.4677 D(x): 0.6392, D(G(z)): 0.2706 Epoch: [19/20], Batch Num: [532/600] Discriminator Loss: 0.8770, Generator Loss: 1.4454 D(x): 0.6654, D(G(z)): 0.2628 Epoch: [19/20], Batch Num: [533/600] Discriminator Loss: 0.8768, Generator Loss: 1.3899 D(x): 0.6885, D(G(z)): 0.2971 Epoch: [19/20], Batch Num: [534/600] Discriminator Loss: 0.8875, Generator Loss: 1.2206 D(x): 0.7011, D(G(z)): 0.2977 Epoch: [19/20], Batch Num: [535/600] Discriminator Loss: 0.9320, Generator Loss: 1.2490 D(x): 0.7792, D(G(z)): 0.3800 Epoch: [19/20], Batch Num: [536/600] Discriminator Loss: 1.0315, Generator Loss: 1.5724 D(x): 0.7181, D(G(z)): 0.3940 Epoch: [19/20], Batch Num: [537/600] Discriminator Loss: 0.8446, Generator Loss: 1.5662 D(x): 0.7084, D(G(z)): 0.2717 Epoch: [19/20], Batch Num: [538/600] Discriminator Loss: 0.9172, Generator Loss: 1.7035 D(x): 0.6379, D(G(z)): 0.2342 Epoch: [19/20], Batch Num: [539/600] Discriminator Loss: 0.8595, Generator Loss: 1.5574 D(x): 0.6684, D(G(z)): 0.2301 Epoch: [19/20], Batch Num: [540/600] Discriminator Loss: 0.9794, Generator Loss: 1.5750 D(x): 0.6505, D(G(z)): 0.2802 Epoch: [19/20], Batch Num: [541/600] Discriminator Loss: 0.8423, Generator Loss: 1.4047 D(x): 0.6944, D(G(z)): 0.2721 Epoch: [19/20], Batch Num: [542/600] Discriminator Loss: 0.8565, Generator Loss: 1.2502 D(x): 0.7367, D(G(z)): 0.3188 Epoch: [19/20], Batch Num: [543/600] Discriminator Loss: 0.9156, Generator Loss: 1.2518 D(x): 0.7384, D(G(z)): 0.3232 Epoch: [19/20], Batch Num: [544/600] Discriminator Loss: 0.8805, Generator Loss: 1.4900 D(x): 0.7436, D(G(z)): 0.3296 Epoch: [19/20], Batch Num: [545/600] Discriminator Loss: 0.7759, Generator Loss: 1.7566 D(x): 0.7508, D(G(z)): 0.2919 Epoch: [19/20], Batch Num: [546/600] Discriminator Loss: 0.8627, Generator Loss: 1.6455 D(x): 0.7191, D(G(z)): 0.3046 Epoch: [19/20], Batch Num: [547/600] Discriminator Loss: 0.8053, Generator Loss: 1.7827 D(x): 0.6843, D(G(z)): 0.2436 Epoch: [19/20], Batch Num: [548/600] Discriminator Loss: 0.8807, Generator Loss: 1.7628 D(x): 0.6638, D(G(z)): 0.2347 Epoch: [19/20], Batch Num: [549/600] Discriminator Loss: 0.8122, Generator Loss: 1.5125 D(x): 0.7006, D(G(z)): 0.2625 Epoch: [19/20], Batch Num: [550/600] Discriminator Loss: 0.9888, Generator Loss: 1.5194 D(x): 0.6723, D(G(z)): 0.2951 Epoch: [19/20], Batch Num: [551/600] Discriminator Loss: 0.8347, Generator Loss: 1.4668 D(x): 0.7626, D(G(z)): 0.3041 Epoch: [19/20], Batch Num: [552/600] Discriminator Loss: 1.0164, Generator Loss: 1.3445 D(x): 0.7251, D(G(z)): 0.3588 Epoch: [19/20], Batch Num: [553/600] Discriminator Loss: 0.8249, Generator Loss: 1.5224 D(x): 0.7526, D(G(z)): 0.2990 Epoch: [19/20], Batch Num: [554/600] Discriminator Loss: 1.1084, Generator Loss: 1.6662 D(x): 0.6833, D(G(z)): 0.3432 Epoch: [19/20], Batch Num: [555/600] Discriminator Loss: 0.9509, Generator Loss: 1.6981 D(x): 0.7064, D(G(z)): 0.3012 Epoch: [19/20], Batch Num: [556/600] Discriminator Loss: 0.8632, Generator Loss: 1.9439 D(x): 0.7149, D(G(z)): 0.2560 Epoch: [19/20], Batch Num: [557/600] Discriminator Loss: 0.9870, Generator Loss: 1.6163 D(x): 0.6205, D(G(z)): 0.2165 Epoch: [19/20], Batch Num: [558/600] Discriminator Loss: 0.9499, Generator Loss: 1.4881 D(x): 0.6653, D(G(z)): 0.2876 Epoch: [19/20], Batch Num: [559/600] Discriminator Loss: 0.9328, Generator Loss: 1.3306 D(x): 0.7236, D(G(z)): 0.3277 Epoch: [19/20], Batch Num: [560/600] Discriminator Loss: 1.1425, Generator Loss: 1.4574 D(x): 0.7599, D(G(z)): 0.4256 Epoch: [19/20], Batch Num: [561/600] Discriminator Loss: 0.9190, Generator Loss: 1.7029 D(x): 0.7282, D(G(z)): 0.3421 Epoch: [19/20], Batch Num: [562/600] Discriminator Loss: 0.8444, Generator Loss: 1.8544 D(x): 0.6951, D(G(z)): 0.2689 Epoch: [19/20], Batch Num: [563/600] Discriminator Loss: 0.9701, Generator Loss: 1.4462 D(x): 0.5882, D(G(z)): 0.2115 Epoch: [19/20], Batch Num: [564/600] Discriminator Loss: 0.9251, Generator Loss: 1.3214 D(x): 0.6325, D(G(z)): 0.2523 Epoch: [19/20], Batch Num: [565/600] Discriminator Loss: 1.0439, Generator Loss: 1.1818 D(x): 0.7110, D(G(z)): 0.3637 Epoch: [19/20], Batch Num: [566/600] Discriminator Loss: 0.9524, Generator Loss: 1.1332 D(x): 0.7151, D(G(z)): 0.3519 Epoch: [19/20], Batch Num: [567/600] Discriminator Loss: 0.9407, Generator Loss: 1.3459 D(x): 0.7469, D(G(z)): 0.3758 Epoch: [19/20], Batch Num: [568/600] Discriminator Loss: 0.9846, Generator Loss: 1.2985 D(x): 0.6831, D(G(z)): 0.3332 Epoch: [19/20], Batch Num: [569/600] Discriminator Loss: 0.9753, Generator Loss: 1.5268 D(x): 0.7115, D(G(z)): 0.3563 Epoch: [19/20], Batch Num: [570/600] Discriminator Loss: 0.9035, Generator Loss: 1.6883 D(x): 0.6729, D(G(z)): 0.3033 Epoch: [19/20], Batch Num: [571/600] Discriminator Loss: 0.9108, Generator Loss: 1.7449 D(x): 0.6514, D(G(z)): 0.2521 Epoch: [19/20], Batch Num: [572/600] Discriminator Loss: 1.0743, Generator Loss: 1.7815 D(x): 0.5876, D(G(z)): 0.2788 Epoch: [19/20], Batch Num: [573/600] Discriminator Loss: 0.9010, Generator Loss: 1.4728 D(x): 0.6733, D(G(z)): 0.2756 Epoch: [19/20], Batch Num: [574/600] Discriminator Loss: 0.9593, Generator Loss: 1.3822 D(x): 0.6981, D(G(z)): 0.3282 Epoch: [19/20], Batch Num: [575/600] Discriminator Loss: 0.9676, Generator Loss: 1.3502 D(x): 0.6750, D(G(z)): 0.3321 Epoch: [19/20], Batch Num: [576/600] Discriminator Loss: 0.8961, Generator Loss: 1.3806 D(x): 0.7207, D(G(z)): 0.3254 Epoch: [19/20], Batch Num: [577/600] Discriminator Loss: 0.9443, Generator Loss: 1.5184 D(x): 0.7638, D(G(z)): 0.3844 Epoch: [19/20], Batch Num: [578/600] Discriminator Loss: 0.8601, Generator Loss: 1.6301 D(x): 0.6972, D(G(z)): 0.2998 Epoch: [19/20], Batch Num: [579/600] Discriminator Loss: 0.9200, Generator Loss: 1.7937 D(x): 0.6662, D(G(z)): 0.2711 Epoch: [19/20], Batch Num: [580/600] Discriminator Loss: 0.8753, Generator Loss: 1.7153 D(x): 0.6358, D(G(z)): 0.2417 Epoch: [19/20], Batch Num: [581/600] Discriminator Loss: 0.9943, Generator Loss: 1.3385 D(x): 0.6376, D(G(z)): 0.2702 Epoch: [19/20], Batch Num: [582/600] Discriminator Loss: 1.0073, Generator Loss: 1.3969 D(x): 0.6579, D(G(z)): 0.3058 Epoch: [19/20], Batch Num: [583/600] Discriminator Loss: 1.0473, Generator Loss: 1.1281 D(x): 0.7292, D(G(z)): 0.3840 Epoch: [19/20], Batch Num: [584/600] Discriminator Loss: 0.9112, Generator Loss: 1.2916 D(x): 0.7443, D(G(z)): 0.3760 Epoch: [19/20], Batch Num: [585/600] Discriminator Loss: 0.9126, Generator Loss: 1.5122 D(x): 0.7471, D(G(z)): 0.3721 Epoch: [19/20], Batch Num: [586/600] Discriminator Loss: 0.9752, Generator Loss: 1.6812 D(x): 0.6870, D(G(z)): 0.3177 Epoch: [19/20], Batch Num: [587/600] Discriminator Loss: 0.8806, Generator Loss: 1.5999 D(x): 0.6845, D(G(z)): 0.2880 Epoch: [19/20], Batch Num: [588/600] Discriminator Loss: 0.9719, Generator Loss: 1.5398 D(x): 0.6117, D(G(z)): 0.2629 Epoch: [19/20], Batch Num: [589/600] Discriminator Loss: 0.8858, Generator Loss: 1.2548 D(x): 0.6553, D(G(z)): 0.2700 Epoch: [19/20], Batch Num: [590/600] Discriminator Loss: 0.9199, Generator Loss: 1.1884 D(x): 0.6768, D(G(z)): 0.3173 Epoch: [19/20], Batch Num: [591/600] Discriminator Loss: 1.1235, Generator Loss: 1.1826 D(x): 0.6902, D(G(z)): 0.4004 Epoch: [19/20], Batch Num: [592/600] Discriminator Loss: 0.8713, Generator Loss: 1.2220 D(x): 0.7789, D(G(z)): 0.3824 Epoch: [19/20], Batch Num: [593/600] Discriminator Loss: 0.8021, Generator Loss: 1.4873 D(x): 0.7725, D(G(z)): 0.3357 Epoch: [19/20], Batch Num: [594/600] Discriminator Loss: 0.9842, Generator Loss: 1.6934 D(x): 0.6687, D(G(z)): 0.3114 Epoch: [19/20], Batch Num: [595/600] Discriminator Loss: 1.0628, Generator Loss: 1.6284 D(x): 0.5692, D(G(z)): 0.2551 Epoch: [19/20], Batch Num: [596/600] Discriminator Loss: 0.9686, Generator Loss: 1.6454 D(x): 0.6382, D(G(z)): 0.2790 Epoch: [19/20], Batch Num: [597/600] Discriminator Loss: 0.8623, Generator Loss: 1.4669 D(x): 0.6872, D(G(z)): 0.2639 Epoch: [19/20], Batch Num: [598/600] Discriminator Loss: 0.9441, Generator Loss: 1.4213 D(x): 0.7073, D(G(z)): 0.3250 Epoch: [19/20], Batch Num: [599/600] Discriminator Loss: 1.0135, Generator Loss: 1.3210 D(x): 0.7029, D(G(z)): 0.3679
L'entraînement des modèles GAN demande beaucoup de patience en raison de sa profondeur. Nous avons décidé de resumer ce que l'on a appris à travers la modification des paramètres sous forme de conseils: